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src/f5_tts/api.py

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+import random
+import sys
+from importlib.resources import files
+
+import soundfile as sf
+import tqdm
+from cached_path import cached_path
+from hydra.utils import get_class
+from omegaconf import OmegaConf
+
+from f5_tts.infer.utils_infer import (
+    infer_process,
+    load_model,
+    load_vocoder,
+    preprocess_ref_audio_text,
+    remove_silence_for_generated_wav,
+    save_spectrogram,
+    transcribe,
+)
+from f5_tts.model.utils import seed_everything
+
+
+class F5TTS:
+    def __init__(
+        self,
+        model="F5TTS_v1_Base",
+        ckpt_file="",
+        vocab_file="",
+        ode_method="euler",
+        use_ema=True,
+        vocoder_local_path=None,
+        device=None,
+        hf_cache_dir=None,
+    ):
+        model_cfg = OmegaConf.load(str(files("f5_tts").joinpath(f"configs/{model}.yaml")))
+        model_cls = get_class(f"f5_tts.model.{model_cfg.model.backbone}")
+        model_arc = model_cfg.model.arch
+
+        self.mel_spec_type = model_cfg.model.mel_spec.mel_spec_type
+        self.target_sample_rate = model_cfg.model.mel_spec.target_sample_rate
+
+        self.ode_method = ode_method
+        self.use_ema = use_ema
+
+        if device is not None:
+            self.device = device
+        else:
+            import torch
+
+            self.device = (
+                "cuda"
+                if torch.cuda.is_available()
+                else "xpu"
+                if torch.xpu.is_available()
+                else "mps"
+                if torch.backends.mps.is_available()
+                else "cpu"
+            )
+
+        # Load models
+        self.vocoder = load_vocoder(
+            self.mel_spec_type, vocoder_local_path is not None, vocoder_local_path, self.device, hf_cache_dir
+        )
+
+        repo_name, ckpt_step, ckpt_type = "F5-TTS", 1250000, "safetensors"
+
+        # override for previous models
+        if model == "F5TTS_Base":
+            if self.mel_spec_type == "vocos":
+                ckpt_step = 1200000
+            elif self.mel_spec_type == "bigvgan":
+                model = "F5TTS_Base_bigvgan"
+                ckpt_type = "pt"
+        elif model == "E2TTS_Base":
+            repo_name = "E2-TTS"
+            ckpt_step = 1200000
+
+        if not ckpt_file:
+            ckpt_file = str(
+                cached_path(f"hf://SWivid/{repo_name}/{model}/model_{ckpt_step}.{ckpt_type}", cache_dir=hf_cache_dir)
+            )
+        self.ema_model = load_model(
+            model_cls, model_arc, ckpt_file, self.mel_spec_type, vocab_file, self.ode_method, self.use_ema, self.device
+        )
+
+    def transcribe(self, ref_audio, language=None):
+        return transcribe(ref_audio, language)
+
+    def export_wav(self, wav, file_wave, remove_silence=False):
+        sf.write(file_wave, wav, self.target_sample_rate)
+
+        if remove_silence:
+            remove_silence_for_generated_wav(file_wave)
+
+    def export_spectrogram(self, spec, file_spec):
+        save_spectrogram(spec, file_spec)
+
+    def infer(
+        self,
+        ref_file,
+        ref_text,
+        gen_text,
+        show_info=print,
+        progress=tqdm,
+        target_rms=0.1,
+        cross_fade_duration=0.15,
+        sway_sampling_coef=-1,
+        cfg_strength=2,
+        nfe_step=32,
+        speed=1.0,
+        fix_duration=None,
+        remove_silence=False,
+        file_wave=None,
+        file_spec=None,
+        seed=None,
+    ):
+        if seed is None:
+            seed = random.randint(0, sys.maxsize)
+        seed_everything(seed)
+        self.seed = seed
+
+        ref_file, ref_text = preprocess_ref_audio_text(ref_file, ref_text)
+
+        wav, sr, spec = infer_process(
+            ref_file,
+            ref_text,
+            gen_text,
+            self.ema_model,
+            self.vocoder,
+            self.mel_spec_type,
+            show_info=show_info,
+            progress=progress,
+            target_rms=target_rms,
+            cross_fade_duration=cross_fade_duration,
+            nfe_step=nfe_step,
+            cfg_strength=cfg_strength,
+            sway_sampling_coef=sway_sampling_coef,
+            speed=speed,
+            fix_duration=fix_duration,
+            device=self.device,
+        )
+
+        if file_wave is not None:
+            self.export_wav(wav, file_wave, remove_silence)
+
+        if file_spec is not None:
+            self.export_spectrogram(spec, file_spec)
+
+        return wav, sr, spec
+
+
+if __name__ == "__main__":
+    f5tts = F5TTS()
+
+    wav, sr, spec = f5tts.infer(
+        ref_file=str(files("f5_tts").joinpath("infer/examples/basic/basic_ref_en.wav")),
+        ref_text="some call me nature, others call me mother nature.",
+        gen_text="""I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring. Respect me and I'll nurture you; ignore me and you shall face the consequences.""",
+        file_wave=str(files("f5_tts").joinpath("../../tests/api_out.wav")),
+        file_spec=str(files("f5_tts").joinpath("../../tests/api_out.png")),
+        seed=None,
+    )
+
+    print("seed :", f5tts.seed)

src/f5_tts/configs/E2TTS_Base.yaml

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+hydra:
+  run:
+    dir: ckpts/${model.name}_${model.mel_spec.mel_spec_type}_${model.tokenizer}_${datasets.name}/${now:%Y-%m-%d}/${now:%H-%M-%S}
+
+datasets:
+  name: Emilia_ZH_EN  # dataset name
+  batch_size_per_gpu: 38400  # 8 GPUs, 8 * 38400 = 307200
+  batch_size_type: frame  # frame | sample
+  max_samples: 64  # max sequences per batch if use frame-wise batch_size. we set 32 for small models, 64 for base models
+  num_workers: 16
+
+optim:
+  epochs: 11
+  learning_rate: 7.5e-5
+  num_warmup_updates: 20000  # warmup updates
+  grad_accumulation_steps: 1  # note: updates = steps / grad_accumulation_steps
+  max_grad_norm: 1.0  # gradient clipping
+  bnb_optimizer: False  # use bnb 8bit AdamW optimizer or not
+
+model:
+  name: E2TTS_Base
+  tokenizer: pinyin
+  tokenizer_path: null  # if 'custom' tokenizer, define the path want to use (should be vocab.txt)
+  backbone: UNetT
+  arch:
+    dim: 1024
+    depth: 24
+    heads: 16
+    ff_mult: 4
+    text_mask_padding: False
+    pe_attn_head: 1
+  mel_spec:
+    target_sample_rate: 24000
+    n_mel_channels: 100
+    hop_length: 256
+    win_length: 1024
+    n_fft: 1024
+    mel_spec_type: vocos  # vocos | bigvgan
+  vocoder:
+    is_local: False  # use local offline ckpt or not
+    local_path: null  # local vocoder path
+
+ckpts:
+  logger: wandb  # wandb | tensorboard | null
+  log_samples: True  # infer random sample per save checkpoint. wip, normal to fail with extra long samples
+  save_per_updates: 50000  # save checkpoint per updates
+  keep_last_n_checkpoints: -1  # -1 to keep all, 0 to not save intermediate, > 0 to keep last N checkpoints
+  last_per_updates: 5000  # save last checkpoint per updates
+  save_dir: ckpts/${model.name}_${model.mel_spec.mel_spec_type}_${model.tokenizer}_${datasets.name}

src/f5_tts/configs/E2TTS_Small.yaml

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+hydra:
+  run:
+    dir: ckpts/${model.name}_${model.mel_spec.mel_spec_type}_${model.tokenizer}_${datasets.name}/${now:%Y-%m-%d}/${now:%H-%M-%S}
+
+datasets:
+  name: Emilia_ZH_EN
+  batch_size_per_gpu: 38400  # 8 GPUs, 8 * 38400 = 307200
+  batch_size_type: frame  # frame | sample
+  max_samples: 64  # max sequences per batch if use frame-wise batch_size. we set 32 for small models, 64 for base models
+  num_workers: 16
+
+optim:
+  epochs: 11
+  learning_rate: 7.5e-5
+  num_warmup_updates: 20000  # warmup updates
+  grad_accumulation_steps: 1  # note: updates = steps / grad_accumulation_steps
+  max_grad_norm: 1.0
+  bnb_optimizer: False  
+
+model:
+  name: E2TTS_Small
+  tokenizer: pinyin
+  tokenizer_path: null  # if 'custom' tokenizer, define the path want to use (should be vocab.txt)
+  backbone: UNetT
+  arch:
+    dim: 768
+    depth: 20
+    heads: 12
+    ff_mult: 4
+    text_mask_padding: False
+    pe_attn_head: 1
+  mel_spec:
+    target_sample_rate: 24000
+    n_mel_channels: 100
+    hop_length: 256
+    win_length: 1024
+    n_fft: 1024
+    mel_spec_type: vocos  # vocos | bigvgan
+  vocoder:
+    is_local: False  # use local offline ckpt or not
+    local_path: null  # local vocoder path
+
+ckpts:
+  logger: wandb  # wandb | tensorboard | null
+  log_samples: True  # infer random sample per save checkpoint. wip, normal to fail with extra long samples
+  save_per_updates: 50000  # save checkpoint per updates
+  keep_last_n_checkpoints: -1  # -1 to keep all, 0 to not save intermediate, > 0 to keep last N checkpoints
+  last_per_updates: 5000  # save last checkpoint per updates
+  save_dir: ckpts/${model.name}_${model.mel_spec.mel_spec_type}_${model.tokenizer}_${datasets.name}

src/f5_tts/configs/F5TTS_Base.yaml

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+hydra:
+  run:
+    dir: ckpts/${model.name}_${model.mel_spec.mel_spec_type}_${model.tokenizer}_${datasets.name}/${now:%Y-%m-%d}/${now:%H-%M-%S}
+
+datasets:
+  name: Emilia_ZH_EN  # dataset name
+  batch_size_per_gpu: 38400  # 8 GPUs, 8 * 38400 = 307200
+  batch_size_type: frame  # frame | sample
+  max_samples: 64  # max sequences per batch if use frame-wise batch_size. we set 32 for small models, 64 for base models
+  num_workers: 16
+
+optim:
+  epochs: 11
+  learning_rate: 7.5e-5
+  num_warmup_updates: 20000  # warmup updates
+  grad_accumulation_steps: 1  # note: updates = steps / grad_accumulation_steps
+  max_grad_norm: 1.0  # gradient clipping
+  bnb_optimizer: False  # use bnb 8bit AdamW optimizer or not
+
+model:
+  name: F5TTS_Base  # model name
+  tokenizer: pinyin  # tokenizer type
+  tokenizer_path: null  # if 'custom' tokenizer, define the path want to use (should be vocab.txt)
+  backbone: DiT
+  arch:
+    dim: 1024
+    depth: 22
+    heads: 16
+    ff_mult: 2
+    text_dim: 512
+    text_mask_padding: False
+    conv_layers: 4
+    pe_attn_head: 1
+    attn_backend: torch  # torch | flash_attn
+    attn_mask_enabled: False
+    checkpoint_activations: False  # recompute activations and save memory for extra compute
+  mel_spec:
+    target_sample_rate: 24000
+    n_mel_channels: 100
+    hop_length: 256
+    win_length: 1024
+    n_fft: 1024
+    mel_spec_type: vocos  # vocos | bigvgan
+  vocoder:
+    is_local: False  # use local offline ckpt or not
+    local_path: null  # local vocoder path
+
+ckpts:
+  logger: wandb  # wandb | tensorboard | null
+  log_samples: True  # infer random sample per save checkpoint. wip, normal to fail with extra long samples
+  save_per_updates: 50000  # save checkpoint per updates
+  keep_last_n_checkpoints: -1  # -1 to keep all, 0 to not save intermediate, > 0 to keep last N checkpoints
+  last_per_updates: 5000  # save last checkpoint per updates
+  save_dir: ckpts/${model.name}_${model.mel_spec.mel_spec_type}_${model.tokenizer}_${datasets.name}

src/f5_tts/configs/F5TTS_Small.yaml

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+hydra:
+  run:
+    dir: ckpts/${model.name}_${model.mel_spec.mel_spec_type}_${model.tokenizer}_${datasets.name}/${now:%Y-%m-%d}/${now:%H-%M-%S}
+
+datasets:
+  name: Emilia_ZH_EN
+  batch_size_per_gpu: 38400  # 8 GPUs, 8 * 38400 = 307200
+  batch_size_type: frame  # frame | sample
+  max_samples: 64  # max sequences per batch if use frame-wise batch_size. we set 32 for small models, 64 for base models
+  num_workers: 16
+
+optim:
+  epochs: 11  # only suitable for Emilia, if you want to train it on LibriTTS, set epoch 686
+  learning_rate: 7.5e-5
+  num_warmup_updates: 20000  # warmup updates
+  grad_accumulation_steps: 1  # note: updates = steps / grad_accumulation_steps
+  max_grad_norm: 1.0  # gradient clipping
+  bnb_optimizer: False  # use bnb 8bit AdamW optimizer or not
+
+model:
+  name: F5TTS_Small
+  tokenizer: pinyin
+  tokenizer_path: null  # if 'custom' tokenizer, define the path want to use (should be vocab.txt)
+  backbone: DiT
+  arch:
+    dim: 768
+    depth: 18
+    heads: 12
+    ff_mult: 2
+    text_dim: 512
+    text_mask_padding: False
+    conv_layers: 4
+    pe_attn_head: 1
+    attn_backend: torch  # torch | flash_attn
+    attn_mask_enabled: False
+    checkpoint_activations: False  # recompute activations and save memory for extra compute
+  mel_spec:
+    target_sample_rate: 24000
+    n_mel_channels: 100
+    hop_length: 256
+    win_length: 1024
+    n_fft: 1024
+    mel_spec_type: vocos  # vocos | bigvgan
+  vocoder:
+    is_local: False  # use local offline ckpt or not
+    local_path: null  # local vocoder path
+
+ckpts:
+  logger: wandb  # wandb | tensorboard | null
+  log_samples: True  # infer random sample per save checkpoint. wip, normal to fail with extra long samples
+  save_per_updates: 50000  # save checkpoint per updates
+  keep_last_n_checkpoints: -1  # -1 to keep all, 0 to not save intermediate, > 0 to keep last N checkpoints
+  last_per_updates: 5000  # save last checkpoint per updates
+  save_dir: ckpts/${model.name}_${model.mel_spec.mel_spec_type}_${model.tokenizer}_${datasets.name}

src/f5_tts/configs/F5TTS_v1_Base.yaml

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+hydra:
+  run:
+    dir: ckpts/${model.name}_${model.mel_spec.mel_spec_type}_${model.tokenizer}_${datasets.name}/${now:%Y-%m-%d}/${now:%H-%M-%S}
+
+datasets:
+  name: Emilia_ZH_EN  # dataset name
+  batch_size_per_gpu: 38400  # 8 GPUs, 8 * 38400 = 307200
+  batch_size_type: frame  # frame | sample
+  max_samples: 64  # max sequences per batch if use frame-wise batch_size. we set 32 for small models, 64 for base models
+  num_workers: 16
+
+optim:
+  epochs: 11
+  learning_rate: 7.5e-5
+  num_warmup_updates: 20000  # warmup updates
+  grad_accumulation_steps: 1  # note: updates = steps / grad_accumulation_steps
+  max_grad_norm: 1.0  # gradient clipping
+  bnb_optimizer: False  # use bnb 8bit AdamW optimizer or not
+
+model:
+  name: F5TTS_v1_Base  # model name
+  tokenizer: pinyin  # tokenizer type
+  tokenizer_path: null  # if 'custom' tokenizer, define the path want to use (should be vocab.txt)
+  backbone: DiT
+  arch:
+    dim: 1024
+    depth: 22
+    heads: 16
+    ff_mult: 2
+    text_dim: 512
+    text_mask_padding: True
+    qk_norm: null  # null | rms_norm
+    conv_layers: 4
+    pe_attn_head: null
+    attn_backend: torch  # torch | flash_attn
+    attn_mask_enabled: False
+    checkpoint_activations: False  # recompute activations and save memory for extra compute
+  mel_spec:
+    target_sample_rate: 24000
+    n_mel_channels: 100
+    hop_length: 256
+    win_length: 1024
+    n_fft: 1024
+    mel_spec_type: vocos  # vocos | bigvgan
+  vocoder:
+    is_local: False  # use local offline ckpt or not
+    local_path: null  # local vocoder path
+
+ckpts:
+  logger: wandb  # wandb | tensorboard | null
+  log_samples: True  # infer random sample per save checkpoint. wip, normal to fail with extra long samples
+  save_per_updates: 50000  # save checkpoint per updates
+  keep_last_n_checkpoints: -1  # -1 to keep all, 0 to not save intermediate, > 0 to keep last N checkpoints
+  last_per_updates: 5000  # save last checkpoint per updates
+  save_dir: ckpts/${model.name}_${model.mel_spec.mel_spec_type}_${model.tokenizer}_${datasets.name}

src/f5_tts/eval/README.md

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+
+# Evaluation
+
+Install packages for evaluation:
+
+```bash
+pip install -e .[eval]
+```
+
+## Generating Samples for Evaluation
+
+### Prepare Test Datasets
+
+1. *Seed-TTS testset*: Download from [seed-tts-eval](https://github.com/BytedanceSpeech/seed-tts-eval).
+2. *LibriSpeech test-clean*: Download from [OpenSLR](http://www.openslr.org/12/).
+3. Unzip the downloaded datasets and place them in the `data/` directory.
+4. Update the path for *LibriSpeech test-clean* data in `src/f5_tts/eval/eval_infer_batch.py`
+5. Our filtered LibriSpeech-PC 4-10s subset: `data/librispeech_pc_test_clean_cross_sentence.lst`
+
+### Batch Inference for Test Set
+
+To run batch inference for evaluations, execute the following commands:
+
+```bash
+# batch inference for evaluations
+accelerate config  # if not set before
+bash src/f5_tts/eval/eval_infer_batch.sh
+```
+
+## Objective Evaluation on Generated Results
+
+### Download Evaluation Model Checkpoints
+
+1. Chinese ASR Model: [Paraformer-zh](https://huggingface.co/funasr/paraformer-zh)
+2. English ASR Model: [Faster-Whisper](https://huggingface.co/Systran/faster-whisper-large-v3)
+3. WavLM Model: Download from [Google Drive](https://drive.google.com/file/d/1-aE1NfzpRCLxA4GUxX9ITI3F9LlbtEGP/view).
+
+Then update in the following scripts with the paths you put evaluation model ckpts to.
+
+### Objective Evaluation
+
+Update the path with your batch-inferenced results, and carry out WER / SIM / UTMOS evaluations:
+```bash
+# Evaluation [WER] for Seed-TTS test [ZH] set
+python src/f5_tts/eval/eval_seedtts_testset.py --eval_task wer --lang zh --gen_wav_dir <GEN_WAV_DIR> --gpu_nums 8
+
+# Evaluation [SIM] for LibriSpeech-PC test-clean (cross-sentence)
+python src/f5_tts/eval/eval_librispeech_test_clean.py --eval_task sim --gen_wav_dir <GEN_WAV_DIR> --librispeech_test_clean_path <TEST_CLEAN_PATH>
+
+# Evaluation [UTMOS]. --ext: Audio extension
+python src/f5_tts/eval/eval_utmos.py --audio_dir <WAV_DIR> --ext wav
+```

src/f5_tts/eval/ecapa_tdnn.py

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+# just for speaker similarity evaluation, third-party code
+
+# From https://github.com/microsoft/UniSpeech/blob/main/downstreams/speaker_verification/models/
+# part of the code is borrowed from https://github.com/lawlict/ECAPA-TDNN
+
+import os
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+""" Res2Conv1d + BatchNorm1d + ReLU
+"""
+
+
+class Res2Conv1dReluBn(nn.Module):
+    """
+    in_channels == out_channels == channels
+    """
+
+    def __init__(self, channels, kernel_size=1, stride=1, padding=0, dilation=1, bias=True, scale=4):
+        super().__init__()
+        assert channels % scale == 0, "{} % {} != 0".format(channels, scale)
+        self.scale = scale
+        self.width = channels // scale
+        self.nums = scale if scale == 1 else scale - 1
+
+        self.convs = []
+        self.bns = []
+        for i in range(self.nums):
+            self.convs.append(nn.Conv1d(self.width, self.width, kernel_size, stride, padding, dilation, bias=bias))
+            self.bns.append(nn.BatchNorm1d(self.width))
+        self.convs = nn.ModuleList(self.convs)
+        self.bns = nn.ModuleList(self.bns)
+
+    def forward(self, x):
+        out = []
+        spx = torch.split(x, self.width, 1)
+        for i in range(self.nums):
+            if i == 0:
+                sp = spx[i]
+            else:
+                sp = sp + spx[i]
+            # Order: conv -> relu -> bn
+            sp = self.convs[i](sp)
+            sp = self.bns[i](F.relu(sp))
+            out.append(sp)
+        if self.scale != 1:
+            out.append(spx[self.nums])
+        out = torch.cat(out, dim=1)
+
+        return out
+
+
+""" Conv1d + BatchNorm1d + ReLU
+"""
+
+
+class Conv1dReluBn(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, dilation=1, bias=True):
+        super().__init__()
+        self.conv = nn.Conv1d(in_channels, out_channels, kernel_size, stride, padding, dilation, bias=bias)
+        self.bn = nn.BatchNorm1d(out_channels)
+
+    def forward(self, x):
+        return self.bn(F.relu(self.conv(x)))
+
+
+""" The SE connection of 1D case.
+"""
+
+
+class SE_Connect(nn.Module):
+    def __init__(self, channels, se_bottleneck_dim=128):
+        super().__init__()
+        self.linear1 = nn.Linear(channels, se_bottleneck_dim)
+        self.linear2 = nn.Linear(se_bottleneck_dim, channels)
+
+    def forward(self, x):
+        out = x.mean(dim=2)
+        out = F.relu(self.linear1(out))
+        out = torch.sigmoid(self.linear2(out))
+        out = x * out.unsqueeze(2)
+
+        return out
+
+
+""" SE-Res2Block of the ECAPA-TDNN architecture.
+"""
+
+# def SE_Res2Block(channels, kernel_size, stride, padding, dilation, scale):
+#     return nn.Sequential(
+#         Conv1dReluBn(channels, 512, kernel_size=1, stride=1, padding=0),
+#         Res2Conv1dReluBn(512, kernel_size, stride, padding, dilation, scale=scale),
+#         Conv1dReluBn(512, channels, kernel_size=1, stride=1, padding=0),
+#         SE_Connect(channels)
+#     )
+
+
+class SE_Res2Block(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, scale, se_bottleneck_dim):
+        super().__init__()
+        self.Conv1dReluBn1 = Conv1dReluBn(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+        self.Res2Conv1dReluBn = Res2Conv1dReluBn(out_channels, kernel_size, stride, padding, dilation, scale=scale)
+        self.Conv1dReluBn2 = Conv1dReluBn(out_channels, out_channels, kernel_size=1, stride=1, padding=0)
+        self.SE_Connect = SE_Connect(out_channels, se_bottleneck_dim)
+
+        self.shortcut = None
+        if in_channels != out_channels:
+            self.shortcut = nn.Conv1d(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=1,
+            )
+
+    def forward(self, x):
+        residual = x
+        if self.shortcut:
+            residual = self.shortcut(x)
+
+        x = self.Conv1dReluBn1(x)
+        x = self.Res2Conv1dReluBn(x)
+        x = self.Conv1dReluBn2(x)
+        x = self.SE_Connect(x)
+
+        return x + residual
+
+
+""" Attentive weighted mean and standard deviation pooling.
+"""
+
+
+class AttentiveStatsPool(nn.Module):
+    def __init__(self, in_dim, attention_channels=128, global_context_att=False):
+        super().__init__()
+        self.global_context_att = global_context_att
+
+        # Use Conv1d with stride == 1 rather than Linear, then we don't need to transpose inputs.
+        if global_context_att:
+            self.linear1 = nn.Conv1d(in_dim * 3, attention_channels, kernel_size=1)  # equals W and b in the paper
+        else:
+            self.linear1 = nn.Conv1d(in_dim, attention_channels, kernel_size=1)  # equals W and b in the paper
+        self.linear2 = nn.Conv1d(attention_channels, in_dim, kernel_size=1)  # equals V and k in the paper
+
+    def forward(self, x):
+        if self.global_context_att:
+            context_mean = torch.mean(x, dim=-1, keepdim=True).expand_as(x)
+            context_std = torch.sqrt(torch.var(x, dim=-1, keepdim=True) + 1e-10).expand_as(x)
+            x_in = torch.cat((x, context_mean, context_std), dim=1)
+        else:
+            x_in = x
+
+        # DON'T use ReLU here! In experiments, I find ReLU hard to converge.
+        alpha = torch.tanh(self.linear1(x_in))
+        # alpha = F.relu(self.linear1(x_in))
+        alpha = torch.softmax(self.linear2(alpha), dim=2)
+        mean = torch.sum(alpha * x, dim=2)
+        residuals = torch.sum(alpha * (x**2), dim=2) - mean**2
+        std = torch.sqrt(residuals.clamp(min=1e-9))
+        return torch.cat([mean, std], dim=1)
+
+
+class ECAPA_TDNN(nn.Module):
+    def __init__(
+        self,
+        feat_dim=80,
+        channels=512,
+        emb_dim=192,
+        global_context_att=False,
+        feat_type="wavlm_large",
+        sr=16000,
+        feature_selection="hidden_states",
+        update_extract=False,
+        config_path=None,
+    ):
+        super().__init__()
+
+        self.feat_type = feat_type
+        self.feature_selection = feature_selection
+        self.update_extract = update_extract
+        self.sr = sr
+
+        torch.hub._validate_not_a_forked_repo = lambda a, b, c: True
+        try:
+            local_s3prl_path = os.path.expanduser("~/.cache/torch/hub/s3prl_s3prl_main")
+            self.feature_extract = torch.hub.load(local_s3prl_path, feat_type, source="local", config_path=config_path)
+        except:  # noqa: E722
+            self.feature_extract = torch.hub.load("s3prl/s3prl", feat_type)
+
+        if len(self.feature_extract.model.encoder.layers) == 24 and hasattr(
+            self.feature_extract.model.encoder.layers[23].self_attn, "fp32_attention"
+        ):
+            self.feature_extract.model.encoder.layers[23].self_attn.fp32_attention = False
+        if len(self.feature_extract.model.encoder.layers) == 24 and hasattr(
+            self.feature_extract.model.encoder.layers[11].self_attn, "fp32_attention"
+        ):
+            self.feature_extract.model.encoder.layers[11].self_attn.fp32_attention = False
+
+        self.feat_num = self.get_feat_num()
+        self.feature_weight = nn.Parameter(torch.zeros(self.feat_num))
+
+        if feat_type != "fbank" and feat_type != "mfcc":
+            freeze_list = ["final_proj", "label_embs_concat", "mask_emb", "project_q", "quantizer"]
+            for name, param in self.feature_extract.named_parameters():
+                for freeze_val in freeze_list:
+                    if freeze_val in name:
+                        param.requires_grad = False
+                        break
+
+        if not self.update_extract:
+            for param in self.feature_extract.parameters():
+                param.requires_grad = False
+
+        self.instance_norm = nn.InstanceNorm1d(feat_dim)
+        # self.channels = [channels] * 4 + [channels * 3]
+        self.channels = [channels] * 4 + [1536]
+
+        self.layer1 = Conv1dReluBn(feat_dim, self.channels[0], kernel_size=5, padding=2)
+        self.layer2 = SE_Res2Block(
+            self.channels[0],
+            self.channels[1],
+            kernel_size=3,
+            stride=1,
+            padding=2,
+            dilation=2,
+            scale=8,
+            se_bottleneck_dim=128,
+        )
+        self.layer3 = SE_Res2Block(
+            self.channels[1],
+            self.channels[2],
+            kernel_size=3,
+            stride=1,
+            padding=3,
+            dilation=3,
+            scale=8,
+            se_bottleneck_dim=128,
+        )
+        self.layer4 = SE_Res2Block(
+            self.channels[2],
+            self.channels[3],
+            kernel_size=3,
+            stride=1,
+            padding=4,
+            dilation=4,
+            scale=8,
+            se_bottleneck_dim=128,
+        )
+
+        # self.conv = nn.Conv1d(self.channels[-1], self.channels[-1], kernel_size=1)
+        cat_channels = channels * 3
+        self.conv = nn.Conv1d(cat_channels, self.channels[-1], kernel_size=1)
+        self.pooling = AttentiveStatsPool(
+            self.channels[-1], attention_channels=128, global_context_att=global_context_att
+        )
+        self.bn = nn.BatchNorm1d(self.channels[-1] * 2)
+        self.linear = nn.Linear(self.channels[-1] * 2, emb_dim)
+
+    def get_feat_num(self):
+        self.feature_extract.eval()
+        wav = [torch.randn(self.sr).to(next(self.feature_extract.parameters()).device)]
+        with torch.no_grad():
+            features = self.feature_extract(wav)
+        select_feature = features[self.feature_selection]
+        if isinstance(select_feature, (list, tuple)):
+            return len(select_feature)
+        else:
+            return 1
+
+    def get_feat(self, x):
+        if self.update_extract:
+            x = self.feature_extract([sample for sample in x])
+        else:
+            with torch.no_grad():
+                if self.feat_type == "fbank" or self.feat_type == "mfcc":
+                    x = self.feature_extract(x) + 1e-6  # B x feat_dim x time_len
+                else:
+                    x = self.feature_extract([sample for sample in x])
+
+        if self.feat_type == "fbank":
+            x = x.log()
+
+        if self.feat_type != "fbank" and self.feat_type != "mfcc":
+            x = x[self.feature_selection]
+            if isinstance(x, (list, tuple)):
+                x = torch.stack(x, dim=0)
+            else:
+                x = x.unsqueeze(0)
+            norm_weights = F.softmax(self.feature_weight, dim=-1).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
+            x = (norm_weights * x).sum(dim=0)
+            x = torch.transpose(x, 1, 2) + 1e-6
+
+        x = self.instance_norm(x)
+        return x
+
+    def forward(self, x):
+        x = self.get_feat(x)
+
+        out1 = self.layer1(x)
+        out2 = self.layer2(out1)
+        out3 = self.layer3(out2)
+        out4 = self.layer4(out3)
+
+        out = torch.cat([out2, out3, out4], dim=1)
+        out = F.relu(self.conv(out))
+        out = self.bn(self.pooling(out))
+        out = self.linear(out)
+
+        return out
+
+
+def ECAPA_TDNN_SMALL(
+    feat_dim,
+    emb_dim=256,
+    feat_type="wavlm_large",
+    sr=16000,
+    feature_selection="hidden_states",
+    update_extract=False,
+    config_path=None,
+):
+    return ECAPA_TDNN(
+        feat_dim=feat_dim,
+        channels=512,
+        emb_dim=emb_dim,
+        feat_type=feat_type,
+        sr=sr,
+        feature_selection=feature_selection,
+        update_extract=update_extract,
+        config_path=config_path,
+    )

src/f5_tts/eval/eval_infer_batch.py

@@ -0,0 +1,210 @@
+import os
+import sys
+
+
+sys.path.append(os.getcwd())
+
+import argparse
+import time
+from importlib.resources import files
+
+import torch
+import torchaudio
+from accelerate import Accelerator
+from hydra.utils import get_class
+from omegaconf import OmegaConf
+from tqdm import tqdm
+
+from f5_tts.eval.utils_eval import (
+    get_inference_prompt,
+    get_librispeech_test_clean_metainfo,
+    get_seedtts_testset_metainfo,
+)
+from f5_tts.infer.utils_infer import load_checkpoint, load_vocoder
+from f5_tts.model import CFM
+from f5_tts.model.utils import get_tokenizer
+
+
+accelerator = Accelerator()
+device = f"cuda:{accelerator.process_index}"
+
+
+use_ema = True
+target_rms = 0.1
+
+
+rel_path = str(files("f5_tts").joinpath("../../"))
+
+
+def main():
+    parser = argparse.ArgumentParser(description="batch inference")
+
+    parser.add_argument("-s", "--seed", default=None, type=int)
+    parser.add_argument("-n", "--expname", required=True)
+    parser.add_argument("-c", "--ckptstep", default=1250000, type=int)
+
+    parser.add_argument("-nfe", "--nfestep", default=32, type=int)
+    parser.add_argument("-o", "--odemethod", default="euler")
+    parser.add_argument("-ss", "--swaysampling", default=-1, type=float)
+
+    parser.add_argument("-t", "--testset", required=True)
+
+    args = parser.parse_args()
+
+    seed = args.seed
+    exp_name = args.expname
+    ckpt_step = args.ckptstep
+
+    nfe_step = args.nfestep
+    ode_method = args.odemethod
+    sway_sampling_coef = args.swaysampling
+
+    testset = args.testset
+
+    infer_batch_size = 1  # max frames. 1 for ddp single inference (recommended)
+    cfg_strength = 2.0
+    speed = 1.0
+    use_truth_duration = False
+    no_ref_audio = False
+
+    model_cfg = OmegaConf.load(str(files("f5_tts").joinpath(f"configs/{exp_name}.yaml")))
+    model_cls = get_class(f"f5_tts.model.{model_cfg.model.backbone}")
+    model_arc = model_cfg.model.arch
+
+    dataset_name = model_cfg.datasets.name
+    tokenizer = model_cfg.model.tokenizer
+
+    mel_spec_type = model_cfg.model.mel_spec.mel_spec_type
+    target_sample_rate = model_cfg.model.mel_spec.target_sample_rate
+    n_mel_channels = model_cfg.model.mel_spec.n_mel_channels
+    hop_length = model_cfg.model.mel_spec.hop_length
+    win_length = model_cfg.model.mel_spec.win_length
+    n_fft = model_cfg.model.mel_spec.n_fft
+
+    if testset == "ls_pc_test_clean":
+        metalst = rel_path + "/data/librispeech_pc_test_clean_cross_sentence.lst"
+        librispeech_test_clean_path = "<SOME_PATH>/LibriSpeech/test-clean"  # test-clean path
+        metainfo = get_librispeech_test_clean_metainfo(metalst, librispeech_test_clean_path)
+
+    elif testset == "seedtts_test_zh":
+        metalst = rel_path + "/data/seedtts_testset/zh/meta.lst"
+        metainfo = get_seedtts_testset_metainfo(metalst)
+
+    elif testset == "seedtts_test_en":
+        metalst = rel_path + "/data/seedtts_testset/en/meta.lst"
+        metainfo = get_seedtts_testset_metainfo(metalst)
+
+    # path to save genereted wavs
+    output_dir = (
+        f"{rel_path}/"
+        f"results/{exp_name}_{ckpt_step}/{testset}/"
+        f"seed{seed}_{ode_method}_nfe{nfe_step}_{mel_spec_type}"
+        f"{f'_ss{sway_sampling_coef}' if sway_sampling_coef else ''}"
+        f"_cfg{cfg_strength}_speed{speed}"
+        f"{'_gt-dur' if use_truth_duration else ''}"
+        f"{'_no-ref-audio' if no_ref_audio else ''}"
+    )
+
+    # -------------------------------------------------#
+
+    prompts_all = get_inference_prompt(
+        metainfo,
+        speed=speed,
+        tokenizer=tokenizer,
+        target_sample_rate=target_sample_rate,
+        n_mel_channels=n_mel_channels,
+        hop_length=hop_length,
+        mel_spec_type=mel_spec_type,
+        target_rms=target_rms,
+        use_truth_duration=use_truth_duration,
+        infer_batch_size=infer_batch_size,
+    )
+
+    # Vocoder model
+    local = False
+    if mel_spec_type == "vocos":
+        vocoder_local_path = "../checkpoints/charactr/vocos-mel-24khz"
+    elif mel_spec_type == "bigvgan":
+        vocoder_local_path = "../checkpoints/bigvgan_v2_24khz_100band_256x"
+    vocoder = load_vocoder(vocoder_name=mel_spec_type, is_local=local, local_path=vocoder_local_path)
+
+    # Tokenizer
+    vocab_char_map, vocab_size = get_tokenizer(dataset_name, tokenizer)
+
+    # Model
+    model = CFM(
+        transformer=model_cls(**model_arc, text_num_embeds=vocab_size, mel_dim=n_mel_channels),
+        mel_spec_kwargs=dict(
+            n_fft=n_fft,
+            hop_length=hop_length,
+            win_length=win_length,
+            n_mel_channels=n_mel_channels,
+            target_sample_rate=target_sample_rate,
+            mel_spec_type=mel_spec_type,
+        ),
+        odeint_kwargs=dict(
+            method=ode_method,
+        ),
+        vocab_char_map=vocab_char_map,
+    ).to(device)
+
+    ckpt_prefix = rel_path + f"/ckpts/{exp_name}/model_{ckpt_step}"
+    if os.path.exists(ckpt_prefix + ".pt"):
+        ckpt_path = ckpt_prefix + ".pt"
+    elif os.path.exists(ckpt_prefix + ".safetensors"):
+        ckpt_path = ckpt_prefix + ".safetensors"
+    else:
+        print("Loading from self-organized training checkpoints rather than released pretrained.")
+        ckpt_path = rel_path + f"/{model_cfg.ckpts.save_dir}/model_{ckpt_step}.pt"
+
+    dtype = torch.float32 if mel_spec_type == "bigvgan" else None
+    model = load_checkpoint(model, ckpt_path, device, dtype=dtype, use_ema=use_ema)
+
+    if not os.path.exists(output_dir) and accelerator.is_main_process:
+        os.makedirs(output_dir)
+
+    # start batch inference
+    accelerator.wait_for_everyone()
+    start = time.time()
+
+    with accelerator.split_between_processes(prompts_all) as prompts:
+        for prompt in tqdm(prompts, disable=not accelerator.is_local_main_process):
+            utts, ref_rms_list, ref_mels, ref_mel_lens, total_mel_lens, final_text_list = prompt
+            ref_mels = ref_mels.to(device)
+            ref_mel_lens = torch.tensor(ref_mel_lens, dtype=torch.long).to(device)
+            total_mel_lens = torch.tensor(total_mel_lens, dtype=torch.long).to(device)
+
+            # Inference
+            with torch.inference_mode():
+                generated, _ = model.sample(
+                    cond=ref_mels,
+                    text=final_text_list,
+                    duration=total_mel_lens,
+                    lens=ref_mel_lens,
+                    steps=nfe_step,
+                    cfg_strength=cfg_strength,
+                    sway_sampling_coef=sway_sampling_coef,
+                    no_ref_audio=no_ref_audio,
+                    seed=seed,
+                )
+                # Final result
+                for i, gen in enumerate(generated):
+                    gen = gen[ref_mel_lens[i] : total_mel_lens[i], :].unsqueeze(0)
+                    gen_mel_spec = gen.permute(0, 2, 1).to(torch.float32)
+                    if mel_spec_type == "vocos":
+                        generated_wave = vocoder.decode(gen_mel_spec).cpu()
+                    elif mel_spec_type == "bigvgan":
+                        generated_wave = vocoder(gen_mel_spec).squeeze(0).cpu()
+
+                    if ref_rms_list[i] < target_rms:
+                        generated_wave = generated_wave * ref_rms_list[i] / target_rms
+                    torchaudio.save(f"{output_dir}/{utts[i]}.wav", generated_wave, target_sample_rate)
+
+    accelerator.wait_for_everyone()
+    if accelerator.is_main_process:
+        timediff = time.time() - start
+        print(f"Done batch inference in {timediff / 60:.2f} minutes.")
+
+
+if __name__ == "__main__":
+    main()

src/f5_tts/eval/eval_infer_batch.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+
+# e.g. F5-TTS, 16 NFE
+accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "F5TTS_v1_Base" -t "seedtts_test_zh" -nfe 16
+accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "F5TTS_v1_Base" -t "seedtts_test_en" -nfe 16
+accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "F5TTS_v1_Base" -t "ls_pc_test_clean" -nfe 16
+
+# e.g. Vanilla E2 TTS, 32 NFE
+accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "E2TTS_Base" -c 1200000 -t "seedtts_test_zh" -o "midpoint" -ss 0
+accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "E2TTS_Base" -c 1200000 -t "seedtts_test_en" -o "midpoint" -ss 0
+accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "E2TTS_Base" -c 1200000 -t "ls_pc_test_clean" -o "midpoint" -ss 0
+
+# e.g. evaluate F5-TTS 16 NFE result on Seed-TTS test-zh
+python src/f5_tts/eval/eval_seedtts_testset.py -e wer -l zh --gen_wav_dir results/F5TTS_v1_Base_1250000/seedtts_test_zh/seed0_euler_nfe32_vocos_ss-1_cfg2.0_speed1.0 --gpu_nums 8
+python src/f5_tts/eval/eval_seedtts_testset.py -e sim -l zh --gen_wav_dir results/F5TTS_v1_Base_1250000/seedtts_test_zh/seed0_euler_nfe32_vocos_ss-1_cfg2.0_speed1.0 --gpu_nums 8
+python src/f5_tts/eval/eval_utmos.py --audio_dir results/F5TTS_v1_Base_1250000/seedtts_test_zh/seed0_euler_nfe32_vocos_ss-1_cfg2.0_speed1.0
+
+# etc.

src/f5_tts/eval/eval_librispeech_test_clean.py

@@ -0,0 +1,89 @@
+# Evaluate with Librispeech test-clean, ~3s prompt to generate 4-10s audio (the way of valle/voicebox evaluation)
+
+import argparse
+import json
+import os
+import sys
+
+
+sys.path.append(os.getcwd())
+
+import multiprocessing as mp
+from importlib.resources import files
+
+import numpy as np
+
+from f5_tts.eval.utils_eval import get_librispeech_test, run_asr_wer, run_sim
+
+
+rel_path = str(files("f5_tts").joinpath("../../"))
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-e", "--eval_task", type=str, default="wer", choices=["sim", "wer"])
+    parser.add_argument("-l", "--lang", type=str, default="en")
+    parser.add_argument("-g", "--gen_wav_dir", type=str, required=True)
+    parser.add_argument("-p", "--librispeech_test_clean_path", type=str, required=True)
+    parser.add_argument("-n", "--gpu_nums", type=int, default=8, help="Number of GPUs to use")
+    parser.add_argument("--local", action="store_true", help="Use local custom checkpoint directory")
+    return parser.parse_args()
+
+
+def main():
+    args = get_args()
+    eval_task = args.eval_task
+    lang = args.lang
+    librispeech_test_clean_path = args.librispeech_test_clean_path  # test-clean path
+    gen_wav_dir = args.gen_wav_dir
+    metalst = rel_path + "/data/librispeech_pc_test_clean_cross_sentence.lst"
+
+    gpus = list(range(args.gpu_nums))
+    test_set = get_librispeech_test(metalst, gen_wav_dir, gpus, librispeech_test_clean_path)
+
+    ## In LibriSpeech, some speakers utilized varying voice characteristics for different characters in the book,
+    ## leading to a low similarity for the ground truth in some cases.
+    # test_set = get_librispeech_test(metalst, gen_wav_dir, gpus, librispeech_test_clean_path, eval_ground_truth = True)  # eval ground truth
+
+    local = args.local
+    if local:  # use local custom checkpoint dir
+        asr_ckpt_dir = "../checkpoints/Systran/faster-whisper-large-v3"
+    else:
+        asr_ckpt_dir = ""  # auto download to cache dir
+    wavlm_ckpt_dir = "../checkpoints/UniSpeech/wavlm_large_finetune.pth"
+
+    # --------------------------------------------------------------------------
+
+    full_results = []
+    metrics = []
+
+    if eval_task == "wer":
+        with mp.Pool(processes=len(gpus)) as pool:
+            args = [(rank, lang, sub_test_set, asr_ckpt_dir) for (rank, sub_test_set) in test_set]
+            results = pool.map(run_asr_wer, args)
+            for r in results:
+                full_results.extend(r)
+    elif eval_task == "sim":
+        with mp.Pool(processes=len(gpus)) as pool:
+            args = [(rank, sub_test_set, wavlm_ckpt_dir) for (rank, sub_test_set) in test_set]
+            results = pool.map(run_sim, args)
+            for r in results:
+                full_results.extend(r)
+    else:
+        raise ValueError(f"Unknown metric type: {eval_task}")
+
+    result_path = f"{gen_wav_dir}/_{eval_task}_results.jsonl"
+    with open(result_path, "w") as f:
+        for line in full_results:
+            metrics.append(line[eval_task])
+            f.write(json.dumps(line, ensure_ascii=False) + "\n")
+        metric = round(np.mean(metrics), 5)
+        f.write(f"\n{eval_task.upper()}: {metric}\n")
+
+    print(f"\nTotal {len(metrics)} samples")
+    print(f"{eval_task.upper()}: {metric}")
+    print(f"{eval_task.upper()} results saved to {result_path}")
+
+
+if __name__ == "__main__":
+    main()

src/f5_tts/eval/eval_seedtts_testset.py

@@ -0,0 +1,88 @@
+# Evaluate with Seed-TTS testset
+
+import argparse
+import json
+import os
+import sys
+
+
+sys.path.append(os.getcwd())
+
+import multiprocessing as mp
+from importlib.resources import files
+
+import numpy as np
+
+from f5_tts.eval.utils_eval import get_seed_tts_test, run_asr_wer, run_sim
+
+
+rel_path = str(files("f5_tts").joinpath("../../"))
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-e", "--eval_task", type=str, default="wer", choices=["sim", "wer"])
+    parser.add_argument("-l", "--lang", type=str, default="en", choices=["zh", "en"])
+    parser.add_argument("-g", "--gen_wav_dir", type=str, required=True)
+    parser.add_argument("-n", "--gpu_nums", type=int, default=8, help="Number of GPUs to use")
+    parser.add_argument("--local", action="store_true", help="Use local custom checkpoint directory")
+    return parser.parse_args()
+
+
+def main():
+    args = get_args()
+    eval_task = args.eval_task
+    lang = args.lang
+    gen_wav_dir = args.gen_wav_dir
+    metalst = rel_path + f"/data/seedtts_testset/{lang}/meta.lst"  # seed-tts testset
+
+    # NOTE. paraformer-zh result will be slightly different according to the number of gpus, cuz batchsize is different
+    #       zh 1.254 seems a result of 4 workers wer_seed_tts
+    gpus = list(range(args.gpu_nums))
+    test_set = get_seed_tts_test(metalst, gen_wav_dir, gpus)
+
+    local = args.local
+    if local:  # use local custom checkpoint dir
+        if lang == "zh":
+            asr_ckpt_dir = "../checkpoints/funasr"  # paraformer-zh dir under funasr
+        elif lang == "en":
+            asr_ckpt_dir = "../checkpoints/Systran/faster-whisper-large-v3"
+    else:
+        asr_ckpt_dir = ""  # auto download to cache dir
+    wavlm_ckpt_dir = "../checkpoints/UniSpeech/wavlm_large_finetune.pth"
+
+    # --------------------------------------------------------------------------
+
+    full_results = []
+    metrics = []
+
+    if eval_task == "wer":
+        with mp.Pool(processes=len(gpus)) as pool:
+            args = [(rank, lang, sub_test_set, asr_ckpt_dir) for (rank, sub_test_set) in test_set]
+            results = pool.map(run_asr_wer, args)
+            for r in results:
+                full_results.extend(r)
+    elif eval_task == "sim":
+        with mp.Pool(processes=len(gpus)) as pool:
+            args = [(rank, sub_test_set, wavlm_ckpt_dir) for (rank, sub_test_set) in test_set]
+            results = pool.map(run_sim, args)
+            for r in results:
+                full_results.extend(r)
+    else:
+        raise ValueError(f"Unknown metric type: {eval_task}")
+
+    result_path = f"{gen_wav_dir}/_{eval_task}_results.jsonl"
+    with open(result_path, "w") as f:
+        for line in full_results:
+            metrics.append(line[eval_task])
+            f.write(json.dumps(line, ensure_ascii=False) + "\n")
+        metric = round(np.mean(metrics), 5)
+        f.write(f"\n{eval_task.upper()}: {metric}\n")
+
+    print(f"\nTotal {len(metrics)} samples")
+    print(f"{eval_task.upper()}: {metric}")
+    print(f"{eval_task.upper()} results saved to {result_path}")
+
+
+if __name__ == "__main__":
+    main()

src/f5_tts/eval/eval_utmos.py

@@ -0,0 +1,42 @@
+import argparse
+import json
+from pathlib import Path
+
+import librosa
+import torch
+from tqdm import tqdm
+
+
+def main():
+    parser = argparse.ArgumentParser(description="UTMOS Evaluation")
+    parser.add_argument("--audio_dir", type=str, required=True, help="Audio file path.")
+    parser.add_argument("--ext", type=str, default="wav", help="Audio extension.")
+    args = parser.parse_args()
+
+    device = "cuda" if torch.cuda.is_available() else "xpu" if torch.xpu.is_available() else "cpu"
+
+    predictor = torch.hub.load("tarepan/SpeechMOS:v1.2.0", "utmos22_strong", trust_repo=True)
+    predictor = predictor.to(device)
+
+    audio_paths = list(Path(args.audio_dir).rglob(f"*.{args.ext}"))
+    utmos_score = 0
+
+    utmos_result_path = Path(args.audio_dir) / "_utmos_results.jsonl"
+    with open(utmos_result_path, "w", encoding="utf-8") as f:
+        for audio_path in tqdm(audio_paths, desc="Processing"):
+            wav, sr = librosa.load(audio_path, sr=None, mono=True)
+            wav_tensor = torch.from_numpy(wav).to(device).unsqueeze(0)
+            score = predictor(wav_tensor, sr)
+            line = {}
+            line["wav"], line["utmos"] = str(audio_path.stem), score.item()
+            utmos_score += score.item()
+            f.write(json.dumps(line, ensure_ascii=False) + "\n")
+        avg_score = utmos_score / len(audio_paths) if len(audio_paths) > 0 else 0
+        f.write(f"\nUTMOS: {avg_score:.4f}\n")
+
+    print(f"UTMOS: {avg_score:.4f}")
+    print(f"UTMOS results saved to {utmos_result_path}")
+
+
+if __name__ == "__main__":
+    main()

src/f5_tts/eval/utils_eval.py

@@ -0,0 +1,419 @@
+import math
+import os
+import random
+import string
+from pathlib import Path
+
+import torch
+import torch.nn.functional as F
+import torchaudio
+from tqdm import tqdm
+
+from f5_tts.eval.ecapa_tdnn import ECAPA_TDNN_SMALL
+from f5_tts.model.modules import MelSpec
+from f5_tts.model.utils import convert_char_to_pinyin
+
+
+# seedtts testset metainfo: utt, prompt_text, prompt_wav, gt_text, gt_wav
+def get_seedtts_testset_metainfo(metalst):
+    f = open(metalst)
+    lines = f.readlines()
+    f.close()
+    metainfo = []
+    for line in lines:
+        if len(line.strip().split("|")) == 5:
+            utt, prompt_text, prompt_wav, gt_text, gt_wav = line.strip().split("|")
+        elif len(line.strip().split("|")) == 4:
+            utt, prompt_text, prompt_wav, gt_text = line.strip().split("|")
+            gt_wav = os.path.join(os.path.dirname(metalst), "wavs", utt + ".wav")
+        if not os.path.isabs(prompt_wav):
+            prompt_wav = os.path.join(os.path.dirname(metalst), prompt_wav)
+        metainfo.append((utt, prompt_text, prompt_wav, gt_text, gt_wav))
+    return metainfo
+
+
+# librispeech test-clean metainfo: gen_utt, ref_txt, ref_wav, gen_txt, gen_wav
+def get_librispeech_test_clean_metainfo(metalst, librispeech_test_clean_path):
+    f = open(metalst)
+    lines = f.readlines()
+    f.close()
+    metainfo = []
+    for line in lines:
+        ref_utt, ref_dur, ref_txt, gen_utt, gen_dur, gen_txt = line.strip().split("\t")
+
+        # ref_txt = ref_txt[0] + ref_txt[1:].lower() + '.'  # if use librispeech test-clean (no-pc)
+        ref_spk_id, ref_chaptr_id, _ = ref_utt.split("-")
+        ref_wav = os.path.join(librispeech_test_clean_path, ref_spk_id, ref_chaptr_id, ref_utt + ".flac")
+
+        # gen_txt = gen_txt[0] + gen_txt[1:].lower() + '.'  # if use librispeech test-clean (no-pc)
+        gen_spk_id, gen_chaptr_id, _ = gen_utt.split("-")
+        gen_wav = os.path.join(librispeech_test_clean_path, gen_spk_id, gen_chaptr_id, gen_utt + ".flac")
+
+        metainfo.append((gen_utt, ref_txt, ref_wav, " " + gen_txt, gen_wav))
+
+    return metainfo
+
+
+# padded to max length mel batch
+def padded_mel_batch(ref_mels):
+    max_mel_length = torch.LongTensor([mel.shape[-1] for mel in ref_mels]).amax()
+    padded_ref_mels = []
+    for mel in ref_mels:
+        padded_ref_mel = F.pad(mel, (0, max_mel_length - mel.shape[-1]), value=0)
+        padded_ref_mels.append(padded_ref_mel)
+    padded_ref_mels = torch.stack(padded_ref_mels)
+    padded_ref_mels = padded_ref_mels.permute(0, 2, 1)
+    return padded_ref_mels
+
+
+# get prompts from metainfo containing: utt, prompt_text, prompt_wav, gt_text, gt_wav
+
+
+def get_inference_prompt(
+    metainfo,
+    speed=1.0,
+    tokenizer="pinyin",
+    polyphone=True,
+    target_sample_rate=24000,
+    n_fft=1024,
+    win_length=1024,
+    n_mel_channels=100,
+    hop_length=256,
+    mel_spec_type="vocos",
+    target_rms=0.1,
+    use_truth_duration=False,
+    infer_batch_size=1,
+    num_buckets=200,
+    min_secs=3,
+    max_secs=40,
+):
+    prompts_all = []
+
+    min_tokens = min_secs * target_sample_rate // hop_length
+    max_tokens = max_secs * target_sample_rate // hop_length
+
+    batch_accum = [0] * num_buckets
+    utts, ref_rms_list, ref_mels, ref_mel_lens, total_mel_lens, final_text_list = (
+        [[] for _ in range(num_buckets)] for _ in range(6)
+    )
+
+    mel_spectrogram = MelSpec(
+        n_fft=n_fft,
+        hop_length=hop_length,
+        win_length=win_length,
+        n_mel_channels=n_mel_channels,
+        target_sample_rate=target_sample_rate,
+        mel_spec_type=mel_spec_type,
+    )
+
+    for utt, prompt_text, prompt_wav, gt_text, gt_wav in tqdm(metainfo, desc="Processing prompts..."):
+        # Audio
+        ref_audio, ref_sr = torchaudio.load(prompt_wav)
+        ref_rms = torch.sqrt(torch.mean(torch.square(ref_audio)))
+        if ref_rms < target_rms:
+            ref_audio = ref_audio * target_rms / ref_rms
+        assert ref_audio.shape[-1] > 5000, f"Empty prompt wav: {prompt_wav}, or torchaudio backend issue."
+        if ref_sr != target_sample_rate:
+            resampler = torchaudio.transforms.Resample(ref_sr, target_sample_rate)
+            ref_audio = resampler(ref_audio)
+
+        # Text
+        if len(prompt_text[-1].encode("utf-8")) == 1:
+            prompt_text = prompt_text + " "
+        text = [prompt_text + gt_text]
+        if tokenizer == "pinyin":
+            text_list = convert_char_to_pinyin(text, polyphone=polyphone)
+        else:
+            text_list = text
+
+        # to mel spectrogram
+        ref_mel = mel_spectrogram(ref_audio)
+        ref_mel = ref_mel.squeeze(0)
+
+        # Duration, mel frame length
+        ref_mel_len = ref_mel.shape[-1]
+
+        if use_truth_duration:
+            gt_audio, gt_sr = torchaudio.load(gt_wav)
+            if gt_sr != target_sample_rate:
+                resampler = torchaudio.transforms.Resample(gt_sr, target_sample_rate)
+                gt_audio = resampler(gt_audio)
+            total_mel_len = ref_mel_len + int(gt_audio.shape[-1] / hop_length / speed)
+
+            # # test vocoder resynthesis
+            # ref_audio = gt_audio
+        else:
+            ref_text_len = len(prompt_text.encode("utf-8"))
+            gen_text_len = len(gt_text.encode("utf-8"))
+            total_mel_len = ref_mel_len + int(ref_mel_len / ref_text_len * gen_text_len / speed)
+
+        # deal with batch
+        assert infer_batch_size > 0, "infer_batch_size should be greater than 0."
+        assert min_tokens <= total_mel_len <= max_tokens, (
+            f"Audio {utt} has duration {total_mel_len * hop_length // target_sample_rate}s out of range [{min_secs}, {max_secs}]."
+        )
+        bucket_i = math.floor((total_mel_len - min_tokens) / (max_tokens - min_tokens + 1) * num_buckets)
+
+        utts[bucket_i].append(utt)
+        ref_rms_list[bucket_i].append(ref_rms)
+        ref_mels[bucket_i].append(ref_mel)
+        ref_mel_lens[bucket_i].append(ref_mel_len)
+        total_mel_lens[bucket_i].append(total_mel_len)
+        final_text_list[bucket_i].extend(text_list)
+
+        batch_accum[bucket_i] += total_mel_len
+
+        if batch_accum[bucket_i] >= infer_batch_size:
+            # print(f"\n{len(ref_mels[bucket_i][0][0])}\n{ref_mel_lens[bucket_i]}\n{total_mel_lens[bucket_i]}")
+            prompts_all.append(
+                (
+                    utts[bucket_i],
+                    ref_rms_list[bucket_i],
+                    padded_mel_batch(ref_mels[bucket_i]),
+                    ref_mel_lens[bucket_i],
+                    total_mel_lens[bucket_i],
+                    final_text_list[bucket_i],
+                )
+            )
+            batch_accum[bucket_i] = 0
+            (
+                utts[bucket_i],
+                ref_rms_list[bucket_i],
+                ref_mels[bucket_i],
+                ref_mel_lens[bucket_i],
+                total_mel_lens[bucket_i],
+                final_text_list[bucket_i],
+            ) = [], [], [], [], [], []
+
+    # add residual
+    for bucket_i, bucket_frames in enumerate(batch_accum):
+        if bucket_frames > 0:
+            prompts_all.append(
+                (
+                    utts[bucket_i],
+                    ref_rms_list[bucket_i],
+                    padded_mel_batch(ref_mels[bucket_i]),
+                    ref_mel_lens[bucket_i],
+                    total_mel_lens[bucket_i],
+                    final_text_list[bucket_i],
+                )
+            )
+    # not only leave easy work for last workers
+    random.seed(666)
+    random.shuffle(prompts_all)
+
+    return prompts_all
+
+
+# get wav_res_ref_text of seed-tts test metalst
+# https://github.com/BytedanceSpeech/seed-tts-eval
+
+
+def get_seed_tts_test(metalst, gen_wav_dir, gpus):
+    f = open(metalst)
+    lines = f.readlines()
+    f.close()
+
+    test_set_ = []
+    for line in tqdm(lines):
+        if len(line.strip().split("|")) == 5:
+            utt, prompt_text, prompt_wav, gt_text, gt_wav = line.strip().split("|")
+        elif len(line.strip().split("|")) == 4:
+            utt, prompt_text, prompt_wav, gt_text = line.strip().split("|")
+
+        if not os.path.exists(os.path.join(gen_wav_dir, utt + ".wav")):
+            continue
+        gen_wav = os.path.join(gen_wav_dir, utt + ".wav")
+        if not os.path.isabs(prompt_wav):
+            prompt_wav = os.path.join(os.path.dirname(metalst), prompt_wav)
+
+        test_set_.append((gen_wav, prompt_wav, gt_text))
+
+    num_jobs = len(gpus)
+    if num_jobs == 1:
+        return [(gpus[0], test_set_)]
+
+    wav_per_job = len(test_set_) // num_jobs + 1
+    test_set = []
+    for i in range(num_jobs):
+        test_set.append((gpus[i], test_set_[i * wav_per_job : (i + 1) * wav_per_job]))
+
+    return test_set
+
+
+# get librispeech test-clean cross sentence test
+
+
+def get_librispeech_test(metalst, gen_wav_dir, gpus, librispeech_test_clean_path, eval_ground_truth=False):
+    f = open(metalst)
+    lines = f.readlines()
+    f.close()
+
+    test_set_ = []
+    for line in tqdm(lines):
+        ref_utt, ref_dur, ref_txt, gen_utt, gen_dur, gen_txt = line.strip().split("\t")
+
+        if eval_ground_truth:
+            gen_spk_id, gen_chaptr_id, _ = gen_utt.split("-")
+            gen_wav = os.path.join(librispeech_test_clean_path, gen_spk_id, gen_chaptr_id, gen_utt + ".flac")
+        else:
+            if not os.path.exists(os.path.join(gen_wav_dir, gen_utt + ".wav")):
+                raise FileNotFoundError(f"Generated wav not found: {gen_utt}")
+            gen_wav = os.path.join(gen_wav_dir, gen_utt + ".wav")
+
+        ref_spk_id, ref_chaptr_id, _ = ref_utt.split("-")
+        ref_wav = os.path.join(librispeech_test_clean_path, ref_spk_id, ref_chaptr_id, ref_utt + ".flac")
+
+        test_set_.append((gen_wav, ref_wav, gen_txt))
+
+    num_jobs = len(gpus)
+    if num_jobs == 1:
+        return [(gpus[0], test_set_)]
+
+    wav_per_job = len(test_set_) // num_jobs + 1
+    test_set = []
+    for i in range(num_jobs):
+        test_set.append((gpus[i], test_set_[i * wav_per_job : (i + 1) * wav_per_job]))
+
+    return test_set
+
+
+# load asr model
+
+
+def load_asr_model(lang, ckpt_dir=""):
+    if lang == "zh":
+        from funasr import AutoModel
+
+        model = AutoModel(
+            model=os.path.join(ckpt_dir, "paraformer-zh"),
+            # vad_model = os.path.join(ckpt_dir, "fsmn-vad"),
+            # punc_model = os.path.join(ckpt_dir, "ct-punc"),
+            # spk_model = os.path.join(ckpt_dir, "cam++"),
+            disable_update=True,
+        )  # following seed-tts setting
+    elif lang == "en":
+        from faster_whisper import WhisperModel
+
+        model_size = "large-v3" if ckpt_dir == "" else ckpt_dir
+        model = WhisperModel(model_size, device="cuda", compute_type="float16")
+    return model
+
+
+# WER Evaluation, the way Seed-TTS does
+
+
+def run_asr_wer(args):
+    rank, lang, test_set, ckpt_dir = args
+
+    if lang == "zh":
+        import zhconv
+
+        torch.cuda.set_device(rank)
+    elif lang == "en":
+        os.environ["CUDA_VISIBLE_DEVICES"] = str(rank)
+    else:
+        raise NotImplementedError(
+            "lang support only 'zh' (funasr paraformer-zh), 'en' (faster-whisper-large-v3), for now."
+        )
+
+    asr_model = load_asr_model(lang, ckpt_dir=ckpt_dir)
+
+    from zhon.hanzi import punctuation
+
+    punctuation_all = punctuation + string.punctuation
+    wer_results = []
+
+    from jiwer import compute_measures
+
+    for gen_wav, prompt_wav, truth in tqdm(test_set):
+        if lang == "zh":
+            res = asr_model.generate(input=gen_wav, batch_size_s=300, disable_pbar=True)
+            hypo = res[0]["text"]
+            hypo = zhconv.convert(hypo, "zh-cn")
+        elif lang == "en":
+            segments, _ = asr_model.transcribe(gen_wav, beam_size=5, language="en")
+            hypo = ""
+            for segment in segments:
+                hypo = hypo + " " + segment.text
+
+        raw_truth = truth
+        raw_hypo = hypo
+
+        for x in punctuation_all:
+            truth = truth.replace(x, "")
+            hypo = hypo.replace(x, "")
+
+        truth = truth.replace("  ", " ")
+        hypo = hypo.replace("  ", " ")
+
+        if lang == "zh":
+            truth = " ".join([x for x in truth])
+            hypo = " ".join([x for x in hypo])
+        elif lang == "en":
+            truth = truth.lower()
+            hypo = hypo.lower()
+
+        measures = compute_measures(truth, hypo)
+        wer = measures["wer"]
+
+        # ref_list = truth.split(" ")
+        # subs = measures["substitutions"] / len(ref_list)
+        # dele = measures["deletions"] / len(ref_list)
+        # inse = measures["insertions"] / len(ref_list)
+
+        wer_results.append(
+            {
+                "wav": Path(gen_wav).stem,
+                "truth": raw_truth,
+                "hypo": raw_hypo,
+                "wer": wer,
+            }
+        )
+
+    return wer_results
+
+
+# SIM Evaluation
+
+
+def run_sim(args):
+    rank, test_set, ckpt_dir = args
+    device = f"cuda:{rank}"
+
+    model = ECAPA_TDNN_SMALL(feat_dim=1024, feat_type="wavlm_large", config_path=None)
+    state_dict = torch.load(ckpt_dir, weights_only=True, map_location=lambda storage, loc: storage)
+    model.load_state_dict(state_dict["model"], strict=False)
+
+    use_gpu = True if torch.cuda.is_available() else False
+    if use_gpu:
+        model = model.cuda(device)
+    model.eval()
+
+    sim_results = []
+    for gen_wav, prompt_wav, truth in tqdm(test_set):
+        wav1, sr1 = torchaudio.load(gen_wav)
+        wav2, sr2 = torchaudio.load(prompt_wav)
+
+        resample1 = torchaudio.transforms.Resample(orig_freq=sr1, new_freq=16000)
+        resample2 = torchaudio.transforms.Resample(orig_freq=sr2, new_freq=16000)
+        wav1 = resample1(wav1)
+        wav2 = resample2(wav2)
+
+        if use_gpu:
+            wav1 = wav1.cuda(device)
+            wav2 = wav2.cuda(device)
+        with torch.no_grad():
+            emb1 = model(wav1)
+            emb2 = model(wav2)
+
+        sim = F.cosine_similarity(emb1, emb2)[0].item()
+        # print(f"VSim score between two audios: {sim:.4f} (-1.0, 1.0).")
+        sim_results.append(
+            {
+                "wav": Path(gen_wav).stem,
+                "sim": sim,
+            }
+        )
+
+    return sim_results

src/f5_tts/infer/README.md

@@ -0,0 +1,177 @@
+# Inference
+
+The pretrained model checkpoints can be reached at [🤗 Hugging Face](https://huggingface.co/SWivid/F5-TTS) and [🤖 Model Scope](https://www.modelscope.cn/models/SWivid/F5-TTS_Emilia-ZH-EN), or will be automatically downloaded when running inference scripts.
+
+**More checkpoints with whole community efforts can be found in [SHARED.md](SHARED.md), supporting more languages.**
+
+Currently support **30s for a single** generation, which is the **total length** (same logic if `fix_duration`) including both prompt and output audio. However, `infer_cli` and `infer_gradio` will automatically do chunk generation for longer text. Long reference audio will be **clip short to ~12s**.
+
+To avoid possible inference failures, make sure you have seen through the following instructions.
+
+- Use reference audio <12s and leave proper silence space (e.g. 1s) at the end. Otherwise there is a risk of truncating in the middle of word, leading to suboptimal generation.
+- <ins>Uppercased letters</ins> (best with form like K.F.C.) will be uttered letter by letter, and lowercased letters used for common words. 
+- Add some spaces (blank: " ") or punctuations (e.g. "," ".") <ins>to explicitly introduce some pauses</ins>.
+- If English punctuation marks the end of a sentence, make sure there is a space " " after it. Otherwise not regarded as when chunk.
+- <ins>Preprocess numbers</ins> to Chinese letters if you want to have them read in Chinese, otherwise in English.
+- If the generation output is blank (pure silence), <ins>check for FFmpeg installation</ins>.
+- Try <ins>turn off `use_ema` if using an early-stage</ins> finetuned checkpoint (which goes just few updates).
+
+
+## Gradio App
+
+Currently supported features:
+
+- Basic TTS with Chunk Inference
+- Multi-Style / Multi-Speaker Generation
+- Voice Chat powered by Qwen2.5-3B-Instruct
+- [Custom inference with more language support](SHARED.md)
+
+The cli command `f5-tts_infer-gradio` equals to `python src/f5_tts/infer/infer_gradio.py`, which launches a Gradio APP (web interface) for inference.
+
+The script will load model checkpoints from Huggingface. You can also manually download files and update the path to `load_model()` in `infer_gradio.py`. Currently only load TTS models first, will load ASR model to do transcription if `ref_text` not provided, will load LLM model if use Voice Chat.
+
+More flags options:
+
+```bash
+# Automatically launch the interface in the default web browser
+f5-tts_infer-gradio --inbrowser
+
+# Set the root path of the application, if it's not served from the root ("/") of the domain
+# For example, if the application is served at "https://example.com/myapp"
+f5-tts_infer-gradio --root_path "/myapp"
+```
+
+Could also be used as a component for larger application:
+```python
+import gradio as gr
+from f5_tts.infer.infer_gradio import app
+
+with gr.Blocks() as main_app:
+    gr.Markdown("# This is an example of using F5-TTS within a bigger Gradio app")
+
+    # ... other Gradio components
+
+    app.render()
+
+main_app.launch()
+```
+
+
+## CLI Inference
+
+The cli command `f5-tts_infer-cli` equals to `python src/f5_tts/infer/infer_cli.py`, which is a command line tool for inference.
+
+The script will load model checkpoints from Huggingface. You can also manually download files and use `--ckpt_file` to specify the model you want to load, or directly update in `infer_cli.py`.
+
+For change vocab.txt use `--vocab_file` to provide your `vocab.txt` file.
+
+Basically you can inference with flags:
+```bash
+# Leave --ref_text "" will have ASR model transcribe (extra GPU memory usage)
+f5-tts_infer-cli \
+--model F5TTS_v1_Base \
+--ref_audio "ref_audio.wav" \
+--ref_text "The content, subtitle or transcription of reference audio." \
+--gen_text "Some text you want TTS model generate for you."
+
+# Use BigVGAN as vocoder. Currently only support F5TTS_Base. 
+f5-tts_infer-cli --model F5TTS_Base --vocoder_name bigvgan --load_vocoder_from_local
+
+# Use custom path checkpoint, e.g.
+f5-tts_infer-cli --ckpt_file ckpts/F5TTS_v1_Base/model_1250000.safetensors
+
+# More instructions
+f5-tts_infer-cli --help
+```
+
+And a `.toml` file would help with more flexible usage.
+
+```bash
+f5-tts_infer-cli -c custom.toml
+```
+
+For example, you can use `.toml` to pass in variables, refer to `src/f5_tts/infer/examples/basic/basic.toml`:
+
+```toml
+# F5TTS_v1_Base | E2TTS_Base
+model = "F5TTS_v1_Base"
+ref_audio = "infer/examples/basic/basic_ref_en.wav"
+# If an empty "", transcribes the reference audio automatically.
+ref_text = "Some call me nature, others call me mother nature."
+gen_text = "I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring."
+# File with text to generate. Ignores the text above.
+gen_file = ""
+remove_silence = false
+output_dir = "tests"
+```
+
+You can also leverage `.toml` file to do multi-style generation, refer to `src/f5_tts/infer/examples/multi/story.toml`.
+
+```toml
+# F5TTS_v1_Base | E2TTS_Base
+model = "F5TTS_v1_Base"
+ref_audio = "infer/examples/multi/main.flac"
+# If an empty "", transcribes the reference audio automatically.
+ref_text = ""
+gen_text = ""
+# File with text to generate. Ignores the text above.
+gen_file = "infer/examples/multi/story.txt"
+remove_silence = true
+output_dir = "tests"
+
+[voices.town]
+ref_audio = "infer/examples/multi/town.flac"
+ref_text = ""
+
+[voices.country]
+ref_audio = "infer/examples/multi/country.flac"
+ref_text = ""
+```
+You should mark the voice with `[main]` `[town]` `[country]` whenever you want to change voice, refer to `src/f5_tts/infer/examples/multi/story.txt`.
+
+## API Usage
+
+```python
+from importlib.resources import files
+from f5_tts.api import F5TTS
+
+f5tts = F5TTS()
+wav, sr, spec = f5tts.infer(
+    ref_file=str(files("f5_tts").joinpath("infer/examples/basic/basic_ref_en.wav")),
+    ref_text="some call me nature, others call me mother nature.",
+    gen_text="""I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring. Respect me and I'll nurture you; ignore me and you shall face the consequences.""",
+    file_wave=str(files("f5_tts").joinpath("../../tests/api_out.wav")),
+    file_spec=str(files("f5_tts").joinpath("../../tests/api_out.png")),
+    seed=None,
+)
+```
+Check [api.py](../api.py) for more details.
+
+## TensorRT-LLM Deployment
+
+See [detailed instructions](../runtime/triton_trtllm/README.md) for more information.
+
+## Socket Real-time Service
+
+Real-time voice output with chunk stream:
+
+```bash
+# Start socket server
+python src/f5_tts/socket_server.py
+
+# If PyAudio not installed
+sudo apt-get install portaudio19-dev
+pip install pyaudio
+
+# Communicate with socket client
+python src/f5_tts/socket_client.py
+```
+
+## Speech Editing
+
+To test speech editing capabilities, use the following command:
+
+```bash
+python src/f5_tts/infer/speech_edit.py
+```
+

src/f5_tts/infer/SHARED.md

@@ -0,0 +1,193 @@
+<!-- omit in toc -->
+# Shared Model Cards
+
+<!-- omit in toc -->
+### **Prerequisites of using**
+- This document is serving as a quick lookup table for the community training/finetuning result, with various language support.
+- The models in this repository are open source and are based on voluntary contributions from contributors.
+- The use of models must be conditioned on respect for the respective creators. The convenience brought comes from their efforts.
+
+<!-- omit in toc -->
+### **Welcome to share here**
+- Have a pretrained/finetuned result: model checkpoint (pruned best to facilitate inference, i.e. leave only `ema_model_state_dict`) and corresponding vocab file (for tokenization).
+- Host a public [huggingface model repository](https://huggingface.co/new) and upload the model related files.
+- Make a pull request adding a model card to the current page, i.e. `src\f5_tts\infer\SHARED.md`.
+
+<!-- omit in toc -->
+### Supported Languages
+- [Multilingual](#multilingual)
+    - [F5-TTS v1 v0 Base @ zh \& en @ F5-TTS](#f5-tts-v1-v0-base--zh--en--f5-tts)
+- [English](#english)
+- [Finnish](#finnish)
+    - [F5-TTS Base @ fi @ AsmoKoskinen](#f5-tts-base--fi--asmokoskinen)
+- [French](#french)
+    - [F5-TTS Base @ fr @ RASPIAUDIO](#f5-tts-base--fr--raspiaudio)
+- [German](#german)
+    - [F5-TTS Base @ de @ hvoss-techfak](#f5-tts-base--de--hvoss-techfak)
+- [Hindi](#hindi)
+    - [F5-TTS Small @ hi @ SPRINGLab](#f5-tts-small--hi--springlab)
+- [Italian](#italian)
+    - [F5-TTS Base @ it @ alien79](#f5-tts-base--it--alien79)
+- [Japanese](#japanese)
+    - [F5-TTS Base @ ja @ Jmica](#f5-tts-base--ja--jmica)
+- [Mandarin](#mandarin)
+- [Russian](#russian)
+    - [F5-TTS Base @ ru @ HotDro4illa](#f5-tts-base--ru--hotdro4illa)
+- [Spanish](#spanish)
+    - [F5-TTS Base @ es @ jpgallegoar](#f5-tts-base--es--jpgallegoar)
+
+
+## Multilingual
+
+#### F5-TTS v1 v0 Base @ zh & en @ F5-TTS
+|Model|🤗Hugging Face|Data (Hours)|Model License|
+|:---:|:------------:|:-----------:|:-------------:|
+|F5-TTS v1 Base|[ckpt & vocab](https://huggingface.co/SWivid/F5-TTS/tree/main/F5TTS_v1_Base)|[Emilia 95K zh&en](https://huggingface.co/datasets/amphion/Emilia-Dataset/tree/fc71e07)|cc-by-nc-4.0|
+
+```bash
+Model: hf://SWivid/F5-TTS/F5TTS_v1_Base/model_1250000.safetensors
+# A Variant Model: hf://SWivid/F5-TTS/F5TTS_v1_Base_no_zero_init/model_1250000.safetensors
+Vocab: hf://SWivid/F5-TTS/F5TTS_v1_Base/vocab.txt
+Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "conv_layers": 4}
+```
+
+|Model|🤗Hugging Face|Data (Hours)|Model License|
+|:---:|:------------:|:-----------:|:-------------:|
+|F5-TTS Base|[ckpt & vocab](https://huggingface.co/SWivid/F5-TTS/tree/main/F5TTS_Base)|[Emilia 95K zh&en](https://huggingface.co/datasets/amphion/Emilia-Dataset/tree/fc71e07)|cc-by-nc-4.0|
+
+```bash
+Model: hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.safetensors
+Vocab: hf://SWivid/F5-TTS/F5TTS_Base/vocab.txt
+Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "text_mask_padding": False, "conv_layers": 4, "pe_attn_head": 1}
+```
+
+*Other infos, e.g. Author info, Github repo, Link to some sampled results, Usage instruction, Tutorial (Blog, Video, etc.) ...*
+
+
+## English
+
+
+## Finnish
+
+#### F5-TTS Base @ fi @ AsmoKoskinen
+|Model|🤗Hugging Face|Data|Model License|
+|:---:|:------------:|:-----------:|:-------------:|
+|F5-TTS Base|[ckpt & vocab](https://huggingface.co/AsmoKoskinen/F5-TTS_Finnish_Model)|[Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0), [Vox Populi](https://huggingface.co/datasets/facebook/voxpopuli)|cc-by-nc-4.0|
+
+```bash
+Model: hf://AsmoKoskinen/F5-TTS_Finnish_Model/model_common_voice_fi_vox_populi_fi_20241206.safetensors
+Vocab: hf://AsmoKoskinen/F5-TTS_Finnish_Model/vocab.txt
+Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "text_mask_padding": False, "conv_layers": 4, "pe_attn_head": 1}
+```
+
+
+## French
+
+#### F5-TTS Base @ fr @ RASPIAUDIO
+|Model|🤗Hugging Face|Data (Hours)|Model License|
+|:---:|:------------:|:-----------:|:-------------:|
+|F5-TTS Base|[ckpt & vocab](https://huggingface.co/RASPIAUDIO/F5-French-MixedSpeakers-reduced)|[LibriVox](https://librivox.org/)|cc-by-nc-4.0|
+
+```bash
+Model: hf://RASPIAUDIO/F5-French-MixedSpeakers-reduced/model_last_reduced.pt
+Vocab: hf://RASPIAUDIO/F5-French-MixedSpeakers-reduced/vocab.txt
+Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "text_mask_padding": False, "conv_layers": 4, "pe_attn_head": 1}
+```
+
+- [Online Inference with Hugging Face Space](https://huggingface.co/spaces/RASPIAUDIO/f5-tts_french).
+- [Tutorial video to train a new language model](https://www.youtube.com/watch?v=UO4usaOojys).
+- [Discussion about this training can be found here](https://github.com/SWivid/F5-TTS/issues/434).
+
+
+## German
+
+#### F5-TTS Base @ de @ hvoss-techfak
+|Model|🤗Hugging Face|Data (Hours)|Model License|
+|:---:|:------------:|:-----------:|:-------------:|
+|F5-TTS Base|[ckpt & vocab](https://huggingface.co/hvoss-techfak/F5-TTS-German)|[Mozilla Common Voice 19.0](https://commonvoice.mozilla.org/en/datasets) & 800 hours Crowdsourced |cc-by-nc-4.0|
+
+```bash
+Model: hf://hvoss-techfak/F5-TTS-German/model_f5tts_german.pt
+Vocab: hf://hvoss-techfak/F5-TTS-German/vocab.txt
+Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "text_mask_padding": False, "conv_layers": 4, "pe_attn_head": 1}
+```
+
+- Finetuned by [@hvoss-techfak](https://github.com/hvoss-techfak)
+
+
+## Hindi
+
+#### F5-TTS Small @ hi @ SPRINGLab
+|Model|🤗Hugging Face|Data (Hours)|Model License|
+|:---:|:------------:|:-----------:|:-------------:|
+|F5-TTS Small|[ckpt & vocab](https://huggingface.co/SPRINGLab/F5-Hindi-24KHz)|[IndicTTS Hi](https://huggingface.co/datasets/SPRINGLab/IndicTTS-Hindi) & [IndicVoices-R Hi](https://huggingface.co/datasets/SPRINGLab/IndicVoices-R_Hindi) |cc-by-4.0|
+
+```bash
+Model: hf://SPRINGLab/F5-Hindi-24KHz/model_2500000.safetensors
+Vocab: hf://SPRINGLab/F5-Hindi-24KHz/vocab.txt
+Config: {"dim": 768, "depth": 18, "heads": 12, "ff_mult": 2, "text_dim": 512, "text_mask_padding": False, "conv_layers": 4, "pe_attn_head": 1}
+```
+
+- Authors: SPRING Lab, Indian Institute of Technology, Madras
+- Website: https://asr.iitm.ac.in/
+
+
+## Italian
+
+#### F5-TTS Base @ it @ alien79
+|Model|🤗Hugging Face|Data|Model License|
+|:---:|:------------:|:-----------:|:-------------:|
+|F5-TTS Base|[ckpt & vocab](https://huggingface.co/alien79/F5-TTS-italian)|[ylacombe/cml-tts](https://huggingface.co/datasets/ylacombe/cml-tts) |cc-by-nc-4.0|
+
+```bash
+Model: hf://alien79/F5-TTS-italian/model_159600.safetensors
+Vocab: hf://alien79/F5-TTS-italian/vocab.txt
+Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "text_mask_padding": False, "conv_layers": 4, "pe_attn_head": 1}
+```
+
+- Trained by [Mithril Man](https://github.com/MithrilMan)
+- Model details on [hf project home](https://huggingface.co/alien79/F5-TTS-italian)
+- Open to collaborations to further improve the model
+
+
+## Japanese
+
+#### F5-TTS Base @ ja @ Jmica
+|Model|🤗Hugging Face|Data (Hours)|Model License|
+|:---:|:------------:|:-----------:|:-------------:|
+|F5-TTS Base|[ckpt & vocab](https://huggingface.co/Jmica/F5TTS/tree/main/JA_21999120)|[Emilia 1.7k JA](https://huggingface.co/datasets/amphion/Emilia-Dataset/tree/fc71e07) & [Galgame Dataset 5.4k](https://huggingface.co/datasets/OOPPEENN/Galgame_Dataset)|cc-by-nc-4.0|
+
+```bash
+Model: hf://Jmica/F5TTS/JA_21999120/model_21999120.pt
+Vocab: hf://Jmica/F5TTS/JA_21999120/vocab_japanese.txt
+Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "text_mask_padding": False, "conv_layers": 4, "pe_attn_head": 1}
+```
+
+
+## Mandarin
+
+
+## Russian
+
+#### F5-TTS Base @ ru @ HotDro4illa
+|Model|🤗Hugging Face|Data (Hours)|Model License|
+|:---:|:------------:|:-----------:|:-------------:|
+|F5-TTS Base|[ckpt & vocab](https://huggingface.co/hotstone228/F5-TTS-Russian)|[Common voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0)|cc-by-nc-4.0|
+
+```bash
+Model: hf://hotstone228/F5-TTS-Russian/model_last.safetensors
+Vocab: hf://hotstone228/F5-TTS-Russian/vocab.txt
+Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "text_mask_padding": False, "conv_layers": 4, "pe_attn_head": 1}
+```
+- Finetuned by [HotDro4illa](https://github.com/HotDro4illa)
+- Any improvements are welcome
+
+
+## Spanish
+
+#### F5-TTS Base @ es @ jpgallegoar
+|Model|🤗Hugging Face|Data (Hours)|Model License|
+|:---:|:------------:|:-----------:|:-------------:|
+|F5-TTS Base|[ckpt & vocab](https://huggingface.co/jpgallegoar/F5-Spanish)|[Voxpopuli](https://huggingface.co/datasets/facebook/voxpopuli) & Crowdsourced & TEDx, 218 hours|cc0-1.0|
+
+- @jpgallegoar [GitHub repo](https://github.com/jpgallegoar/Spanish-F5), Jupyter Notebook and Gradio usage for Spanish model.

src/f5_tts/infer/examples/basic/basic.toml

@@ -0,0 +1,11 @@
+# F5TTS_v1_Base | E2TTS_Base
+model = "F5TTS_v1_Base"
+ref_audio = "infer/examples/basic/basic_ref_en.wav"
+# If an empty "", transcribes the reference audio automatically.
+ref_text = "Some call me nature, others call me mother nature."
+gen_text = "I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring."
+# File with text to generate. Ignores the text above.
+gen_file = ""
+remove_silence = false
+output_dir = "tests"
+output_file = "infer_cli_basic.wav"

src/f5_tts/infer/examples/basic/basic_ref_en.wav

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+oid sha256:b0e22048e72414fcc1e6b6342e47a774d748a195ed34e4a5b3fcf416707f2b71
+size 256018

src/f5_tts/infer/examples/basic/basic_ref_zh.wav

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+oid sha256:96724a113240d1f82c6ded1334122f0176b96c9226ccd3c919e625bcfd2a3ede
+size 324558

src/f5_tts/infer/examples/multi/country.flac

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+oid sha256:bb15708b4b3875e37beec46591a5d89e1a9a63fdad3b8fe4a5c8738f4f554400
+size 180321

src/f5_tts/infer/examples/multi/main.flac

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4abb1107771ce7e14926fde879b959dde6db6e572476b98684f04e45e978ab19
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src/f5_tts/infer/examples/multi/story.toml

@@ -0,0 +1,20 @@
+# F5TTS_v1_Base | E2TTS_Base
+model = "F5TTS_v1_Base"
+ref_audio = "infer/examples/multi/main.flac"
+# If an empty "", transcribes the reference audio automatically.
+ref_text = ""
+gen_text = ""
+# File with text to generate. Ignores the text above.
+gen_file = "infer/examples/multi/story.txt"
+remove_silence = true
+output_dir = "tests"
+output_file = "infer_cli_story.wav"
+
+[voices.town]
+ref_audio = "infer/examples/multi/town.flac"
+ref_text = ""
+speed = 0.8  # will ignore global speed
+
+[voices.country]
+ref_audio = "infer/examples/multi/country.flac"
+ref_text = ""

src/f5_tts/infer/examples/multi/story.txt

@@ -0,0 +1 @@
+A Town Mouse and a Country Mouse were acquaintances, and the Country Mouse one day invited his friend to come and see him at his home in the fields. The Town Mouse came, and they sat down to a dinner of barleycorns and roots, the latter of which had a distinctly earthy flavour. The fare was not much to the taste of the guest, and presently he broke out with [town] "My poor dear friend, you live here no better than the ants! Now, you should just see how I fare! My larder is a regular horn of plenty. You must come and stay with me, and I promise you you shall live on the fat of the land." [main] So when he returned to town he took the Country Mouse with him, and showed him into a larder containing flour and oatmeal and figs and honey and dates. The Country Mouse had never seen anything like it, and sat down to enjoy the luxuries his friend provided: but before they had well begun, the door of the larder opened and someone came in. The two Mice scampered off and hid themselves in a narrow and exceedingly uncomfortable hole. Presently, when all was quiet, they ventured out again; but someone else came in, and off they scuttled again. This was too much for the visitor. [country] "Goodbye," [main] said he, [country] "I'm off. You live in the lap of luxury, I can see, but you are surrounded by dangers; whereas at home I can enjoy my simple dinner of roots and corn in peace."

src/f5_tts/infer/examples/multi/town.flac

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+size 229383

src/f5_tts/infer/examples/vocab.txt

@@ -0,0 +1,2545 @@
+ 
+!
+"
+#
+$
+%
+&
+'
+(
+)
+*
++
+,
+-
+.
+/
+0
+1
+2
+3
+4
+5
+6
+7
+8
+9
+:
+;
+=
+>
+?
+@
+A
+B
+C
+D
+E
+F
+G
+H
+I
+J
+K
+L
+M
+N
+O
+P
+Q
+R
+S
+T
+U
+V
+W
+X
+Y
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+히
+힘
+ﷺ
+ﷻ
+！
+，
+？
+�
+𠮶

src/f5_tts/infer/infer_cli.py

@@ -0,0 +1,383 @@
+import argparse
+import codecs
+import os
+import re
+from datetime import datetime
+from importlib.resources import files
+from pathlib import Path
+
+import numpy as np
+import soundfile as sf
+import tomli
+from cached_path import cached_path
+from hydra.utils import get_class
+from omegaconf import OmegaConf
+from unidecode import unidecode
+
+from f5_tts.infer.utils_infer import (
+    cfg_strength,
+    cross_fade_duration,
+    device,
+    fix_duration,
+    infer_process,
+    load_model,
+    load_vocoder,
+    mel_spec_type,
+    nfe_step,
+    preprocess_ref_audio_text,
+    remove_silence_for_generated_wav,
+    speed,
+    sway_sampling_coef,
+    target_rms,
+)
+
+
+parser = argparse.ArgumentParser(
+    prog="python3 infer-cli.py",
+    description="Commandline interface for E2/F5 TTS with Advanced Batch Processing.",
+    epilog="Specify options above to override one or more settings from config.",
+)
+parser.add_argument(
+    "-c",
+    "--config",
+    type=str,
+    default=os.path.join(files("f5_tts").joinpath("infer/examples/basic"), "basic.toml"),
+    help="The configuration file, default see infer/examples/basic/basic.toml",
+)
+
+
+# Note. Not to provide default value here in order to read default from config file
+
+parser.add_argument(
+    "-m",
+    "--model",
+    type=str,
+    help="The model name: F5TTS_v1_Base | F5TTS_Base | E2TTS_Base | etc.",
+)
+parser.add_argument(
+    "-mc",
+    "--model_cfg",
+    type=str,
+    help="The path to F5-TTS model config file .yaml",
+)
+parser.add_argument(
+    "-p",
+    "--ckpt_file",
+    type=str,
+    help="The path to model checkpoint .pt, leave blank to use default",
+)
+parser.add_argument(
+    "-v",
+    "--vocab_file",
+    type=str,
+    help="The path to vocab file .txt, leave blank to use default",
+)
+parser.add_argument(
+    "-r",
+    "--ref_audio",
+    type=str,
+    help="The reference audio file.",
+)
+parser.add_argument(
+    "-s",
+    "--ref_text",
+    type=str,
+    help="The transcript/subtitle for the reference audio",
+)
+parser.add_argument(
+    "-t",
+    "--gen_text",
+    type=str,
+    help="The text to make model synthesize a speech",
+)
+parser.add_argument(
+    "-f",
+    "--gen_file",
+    type=str,
+    help="The file with text to generate, will ignore --gen_text",
+)
+parser.add_argument(
+    "-o",
+    "--output_dir",
+    type=str,
+    help="The path to output folder",
+)
+parser.add_argument(
+    "-w",
+    "--output_file",
+    type=str,
+    help="The name of output file",
+)
+parser.add_argument(
+    "--save_chunk",
+    action="store_true",
+    help="To save each audio chunks during inference",
+)
+parser.add_argument(
+    "--no_legacy_text",
+    action="store_false",
+    help="Not to use lossy ASCII transliterations of unicode text in saved file names.",
+)
+parser.add_argument(
+    "--remove_silence",
+    action="store_true",
+    help="To remove long silence found in ouput",
+)
+parser.add_argument(
+    "--load_vocoder_from_local",
+    action="store_true",
+    help="To load vocoder from local dir, default to ../checkpoints/vocos-mel-24khz",
+)
+parser.add_argument(
+    "--vocoder_name",
+    type=str,
+    choices=["vocos", "bigvgan"],
+    help=f"Used vocoder name: vocos | bigvgan, default {mel_spec_type}",
+)
+parser.add_argument(
+    "--target_rms",
+    type=float,
+    help=f"Target output speech loudness normalization value, default {target_rms}",
+)
+parser.add_argument(
+    "--cross_fade_duration",
+    type=float,
+    help=f"Duration of cross-fade between audio segments in seconds, default {cross_fade_duration}",
+)
+parser.add_argument(
+    "--nfe_step",
+    type=int,
+    help=f"The number of function evaluation (denoising steps), default {nfe_step}",
+)
+parser.add_argument(
+    "--cfg_strength",
+    type=float,
+    help=f"Classifier-free guidance strength, default {cfg_strength}",
+)
+parser.add_argument(
+    "--sway_sampling_coef",
+    type=float,
+    help=f"Sway Sampling coefficient, default {sway_sampling_coef}",
+)
+parser.add_argument(
+    "--speed",
+    type=float,
+    help=f"The speed of the generated audio, default {speed}",
+)
+parser.add_argument(
+    "--fix_duration",
+    type=float,
+    help=f"Fix the total duration (ref and gen audios) in seconds, default {fix_duration}",
+)
+parser.add_argument(
+    "--device",
+    type=str,
+    help="Specify the device to run on",
+)
+args = parser.parse_args()
+
+
+# config file
+
+config = tomli.load(open(args.config, "rb"))
+
+
+# command-line interface parameters
+
+model = args.model or config.get("model", "F5TTS_v1_Base")
+ckpt_file = args.ckpt_file or config.get("ckpt_file", "")
+vocab_file = args.vocab_file or config.get("vocab_file", "")
+
+ref_audio = args.ref_audio or config.get("ref_audio", "infer/examples/basic/basic_ref_en.wav")
+ref_text = (
+    args.ref_text
+    if args.ref_text is not None
+    else config.get("ref_text", "Some call me nature, others call me mother nature.")
+)
+gen_text = args.gen_text or config.get("gen_text", "Here we generate something just for test.")
+gen_file = args.gen_file or config.get("gen_file", "")
+
+output_dir = args.output_dir or config.get("output_dir", "tests")
+output_file = args.output_file or config.get(
+    "output_file", f"infer_cli_{datetime.now().strftime(r'%Y%m%d_%H%M%S')}.wav"
+)
+
+save_chunk = args.save_chunk or config.get("save_chunk", False)
+use_legacy_text = args.no_legacy_text or config.get("no_legacy_text", False)  # no_legacy_text is a store_false arg
+if save_chunk and use_legacy_text:
+    print(
+        "\nWarning to --save_chunk: lossy ASCII transliterations of unicode text for legacy (.wav) file names, --no_legacy_text to disable.\n"
+    )
+
+remove_silence = args.remove_silence or config.get("remove_silence", False)
+load_vocoder_from_local = args.load_vocoder_from_local or config.get("load_vocoder_from_local", False)
+
+vocoder_name = args.vocoder_name or config.get("vocoder_name", mel_spec_type)
+target_rms = args.target_rms or config.get("target_rms", target_rms)
+cross_fade_duration = args.cross_fade_duration or config.get("cross_fade_duration", cross_fade_duration)
+nfe_step = args.nfe_step or config.get("nfe_step", nfe_step)
+cfg_strength = args.cfg_strength or config.get("cfg_strength", cfg_strength)
+sway_sampling_coef = args.sway_sampling_coef or config.get("sway_sampling_coef", sway_sampling_coef)
+speed = args.speed or config.get("speed", speed)
+fix_duration = args.fix_duration or config.get("fix_duration", fix_duration)
+device = args.device or config.get("device", device)
+
+
+# patches for pip pkg user
+if "infer/examples/" in ref_audio:
+    ref_audio = str(files("f5_tts").joinpath(f"{ref_audio}"))
+if "infer/examples/" in gen_file:
+    gen_file = str(files("f5_tts").joinpath(f"{gen_file}"))
+if "voices" in config:
+    for voice in config["voices"]:
+        voice_ref_audio = config["voices"][voice]["ref_audio"]
+        if "infer/examples/" in voice_ref_audio:
+            config["voices"][voice]["ref_audio"] = str(files("f5_tts").joinpath(f"{voice_ref_audio}"))
+
+
+# ignore gen_text if gen_file provided
+
+if gen_file:
+    gen_text = codecs.open(gen_file, "r", "utf-8").read()
+
+
+# output path
+
+wave_path = Path(output_dir) / output_file
+# spectrogram_path = Path(output_dir) / "infer_cli_out.png"
+if save_chunk:
+    output_chunk_dir = os.path.join(output_dir, f"{Path(output_file).stem}_chunks")
+    if not os.path.exists(output_chunk_dir):
+        os.makedirs(output_chunk_dir)
+
+
+# load vocoder
+
+if vocoder_name == "vocos":
+    vocoder_local_path = "../checkpoints/vocos-mel-24khz"
+elif vocoder_name == "bigvgan":
+    vocoder_local_path = "../checkpoints/bigvgan_v2_24khz_100band_256x"
+
+vocoder = load_vocoder(
+    vocoder_name=vocoder_name, is_local=load_vocoder_from_local, local_path=vocoder_local_path, device=device
+)
+
+
+# load TTS model
+
+model_cfg = OmegaConf.load(
+    args.model_cfg or config.get("model_cfg", str(files("f5_tts").joinpath(f"configs/{model}.yaml")))
+)
+model_cls = get_class(f"f5_tts.model.{model_cfg.model.backbone}")
+model_arc = model_cfg.model.arch
+
+repo_name, ckpt_step, ckpt_type = "F5-TTS", 1250000, "safetensors"
+
+if model != "F5TTS_Base":
+    assert vocoder_name == model_cfg.model.mel_spec.mel_spec_type
+
+# override for previous models
+if model == "F5TTS_Base":
+    if vocoder_name == "vocos":
+        ckpt_step = 1200000
+    elif vocoder_name == "bigvgan":
+        model = "F5TTS_Base_bigvgan"
+        ckpt_type = "pt"
+elif model == "E2TTS_Base":
+    repo_name = "E2-TTS"
+    ckpt_step = 1200000
+
+if not ckpt_file:
+    ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{model}/model_{ckpt_step}.{ckpt_type}"))
+
+print(f"Using {model}...")
+ema_model = load_model(
+    model_cls, model_arc, ckpt_file, mel_spec_type=vocoder_name, vocab_file=vocab_file, device=device
+)
+
+
+# inference process
+
+
+def main():
+    main_voice = {"ref_audio": ref_audio, "ref_text": ref_text}
+    if "voices" not in config:
+        voices = {"main": main_voice}
+    else:
+        voices = config["voices"]
+        voices["main"] = main_voice
+    for voice in voices:
+        print("Voice:", voice)
+        print("ref_audio ", voices[voice]["ref_audio"])
+        voices[voice]["ref_audio"], voices[voice]["ref_text"] = preprocess_ref_audio_text(
+            voices[voice]["ref_audio"], voices[voice]["ref_text"]
+        )
+        print("ref_audio_", voices[voice]["ref_audio"], "\n\n")
+
+    generated_audio_segments = []
+    reg1 = r"(?=\[\w+\])"
+    chunks = re.split(reg1, gen_text)
+    reg2 = r"\[(\w+)\]"
+    for text in chunks:
+        if not text.strip():
+            continue
+        match = re.match(reg2, text)
+        if match:
+            voice = match[1]
+        else:
+            print("No voice tag found, using main.")
+            voice = "main"
+        if voice not in voices:
+            print(f"Voice {voice} not found, using main.")
+            voice = "main"
+        text = re.sub(reg2, "", text)
+        ref_audio_ = voices[voice]["ref_audio"]
+        ref_text_ = voices[voice]["ref_text"]
+        local_speed = voices[voice].get("speed", speed)
+        gen_text_ = text.strip()
+        print(f"Voice: {voice}")
+        audio_segment, final_sample_rate, spectrogram = infer_process(
+            ref_audio_,
+            ref_text_,
+            gen_text_,
+            ema_model,
+            vocoder,
+            mel_spec_type=vocoder_name,
+            target_rms=target_rms,
+            cross_fade_duration=cross_fade_duration,
+            nfe_step=nfe_step,
+            cfg_strength=cfg_strength,
+            sway_sampling_coef=sway_sampling_coef,
+            speed=local_speed,
+            fix_duration=fix_duration,
+            device=device,
+        )
+        generated_audio_segments.append(audio_segment)
+
+        if save_chunk:
+            if len(gen_text_) > 200:
+                gen_text_ = gen_text_[:200] + " ... "
+            if use_legacy_text:
+                gen_text_ = unidecode(gen_text_)
+            sf.write(
+                os.path.join(output_chunk_dir, f"{len(generated_audio_segments) - 1}_{gen_text_}.wav"),
+                audio_segment,
+                final_sample_rate,
+            )
+
+    if generated_audio_segments:
+        final_wave = np.concatenate(generated_audio_segments)
+
+        if not os.path.exists(output_dir):
+            os.makedirs(output_dir)
+
+        with open(wave_path, "wb") as f:
+            sf.write(f.name, final_wave, final_sample_rate)
+            # Remove silence
+            if remove_silence:
+                remove_silence_for_generated_wav(f.name)
+            print(f.name)
+
+
+if __name__ == "__main__":
+    main()

src/f5_tts/infer/infer_gradio.py

@@ -0,0 +1,1121 @@
+# ruff: noqa: E402
+# Above allows ruff to ignore E402: module level import not at top of file
+
+import gc
+import json
+import os
+import re
+import tempfile
+from collections import OrderedDict
+from functools import lru_cache
+from importlib.resources import files
+
+import click
+import gradio as gr
+import numpy as np
+import soundfile as sf
+import torch
+import torchaudio
+from cached_path import cached_path
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+try:
+    import spaces
+
+    USING_SPACES = True
+except ImportError:
+    USING_SPACES = False
+
+
+def gpu_decorator(func):
+    if USING_SPACES:
+        return spaces.GPU(func)
+    else:
+        return func
+
+
+from f5_tts.infer.utils_infer import (
+    infer_process,
+    load_model,
+    load_vocoder,
+    preprocess_ref_audio_text,
+    remove_silence_for_generated_wav,
+    save_spectrogram,
+    tempfile_kwargs,
+)
+from f5_tts.model import DiT, UNetT
+
+
+DEFAULT_TTS_MODEL = "F5-TTS_v1"
+tts_model_choice = DEFAULT_TTS_MODEL
+
+DEFAULT_TTS_MODEL_CFG = [
+    "hf://SWivid/F5-TTS/F5TTS_v1_Base/model_1250000.safetensors",
+    "hf://SWivid/F5-TTS/F5TTS_v1_Base/vocab.txt",
+    json.dumps(dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)),
+]
+
+
+# load models
+
+vocoder = load_vocoder()
+
+
+def load_f5tts():
+    ckpt_path = str(cached_path(DEFAULT_TTS_MODEL_CFG[0]))
+    F5TTS_model_cfg = json.loads(DEFAULT_TTS_MODEL_CFG[2])
+    return load_model(DiT, F5TTS_model_cfg, ckpt_path)
+
+
+def load_e2tts():
+    ckpt_path = str(cached_path("hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.safetensors"))
+    E2TTS_model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4, text_mask_padding=False, pe_attn_head=1)
+    return load_model(UNetT, E2TTS_model_cfg, ckpt_path)
+
+
+def load_custom(ckpt_path: str, vocab_path="", model_cfg=None):
+    ckpt_path, vocab_path = ckpt_path.strip(), vocab_path.strip()
+    if ckpt_path.startswith("hf://"):
+        ckpt_path = str(cached_path(ckpt_path))
+    if vocab_path.startswith("hf://"):
+        vocab_path = str(cached_path(vocab_path))
+    if model_cfg is None:
+        model_cfg = json.loads(DEFAULT_TTS_MODEL_CFG[2])
+    elif isinstance(model_cfg, str):
+        model_cfg = json.loads(model_cfg)
+    return load_model(DiT, model_cfg, ckpt_path, vocab_file=vocab_path)
+
+
+F5TTS_ema_model = load_f5tts()
+E2TTS_ema_model = load_e2tts() if USING_SPACES else None
+custom_ema_model, pre_custom_path = None, ""
+
+chat_model_state = None
+chat_tokenizer_state = None
+
+
+@gpu_decorator
+def chat_model_inference(messages, model, tokenizer):
+    """Generate response using Qwen"""
+    text = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True,
+    )
+
+    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+    generated_ids = model.generate(
+        **model_inputs,
+        max_new_tokens=512,
+        temperature=0.7,
+        top_p=0.95,
+    )
+
+    generated_ids = [
+        output_ids[len(input_ids) :] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+    ]
+    return tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+
+@gpu_decorator
+def load_text_from_file(file):
+    if file:
+        with open(file, "r", encoding="utf-8") as f:
+            text = f.read().strip()
+    else:
+        text = ""
+    return gr.update(value=text)
+
+
+@lru_cache(maxsize=1000)  # NOTE. need to ensure params of infer() hashable
+@gpu_decorator
+def infer(
+    ref_audio_orig,
+    ref_text,
+    gen_text,
+    model,
+    remove_silence,
+    seed,
+    cross_fade_duration=0.15,
+    nfe_step=32,
+    speed=1,
+    show_info=gr.Info,
+):
+    if not ref_audio_orig:
+        gr.Warning("Please provide reference audio.")
+        return gr.update(), gr.update(), ref_text
+
+    # Set inference seed
+    if seed < 0 or seed > 2**31 - 1:
+        gr.Warning("Seed must in range 0 ~ 2147483647. Using random seed instead.")
+        seed = np.random.randint(0, 2**31 - 1)
+    torch.manual_seed(seed)
+    used_seed = seed
+
+    if not gen_text.strip():
+        gr.Warning("Please enter text to generate or upload a text file.")
+        return gr.update(), gr.update(), ref_text
+
+    ref_audio, ref_text = preprocess_ref_audio_text(ref_audio_orig, ref_text, show_info=show_info)
+
+    if model == DEFAULT_TTS_MODEL:
+        ema_model = F5TTS_ema_model
+    elif model == "E2-TTS":
+        global E2TTS_ema_model
+        if E2TTS_ema_model is None:
+            show_info("Loading E2-TTS model...")
+            E2TTS_ema_model = load_e2tts()
+        ema_model = E2TTS_ema_model
+    elif isinstance(model, tuple) and model[0] == "Custom":
+        assert not USING_SPACES, "Only official checkpoints allowed in Spaces."
+        global custom_ema_model, pre_custom_path
+        if pre_custom_path != model[1]:
+            show_info("Loading Custom TTS model...")
+            custom_ema_model = load_custom(model[1], vocab_path=model[2], model_cfg=model[3])
+            pre_custom_path = model[1]
+        ema_model = custom_ema_model
+
+    final_wave, final_sample_rate, combined_spectrogram = infer_process(
+        ref_audio,
+        ref_text,
+        gen_text,
+        ema_model,
+        vocoder,
+        cross_fade_duration=cross_fade_duration,
+        nfe_step=nfe_step,
+        speed=speed,
+        show_info=show_info,
+        progress=gr.Progress(),
+    )
+
+    # Remove silence
+    if remove_silence:
+        with tempfile.NamedTemporaryFile(suffix=".wav", **tempfile_kwargs) as f:
+            temp_path = f.name
+        try:
+            sf.write(temp_path, final_wave, final_sample_rate)
+            remove_silence_for_generated_wav(f.name)
+            final_wave, _ = torchaudio.load(f.name)
+        finally:
+            os.unlink(temp_path)
+        final_wave = final_wave.squeeze().cpu().numpy()
+
+    # Save the spectrogram
+    with tempfile.NamedTemporaryFile(suffix=".png", **tempfile_kwargs) as tmp_spectrogram:
+        spectrogram_path = tmp_spectrogram.name
+    save_spectrogram(combined_spectrogram, spectrogram_path)
+
+    return (final_sample_rate, final_wave), spectrogram_path, ref_text, used_seed
+
+
+with gr.Blocks() as app_tts:
+    gr.Markdown("# Batched TTS")
+    ref_audio_input = gr.Audio(label="Reference Audio", type="filepath")
+    with gr.Row():
+        gen_text_input = gr.Textbox(
+            label="Text to Generate",
+            lines=10,
+            max_lines=40,
+            scale=4,
+        )
+        gen_text_file = gr.File(label="Load Text to Generate from File (.txt)", file_types=[".txt"], scale=1)
+    generate_btn = gr.Button("Synthesize", variant="primary")
+    with gr.Accordion("Advanced Settings", open=False):
+        with gr.Row():
+            ref_text_input = gr.Textbox(
+                label="Reference Text",
+                info="Leave blank to automatically transcribe the reference audio. If you enter text or upload a file, it will override automatic transcription.",
+                lines=2,
+                scale=4,
+            )
+            ref_text_file = gr.File(label="Load Reference Text from File (.txt)", file_types=[".txt"], scale=1)
+        with gr.Row():
+            randomize_seed = gr.Checkbox(
+                label="Randomize Seed",
+                info="Check to use a random seed for each generation. Uncheck to use the seed specified.",
+                value=True,
+                scale=3,
+            )
+            seed_input = gr.Number(show_label=False, value=0, precision=0, scale=1)
+            with gr.Column(scale=4):
+                remove_silence = gr.Checkbox(
+                    label="Remove Silences",
+                    info="If undesired long silence(s) produced, turn on to automatically detect and crop.",
+                    value=False,
+                )
+        speed_slider = gr.Slider(
+            label="Speed",
+            minimum=0.3,
+            maximum=2.0,
+            value=1.0,
+            step=0.1,
+            info="Adjust the speed of the audio.",
+        )
+        nfe_slider = gr.Slider(
+            label="NFE Steps",
+            minimum=4,
+            maximum=64,
+            value=32,
+            step=2,
+            info="Set the number of denoising steps.",
+        )
+        cross_fade_duration_slider = gr.Slider(
+            label="Cross-Fade Duration (s)",
+            minimum=0.0,
+            maximum=1.0,
+            value=0.15,
+            step=0.01,
+            info="Set the duration of the cross-fade between audio clips.",
+        )
+
+    audio_output = gr.Audio(label="Synthesized Audio")
+    spectrogram_output = gr.Image(label="Spectrogram")
+
+    @gpu_decorator
+    def basic_tts(
+        ref_audio_input,
+        ref_text_input,
+        gen_text_input,
+        remove_silence,
+        randomize_seed,
+        seed_input,
+        cross_fade_duration_slider,
+        nfe_slider,
+        speed_slider,
+    ):
+        if randomize_seed:
+            seed_input = np.random.randint(0, 2**31 - 1)
+
+        audio_out, spectrogram_path, ref_text_out, used_seed = infer(
+            ref_audio_input,
+            ref_text_input,
+            gen_text_input,
+            tts_model_choice,
+            remove_silence,
+            seed=seed_input,
+            cross_fade_duration=cross_fade_duration_slider,
+            nfe_step=nfe_slider,
+            speed=speed_slider,
+        )
+        return audio_out, spectrogram_path, ref_text_out, used_seed
+
+    gen_text_file.upload(
+        load_text_from_file,
+        inputs=[gen_text_file],
+        outputs=[gen_text_input],
+    )
+
+    ref_text_file.upload(
+        load_text_from_file,
+        inputs=[ref_text_file],
+        outputs=[ref_text_input],
+    )
+
+    ref_audio_input.clear(
+        lambda: [None, None],
+        None,
+        [ref_text_input, ref_text_file],
+    )
+
+    generate_btn.click(
+        basic_tts,
+        inputs=[
+            ref_audio_input,
+            ref_text_input,
+            gen_text_input,
+            remove_silence,
+            randomize_seed,
+            seed_input,
+            cross_fade_duration_slider,
+            nfe_slider,
+            speed_slider,
+        ],
+        outputs=[audio_output, spectrogram_output, ref_text_input, seed_input],
+    )
+
+
+def parse_speechtypes_text(gen_text):
+    # Pattern to find {str} or {"name": str, "seed": int, "speed": float}
+    pattern = r"(\{.*?\})"
+
+    # Split the text by the pattern
+    tokens = re.split(pattern, gen_text)
+
+    segments = []
+
+    current_type_dict = {
+        "name": "Regular",
+        "seed": -1,
+        "speed": 1.0,
+    }
+
+    for i in range(len(tokens)):
+        if i % 2 == 0:
+            # This is text
+            text = tokens[i].strip()
+            if text:
+                current_type_dict["text"] = text
+                segments.append(current_type_dict)
+        else:
+            # This is type
+            type_str = tokens[i].strip()
+            try:  # if type dict
+                current_type_dict = json.loads(type_str)
+            except json.decoder.JSONDecodeError:
+                type_str = type_str[1:-1]  # remove brace {}
+                current_type_dict = {"name": type_str, "seed": -1, "speed": 1.0}
+
+    return segments
+
+
+with gr.Blocks() as app_multistyle:
+    # New section for multistyle generation
+    gr.Markdown(
+        """
+    # Multiple Speech-Type Generation
+
+    This section allows you to generate multiple speech types or multiple people's voices. Enter your text in the format shown below, or upload a .txt file with the same format. The system will generate speech using the appropriate type. If unspecified, the model will use the regular speech type. The current speech type will be used until the next speech type is specified.
+    """
+    )
+
+    with gr.Row():
+        gr.Markdown(
+            """
+            **Example Input:** <br>
+            {Regular} Hello, I'd like to order a sandwich please. <br>
+            {Surprised} What do you mean you're out of bread? <br>
+            {Sad} I really wanted a sandwich though... <br>
+            {Angry} You know what, darn you and your little shop! <br>
+            {Whisper} I'll just go back home and cry now. <br>
+            {Shouting} Why me?!
+            """
+        )
+
+        gr.Markdown(
+            """
+            **Example Input 2:** <br>
+            {"name": "Speaker1_Happy", "seed": -1, "speed": 1} Hello, I'd like to order a sandwich please. <br>
+            {"name": "Speaker2_Regular", "seed": -1, "speed": 1} Sorry, we're out of bread. <br>
+            {"name": "Speaker1_Sad", "seed": -1, "speed": 1} I really wanted a sandwich though... <br>
+            {"name": "Speaker2_Whisper", "seed": -1, "speed": 1} I'll give you the last one I was hiding.
+            """
+        )
+
+    gr.Markdown(
+        'Upload different audio clips for each speech type. The first speech type is mandatory. You can add additional speech types by clicking the "Add Speech Type" button.'
+    )
+
+    # Regular speech type (mandatory)
+    with gr.Row(variant="compact") as regular_row:
+        with gr.Column(scale=1, min_width=160):
+            regular_name = gr.Textbox(value="Regular", label="Speech Type Name")
+            regular_insert = gr.Button("Insert Label", variant="secondary")
+        with gr.Column(scale=3):
+            regular_audio = gr.Audio(label="Regular Reference Audio", type="filepath")
+        with gr.Column(scale=3):
+            regular_ref_text = gr.Textbox(label="Reference Text (Regular)", lines=4)
+            with gr.Row():
+                regular_seed_slider = gr.Slider(
+                    show_label=False, minimum=-1, maximum=999, value=-1, step=1, info="Seed, -1 for random"
+                )
+                regular_speed_slider = gr.Slider(
+                    show_label=False, minimum=0.3, maximum=2.0, value=1.0, step=0.1, info="Adjust the speed"
+                )
+        with gr.Column(scale=1, min_width=160):
+            regular_ref_text_file = gr.File(label="Load Reference Text from File (.txt)", file_types=[".txt"])
+
+    # Regular speech type (max 100)
+    max_speech_types = 100
+    speech_type_rows = [regular_row]
+    speech_type_names = [regular_name]
+    speech_type_audios = [regular_audio]
+    speech_type_ref_texts = [regular_ref_text]
+    speech_type_ref_text_files = [regular_ref_text_file]
+    speech_type_seeds = [regular_seed_slider]
+    speech_type_speeds = [regular_speed_slider]
+    speech_type_delete_btns = [None]
+    speech_type_insert_btns = [regular_insert]
+
+    # Additional speech types (99 more)
+    for i in range(max_speech_types - 1):
+        with gr.Row(variant="compact", visible=False) as row:
+            with gr.Column(scale=1, min_width=160):
+                name_input = gr.Textbox(label="Speech Type Name")
+                insert_btn = gr.Button("Insert Label", variant="secondary")
+                delete_btn = gr.Button("Delete Type", variant="stop")
+            with gr.Column(scale=3):
+                audio_input = gr.Audio(label="Reference Audio", type="filepath")
+            with gr.Column(scale=3):
+                ref_text_input = gr.Textbox(label="Reference Text", lines=4)
+                with gr.Row():
+                    seed_input = gr.Slider(
+                        show_label=False, minimum=-1, maximum=999, value=-1, step=1, info="Seed. -1 for random"
+                    )
+                    speed_input = gr.Slider(
+                        show_label=False, minimum=0.3, maximum=2.0, value=1.0, step=0.1, info="Adjust the speed"
+                    )
+            with gr.Column(scale=1, min_width=160):
+                ref_text_file_input = gr.File(label="Load Reference Text from File (.txt)", file_types=[".txt"])
+        speech_type_rows.append(row)
+        speech_type_names.append(name_input)
+        speech_type_audios.append(audio_input)
+        speech_type_ref_texts.append(ref_text_input)
+        speech_type_ref_text_files.append(ref_text_file_input)
+        speech_type_seeds.append(seed_input)
+        speech_type_speeds.append(speed_input)
+        speech_type_delete_btns.append(delete_btn)
+        speech_type_insert_btns.append(insert_btn)
+
+    # Global logic for all speech types
+    for i in range(max_speech_types):
+        speech_type_audios[i].clear(
+            lambda: [None, None],
+            None,
+            [speech_type_ref_texts[i], speech_type_ref_text_files[i]],
+        )
+        speech_type_ref_text_files[i].upload(
+            load_text_from_file,
+            inputs=[speech_type_ref_text_files[i]],
+            outputs=[speech_type_ref_texts[i]],
+        )
+
+    # Button to add speech type
+    add_speech_type_btn = gr.Button("Add Speech Type")
+
+    # Keep track of autoincrement of speech types, no roll back
+    speech_type_count = 1
+
+    # Function to add a speech type
+    def add_speech_type_fn():
+        row_updates = [gr.update() for _ in range(max_speech_types)]
+        global speech_type_count
+        if speech_type_count < max_speech_types:
+            row_updates[speech_type_count] = gr.update(visible=True)
+            speech_type_count += 1
+        else:
+            gr.Warning("Exhausted maximum number of speech types. Consider restart the app.")
+        return row_updates
+
+    add_speech_type_btn.click(add_speech_type_fn, outputs=speech_type_rows)
+
+    # Function to delete a speech type
+    def delete_speech_type_fn():
+        return gr.update(visible=False), None, None, None, None
+
+    # Update delete button clicks and ref text file changes
+    for i in range(1, len(speech_type_delete_btns)):
+        speech_type_delete_btns[i].click(
+            delete_speech_type_fn,
+            outputs=[
+                speech_type_rows[i],
+                speech_type_names[i],
+                speech_type_audios[i],
+                speech_type_ref_texts[i],
+                speech_type_ref_text_files[i],
+            ],
+        )
+
+    # Text input for the prompt
+    with gr.Row():
+        gen_text_input_multistyle = gr.Textbox(
+            label="Text to Generate",
+            lines=10,
+            max_lines=40,
+            scale=4,
+            placeholder="Enter the script with speaker names (or emotion types) at the start of each block, e.g.:\n\n{Regular} Hello, I'd like to order a sandwich please.\n{Surprised} What do you mean you're out of bread?\n{Sad} I really wanted a sandwich though...\n{Angry} You know what, darn you and your little shop!\n{Whisper} I'll just go back home and cry now.\n{Shouting} Why me?!",
+        )
+        gen_text_file_multistyle = gr.File(label="Load Text to Generate from File (.txt)", file_types=[".txt"], scale=1)
+
+    def make_insert_speech_type_fn(index):
+        def insert_speech_type_fn(current_text, speech_type_name, speech_type_seed, speech_type_speed):
+            current_text = current_text or ""
+            if not speech_type_name:
+                gr.Warning("Please enter speech type name before insert.")
+                return current_text
+            speech_type_dict = {
+                "name": speech_type_name,
+                "seed": speech_type_seed,
+                "speed": speech_type_speed,
+            }
+            updated_text = current_text + json.dumps(speech_type_dict) + " "
+            return updated_text
+
+        return insert_speech_type_fn
+
+    for i, insert_btn in enumerate(speech_type_insert_btns):
+        insert_fn = make_insert_speech_type_fn(i)
+        insert_btn.click(
+            insert_fn,
+            inputs=[gen_text_input_multistyle, speech_type_names[i], speech_type_seeds[i], speech_type_speeds[i]],
+            outputs=gen_text_input_multistyle,
+        )
+
+    with gr.Accordion("Advanced Settings", open=True):
+        with gr.Row():
+            with gr.Column():
+                show_cherrypick_multistyle = gr.Checkbox(
+                    label="Show Cherry-pick Interface",
+                    info="Turn on to show interface, picking seeds from previous generations.",
+                    value=False,
+                )
+            with gr.Column():
+                remove_silence_multistyle = gr.Checkbox(
+                    label="Remove Silences",
+                    info="Turn on to automatically detect and crop long silences.",
+                    value=True,
+                )
+
+    # Generate button
+    generate_multistyle_btn = gr.Button("Generate Multi-Style Speech", variant="primary")
+
+    # Output audio
+    audio_output_multistyle = gr.Audio(label="Synthesized Audio")
+
+    # Used seed gallery
+    cherrypick_interface_multistyle = gr.Textbox(
+        label="Cherry-pick Interface",
+        lines=10,
+        max_lines=40,
+        show_copy_button=True,
+        interactive=False,
+        visible=False,
+    )
+
+    # Logic control to show/hide the cherrypick interface
+    show_cherrypick_multistyle.change(
+        lambda is_visible: gr.update(visible=is_visible),
+        show_cherrypick_multistyle,
+        cherrypick_interface_multistyle,
+    )
+
+    # Function to load text to generate from file
+    gen_text_file_multistyle.upload(
+        load_text_from_file,
+        inputs=[gen_text_file_multistyle],
+        outputs=[gen_text_input_multistyle],
+    )
+
+    @gpu_decorator
+    def generate_multistyle_speech(
+        gen_text,
+        *args,
+    ):
+        speech_type_names_list = args[:max_speech_types]
+        speech_type_audios_list = args[max_speech_types : 2 * max_speech_types]
+        speech_type_ref_texts_list = args[2 * max_speech_types : 3 * max_speech_types]
+        remove_silence = args[3 * max_speech_types]
+        # Collect the speech types and their audios into a dict
+        speech_types = OrderedDict()
+
+        ref_text_idx = 0
+        for name_input, audio_input, ref_text_input in zip(
+            speech_type_names_list, speech_type_audios_list, speech_type_ref_texts_list
+        ):
+            if name_input and audio_input:
+                speech_types[name_input] = {"audio": audio_input, "ref_text": ref_text_input}
+            else:
+                speech_types[f"@{ref_text_idx}@"] = {"audio": "", "ref_text": ""}
+            ref_text_idx += 1
+
+        # Parse the gen_text into segments
+        segments = parse_speechtypes_text(gen_text)
+
+        # For each segment, generate speech
+        generated_audio_segments = []
+        current_type_name = "Regular"
+        inference_meta_data = ""
+
+        for segment in segments:
+            name = segment["name"]
+            seed_input = segment["seed"]
+            speed = segment["speed"]
+            text = segment["text"]
+
+            if name in speech_types:
+                current_type_name = name
+            else:
+                gr.Warning(f"Type {name} is not available, will use Regular as default.")
+                current_type_name = "Regular"
+
+            try:
+                ref_audio = speech_types[current_type_name]["audio"]
+            except KeyError:
+                gr.Warning(f"Please provide reference audio for type {current_type_name}.")
+                return [None] + [speech_types[name]["ref_text"] for name in speech_types] + [None]
+            ref_text = speech_types[current_type_name].get("ref_text", "")
+
+            if seed_input == -1:
+                seed_input = np.random.randint(0, 2**31 - 1)
+
+            # Generate or retrieve speech for this segment
+            audio_out, _, ref_text_out, used_seed = infer(
+                ref_audio,
+                ref_text,
+                text,
+                tts_model_choice,
+                remove_silence,
+                seed=seed_input,
+                cross_fade_duration=0,
+                speed=speed,
+                show_info=print,  # no pull to top when generating
+            )
+            sr, audio_data = audio_out
+
+            generated_audio_segments.append(audio_data)
+            speech_types[current_type_name]["ref_text"] = ref_text_out
+            inference_meta_data += json.dumps(dict(name=name, seed=used_seed, speed=speed)) + f" {text}\n"
+
+        # Concatenate all audio segments
+        if generated_audio_segments:
+            final_audio_data = np.concatenate(generated_audio_segments)
+            return (
+                [(sr, final_audio_data)]
+                + [speech_types[name]["ref_text"] for name in speech_types]
+                + [inference_meta_data]
+            )
+        else:
+            gr.Warning("No audio generated.")
+            return [None] + [speech_types[name]["ref_text"] for name in speech_types] + [None]
+
+    generate_multistyle_btn.click(
+        generate_multistyle_speech,
+        inputs=[
+            gen_text_input_multistyle,
+        ]
+        + speech_type_names
+        + speech_type_audios
+        + speech_type_ref_texts
+        + [
+            remove_silence_multistyle,
+        ],
+        outputs=[audio_output_multistyle] + speech_type_ref_texts + [cherrypick_interface_multistyle],
+    )
+
+    # Validation function to disable Generate button if speech types are missing
+    def validate_speech_types(gen_text, regular_name, *args):
+        speech_type_names_list = args
+
+        # Collect the speech types names
+        speech_types_available = set()
+        if regular_name:
+            speech_types_available.add(regular_name)
+        for name_input in speech_type_names_list:
+            if name_input:
+                speech_types_available.add(name_input)
+
+        # Parse the gen_text to get the speech types used
+        segments = parse_speechtypes_text(gen_text)
+        speech_types_in_text = set(segment["name"] for segment in segments)
+
+        # Check if all speech types in text are available
+        missing_speech_types = speech_types_in_text - speech_types_available
+
+        if missing_speech_types:
+            # Disable the generate button
+            return gr.update(interactive=False)
+        else:
+            # Enable the generate button
+            return gr.update(interactive=True)
+
+    gen_text_input_multistyle.change(
+        validate_speech_types,
+        inputs=[gen_text_input_multistyle, regular_name] + speech_type_names,
+        outputs=generate_multistyle_btn,
+    )
+
+
+with gr.Blocks() as app_chat:
+    gr.Markdown(
+        """
+# Voice Chat
+Have a conversation with an AI using your reference voice!
+1. Upload a reference audio clip and optionally its transcript (via text or .txt file).
+2. Load the chat model.
+3. Record your message through your microphone or type it.
+4. The AI will respond using the reference voice.
+"""
+    )
+
+    chat_model_name_list = [
+        "Qwen/Qwen2.5-3B-Instruct",
+        "microsoft/Phi-4-mini-instruct",
+    ]
+
+    @gpu_decorator
+    def load_chat_model(chat_model_name):
+        show_info = gr.Info
+        global chat_model_state, chat_tokenizer_state
+        if chat_model_state is not None:
+            chat_model_state = None
+            chat_tokenizer_state = None
+            gc.collect()
+            torch.cuda.empty_cache()
+
+        show_info(f"Loading chat model: {chat_model_name}")
+        chat_model_state = AutoModelForCausalLM.from_pretrained(chat_model_name, torch_dtype="auto", device_map="auto")
+        chat_tokenizer_state = AutoTokenizer.from_pretrained(chat_model_name)
+        show_info(f"Chat model {chat_model_name} loaded successfully!")
+
+        return gr.update(visible=False), gr.update(visible=True)
+
+    if USING_SPACES:
+        load_chat_model(chat_model_name_list[0])
+
+    chat_model_name_input = gr.Dropdown(
+        choices=chat_model_name_list,
+        value=chat_model_name_list[0],
+        label="Chat Model Name",
+        info="Enter the name of a HuggingFace chat model",
+        allow_custom_value=not USING_SPACES,
+    )
+    load_chat_model_btn = gr.Button("Load Chat Model", variant="primary", visible=not USING_SPACES)
+    chat_interface_container = gr.Column(visible=USING_SPACES)
+
+    chat_model_name_input.change(
+        lambda: gr.update(visible=True),
+        None,
+        load_chat_model_btn,
+        show_progress="hidden",
+    )
+    load_chat_model_btn.click(
+        load_chat_model, inputs=[chat_model_name_input], outputs=[load_chat_model_btn, chat_interface_container]
+    )
+
+    with chat_interface_container:
+        with gr.Row():
+            with gr.Column():
+                ref_audio_chat = gr.Audio(label="Reference Audio", type="filepath")
+            with gr.Column():
+                with gr.Accordion("Advanced Settings", open=False):
+                    with gr.Row():
+                        ref_text_chat = gr.Textbox(
+                            label="Reference Text",
+                            info="Optional: Leave blank to auto-transcribe",
+                            lines=2,
+                            scale=3,
+                        )
+                        ref_text_file_chat = gr.File(
+                            label="Load Reference Text from File (.txt)", file_types=[".txt"], scale=1
+                        )
+                    with gr.Row():
+                        randomize_seed_chat = gr.Checkbox(
+                            label="Randomize Seed",
+                            value=True,
+                            info="Uncheck to use the seed specified.",
+                            scale=3,
+                        )
+                        seed_input_chat = gr.Number(show_label=False, value=0, precision=0, scale=1)
+                    remove_silence_chat = gr.Checkbox(
+                        label="Remove Silences",
+                        value=True,
+                    )
+                    system_prompt_chat = gr.Textbox(
+                        label="System Prompt",
+                        value="You are not an AI assistant, you are whoever the user says you are. You must stay in character. Keep your responses concise since they will be spoken out loud.",
+                        lines=2,
+                    )
+
+        chatbot_interface = gr.Chatbot(label="Conversation", type="messages")
+
+        with gr.Row():
+            with gr.Column():
+                audio_input_chat = gr.Microphone(
+                    label="Speak your message",
+                    type="filepath",
+                )
+                audio_output_chat = gr.Audio(autoplay=True)
+            with gr.Column():
+                text_input_chat = gr.Textbox(
+                    label="Type your message",
+                    lines=1,
+                )
+                send_btn_chat = gr.Button("Send Message")
+                clear_btn_chat = gr.Button("Clear Conversation")
+
+        # Modify process_audio_input to generate user input
+        @gpu_decorator
+        def process_audio_input(conv_state, audio_path, text):
+            """Handle audio or text input from user"""
+
+            if not audio_path and not text.strip():
+                return conv_state
+
+            if audio_path:
+                text = preprocess_ref_audio_text(audio_path, text)[1]
+            if not text.strip():
+                return conv_state
+
+            conv_state.append({"role": "user", "content": text})
+            return conv_state
+
+        # Use model and tokenizer from state to get text response
+        @gpu_decorator
+        def generate_text_response(conv_state, system_prompt):
+            """Generate text response from AI"""
+
+            system_prompt_state = [{"role": "system", "content": system_prompt}]
+            response = chat_model_inference(system_prompt_state + conv_state, chat_model_state, chat_tokenizer_state)
+
+            conv_state.append({"role": "assistant", "content": response})
+            return conv_state
+
+        @gpu_decorator
+        def generate_audio_response(conv_state, ref_audio, ref_text, remove_silence, randomize_seed, seed_input):
+            """Generate TTS audio for AI response"""
+            if not conv_state or not ref_audio:
+                return None, ref_text, seed_input
+
+            last_ai_response = conv_state[-1]["content"]
+            if not last_ai_response or conv_state[-1]["role"] != "assistant":
+                return None, ref_text, seed_input
+
+            if randomize_seed:
+                seed_input = np.random.randint(0, 2**31 - 1)
+
+            audio_result, _, ref_text_out, used_seed = infer(
+                ref_audio,
+                ref_text,
+                last_ai_response,
+                tts_model_choice,
+                remove_silence,
+                seed=seed_input,
+                cross_fade_duration=0.15,
+                speed=1.0,
+                show_info=print,  # show_info=print no pull to top when generating
+            )
+            return audio_result, ref_text_out, used_seed
+
+        def clear_conversation():
+            """Reset the conversation"""
+            return [], None
+
+        ref_text_file_chat.upload(
+            load_text_from_file,
+            inputs=[ref_text_file_chat],
+            outputs=[ref_text_chat],
+        )
+
+        for user_operation in [audio_input_chat.stop_recording, text_input_chat.submit, send_btn_chat.click]:
+            user_operation(
+                process_audio_input,
+                inputs=[chatbot_interface, audio_input_chat, text_input_chat],
+                outputs=[chatbot_interface],
+            ).then(
+                generate_text_response,
+                inputs=[chatbot_interface, system_prompt_chat],
+                outputs=[chatbot_interface],
+            ).then(
+                generate_audio_response,
+                inputs=[
+                    chatbot_interface,
+                    ref_audio_chat,
+                    ref_text_chat,
+                    remove_silence_chat,
+                    randomize_seed_chat,
+                    seed_input_chat,
+                ],
+                outputs=[audio_output_chat, ref_text_chat, seed_input_chat],
+            ).then(
+                lambda: [None, None],
+                None,
+                [audio_input_chat, text_input_chat],
+            )
+
+        # Handle clear button or system prompt change and reset conversation
+        for user_operation in [clear_btn_chat.click, system_prompt_chat.change, chatbot_interface.clear]:
+            user_operation(
+                clear_conversation,
+                outputs=[chatbot_interface, audio_output_chat],
+            )
+
+
+with gr.Blocks() as app_credits:
+    gr.Markdown("""
+# Credits
+
+* [mrfakename](https://github.com/fakerybakery) for the original [online demo](https://huggingface.co/spaces/mrfakename/E2-F5-TTS)
+* [RootingInLoad](https://github.com/RootingInLoad) for initial chunk generation and podcast app exploration
+* [jpgallegoar](https://github.com/jpgallegoar) for multiple speech-type generation & voice chat
+""")
+
+
+with gr.Blocks() as app:
+    gr.Markdown(
+        f"""
+# E2/F5 TTS
+
+This is {"a local web UI for [F5 TTS](https://github.com/SWivid/F5-TTS)" if not USING_SPACES else "an online demo for [F5-TTS](https://github.com/SWivid/F5-TTS)"} with advanced batch processing support. This app supports the following TTS models:
+
+* [F5-TTS](https://arxiv.org/abs/2410.06885) (A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching)
+* [E2 TTS](https://arxiv.org/abs/2406.18009) (Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS)
+
+The checkpoints currently support English and Chinese.
+
+If you're having issues, try converting your reference audio to WAV or MP3, clipping it to 12s with  ✂  in the bottom right corner (otherwise might have non-optimal auto-trimmed result).
+
+**NOTE: Reference text will be automatically transcribed with Whisper if not provided. For best results, keep your reference clips short (<12s). Ensure the audio is fully uploaded before generating.**
+"""
+    )
+
+    last_used_custom = files("f5_tts").joinpath("infer/.cache/last_used_custom_model_info_v1.txt")
+
+    def load_last_used_custom():
+        try:
+            custom = []
+            with open(last_used_custom, "r", encoding="utf-8") as f:
+                for line in f:
+                    custom.append(line.strip())
+            return custom
+        except FileNotFoundError:
+            last_used_custom.parent.mkdir(parents=True, exist_ok=True)
+            return DEFAULT_TTS_MODEL_CFG
+
+    def switch_tts_model(new_choice):
+        global tts_model_choice
+        if new_choice == "Custom":  # override in case webpage is refreshed
+            custom_ckpt_path, custom_vocab_path, custom_model_cfg = load_last_used_custom()
+            tts_model_choice = ("Custom", custom_ckpt_path, custom_vocab_path, custom_model_cfg)
+            return (
+                gr.update(visible=True, value=custom_ckpt_path),
+                gr.update(visible=True, value=custom_vocab_path),
+                gr.update(visible=True, value=custom_model_cfg),
+            )
+        else:
+            tts_model_choice = new_choice
+            return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
+
+    def set_custom_model(custom_ckpt_path, custom_vocab_path, custom_model_cfg):
+        global tts_model_choice
+        tts_model_choice = ("Custom", custom_ckpt_path, custom_vocab_path, custom_model_cfg)
+        with open(last_used_custom, "w", encoding="utf-8") as f:
+            f.write(custom_ckpt_path + "\n" + custom_vocab_path + "\n" + custom_model_cfg + "\n")
+
+    with gr.Row():
+        if not USING_SPACES:
+            choose_tts_model = gr.Radio(
+                choices=[DEFAULT_TTS_MODEL, "E2-TTS", "Custom"], label="Choose TTS Model", value=DEFAULT_TTS_MODEL
+            )
+        else:
+            choose_tts_model = gr.Radio(
+                choices=[DEFAULT_TTS_MODEL, "E2-TTS"], label="Choose TTS Model", value=DEFAULT_TTS_MODEL
+            )
+        custom_ckpt_path = gr.Dropdown(
+            choices=[DEFAULT_TTS_MODEL_CFG[0]],
+            value=load_last_used_custom()[0],
+            allow_custom_value=True,
+            label="Model: local_path | hf://user_id/repo_id/model_ckpt",
+            visible=False,
+        )
+        custom_vocab_path = gr.Dropdown(
+            choices=[DEFAULT_TTS_MODEL_CFG[1]],
+            value=load_last_used_custom()[1],
+            allow_custom_value=True,
+            label="Vocab: local_path | hf://user_id/repo_id/vocab_file",
+            visible=False,
+        )
+        custom_model_cfg = gr.Dropdown(
+            choices=[
+                DEFAULT_TTS_MODEL_CFG[2],
+                json.dumps(
+                    dict(
+                        dim=1024,
+                        depth=22,
+                        heads=16,
+                        ff_mult=2,
+                        text_dim=512,
+                        text_mask_padding=False,
+                        conv_layers=4,
+                        pe_attn_head=1,
+                    )
+                ),
+                json.dumps(
+                    dict(
+                        dim=768,
+                        depth=18,
+                        heads=12,
+                        ff_mult=2,
+                        text_dim=512,
+                        text_mask_padding=False,
+                        conv_layers=4,
+                        pe_attn_head=1,
+                    )
+                ),
+            ],
+            value=load_last_used_custom()[2],
+            allow_custom_value=True,
+            label="Config: in a dictionary form",
+            visible=False,
+        )
+
+    choose_tts_model.change(
+        switch_tts_model,
+        inputs=[choose_tts_model],
+        outputs=[custom_ckpt_path, custom_vocab_path, custom_model_cfg],
+        show_progress="hidden",
+    )
+    custom_ckpt_path.change(
+        set_custom_model,
+        inputs=[custom_ckpt_path, custom_vocab_path, custom_model_cfg],
+        show_progress="hidden",
+    )
+    custom_vocab_path.change(
+        set_custom_model,
+        inputs=[custom_ckpt_path, custom_vocab_path, custom_model_cfg],
+        show_progress="hidden",
+    )
+    custom_model_cfg.change(
+        set_custom_model,
+        inputs=[custom_ckpt_path, custom_vocab_path, custom_model_cfg],
+        show_progress="hidden",
+    )
+
+    gr.TabbedInterface(
+        [app_tts, app_multistyle, app_chat, app_credits],
+        ["Basic-TTS", "Multi-Speech", "Voice-Chat", "Credits"],
+    )
+
+
+@click.command()
+@click.option("--port", "-p", default=None, type=int, help="Port to run the app on")
+@click.option("--host", "-H", default=None, help="Host to run the app on")
+@click.option(
+    "--share",
+    "-s",
+    default=False,
+    is_flag=True,
+    help="Share the app via Gradio share link",
+)
+@click.option("--api", "-a", default=True, is_flag=True, help="Allow API access")
+@click.option(
+    "--root_path",
+    "-r",
+    default=None,
+    type=str,
+    help='The root path (or "mount point") of the application, if it\'s not served from the root ("/") of the domain. Often used when the application is behind a reverse proxy that forwards requests to the application, e.g. set "/myapp" or full URL for application served at "https://example.com/myapp".',
+)
+@click.option(
+    "--inbrowser",
+    "-i",
+    is_flag=True,
+    default=False,
+    help="Automatically launch the interface in the default web browser",
+)
+def main(port, host, share, api, root_path, inbrowser):
+    global app
+    print("Starting app...")
+    app.queue(api_open=api).launch(
+        server_name=host,
+        server_port=port,
+        share=share,
+        show_api=api,
+        root_path=root_path,
+        inbrowser=inbrowser,
+    )
+
+
+if __name__ == "__main__":
+    if not USING_SPACES:
+        main()
+    else:
+        app.queue().launch()

src/f5_tts/infer/speech_edit.py

@@ -0,0 +1,205 @@
+import os
+
+
+os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"  # for MPS device compatibility
+
+from importlib.resources import files
+
+import torch
+import torch.nn.functional as F
+import torchaudio
+from cached_path import cached_path
+from hydra.utils import get_class
+from omegaconf import OmegaConf
+
+from f5_tts.infer.utils_infer import load_checkpoint, load_vocoder, save_spectrogram
+from f5_tts.model import CFM
+from f5_tts.model.utils import convert_char_to_pinyin, get_tokenizer
+
+
+device = (
+    "cuda"
+    if torch.cuda.is_available()
+    else "xpu"
+    if torch.xpu.is_available()
+    else "mps"
+    if torch.backends.mps.is_available()
+    else "cpu"
+)
+
+
+# ---------------------- infer setting ---------------------- #
+
+seed = None  # int | None
+
+exp_name = "F5TTS_v1_Base"  # F5TTS_v1_Base | E2TTS_Base
+ckpt_step = 1250000
+
+nfe_step = 32  # 16, 32
+cfg_strength = 2.0
+ode_method = "euler"  # euler | midpoint
+sway_sampling_coef = -1.0
+speed = 1.0
+target_rms = 0.1
+
+
+model_cfg = OmegaConf.load(str(files("f5_tts").joinpath(f"configs/{exp_name}.yaml")))
+model_cls = get_class(f"f5_tts.model.{model_cfg.model.backbone}")
+model_arc = model_cfg.model.arch
+
+dataset_name = model_cfg.datasets.name
+tokenizer = model_cfg.model.tokenizer
+
+mel_spec_type = model_cfg.model.mel_spec.mel_spec_type
+target_sample_rate = model_cfg.model.mel_spec.target_sample_rate
+n_mel_channels = model_cfg.model.mel_spec.n_mel_channels
+hop_length = model_cfg.model.mel_spec.hop_length
+win_length = model_cfg.model.mel_spec.win_length
+n_fft = model_cfg.model.mel_spec.n_fft
+
+
+# ckpt_path = str(files("f5_tts").joinpath("../../")) + f"/ckpts/{exp_name}/model_{ckpt_step}.safetensors"
+ckpt_path = str(cached_path(f"hf://SWivid/F5-TTS/{exp_name}/model_{ckpt_step}.safetensors"))
+output_dir = "tests"
+
+
+# [leverage https://github.com/MahmoudAshraf97/ctc-forced-aligner to get char level alignment]
+# pip install git+https://github.com/MahmoudAshraf97/ctc-forced-aligner.git
+# [write the origin_text into a file, e.g. tests/test_edit.txt]
+# ctc-forced-aligner --audio_path "src/f5_tts/infer/examples/basic/basic_ref_en.wav" --text_path "tests/test_edit.txt" --language "zho" --romanize --split_size "char"
+# [result will be saved at same path of audio file]
+# [--language "zho" for Chinese, "eng" for English]
+# [if local ckpt, set --alignment_model "../checkpoints/mms-300m-1130-forced-aligner"]
+
+audio_to_edit = str(files("f5_tts").joinpath("infer/examples/basic/basic_ref_en.wav"))
+origin_text = "Some call me nature, others call me mother nature."
+target_text = "Some call me optimist, others call me realist."
+parts_to_edit = [
+    [1.42, 2.44],
+    [4.04, 4.9],
+]  # stard_ends of "nature" & "mother nature", in seconds
+fix_duration = [
+    1.2,
+    1,
+]  # fix duration for "optimist" & "realist", in seconds
+
+# audio_to_edit = "src/f5_tts/infer/examples/basic/basic_ref_zh.wav"
+# origin_text = "对，这就是我，万人敬仰的太乙真人。"
+# target_text = "对，那就是你，万人敬仰的太白金星。"
+# parts_to_edit = [[0.84, 1.4], [1.92, 2.4], [4.26, 6.26], ]
+# fix_duration = None  # use origin text duration
+
+
+# -------------------------------------------------#
+
+use_ema = True
+
+if not os.path.exists(output_dir):
+    os.makedirs(output_dir)
+
+# Vocoder model
+local = False
+if mel_spec_type == "vocos":
+    vocoder_local_path = "../checkpoints/charactr/vocos-mel-24khz"
+elif mel_spec_type == "bigvgan":
+    vocoder_local_path = "../checkpoints/bigvgan_v2_24khz_100band_256x"
+vocoder = load_vocoder(vocoder_name=mel_spec_type, is_local=local, local_path=vocoder_local_path)
+
+# Tokenizer
+vocab_char_map, vocab_size = get_tokenizer(dataset_name, tokenizer)
+
+# Model
+model = CFM(
+    transformer=model_cls(**model_arc, text_num_embeds=vocab_size, mel_dim=n_mel_channels),
+    mel_spec_kwargs=dict(
+        n_fft=n_fft,
+        hop_length=hop_length,
+        win_length=win_length,
+        n_mel_channels=n_mel_channels,
+        target_sample_rate=target_sample_rate,
+        mel_spec_type=mel_spec_type,
+    ),
+    odeint_kwargs=dict(
+        method=ode_method,
+    ),
+    vocab_char_map=vocab_char_map,
+).to(device)
+
+dtype = torch.float32 if mel_spec_type == "bigvgan" else None
+model = load_checkpoint(model, ckpt_path, device, dtype=dtype, use_ema=use_ema)
+
+# Audio
+audio, sr = torchaudio.load(audio_to_edit)
+if audio.shape[0] > 1:
+    audio = torch.mean(audio, dim=0, keepdim=True)
+rms = torch.sqrt(torch.mean(torch.square(audio)))
+if rms < target_rms:
+    audio = audio * target_rms / rms
+if sr != target_sample_rate:
+    resampler = torchaudio.transforms.Resample(sr, target_sample_rate)
+    audio = resampler(audio)
+offset = 0
+audio_ = torch.zeros(1, 0)
+edit_mask = torch.zeros(1, 0, dtype=torch.bool)
+for part in parts_to_edit:
+    start, end = part
+    part_dur = end - start if fix_duration is None else fix_duration.pop(0)
+    part_dur = part_dur * target_sample_rate
+    start = start * target_sample_rate
+    audio_ = torch.cat((audio_, audio[:, round(offset) : round(start)], torch.zeros(1, round(part_dur))), dim=-1)
+    edit_mask = torch.cat(
+        (
+            edit_mask,
+            torch.ones(1, round((start - offset) / hop_length), dtype=torch.bool),
+            torch.zeros(1, round(part_dur / hop_length), dtype=torch.bool),
+        ),
+        dim=-1,
+    )
+    offset = end * target_sample_rate
+audio = torch.cat((audio_, audio[:, round(offset) :]), dim=-1)
+edit_mask = F.pad(edit_mask, (0, audio.shape[-1] // hop_length - edit_mask.shape[-1] + 1), value=True)
+audio = audio.to(device)
+edit_mask = edit_mask.to(device)
+
+# Text
+text_list = [target_text]
+if tokenizer == "pinyin":
+    final_text_list = convert_char_to_pinyin(text_list)
+else:
+    final_text_list = [text_list]
+print(f"text  : {text_list}")
+print(f"pinyin: {final_text_list}")
+
+# Duration
+ref_audio_len = 0
+duration = audio.shape[-1] // hop_length
+
+# Inference
+with torch.inference_mode():
+    generated, trajectory = model.sample(
+        cond=audio,
+        text=final_text_list,
+        duration=duration,
+        steps=nfe_step,
+        cfg_strength=cfg_strength,
+        sway_sampling_coef=sway_sampling_coef,
+        seed=seed,
+        edit_mask=edit_mask,
+    )
+    print(f"Generated mel: {generated.shape}")
+
+    # Final result
+    generated = generated.to(torch.float32)
+    generated = generated[:, ref_audio_len:, :]
+    gen_mel_spec = generated.permute(0, 2, 1)
+    if mel_spec_type == "vocos":
+        generated_wave = vocoder.decode(gen_mel_spec).cpu()
+    elif mel_spec_type == "bigvgan":
+        generated_wave = vocoder(gen_mel_spec).squeeze(0).cpu()
+
+    if rms < target_rms:
+        generated_wave = generated_wave * rms / target_rms
+
+    save_spectrogram(gen_mel_spec[0].cpu().numpy(), f"{output_dir}/speech_edit_out.png")
+    torchaudio.save(f"{output_dir}/speech_edit_out.wav", generated_wave, target_sample_rate)
+    print(f"Generated wav: {generated_wave.shape}")

src/f5_tts/infer/utils_infer.py

@@ -0,0 +1,605 @@
+# A unified script for inference process
+# Make adjustments inside functions, and consider both gradio and cli scripts if need to change func output format
+import os
+import sys
+from concurrent.futures import ThreadPoolExecutor
+
+
+os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"  # for MPS device compatibility
+sys.path.append(f"{os.path.dirname(os.path.abspath(__file__))}/../../third_party/BigVGAN/")
+
+import hashlib
+import re
+import tempfile
+from importlib.resources import files
+
+import matplotlib
+
+
+matplotlib.use("Agg")
+
+import matplotlib.pylab as plt
+import numpy as np
+import torch
+import torchaudio
+import tqdm
+from huggingface_hub import hf_hub_download
+from pydub import AudioSegment, silence
+from transformers import pipeline
+from vocos import Vocos
+
+from f5_tts.model import CFM
+from f5_tts.model.utils import convert_char_to_pinyin, get_tokenizer
+
+
+_ref_audio_cache = {}
+_ref_text_cache = {}
+
+device = (
+    "cuda"
+    if torch.cuda.is_available()
+    else "xpu"
+    if torch.xpu.is_available()
+    else "mps"
+    if torch.backends.mps.is_available()
+    else "cpu"
+)
+
+tempfile_kwargs = {"delete_on_close": False} if sys.version_info >= (3, 12) else {"delete": False}
+
+# -----------------------------------------
+
+target_sample_rate = 24000
+n_mel_channels = 100
+hop_length = 256
+win_length = 1024
+n_fft = 1024
+mel_spec_type = "vocos"
+target_rms = 0.1
+cross_fade_duration = 0.15
+ode_method = "euler"
+nfe_step = 32  # 16, 32
+cfg_strength = 2.0
+sway_sampling_coef = -1.0
+speed = 1.0
+fix_duration = None
+
+# -----------------------------------------
+
+
+# chunk text into smaller pieces
+
+
+def chunk_text(text, max_chars=135):
+    """
+    Splits the input text into chunks, each with a maximum number of characters.
+
+    Args:
+        text (str): The text to be split.
+        max_chars (int): The maximum number of characters per chunk.
+
+    Returns:
+        List[str]: A list of text chunks.
+    """
+    chunks = []
+    current_chunk = ""
+    # Split the text into sentences based on punctuation followed by whitespace
+    sentences = re.split(r"(?<=[;:,.!?])\s+|(?<=[；：，。！？])", text)
+
+    for sentence in sentences:
+        if len(current_chunk.encode("utf-8")) + len(sentence.encode("utf-8")) <= max_chars:
+            current_chunk += sentence + " " if sentence and len(sentence[-1].encode("utf-8")) == 1 else sentence
+        else:
+            if current_chunk:
+                chunks.append(current_chunk.strip())
+            current_chunk = sentence + " " if sentence and len(sentence[-1].encode("utf-8")) == 1 else sentence
+
+    if current_chunk:
+        chunks.append(current_chunk.strip())
+
+    return chunks
+
+
+# load vocoder
+def load_vocoder(vocoder_name="vocos", is_local=False, local_path="", device=device, hf_cache_dir=None):
+    if vocoder_name == "vocos":
+        # vocoder = Vocos.from_pretrained("charactr/vocos-mel-24khz").to(device)
+        if is_local:
+            print(f"Load vocos from local path {local_path}")
+            config_path = f"{local_path}/config.yaml"
+            model_path = f"{local_path}/pytorch_model.bin"
+        else:
+            print("Download Vocos from huggingface charactr/vocos-mel-24khz")
+            repo_id = "charactr/vocos-mel-24khz"
+            config_path = hf_hub_download(repo_id=repo_id, cache_dir=hf_cache_dir, filename="config.yaml")
+            model_path = hf_hub_download(repo_id=repo_id, cache_dir=hf_cache_dir, filename="pytorch_model.bin")
+        vocoder = Vocos.from_hparams(config_path)
+        state_dict = torch.load(model_path, map_location="cpu", weights_only=True)
+        from vocos.feature_extractors import EncodecFeatures
+
+        if isinstance(vocoder.feature_extractor, EncodecFeatures):
+            encodec_parameters = {
+                "feature_extractor.encodec." + key: value
+                for key, value in vocoder.feature_extractor.encodec.state_dict().items()
+            }
+            state_dict.update(encodec_parameters)
+        vocoder.load_state_dict(state_dict)
+        vocoder = vocoder.eval().to(device)
+    elif vocoder_name == "bigvgan":
+        try:
+            from third_party.BigVGAN import bigvgan
+        except ImportError:
+            print("You need to follow the README to init submodule and change the BigVGAN source code.")
+        if is_local:
+            # download generator from https://huggingface.co/nvidia/bigvgan_v2_24khz_100band_256x/tree/main
+            vocoder = bigvgan.BigVGAN.from_pretrained(local_path, use_cuda_kernel=False)
+        else:
+            vocoder = bigvgan.BigVGAN.from_pretrained(
+                "nvidia/bigvgan_v2_24khz_100band_256x", use_cuda_kernel=False, cache_dir=hf_cache_dir
+            )
+
+        vocoder.remove_weight_norm()
+        vocoder = vocoder.eval().to(device)
+    return vocoder
+
+
+# load asr pipeline
+
+asr_pipe = None
+
+
+def initialize_asr_pipeline(device: str = device, dtype=None):
+    if dtype is None:
+        dtype = (
+            torch.float16
+            if "cuda" in device
+            and torch.cuda.get_device_properties(device).major >= 7
+            and not torch.cuda.get_device_name().endswith("[ZLUDA]")
+            else torch.float32
+        )
+    global asr_pipe
+    asr_pipe = pipeline(
+        "automatic-speech-recognition",
+        model="openai/whisper-large-v3-turbo",
+        torch_dtype=dtype,
+        device=device,
+    )
+
+
+# transcribe
+
+
+def transcribe(ref_audio, language=None):
+    global asr_pipe
+    if asr_pipe is None:
+        initialize_asr_pipeline(device=device)
+    return asr_pipe(
+        ref_audio,
+        chunk_length_s=30,
+        batch_size=128,
+        generate_kwargs={"task": "transcribe", "language": language} if language else {"task": "transcribe"},
+        return_timestamps=False,
+    )["text"].strip()
+
+
+# load model checkpoint for inference
+
+
+def load_checkpoint(model, ckpt_path, device: str, dtype=None, use_ema=True):
+    if dtype is None:
+        dtype = (
+            torch.float16
+            if "cuda" in device
+            and torch.cuda.get_device_properties(device).major >= 7
+            and not torch.cuda.get_device_name().endswith("[ZLUDA]")
+            else torch.float32
+        )
+    model = model.to(dtype)
+
+    ckpt_type = ckpt_path.split(".")[-1]
+    if ckpt_type == "safetensors":
+        from safetensors.torch import load_file
+
+        checkpoint = load_file(ckpt_path, device=device)
+    else:
+        checkpoint = torch.load(ckpt_path, map_location=device, weights_only=True)
+
+    if use_ema:
+        if ckpt_type == "safetensors":
+            checkpoint = {"ema_model_state_dict": checkpoint}
+        checkpoint["model_state_dict"] = {
+            k.replace("ema_model.", ""): v
+            for k, v in checkpoint["ema_model_state_dict"].items()
+            if k not in ["initted", "step"]
+        }
+
+        # patch for backward compatibility, 305e3ea
+        for key in ["mel_spec.mel_stft.mel_scale.fb", "mel_spec.mel_stft.spectrogram.window"]:
+            if key in checkpoint["model_state_dict"]:
+                del checkpoint["model_state_dict"][key]
+
+        model.load_state_dict(checkpoint["model_state_dict"])
+    else:
+        if ckpt_type == "safetensors":
+            checkpoint = {"model_state_dict": checkpoint}
+        model.load_state_dict(checkpoint["model_state_dict"])
+
+    del checkpoint
+    torch.cuda.empty_cache()
+
+    return model.to(device)
+
+
+# load model for inference
+
+
+def load_model(
+    model_cls,
+    model_cfg,
+    ckpt_path,
+    mel_spec_type=mel_spec_type,
+    vocab_file="",
+    ode_method=ode_method,
+    use_ema=True,
+    device=device,
+):
+    if vocab_file == "":
+        vocab_file = str(files("f5_tts").joinpath("infer/examples/vocab.txt"))
+    tokenizer = "custom"
+
+    print("\nvocab : ", vocab_file)
+    print("token : ", tokenizer)
+    print("model : ", ckpt_path, "\n")
+
+    vocab_char_map, vocab_size = get_tokenizer(vocab_file, tokenizer)
+    model = CFM(
+        transformer=model_cls(**model_cfg, text_num_embeds=vocab_size, mel_dim=n_mel_channels),
+        mel_spec_kwargs=dict(
+            n_fft=n_fft,
+            hop_length=hop_length,
+            win_length=win_length,
+            n_mel_channels=n_mel_channels,
+            target_sample_rate=target_sample_rate,
+            mel_spec_type=mel_spec_type,
+        ),
+        odeint_kwargs=dict(
+            method=ode_method,
+        ),
+        vocab_char_map=vocab_char_map,
+    ).to(device)
+
+    dtype = torch.float32 if mel_spec_type == "bigvgan" else None
+    model = load_checkpoint(model, ckpt_path, device, dtype=dtype, use_ema=use_ema)
+
+    return model
+
+
+def remove_silence_edges(audio, silence_threshold=-42):
+    # Remove silence from the start
+    non_silent_start_idx = silence.detect_leading_silence(audio, silence_threshold=silence_threshold)
+    audio = audio[non_silent_start_idx:]
+
+    # Remove silence from the end
+    non_silent_end_duration = audio.duration_seconds
+    for ms in reversed(audio):
+        if ms.dBFS > silence_threshold:
+            break
+        non_silent_end_duration -= 0.001
+    trimmed_audio = audio[: int(non_silent_end_duration * 1000)]
+
+    return trimmed_audio
+
+
+# preprocess reference audio and text
+
+
+def preprocess_ref_audio_text(ref_audio_orig, ref_text, show_info=print):
+    show_info("Converting audio...")
+
+    # Compute a hash of the reference audio file
+    with open(ref_audio_orig, "rb") as audio_file:
+        audio_data = audio_file.read()
+        audio_hash = hashlib.md5(audio_data).hexdigest()
+
+    global _ref_audio_cache
+
+    if audio_hash in _ref_audio_cache:
+        show_info("Using cached preprocessed reference audio...")
+        ref_audio = _ref_audio_cache[audio_hash]
+
+    else:  # first pass, do preprocess
+        with tempfile.NamedTemporaryFile(suffix=".wav", **tempfile_kwargs) as f:
+            temp_path = f.name
+
+        aseg = AudioSegment.from_file(ref_audio_orig)
+
+        # 1. try to find long silence for clipping
+        non_silent_segs = silence.split_on_silence(
+            aseg, min_silence_len=1000, silence_thresh=-50, keep_silence=1000, seek_step=10
+        )
+        non_silent_wave = AudioSegment.silent(duration=0)
+        for non_silent_seg in non_silent_segs:
+            if len(non_silent_wave) > 6000 and len(non_silent_wave + non_silent_seg) > 12000:
+                show_info("Audio is over 12s, clipping short. (1)")
+                break
+            non_silent_wave += non_silent_seg
+
+        # 2. try to find short silence for clipping if 1. failed
+        if len(non_silent_wave) > 12000:
+            non_silent_segs = silence.split_on_silence(
+                aseg, min_silence_len=100, silence_thresh=-40, keep_silence=1000, seek_step=10
+            )
+            non_silent_wave = AudioSegment.silent(duration=0)
+            for non_silent_seg in non_silent_segs:
+                if len(non_silent_wave) > 6000 and len(non_silent_wave + non_silent_seg) > 12000:
+                    show_info("Audio is over 12s, clipping short. (2)")
+                    break
+                non_silent_wave += non_silent_seg
+
+        aseg = non_silent_wave
+
+        # 3. if no proper silence found for clipping
+        if len(aseg) > 12000:
+            aseg = aseg[:12000]
+            show_info("Audio is over 12s, clipping short. (3)")
+
+        aseg = remove_silence_edges(aseg) + AudioSegment.silent(duration=50)
+        aseg.export(temp_path, format="wav")
+        ref_audio = temp_path
+
+        # Cache the processed reference audio
+        _ref_audio_cache[audio_hash] = ref_audio
+
+    if not ref_text.strip():
+        global _ref_text_cache
+        if audio_hash in _ref_text_cache:
+            # Use cached asr transcription
+            show_info("Using cached reference text...")
+            ref_text = _ref_text_cache[audio_hash]
+        else:
+            show_info("No reference text provided, transcribing reference audio...")
+            ref_text = transcribe(ref_audio)
+            # Cache the transcribed text (not caching custom ref_text, enabling users to do manual tweak)
+            _ref_text_cache[audio_hash] = ref_text
+    else:
+        show_info("Using custom reference text...")
+
+    # Ensure ref_text ends with a proper sentence-ending punctuation
+    if not ref_text.endswith(". ") and not ref_text.endswith("。"):
+        if ref_text.endswith("."):
+            ref_text += " "
+        else:
+            ref_text += ". "
+
+    print("\nref_text  ", ref_text)
+
+    return ref_audio, ref_text
+
+
+# infer process: chunk text -> infer batches [i.e. infer_batch_process()]
+
+
+def infer_process(
+    ref_audio,
+    ref_text,
+    gen_text,
+    model_obj,
+    vocoder,
+    mel_spec_type=mel_spec_type,
+    show_info=print,
+    progress=tqdm,
+    target_rms=target_rms,
+    cross_fade_duration=cross_fade_duration,
+    nfe_step=nfe_step,
+    cfg_strength=cfg_strength,
+    sway_sampling_coef=sway_sampling_coef,
+    speed=speed,
+    fix_duration=fix_duration,
+    device=device,
+):
+    # Split the input text into batches
+    audio, sr = torchaudio.load(ref_audio)
+    max_chars = int(len(ref_text.encode("utf-8")) / (audio.shape[-1] / sr) * (22 - audio.shape[-1] / sr) * speed)
+    gen_text_batches = chunk_text(gen_text, max_chars=max_chars)
+    for i, gen_text in enumerate(gen_text_batches):
+        print(f"gen_text {i}", gen_text)
+    print("\n")
+
+    show_info(f"Generating audio in {len(gen_text_batches)} batches...")
+    return next(
+        infer_batch_process(
+            (audio, sr),
+            ref_text,
+            gen_text_batches,
+            model_obj,
+            vocoder,
+            mel_spec_type=mel_spec_type,
+            progress=progress,
+            target_rms=target_rms,
+            cross_fade_duration=cross_fade_duration,
+            nfe_step=nfe_step,
+            cfg_strength=cfg_strength,
+            sway_sampling_coef=sway_sampling_coef,
+            speed=speed,
+            fix_duration=fix_duration,
+            device=device,
+        )
+    )
+
+
+# infer batches
+
+
+def infer_batch_process(
+    ref_audio,
+    ref_text,
+    gen_text_batches,
+    model_obj,
+    vocoder,
+    mel_spec_type="vocos",
+    progress=tqdm,
+    target_rms=0.1,
+    cross_fade_duration=0.15,
+    nfe_step=32,
+    cfg_strength=2.0,
+    sway_sampling_coef=-1,
+    speed=1,
+    fix_duration=None,
+    device=None,
+    streaming=False,
+    chunk_size=2048,
+):
+    audio, sr = ref_audio
+    if audio.shape[0] > 1:
+        audio = torch.mean(audio, dim=0, keepdim=True)
+
+    rms = torch.sqrt(torch.mean(torch.square(audio)))
+    if rms < target_rms:
+        audio = audio * target_rms / rms
+    if sr != target_sample_rate:
+        resampler = torchaudio.transforms.Resample(sr, target_sample_rate)
+        audio = resampler(audio)
+    audio = audio.to(device)
+
+    generated_waves = []
+    spectrograms = []
+
+    if len(ref_text[-1].encode("utf-8")) == 1:
+        ref_text = ref_text + " "
+
+    def process_batch(gen_text):
+        local_speed = speed
+        if len(gen_text.encode("utf-8")) < 10:
+            local_speed = 0.3
+
+        # Prepare the text
+        text_list = [ref_text + gen_text]
+        final_text_list = convert_char_to_pinyin(text_list)
+
+        ref_audio_len = audio.shape[-1] // hop_length
+        if fix_duration is not None:
+            duration = int(fix_duration * target_sample_rate / hop_length)
+        else:
+            # Calculate duration
+            ref_text_len = len(ref_text.encode("utf-8"))
+            gen_text_len = len(gen_text.encode("utf-8"))
+            duration = ref_audio_len + int(ref_audio_len / ref_text_len * gen_text_len / local_speed)
+
+        # inference
+        with torch.inference_mode():
+            generated, _ = model_obj.sample(
+                cond=audio,
+                text=final_text_list,
+                duration=duration,
+                steps=nfe_step,
+                cfg_strength=cfg_strength,
+                sway_sampling_coef=sway_sampling_coef,
+            )
+            del _
+
+            generated = generated.to(torch.float32)  # generated mel spectrogram
+            generated = generated[:, ref_audio_len:, :]
+            generated = generated.permute(0, 2, 1)
+            if mel_spec_type == "vocos":
+                generated_wave = vocoder.decode(generated)
+            elif mel_spec_type == "bigvgan":
+                generated_wave = vocoder(generated)
+            if rms < target_rms:
+                generated_wave = generated_wave * rms / target_rms
+
+            # wav -> numpy
+            generated_wave = generated_wave.squeeze().cpu().numpy()
+
+            if streaming:
+                for j in range(0, len(generated_wave), chunk_size):
+                    yield generated_wave[j : j + chunk_size], target_sample_rate
+            else:
+                generated_cpu = generated[0].cpu().numpy()
+                del generated
+                yield generated_wave, generated_cpu
+
+    if streaming:
+        for gen_text in progress.tqdm(gen_text_batches) if progress is not None else gen_text_batches:
+            for chunk in process_batch(gen_text):
+                yield chunk
+    else:
+        with ThreadPoolExecutor() as executor:
+            futures = [executor.submit(process_batch, gen_text) for gen_text in gen_text_batches]
+            for future in progress.tqdm(futures) if progress is not None else futures:
+                result = future.result()
+                if result:
+                    generated_wave, generated_mel_spec = next(result)
+                    generated_waves.append(generated_wave)
+                    spectrograms.append(generated_mel_spec)
+
+        if generated_waves:
+            if cross_fade_duration <= 0:
+                # Simply concatenate
+                final_wave = np.concatenate(generated_waves)
+            else:
+                # Combine all generated waves with cross-fading
+                final_wave = generated_waves[0]
+                for i in range(1, len(generated_waves)):
+                    prev_wave = final_wave
+                    next_wave = generated_waves[i]
+
+                    # Calculate cross-fade samples, ensuring it does not exceed wave lengths
+                    cross_fade_samples = int(cross_fade_duration * target_sample_rate)
+                    cross_fade_samples = min(cross_fade_samples, len(prev_wave), len(next_wave))
+
+                    if cross_fade_samples <= 0:
+                        # No overlap possible, concatenate
+                        final_wave = np.concatenate([prev_wave, next_wave])
+                        continue
+
+                    # Overlapping parts
+                    prev_overlap = prev_wave[-cross_fade_samples:]
+                    next_overlap = next_wave[:cross_fade_samples]
+
+                    # Fade out and fade in
+                    fade_out = np.linspace(1, 0, cross_fade_samples)
+                    fade_in = np.linspace(0, 1, cross_fade_samples)
+
+                    # Cross-faded overlap
+                    cross_faded_overlap = prev_overlap * fade_out + next_overlap * fade_in
+
+                    # Combine
+                    new_wave = np.concatenate(
+                        [prev_wave[:-cross_fade_samples], cross_faded_overlap, next_wave[cross_fade_samples:]]
+                    )
+
+                    final_wave = new_wave
+
+            # Create a combined spectrogram
+            combined_spectrogram = np.concatenate(spectrograms, axis=1)
+
+            yield final_wave, target_sample_rate, combined_spectrogram
+
+        else:
+            yield None, target_sample_rate, None
+
+
+# remove silence from generated wav
+
+
+def remove_silence_for_generated_wav(filename):
+    aseg = AudioSegment.from_file(filename)
+    non_silent_segs = silence.split_on_silence(
+        aseg, min_silence_len=1000, silence_thresh=-50, keep_silence=500, seek_step=10
+    )
+    non_silent_wave = AudioSegment.silent(duration=0)
+    for non_silent_seg in non_silent_segs:
+        non_silent_wave += non_silent_seg
+    aseg = non_silent_wave
+    aseg.export(filename, format="wav")
+
+
+# save spectrogram
+
+
+def save_spectrogram(spectrogram, path):
+    plt.figure(figsize=(12, 4))
+    plt.imshow(spectrogram, origin="lower", aspect="auto")
+    plt.colorbar()
+    plt.savefig(path)
+    plt.close()

src/f5_tts/model/__init__.py

@@ -0,0 +1,8 @@
+from f5_tts.model.backbones.dit import DiT
+from f5_tts.model.backbones.mmdit import MMDiT
+from f5_tts.model.backbones.unett import UNetT
+from f5_tts.model.cfm import CFM
+from f5_tts.model.trainer import Trainer
+
+
+__all__ = ["CFM", "UNetT", "DiT", "MMDiT", "Trainer"]

src/f5_tts/model/backbones/README.md

@@ -0,0 +1,20 @@
+## Backbones quick introduction
+
+
+### unett.py
+- flat unet transformer
+- structure same as in e2-tts & voicebox paper except using rotary pos emb
+- possible abs pos emb & convnextv2 blocks for embedded text before concat
+
+### dit.py
+- adaln-zero dit
+- embedded timestep as condition
+- concatted noised_input + masked_cond + embedded_text, linear proj in
+- possible abs pos emb & convnextv2 blocks for embedded text before concat
+- possible long skip connection (first layer to last layer)
+
+### mmdit.py
+- stable diffusion 3 block structure
+- timestep as condition
+- left stream: text embedded and applied a abs pos emb
+- right stream: masked_cond & noised_input concatted and with same conv pos emb as unett

src/f5_tts/model/backbones/dit.py

@@ -0,0 +1,259 @@
+"""
+ein notation:
+b - batch
+n - sequence
+nt - text sequence
+nw - raw wave length
+d - dimension
+"""
+
+from __future__ import annotations
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from x_transformers.x_transformers import RotaryEmbedding
+
+from f5_tts.model.modules import (
+    AdaLayerNorm_Final,
+    ConvNeXtV2Block,
+    ConvPositionEmbedding,
+    DiTBlock,
+    TimestepEmbedding,
+    get_pos_embed_indices,
+    precompute_freqs_cis,
+)
+
+
+# Text embedding
+
+
+class TextEmbedding(nn.Module):
+    def __init__(self, text_num_embeds, text_dim, mask_padding=True, conv_layers=0, conv_mult=2):
+        super().__init__()
+        self.text_embed = nn.Embedding(text_num_embeds + 1, text_dim)  # use 0 as filler token
+
+        self.mask_padding = mask_padding  # mask filler and batch padding tokens or not
+
+        if conv_layers > 0:
+            self.extra_modeling = True
+            self.precompute_max_pos = 4096  # ~44s of 24khz audio
+            self.register_buffer("freqs_cis", precompute_freqs_cis(text_dim, self.precompute_max_pos), persistent=False)
+            self.text_blocks = nn.Sequential(
+                *[ConvNeXtV2Block(text_dim, text_dim * conv_mult) for _ in range(conv_layers)]
+            )
+        else:
+            self.extra_modeling = False
+
+    def forward(self, text: int["b nt"], seq_len, drop_text=False):  # noqa: F722
+        text = text + 1  # use 0 as filler token. preprocess of batch pad -1, see list_str_to_idx()
+        text = text[:, :seq_len]  # curtail if character tokens are more than the mel spec tokens
+        batch, text_len = text.shape[0], text.shape[1]
+        text = F.pad(text, (0, seq_len - text_len), value=0)
+        if self.mask_padding:
+            text_mask = text == 0
+
+        if drop_text:  # cfg for text
+            text = torch.zeros_like(text)
+
+        text = self.text_embed(text)  # b n -> b n d
+
+        # possible extra modeling
+        if self.extra_modeling:
+            # sinus pos emb
+            batch_start = torch.zeros((batch,), dtype=torch.long)
+            pos_idx = get_pos_embed_indices(batch_start, seq_len, max_pos=self.precompute_max_pos)
+            text_pos_embed = self.freqs_cis[pos_idx]
+            text = text + text_pos_embed
+
+            # convnextv2 blocks
+            if self.mask_padding:
+                text = text.masked_fill(text_mask.unsqueeze(-1).expand(-1, -1, text.size(-1)), 0.0)
+                for block in self.text_blocks:
+                    text = block(text)
+                    text = text.masked_fill(text_mask.unsqueeze(-1).expand(-1, -1, text.size(-1)), 0.0)
+            else:
+                text = self.text_blocks(text)
+
+        return text
+
+
+# noised input audio and context mixing embedding
+
+
+class InputEmbedding(nn.Module):
+    def __init__(self, mel_dim, text_dim, out_dim):
+        super().__init__()
+        self.proj = nn.Linear(mel_dim * 2 + text_dim, out_dim)
+        self.conv_pos_embed = ConvPositionEmbedding(dim=out_dim)
+
+    def forward(self, x: float["b n d"], cond: float["b n d"], text_embed: float["b n d"], drop_audio_cond=False):  # noqa: F722
+        if drop_audio_cond:  # cfg for cond audio
+            cond = torch.zeros_like(cond)
+
+        x = self.proj(torch.cat((x, cond, text_embed), dim=-1))
+        x = self.conv_pos_embed(x) + x
+        return x
+
+
+# Transformer backbone using DiT blocks
+
+
+class DiT(nn.Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        depth=8,
+        heads=8,
+        dim_head=64,
+        dropout=0.1,
+        ff_mult=4,
+        mel_dim=100,
+        text_num_embeds=256,
+        text_dim=None,
+        text_mask_padding=True,
+        qk_norm=None,
+        conv_layers=0,
+        pe_attn_head=None,
+        attn_backend="torch",  # "torch" | "flash_attn"
+        attn_mask_enabled=False,
+        long_skip_connection=False,
+        checkpoint_activations=False,
+    ):
+        super().__init__()
+
+        self.time_embed = TimestepEmbedding(dim)
+        if text_dim is None:
+            text_dim = mel_dim
+        self.text_embed = TextEmbedding(
+            text_num_embeds, text_dim, mask_padding=text_mask_padding, conv_layers=conv_layers
+        )
+        self.text_cond, self.text_uncond = None, None  # text cache
+        self.input_embed = InputEmbedding(mel_dim, text_dim, dim)
+
+        self.rotary_embed = RotaryEmbedding(dim_head)
+
+        self.dim = dim
+        self.depth = depth
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                DiTBlock(
+                    dim=dim,
+                    heads=heads,
+                    dim_head=dim_head,
+                    ff_mult=ff_mult,
+                    dropout=dropout,
+                    qk_norm=qk_norm,
+                    pe_attn_head=pe_attn_head,
+                    attn_backend=attn_backend,
+                    attn_mask_enabled=attn_mask_enabled,
+                )
+                for _ in range(depth)
+            ]
+        )
+        self.long_skip_connection = nn.Linear(dim * 2, dim, bias=False) if long_skip_connection else None
+
+        self.norm_out = AdaLayerNorm_Final(dim)  # final modulation
+        self.proj_out = nn.Linear(dim, mel_dim)
+
+        self.checkpoint_activations = checkpoint_activations
+
+        self.initialize_weights()
+
+    def initialize_weights(self):
+        # Zero-out AdaLN layers in DiT blocks:
+        for block in self.transformer_blocks:
+            nn.init.constant_(block.attn_norm.linear.weight, 0)
+            nn.init.constant_(block.attn_norm.linear.bias, 0)
+
+        # Zero-out output layers:
+        nn.init.constant_(self.norm_out.linear.weight, 0)
+        nn.init.constant_(self.norm_out.linear.bias, 0)
+        nn.init.constant_(self.proj_out.weight, 0)
+        nn.init.constant_(self.proj_out.bias, 0)
+
+    def ckpt_wrapper(self, module):
+        # https://github.com/chuanyangjin/fast-DiT/blob/main/models.py
+        def ckpt_forward(*inputs):
+            outputs = module(*inputs)
+            return outputs
+
+        return ckpt_forward
+
+    def get_input_embed(
+        self,
+        x,  # b n d
+        cond,  # b n d
+        text,  # b nt
+        drop_audio_cond: bool = False,
+        drop_text: bool = False,
+        cache: bool = True,
+    ):
+        seq_len = x.shape[1]
+        if cache:
+            if drop_text:
+                if self.text_uncond is None:
+                    self.text_uncond = self.text_embed(text, seq_len, drop_text=True)
+                text_embed = self.text_uncond
+            else:
+                if self.text_cond is None:
+                    self.text_cond = self.text_embed(text, seq_len, drop_text=False)
+                text_embed = self.text_cond
+        else:
+            text_embed = self.text_embed(text, seq_len, drop_text=drop_text)
+
+        x = self.input_embed(x, cond, text_embed, drop_audio_cond=drop_audio_cond)
+
+        return x
+
+    def clear_cache(self):
+        self.text_cond, self.text_uncond = None, None
+
+    def forward(
+        self,
+        x: float["b n d"],  # nosied input audio  # noqa: F722
+        cond: float["b n d"],  # masked cond audio  # noqa: F722
+        text: int["b nt"],  # text  # noqa: F722
+        time: float["b"] | float[""],  # time step  # noqa: F821 F722
+        mask: bool["b n"] | None = None,  # noqa: F722
+        drop_audio_cond: bool = False,  # cfg for cond audio
+        drop_text: bool = False,  # cfg for text
+        cfg_infer: bool = False,  # cfg inference, pack cond & uncond forward
+        cache: bool = False,
+    ):
+        batch, seq_len = x.shape[0], x.shape[1]
+        if time.ndim == 0:
+            time = time.repeat(batch)
+
+        # t: conditioning time, text: text, x: noised audio + cond audio + text
+        t = self.time_embed(time)
+        if cfg_infer:  # pack cond & uncond forward: b n d -> 2b n d
+            x_cond = self.get_input_embed(x, cond, text, drop_audio_cond=False, drop_text=False, cache=cache)
+            x_uncond = self.get_input_embed(x, cond, text, drop_audio_cond=True, drop_text=True, cache=cache)
+            x = torch.cat((x_cond, x_uncond), dim=0)
+            t = torch.cat((t, t), dim=0)
+            mask = torch.cat((mask, mask), dim=0) if mask is not None else None
+        else:
+            x = self.get_input_embed(x, cond, text, drop_audio_cond=drop_audio_cond, drop_text=drop_text, cache=cache)
+
+        rope = self.rotary_embed.forward_from_seq_len(seq_len)
+
+        if self.long_skip_connection is not None:
+            residual = x
+
+        for block in self.transformer_blocks:
+            if self.checkpoint_activations:
+                # https://pytorch.org/docs/stable/checkpoint.html#torch.utils.checkpoint.checkpoint
+                x = torch.utils.checkpoint.checkpoint(self.ckpt_wrapper(block), x, t, mask, rope, use_reentrant=False)
+            else:
+                x = block(x, t, mask=mask, rope=rope)
+
+        if self.long_skip_connection is not None:
+            x = self.long_skip_connection(torch.cat((x, residual), dim=-1))
+
+        x = self.norm_out(x, t)
+        output = self.proj_out(x)
+
+        return output

src/f5_tts/model/backbones/mmdit.py

@@ -0,0 +1,212 @@
+"""
+ein notation:
+b - batch
+n - sequence
+nt - text sequence
+nw - raw wave length
+d - dimension
+"""
+
+from __future__ import annotations
+
+import torch
+from torch import nn
+from x_transformers.x_transformers import RotaryEmbedding
+
+from f5_tts.model.modules import (
+    AdaLayerNorm_Final,
+    ConvPositionEmbedding,
+    MMDiTBlock,
+    TimestepEmbedding,
+    get_pos_embed_indices,
+    precompute_freqs_cis,
+)
+
+
+# text embedding
+
+
+class TextEmbedding(nn.Module):
+    def __init__(self, out_dim, text_num_embeds, mask_padding=True):
+        super().__init__()
+        self.text_embed = nn.Embedding(text_num_embeds + 1, out_dim)  # will use 0 as filler token
+
+        self.mask_padding = mask_padding  # mask filler and batch padding tokens or not
+
+        self.precompute_max_pos = 1024
+        self.register_buffer("freqs_cis", precompute_freqs_cis(out_dim, self.precompute_max_pos), persistent=False)
+
+    def forward(self, text: int["b nt"], drop_text=False) -> int["b nt d"]:  # noqa: F722
+        text = text + 1  # use 0 as filler token. preprocess of batch pad -1, see list_str_to_idx()
+        if self.mask_padding:
+            text_mask = text == 0
+
+        if drop_text:  # cfg for text
+            text = torch.zeros_like(text)
+
+        text = self.text_embed(text)  # b nt -> b nt d
+
+        # sinus pos emb
+        batch_start = torch.zeros((text.shape[0],), dtype=torch.long)
+        batch_text_len = text.shape[1]
+        pos_idx = get_pos_embed_indices(batch_start, batch_text_len, max_pos=self.precompute_max_pos)
+        text_pos_embed = self.freqs_cis[pos_idx]
+
+        text = text + text_pos_embed
+
+        if self.mask_padding:
+            text = text.masked_fill(text_mask.unsqueeze(-1).expand(-1, -1, text.size(-1)), 0.0)
+
+        return text
+
+
+# noised input & masked cond audio embedding
+
+
+class AudioEmbedding(nn.Module):
+    def __init__(self, in_dim, out_dim):
+        super().__init__()
+        self.linear = nn.Linear(2 * in_dim, out_dim)
+        self.conv_pos_embed = ConvPositionEmbedding(out_dim)
+
+    def forward(self, x: float["b n d"], cond: float["b n d"], drop_audio_cond=False):  # noqa: F722
+        if drop_audio_cond:
+            cond = torch.zeros_like(cond)
+        x = torch.cat((x, cond), dim=-1)
+        x = self.linear(x)
+        x = self.conv_pos_embed(x) + x
+        return x
+
+
+# Transformer backbone using MM-DiT blocks
+
+
+class MMDiT(nn.Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        depth=8,
+        heads=8,
+        dim_head=64,
+        dropout=0.1,
+        ff_mult=4,
+        mel_dim=100,
+        text_num_embeds=256,
+        text_mask_padding=True,
+        qk_norm=None,
+    ):
+        super().__init__()
+
+        self.time_embed = TimestepEmbedding(dim)
+        self.text_embed = TextEmbedding(dim, text_num_embeds, mask_padding=text_mask_padding)
+        self.text_cond, self.text_uncond = None, None  # text cache
+        self.audio_embed = AudioEmbedding(mel_dim, dim)
+
+        self.rotary_embed = RotaryEmbedding(dim_head)
+
+        self.dim = dim
+        self.depth = depth
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                MMDiTBlock(
+                    dim=dim,
+                    heads=heads,
+                    dim_head=dim_head,
+                    dropout=dropout,
+                    ff_mult=ff_mult,
+                    context_pre_only=i == depth - 1,
+                    qk_norm=qk_norm,
+                )
+                for i in range(depth)
+            ]
+        )
+        self.norm_out = AdaLayerNorm_Final(dim)  # final modulation
+        self.proj_out = nn.Linear(dim, mel_dim)
+
+        self.initialize_weights()
+
+    def initialize_weights(self):
+        # Zero-out AdaLN layers in MMDiT blocks:
+        for block in self.transformer_blocks:
+            nn.init.constant_(block.attn_norm_x.linear.weight, 0)
+            nn.init.constant_(block.attn_norm_x.linear.bias, 0)
+            nn.init.constant_(block.attn_norm_c.linear.weight, 0)
+            nn.init.constant_(block.attn_norm_c.linear.bias, 0)
+
+        # Zero-out output layers:
+        nn.init.constant_(self.norm_out.linear.weight, 0)
+        nn.init.constant_(self.norm_out.linear.bias, 0)
+        nn.init.constant_(self.proj_out.weight, 0)
+        nn.init.constant_(self.proj_out.bias, 0)
+
+    def get_input_embed(
+        self,
+        x,  # b n d
+        cond,  # b n d
+        text,  # b nt
+        drop_audio_cond: bool = False,
+        drop_text: bool = False,
+        cache: bool = True,
+    ):
+        if cache:
+            if drop_text:
+                if self.text_uncond is None:
+                    self.text_uncond = self.text_embed(text, drop_text=True)
+                c = self.text_uncond
+            else:
+                if self.text_cond is None:
+                    self.text_cond = self.text_embed(text, drop_text=False)
+                c = self.text_cond
+        else:
+            c = self.text_embed(text, drop_text=drop_text)
+        x = self.audio_embed(x, cond, drop_audio_cond=drop_audio_cond)
+
+        return x, c
+
+    def clear_cache(self):
+        self.text_cond, self.text_uncond = None, None
+
+    def forward(
+        self,
+        x: float["b n d"],  # nosied input audio  # noqa: F722
+        cond: float["b n d"],  # masked cond audio  # noqa: F722
+        text: int["b nt"],  # text  # noqa: F722
+        time: float["b"] | float[""],  # time step  # noqa: F821 F722
+        mask: bool["b n"] | None = None,  # noqa: F722
+        drop_audio_cond: bool = False,  # cfg for cond audio
+        drop_text: bool = False,  # cfg for text
+        cfg_infer: bool = False,  # cfg inference, pack cond & uncond forward
+        cache: bool = False,
+    ):
+        batch = x.shape[0]
+        if time.ndim == 0:
+            time = time.repeat(batch)
+
+        # t: conditioning (time), c: context (text + masked cond audio), x: noised input audio
+        t = self.time_embed(time)
+        if cfg_infer:  # pack cond & uncond forward: b n d -> 2b n d
+            x_cond, c_cond = self.get_input_embed(x, cond, text, drop_audio_cond=False, drop_text=False, cache=cache)
+            x_uncond, c_uncond = self.get_input_embed(x, cond, text, drop_audio_cond=True, drop_text=True, cache=cache)
+            x = torch.cat((x_cond, x_uncond), dim=0)
+            c = torch.cat((c_cond, c_uncond), dim=0)
+            t = torch.cat((t, t), dim=0)
+            mask = torch.cat((mask, mask), dim=0) if mask is not None else None
+        else:
+            x, c = self.get_input_embed(
+                x, cond, text, drop_audio_cond=drop_audio_cond, drop_text=drop_text, cache=cache
+            )
+
+        seq_len = x.shape[1]
+        text_len = text.shape[1]
+        rope_audio = self.rotary_embed.forward_from_seq_len(seq_len)
+        rope_text = self.rotary_embed.forward_from_seq_len(text_len)
+
+        for block in self.transformer_blocks:
+            c, x = block(x, c, t, mask=mask, rope=rope_audio, c_rope=rope_text)
+
+        x = self.norm_out(x, t)
+        output = self.proj_out(x)
+
+        return output

src/f5_tts/model/backbones/unett.py

@@ -0,0 +1,273 @@
+"""
+ein notation:
+b - batch
+n - sequence
+nt - text sequence
+nw - raw wave length
+d - dimension
+"""
+
+from __future__ import annotations
+
+from typing import Literal
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from x_transformers import RMSNorm
+from x_transformers.x_transformers import RotaryEmbedding
+
+from f5_tts.model.modules import (
+    Attention,
+    AttnProcessor,
+    ConvNeXtV2Block,
+    ConvPositionEmbedding,
+    FeedForward,
+    TimestepEmbedding,
+    get_pos_embed_indices,
+    precompute_freqs_cis,
+)
+
+
+# Text embedding
+
+
+class TextEmbedding(nn.Module):
+    def __init__(self, text_num_embeds, text_dim, mask_padding=True, conv_layers=0, conv_mult=2):
+        super().__init__()
+        self.text_embed = nn.Embedding(text_num_embeds + 1, text_dim)  # use 0 as filler token
+
+        self.mask_padding = mask_padding  # mask filler and batch padding tokens or not
+
+        if conv_layers > 0:
+            self.extra_modeling = True
+            self.precompute_max_pos = 4096  # ~44s of 24khz audio
+            self.register_buffer("freqs_cis", precompute_freqs_cis(text_dim, self.precompute_max_pos), persistent=False)
+            self.text_blocks = nn.Sequential(
+                *[ConvNeXtV2Block(text_dim, text_dim * conv_mult) for _ in range(conv_layers)]
+            )
+        else:
+            self.extra_modeling = False
+
+    def forward(self, text: int["b nt"], seq_len, drop_text=False):  # noqa: F722
+        text = text + 1  # use 0 as filler token. preprocess of batch pad -1, see list_str_to_idx()
+        text = text[:, :seq_len]  # curtail if character tokens are more than the mel spec tokens
+        batch, text_len = text.shape[0], text.shape[1]
+        text = F.pad(text, (0, seq_len - text_len), value=0)
+        if self.mask_padding:
+            text_mask = text == 0
+
+        if drop_text:  # cfg for text
+            text = torch.zeros_like(text)
+
+        text = self.text_embed(text)  # b n -> b n d
+
+        # possible extra modeling
+        if self.extra_modeling:
+            # sinus pos emb
+            batch_start = torch.zeros((batch,), dtype=torch.long)
+            pos_idx = get_pos_embed_indices(batch_start, seq_len, max_pos=self.precompute_max_pos)
+            text_pos_embed = self.freqs_cis[pos_idx]
+            text = text + text_pos_embed
+
+            # convnextv2 blocks
+            if self.mask_padding:
+                text = text.masked_fill(text_mask.unsqueeze(-1).expand(-1, -1, text.size(-1)), 0.0)
+                for block in self.text_blocks:
+                    text = block(text)
+                    text = text.masked_fill(text_mask.unsqueeze(-1).expand(-1, -1, text.size(-1)), 0.0)
+            else:
+                text = self.text_blocks(text)
+
+        return text
+
+
+# noised input audio and context mixing embedding
+
+
+class InputEmbedding(nn.Module):
+    def __init__(self, mel_dim, text_dim, out_dim):
+        super().__init__()
+        self.proj = nn.Linear(mel_dim * 2 + text_dim, out_dim)
+        self.conv_pos_embed = ConvPositionEmbedding(dim=out_dim)
+
+    def forward(self, x: float["b n d"], cond: float["b n d"], text_embed: float["b n d"], drop_audio_cond=False):  # noqa: F722
+        if drop_audio_cond:  # cfg for cond audio
+            cond = torch.zeros_like(cond)
+
+        x = self.proj(torch.cat((x, cond, text_embed), dim=-1))
+        x = self.conv_pos_embed(x) + x
+        return x
+
+
+# Flat UNet Transformer backbone
+
+
+class UNetT(nn.Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        depth=8,
+        heads=8,
+        dim_head=64,
+        dropout=0.1,
+        ff_mult=4,
+        mel_dim=100,
+        text_num_embeds=256,
+        text_dim=None,
+        text_mask_padding=True,
+        qk_norm=None,
+        conv_layers=0,
+        pe_attn_head=None,
+        skip_connect_type: Literal["add", "concat", "none"] = "concat",
+    ):
+        super().__init__()
+        assert depth % 2 == 0, "UNet-Transformer's depth should be even."
+
+        self.time_embed = TimestepEmbedding(dim)
+        if text_dim is None:
+            text_dim = mel_dim
+        self.text_embed = TextEmbedding(
+            text_num_embeds, text_dim, mask_padding=text_mask_padding, conv_layers=conv_layers
+        )
+        self.text_cond, self.text_uncond = None, None  # text cache
+        self.input_embed = InputEmbedding(mel_dim, text_dim, dim)
+
+        self.rotary_embed = RotaryEmbedding(dim_head)
+
+        # transformer layers & skip connections
+
+        self.dim = dim
+        self.skip_connect_type = skip_connect_type
+        needs_skip_proj = skip_connect_type == "concat"
+
+        self.depth = depth
+        self.layers = nn.ModuleList([])
+
+        for idx in range(depth):
+            is_later_half = idx >= (depth // 2)
+
+            attn_norm = RMSNorm(dim)
+            attn = Attention(
+                processor=AttnProcessor(pe_attn_head=pe_attn_head),
+                dim=dim,
+                heads=heads,
+                dim_head=dim_head,
+                dropout=dropout,
+                qk_norm=qk_norm,
+            )
+
+            ff_norm = RMSNorm(dim)
+            ff = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh")
+
+            skip_proj = nn.Linear(dim * 2, dim, bias=False) if needs_skip_proj and is_later_half else None
+
+            self.layers.append(
+                nn.ModuleList(
+                    [
+                        skip_proj,
+                        attn_norm,
+                        attn,
+                        ff_norm,
+                        ff,
+                    ]
+                )
+            )
+
+        self.norm_out = RMSNorm(dim)
+        self.proj_out = nn.Linear(dim, mel_dim)
+
+    def get_input_embed(
+        self,
+        x,  # b n d
+        cond,  # b n d
+        text,  # b nt
+        drop_audio_cond: bool = False,
+        drop_text: bool = False,
+        cache: bool = True,
+    ):
+        seq_len = x.shape[1]
+        if cache:
+            if drop_text:
+                if self.text_uncond is None:
+                    self.text_uncond = self.text_embed(text, seq_len, drop_text=True)
+                text_embed = self.text_uncond
+            else:
+                if self.text_cond is None:
+                    self.text_cond = self.text_embed(text, seq_len, drop_text=False)
+                text_embed = self.text_cond
+        else:
+            text_embed = self.text_embed(text, seq_len, drop_text=drop_text)
+
+        x = self.input_embed(x, cond, text_embed, drop_audio_cond=drop_audio_cond)
+
+        return x
+
+    def clear_cache(self):
+        self.text_cond, self.text_uncond = None, None
+
+    def forward(
+        self,
+        x: float["b n d"],  # nosied input audio  # noqa: F722
+        cond: float["b n d"],  # masked cond audio  # noqa: F722
+        text: int["b nt"],  # text  # noqa: F722
+        time: float["b"] | float[""],  # time step  # noqa: F821 F722
+        mask: bool["b n"] | None = None,  # noqa: F722
+        drop_audio_cond: bool = False,  # cfg for cond audio
+        drop_text: bool = False,  # cfg for text
+        cfg_infer: bool = False,  # cfg inference, pack cond & uncond forward
+        cache: bool = False,
+    ):
+        batch, seq_len = x.shape[0], x.shape[1]
+        if time.ndim == 0:
+            time = time.repeat(batch)
+
+        # t: conditioning time, c: context (text + masked cond audio), x: noised input audio
+        t = self.time_embed(time)
+        if cfg_infer:  # pack cond & uncond forward: b n d -> 2b n d
+            x_cond = self.get_input_embed(x, cond, text, drop_audio_cond=False, drop_text=False, cache=cache)
+            x_uncond = self.get_input_embed(x, cond, text, drop_audio_cond=True, drop_text=True, cache=cache)
+            x = torch.cat((x_cond, x_uncond), dim=0)
+            t = torch.cat((t, t), dim=0)
+            mask = torch.cat((mask, mask), dim=0) if mask is not None else None
+        else:
+            x = self.get_input_embed(x, cond, text, drop_audio_cond=drop_audio_cond, drop_text=drop_text, cache=cache)
+
+        # postfix time t to input x, [b n d] -> [b n+1 d]
+        x = torch.cat([t.unsqueeze(1), x], dim=1)  # pack t to x
+        if mask is not None:
+            mask = F.pad(mask, (1, 0), value=1)
+
+        rope = self.rotary_embed.forward_from_seq_len(seq_len + 1)
+
+        # flat unet transformer
+        skip_connect_type = self.skip_connect_type
+        skips = []
+        for idx, (maybe_skip_proj, attn_norm, attn, ff_norm, ff) in enumerate(self.layers):
+            layer = idx + 1
+
+            # skip connection logic
+            is_first_half = layer <= (self.depth // 2)
+            is_later_half = not is_first_half
+
+            if is_first_half:
+                skips.append(x)
+
+            if is_later_half:
+                skip = skips.pop()
+                if skip_connect_type == "concat":
+                    x = torch.cat((x, skip), dim=-1)
+                    x = maybe_skip_proj(x)
+                elif skip_connect_type == "add":
+                    x = x + skip
+
+            # attention and feedforward blocks
+            x = attn(attn_norm(x), rope=rope, mask=mask) + x
+            x = ff(ff_norm(x)) + x
+
+        assert len(skips) == 0
+
+        x = self.norm_out(x)[:, 1:, :]  # unpack t from x
+
+        return self.proj_out(x)

src/f5_tts/model/cfm.py

@@ -0,0 +1,302 @@
+"""
+ein notation:
+b - batch
+n - sequence
+nt - text sequence
+nw - raw wave length
+d - dimension
+"""
+
+from __future__ import annotations
+
+from random import random
+from typing import Callable
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch.nn.utils.rnn import pad_sequence
+from torchdiffeq import odeint
+
+from f5_tts.model.modules import MelSpec
+from f5_tts.model.utils import (
+    default,
+    exists,
+    get_epss_timesteps,
+    lens_to_mask,
+    list_str_to_idx,
+    list_str_to_tensor,
+    mask_from_frac_lengths,
+)
+
+
+class CFM(nn.Module):
+    def __init__(
+        self,
+        transformer: nn.Module,
+        sigma=0.0,
+        odeint_kwargs: dict = dict(
+            # atol = 1e-5,
+            # rtol = 1e-5,
+            method="euler"  # 'midpoint'
+        ),
+        audio_drop_prob=0.3,
+        cond_drop_prob=0.2,
+        num_channels=None,
+        mel_spec_module: nn.Module | None = None,
+        mel_spec_kwargs: dict = dict(),
+        frac_lengths_mask: tuple[float, float] = (0.7, 1.0),
+        vocab_char_map: dict[str:int] | None = None,
+    ):
+        super().__init__()
+
+        self.frac_lengths_mask = frac_lengths_mask
+
+        # mel spec
+        self.mel_spec = default(mel_spec_module, MelSpec(**mel_spec_kwargs))
+        num_channels = default(num_channels, self.mel_spec.n_mel_channels)
+        self.num_channels = num_channels
+
+        # classifier-free guidance
+        self.audio_drop_prob = audio_drop_prob
+        self.cond_drop_prob = cond_drop_prob
+
+        # transformer
+        self.transformer = transformer
+        dim = transformer.dim
+        self.dim = dim
+
+        # conditional flow related
+        self.sigma = sigma
+
+        # sampling related
+        self.odeint_kwargs = odeint_kwargs
+
+        # vocab map for tokenization
+        self.vocab_char_map = vocab_char_map
+
+    @property
+    def device(self):
+        return next(self.parameters()).device
+
+    @torch.no_grad()
+    def sample(
+        self,
+        cond: float["b n d"] | float["b nw"],  # noqa: F722
+        text: int["b nt"] | list[str],  # noqa: F722
+        duration: int | int["b"],  # noqa: F821
+        *,
+        lens: int["b"] | None = None,  # noqa: F821
+        steps=32,
+        cfg_strength=1.0,
+        sway_sampling_coef=None,
+        seed: int | None = None,
+        max_duration=4096,
+        vocoder: Callable[[float["b d n"]], float["b nw"]] | None = None,  # noqa: F722
+        use_epss=True,
+        no_ref_audio=False,
+        duplicate_test=False,
+        t_inter=0.1,
+        edit_mask=None,
+    ):
+        self.eval()
+        # raw wave
+
+        if cond.ndim == 2:
+            cond = self.mel_spec(cond)
+            cond = cond.permute(0, 2, 1)
+            assert cond.shape[-1] == self.num_channels
+
+        cond = cond.to(next(self.parameters()).dtype)
+
+        batch, cond_seq_len, device = *cond.shape[:2], cond.device
+        if not exists(lens):
+            lens = torch.full((batch,), cond_seq_len, device=device, dtype=torch.long)
+
+        # text
+
+        if isinstance(text, list):
+            if exists(self.vocab_char_map):
+                text = list_str_to_idx(text, self.vocab_char_map).to(device)
+            else:
+                text = list_str_to_tensor(text).to(device)
+            assert text.shape[0] == batch
+
+        # duration
+
+        cond_mask = lens_to_mask(lens)
+        if edit_mask is not None:
+            cond_mask = cond_mask & edit_mask
+
+        if isinstance(duration, int):
+            duration = torch.full((batch,), duration, device=device, dtype=torch.long)
+
+        duration = torch.maximum(
+            torch.maximum((text != -1).sum(dim=-1), lens) + 1, duration
+        )  # duration at least text/audio prompt length plus one token, so something is generated
+        duration = duration.clamp(max=max_duration)
+        max_duration = duration.amax()
+
+        # duplicate test corner for inner time step oberservation
+        if duplicate_test:
+            test_cond = F.pad(cond, (0, 0, cond_seq_len, max_duration - 2 * cond_seq_len), value=0.0)
+
+        cond = F.pad(cond, (0, 0, 0, max_duration - cond_seq_len), value=0.0)
+        if no_ref_audio:
+            cond = torch.zeros_like(cond)
+
+        cond_mask = F.pad(cond_mask, (0, max_duration - cond_mask.shape[-1]), value=False)
+        cond_mask = cond_mask.unsqueeze(-1)
+        step_cond = torch.where(
+            cond_mask, cond, torch.zeros_like(cond)
+        )  # allow direct control (cut cond audio) with lens passed in
+
+        if batch > 1:
+            mask = lens_to_mask(duration)
+        else:  # save memory and speed up, as single inference need no mask currently
+            mask = None
+
+        # neural ode
+
+        def fn(t, x):
+            # at each step, conditioning is fixed
+            # step_cond = torch.where(cond_mask, cond, torch.zeros_like(cond))
+
+            # predict flow (cond)
+            if cfg_strength < 1e-5:
+                pred = self.transformer(
+                    x=x,
+                    cond=step_cond,
+                    text=text,
+                    time=t,
+                    mask=mask,
+                    drop_audio_cond=False,
+                    drop_text=False,
+                    cache=True,
+                )
+                return pred
+
+            # predict flow (cond and uncond), for classifier-free guidance
+            pred_cfg = self.transformer(
+                x=x,
+                cond=step_cond,
+                text=text,
+                time=t,
+                mask=mask,
+                cfg_infer=True,
+                cache=True,
+            )
+            pred, null_pred = torch.chunk(pred_cfg, 2, dim=0)
+            return pred + (pred - null_pred) * cfg_strength
+
+        # noise input
+        # to make sure batch inference result is same with different batch size, and for sure single inference
+        # still some difference maybe due to convolutional layers
+        y0 = []
+        for dur in duration:
+            if exists(seed):
+                torch.manual_seed(seed)
+            y0.append(torch.randn(dur, self.num_channels, device=self.device, dtype=step_cond.dtype))
+        y0 = pad_sequence(y0, padding_value=0, batch_first=True)
+
+        t_start = 0
+
+        # duplicate test corner for inner time step oberservation
+        if duplicate_test:
+            t_start = t_inter
+            y0 = (1 - t_start) * y0 + t_start * test_cond
+            steps = int(steps * (1 - t_start))
+
+        if t_start == 0 and use_epss:  # use Empirically Pruned Step Sampling for low NFE
+            t = get_epss_timesteps(steps, device=self.device, dtype=step_cond.dtype)
+        else:
+            t = torch.linspace(t_start, 1, steps + 1, device=self.device, dtype=step_cond.dtype)
+        if sway_sampling_coef is not None:
+            t = t + sway_sampling_coef * (torch.cos(torch.pi / 2 * t) - 1 + t)
+
+        trajectory = odeint(fn, y0, t, **self.odeint_kwargs)
+        self.transformer.clear_cache()
+
+        sampled = trajectory[-1]
+        out = sampled
+        out = torch.where(cond_mask, cond, out)
+
+        if exists(vocoder):
+            out = out.permute(0, 2, 1)
+            out = vocoder(out)
+
+        return out, trajectory
+
+    def forward(
+        self,
+        inp: float["b n d"] | float["b nw"],  # mel or raw wave  # noqa: F722
+        text: int["b nt"] | list[str],  # noqa: F722
+        *,
+        lens: int["b"] | None = None,  # noqa: F821
+        noise_scheduler: str | None = None,
+    ):
+        # handle raw wave
+        if inp.ndim == 2:
+            inp = self.mel_spec(inp)
+            inp = inp.permute(0, 2, 1)
+            assert inp.shape[-1] == self.num_channels
+
+        batch, seq_len, dtype, device, _σ1 = *inp.shape[:2], inp.dtype, self.device, self.sigma
+
+        # handle text as string
+        if isinstance(text, list):
+            if exists(self.vocab_char_map):
+                text = list_str_to_idx(text, self.vocab_char_map).to(device)
+            else:
+                text = list_str_to_tensor(text).to(device)
+            assert text.shape[0] == batch
+
+        # lens and mask
+        if not exists(lens):
+            lens = torch.full((batch,), seq_len, device=device)
+
+        mask = lens_to_mask(lens, length=seq_len)  # useless here, as collate_fn will pad to max length in batch
+
+        # get a random span to mask out for training conditionally
+        frac_lengths = torch.zeros((batch,), device=self.device).float().uniform_(*self.frac_lengths_mask)
+        rand_span_mask = mask_from_frac_lengths(lens, frac_lengths)
+
+        if exists(mask):
+            rand_span_mask &= mask
+
+        # mel is x1
+        x1 = inp
+
+        # x0 is gaussian noise
+        x0 = torch.randn_like(x1)
+
+        # time step
+        time = torch.rand((batch,), dtype=dtype, device=self.device)
+        # TODO. noise_scheduler
+
+        # sample xt (φ_t(x) in the paper)
+        t = time.unsqueeze(-1).unsqueeze(-1)
+        φ = (1 - t) * x0 + t * x1
+        flow = x1 - x0
+
+        # only predict what is within the random mask span for infilling
+        cond = torch.where(rand_span_mask[..., None], torch.zeros_like(x1), x1)
+
+        # transformer and cfg training with a drop rate
+        drop_audio_cond = random() < self.audio_drop_prob  # p_drop in voicebox paper
+        if random() < self.cond_drop_prob:  # p_uncond in voicebox paper
+            drop_audio_cond = True
+            drop_text = True
+        else:
+            drop_text = False
+
+        # apply mask will use more memory; might adjust batchsize or batchsampler long sequence threshold
+        pred = self.transformer(
+            x=φ, cond=cond, text=text, time=time, drop_audio_cond=drop_audio_cond, drop_text=drop_text, mask=mask
+        )
+
+        # flow matching loss
+        loss = F.mse_loss(pred, flow, reduction="none")
+        loss = loss[rand_span_mask]
+
+        return loss.mean(), cond, pred

src/f5_tts/model/dataset.py

@@ -0,0 +1,330 @@
+import json
+from importlib.resources import files
+
+import torch
+import torch.nn.functional as F
+import torchaudio
+from datasets import Dataset as Dataset_
+from datasets import load_from_disk
+from torch import nn
+from torch.utils.data import Dataset, Sampler
+from tqdm import tqdm
+
+from f5_tts.model.modules import MelSpec
+from f5_tts.model.utils import default
+
+
+class HFDataset(Dataset):
+    def __init__(
+        self,
+        hf_dataset: Dataset,
+        target_sample_rate=24_000,
+        n_mel_channels=100,
+        hop_length=256,
+        n_fft=1024,
+        win_length=1024,
+        mel_spec_type="vocos",
+    ):
+        self.data = hf_dataset
+        self.target_sample_rate = target_sample_rate
+        self.hop_length = hop_length
+
+        self.mel_spectrogram = MelSpec(
+            n_fft=n_fft,
+            hop_length=hop_length,
+            win_length=win_length,
+            n_mel_channels=n_mel_channels,
+            target_sample_rate=target_sample_rate,
+            mel_spec_type=mel_spec_type,
+        )
+
+    def get_frame_len(self, index):
+        row = self.data[index]
+        audio = row["audio"]["array"]
+        sample_rate = row["audio"]["sampling_rate"]
+        return audio.shape[-1] / sample_rate * self.target_sample_rate / self.hop_length
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, index):
+        row = self.data[index]
+        audio = row["audio"]["array"]
+
+        # logger.info(f"Audio shape: {audio.shape}")
+
+        sample_rate = row["audio"]["sampling_rate"]
+        duration = audio.shape[-1] / sample_rate
+
+        if duration > 30 or duration < 0.3:
+            return self.__getitem__((index + 1) % len(self.data))
+
+        audio_tensor = torch.from_numpy(audio).float()
+
+        if sample_rate != self.target_sample_rate:
+            resampler = torchaudio.transforms.Resample(sample_rate, self.target_sample_rate)
+            audio_tensor = resampler(audio_tensor)
+
+        audio_tensor = audio_tensor.unsqueeze(0)  # 't -> 1 t')
+
+        mel_spec = self.mel_spectrogram(audio_tensor)
+
+        mel_spec = mel_spec.squeeze(0)  # '1 d t -> d t'
+
+        text = row["text"]
+
+        return dict(
+            mel_spec=mel_spec,
+            text=text,
+        )
+
+
+class CustomDataset(Dataset):
+    def __init__(
+        self,
+        custom_dataset: Dataset,
+        durations=None,
+        target_sample_rate=24_000,
+        hop_length=256,
+        n_mel_channels=100,
+        n_fft=1024,
+        win_length=1024,
+        mel_spec_type="vocos",
+        preprocessed_mel=False,
+        mel_spec_module: nn.Module | None = None,
+    ):
+        self.data = custom_dataset
+        self.durations = durations
+        self.target_sample_rate = target_sample_rate
+        self.hop_length = hop_length
+        self.n_fft = n_fft
+        self.win_length = win_length
+        self.mel_spec_type = mel_spec_type
+        self.preprocessed_mel = preprocessed_mel
+
+        if not preprocessed_mel:
+            self.mel_spectrogram = default(
+                mel_spec_module,
+                MelSpec(
+                    n_fft=n_fft,
+                    hop_length=hop_length,
+                    win_length=win_length,
+                    n_mel_channels=n_mel_channels,
+                    target_sample_rate=target_sample_rate,
+                    mel_spec_type=mel_spec_type,
+                ),
+            )
+
+    def get_frame_len(self, index):
+        if (
+            self.durations is not None
+        ):  # Please make sure the separately provided durations are correct, otherwise 99.99% OOM
+            return self.durations[index] * self.target_sample_rate / self.hop_length
+        return self.data[index]["duration"] * self.target_sample_rate / self.hop_length
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, index):
+        while True:
+            row = self.data[index]
+            audio_path = row["audio_path"]
+            text = row["text"]
+            duration = row["duration"]
+
+            # filter by given length
+            if 0.3 <= duration <= 30:
+                break  # valid
+
+            index = (index + 1) % len(self.data)
+
+        if self.preprocessed_mel:
+            mel_spec = torch.tensor(row["mel_spec"])
+        else:
+            audio, source_sample_rate = torchaudio.load(audio_path)
+
+            # make sure mono input
+            if audio.shape[0] > 1:
+                audio = torch.mean(audio, dim=0, keepdim=True)
+
+            # resample if necessary
+            if source_sample_rate != self.target_sample_rate:
+                resampler = torchaudio.transforms.Resample(source_sample_rate, self.target_sample_rate)
+                audio = resampler(audio)
+
+            # to mel spectrogram
+            mel_spec = self.mel_spectrogram(audio)
+            mel_spec = mel_spec.squeeze(0)  # '1 d t -> d t'
+
+        return {
+            "mel_spec": mel_spec,
+            "text": text,
+        }
+
+
+# Dynamic Batch Sampler
+class DynamicBatchSampler(Sampler[list[int]]):
+    """Extension of Sampler that will do the following:
+    1.  Change the batch size (essentially number of sequences)
+        in a batch to ensure that the total number of frames are less
+        than a certain threshold.
+    2.  Make sure the padding efficiency in the batch is high.
+    3.  Shuffle batches each epoch while maintaining reproducibility.
+    """
+
+    def __init__(
+        self, sampler: Sampler[int], frames_threshold: int, max_samples=0, random_seed=None, drop_residual: bool = False
+    ):
+        self.sampler = sampler
+        self.frames_threshold = frames_threshold
+        self.max_samples = max_samples
+        self.random_seed = random_seed
+        self.epoch = 0
+
+        indices, batches = [], []
+        data_source = self.sampler.data_source
+
+        for idx in tqdm(
+            self.sampler, desc="Sorting with sampler... if slow, check whether dataset is provided with duration"
+        ):
+            indices.append((idx, data_source.get_frame_len(idx)))
+        indices.sort(key=lambda elem: elem[1])
+
+        batch = []
+        batch_frames = 0
+        for idx, frame_len in tqdm(
+            indices, desc=f"Creating dynamic batches with {frames_threshold} audio frames per gpu"
+        ):
+            if batch_frames + frame_len <= self.frames_threshold and (max_samples == 0 or len(batch) < max_samples):
+                batch.append(idx)
+                batch_frames += frame_len
+            else:
+                if len(batch) > 0:
+                    batches.append(batch)
+                if frame_len <= self.frames_threshold:
+                    batch = [idx]
+                    batch_frames = frame_len
+                else:
+                    batch = []
+                    batch_frames = 0
+
+        if not drop_residual and len(batch) > 0:
+            batches.append(batch)
+
+        del indices
+        self.batches = batches
+
+        # Ensure even batches with accelerate BatchSamplerShard cls under frame_per_batch setting
+        self.drop_last = True
+
+    def set_epoch(self, epoch: int) -> None:
+        """Sets the epoch for this sampler."""
+        self.epoch = epoch
+
+    def __iter__(self):
+        # Use both random_seed and epoch for deterministic but different shuffling per epoch
+        if self.random_seed is not None:
+            g = torch.Generator()
+            g.manual_seed(self.random_seed + self.epoch)
+            # Use PyTorch's random permutation for better reproducibility across PyTorch versions
+            indices = torch.randperm(len(self.batches), generator=g).tolist()
+            batches = [self.batches[i] for i in indices]
+        else:
+            batches = self.batches
+        return iter(batches)
+
+    def __len__(self):
+        return len(self.batches)
+
+
+# Load dataset
+
+
+def load_dataset(
+    dataset_name: str,
+    tokenizer: str = "pinyin",
+    dataset_type: str = "CustomDataset",
+    audio_type: str = "raw",
+    mel_spec_module: nn.Module | None = None,
+    mel_spec_kwargs: dict = dict(),
+) -> CustomDataset | HFDataset:
+    """
+    dataset_type    - "CustomDataset" if you want to use tokenizer name and default data path to load for train_dataset
+                    - "CustomDatasetPath" if you just want to pass the full path to a preprocessed dataset without relying on tokenizer
+    """
+
+    print("Loading dataset ...")
+
+    if dataset_type == "CustomDataset":
+        rel_data_path = str(files("f5_tts").joinpath(f"../../data/{dataset_name}_{tokenizer}"))
+        if audio_type == "raw":
+            try:
+                train_dataset = load_from_disk(f"{rel_data_path}/raw")
+            except:  # noqa: E722
+                train_dataset = Dataset_.from_file(f"{rel_data_path}/raw.arrow")
+            preprocessed_mel = False
+        elif audio_type == "mel":
+            train_dataset = Dataset_.from_file(f"{rel_data_path}/mel.arrow")
+            preprocessed_mel = True
+        with open(f"{rel_data_path}/duration.json", "r", encoding="utf-8") as f:
+            data_dict = json.load(f)
+        durations = data_dict["duration"]
+        train_dataset = CustomDataset(
+            train_dataset,
+            durations=durations,
+            preprocessed_mel=preprocessed_mel,
+            mel_spec_module=mel_spec_module,
+            **mel_spec_kwargs,
+        )
+
+    elif dataset_type == "CustomDatasetPath":
+        try:
+            train_dataset = load_from_disk(f"{dataset_name}/raw")
+        except:  # noqa: E722
+            train_dataset = Dataset_.from_file(f"{dataset_name}/raw.arrow")
+
+        with open(f"{dataset_name}/duration.json", "r", encoding="utf-8") as f:
+            data_dict = json.load(f)
+        durations = data_dict["duration"]
+        train_dataset = CustomDataset(
+            train_dataset, durations=durations, preprocessed_mel=preprocessed_mel, **mel_spec_kwargs
+        )
+
+    elif dataset_type == "HFDataset":
+        print(
+            "Should manually modify the path of huggingface dataset to your need.\n"
+            + "May also the corresponding script cuz different dataset may have different format."
+        )
+        pre, post = dataset_name.split("_")
+        train_dataset = HFDataset(
+            load_dataset(f"{pre}/{pre}", split=f"train.{post}", cache_dir=str(files("f5_tts").joinpath("../../data"))),
+        )
+
+    return train_dataset
+
+
+# collation
+
+
+def collate_fn(batch):
+    mel_specs = [item["mel_spec"].squeeze(0) for item in batch]
+    mel_lengths = torch.LongTensor([spec.shape[-1] for spec in mel_specs])
+    max_mel_length = mel_lengths.amax()
+
+    padded_mel_specs = []
+    for spec in mel_specs:
+        padding = (0, max_mel_length - spec.size(-1))
+        padded_spec = F.pad(spec, padding, value=0)
+        padded_mel_specs.append(padded_spec)
+
+    mel_specs = torch.stack(padded_mel_specs)
+
+    text = [item["text"] for item in batch]
+    text_lengths = torch.LongTensor([len(item) for item in text])
+
+    return dict(
+        mel=mel_specs,
+        mel_lengths=mel_lengths,  # records for padding mask
+        text=text,
+        text_lengths=text_lengths,
+    )

src/f5_tts/model/modules.py

@@ -0,0 +1,784 @@
+"""
+ein notation:
+b - batch
+n - sequence
+nt - text sequence
+nw - raw wave length
+d - dimension
+"""
+# flake8: noqa
+
+from __future__ import annotations
+
+import math
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+import torchaudio
+from librosa.filters import mel as librosa_mel_fn
+from torch import nn
+from x_transformers.x_transformers import apply_rotary_pos_emb
+
+from f5_tts.model.utils import is_package_available
+
+
+# raw wav to mel spec
+
+
+mel_basis_cache = {}
+hann_window_cache = {}
+
+
+def get_bigvgan_mel_spectrogram(
+    waveform,
+    n_fft=1024,
+    n_mel_channels=100,
+    target_sample_rate=24000,
+    hop_length=256,
+    win_length=1024,
+    fmin=0,
+    fmax=None,
+    center=False,
+):  # Copy from https://github.com/NVIDIA/BigVGAN/tree/main
+    device = waveform.device
+    key = f"{n_fft}_{n_mel_channels}_{target_sample_rate}_{hop_length}_{win_length}_{fmin}_{fmax}_{device}"
+
+    if key not in mel_basis_cache:
+        mel = librosa_mel_fn(sr=target_sample_rate, n_fft=n_fft, n_mels=n_mel_channels, fmin=fmin, fmax=fmax)
+        mel_basis_cache[key] = torch.from_numpy(mel).float().to(device)  # TODO: why they need .float()?
+        hann_window_cache[key] = torch.hann_window(win_length).to(device)
+
+    mel_basis = mel_basis_cache[key]
+    hann_window = hann_window_cache[key]
+
+    padding = (n_fft - hop_length) // 2
+    waveform = torch.nn.functional.pad(waveform.unsqueeze(1), (padding, padding), mode="reflect").squeeze(1)
+
+    spec = torch.stft(
+        waveform,
+        n_fft,
+        hop_length=hop_length,
+        win_length=win_length,
+        window=hann_window,
+        center=center,
+        pad_mode="reflect",
+        normalized=False,
+        onesided=True,
+        return_complex=True,
+    )
+    spec = torch.sqrt(torch.view_as_real(spec).pow(2).sum(-1) + 1e-9)
+
+    mel_spec = torch.matmul(mel_basis, spec)
+    mel_spec = torch.log(torch.clamp(mel_spec, min=1e-5))
+
+    return mel_spec
+
+
+def get_vocos_mel_spectrogram(
+    waveform,
+    n_fft=1024,
+    n_mel_channels=100,
+    target_sample_rate=24000,
+    hop_length=256,
+    win_length=1024,
+):
+    mel_stft = torchaudio.transforms.MelSpectrogram(
+        sample_rate=target_sample_rate,
+        n_fft=n_fft,
+        win_length=win_length,
+        hop_length=hop_length,
+        n_mels=n_mel_channels,
+        power=1,
+        center=True,
+        normalized=False,
+        norm=None,
+    ).to(waveform.device)
+    if len(waveform.shape) == 3:
+        waveform = waveform.squeeze(1)  # 'b 1 nw -> b nw'
+
+    assert len(waveform.shape) == 2
+
+    mel = mel_stft(waveform)
+    mel = mel.clamp(min=1e-5).log()
+    return mel
+
+
+class MelSpec(nn.Module):
+    def __init__(
+        self,
+        n_fft=1024,
+        hop_length=256,
+        win_length=1024,
+        n_mel_channels=100,
+        target_sample_rate=24_000,
+        mel_spec_type="vocos",
+    ):
+        super().__init__()
+        assert mel_spec_type in ["vocos", "bigvgan"], print("We only support two extract mel backend: vocos or bigvgan")
+
+        self.n_fft = n_fft
+        self.hop_length = hop_length
+        self.win_length = win_length
+        self.n_mel_channels = n_mel_channels
+        self.target_sample_rate = target_sample_rate
+
+        if mel_spec_type == "vocos":
+            self.extractor = get_vocos_mel_spectrogram
+        elif mel_spec_type == "bigvgan":
+            self.extractor = get_bigvgan_mel_spectrogram
+
+        self.register_buffer("dummy", torch.tensor(0), persistent=False)
+
+    def forward(self, wav):
+        if self.dummy.device != wav.device:
+            self.to(wav.device)
+
+        mel = self.extractor(
+            waveform=wav,
+            n_fft=self.n_fft,
+            n_mel_channels=self.n_mel_channels,
+            target_sample_rate=self.target_sample_rate,
+            hop_length=self.hop_length,
+            win_length=self.win_length,
+        )
+
+        return mel
+
+
+# sinusoidal position embedding
+
+
+class SinusPositionEmbedding(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.dim = dim
+
+    def forward(self, x, scale=1000):
+        device = x.device
+        half_dim = self.dim // 2
+        emb = math.log(10000) / (half_dim - 1)
+        emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb)
+        emb = scale * x.unsqueeze(1) * emb.unsqueeze(0)
+        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
+        return emb
+
+
+# convolutional position embedding
+
+
+class ConvPositionEmbedding(nn.Module):
+    def __init__(self, dim, kernel_size=31, groups=16):
+        super().__init__()
+        assert kernel_size % 2 != 0
+        self.conv1d = nn.Sequential(
+            nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2),
+            nn.Mish(),
+            nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2),
+            nn.Mish(),
+        )
+
+    def forward(self, x: float["b n d"], mask: bool["b n"] | None = None):
+        if mask is not None:
+            mask = mask[..., None]
+            x = x.masked_fill(~mask, 0.0)
+
+        x = x.permute(0, 2, 1)
+        x = self.conv1d(x)
+        out = x.permute(0, 2, 1)
+
+        if mask is not None:
+            out = out.masked_fill(~mask, 0.0)
+
+        return out
+
+
+# rotary positional embedding related
+
+
+def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0, theta_rescale_factor=1.0):
+    # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
+    # has some connection to NTK literature
+    # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
+    # https://github.com/lucidrains/rotary-embedding-torch/blob/main/rotary_embedding_torch/rotary_embedding_torch.py
+    theta *= theta_rescale_factor ** (dim / (dim - 2))
+    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+    t = torch.arange(end, device=freqs.device)  # type: ignore
+    freqs = torch.outer(t, freqs).float()  # type: ignore
+    freqs_cos = torch.cos(freqs)  # real part
+    freqs_sin = torch.sin(freqs)  # imaginary part
+    return torch.cat([freqs_cos, freqs_sin], dim=-1)
+
+
+def get_pos_embed_indices(start, length, max_pos, scale=1.0):
+    # length = length if isinstance(length, int) else length.max()
+    scale = scale * torch.ones_like(start, dtype=torch.float32)  # in case scale is a scalar
+    pos = (
+        start.unsqueeze(1)
+        + (torch.arange(length, device=start.device, dtype=torch.float32).unsqueeze(0) * scale.unsqueeze(1)).long()
+    )
+    # avoid extra long error.
+    pos = torch.where(pos < max_pos, pos, max_pos - 1)
+    return pos
+
+
+# Global Response Normalization layer (Instance Normalization ?)
+
+
+class GRN(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.gamma = nn.Parameter(torch.zeros(1, 1, dim))
+        self.beta = nn.Parameter(torch.zeros(1, 1, dim))
+
+    def forward(self, x):
+        Gx = torch.norm(x, p=2, dim=1, keepdim=True)
+        Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6)
+        return self.gamma * (x * Nx) + self.beta + x
+
+
+# ConvNeXt-V2 Block https://github.com/facebookresearch/ConvNeXt-V2/blob/main/models/convnextv2.py
+# ref: https://github.com/bfs18/e2_tts/blob/main/rfwave/modules.py#L108
+
+
+class ConvNeXtV2Block(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        intermediate_dim: int,
+        dilation: int = 1,
+    ):
+        super().__init__()
+        padding = (dilation * (7 - 1)) // 2
+        self.dwconv = nn.Conv1d(
+            dim, dim, kernel_size=7, padding=padding, groups=dim, dilation=dilation
+        )  # depthwise conv
+        self.norm = nn.LayerNorm(dim, eps=1e-6)
+        self.pwconv1 = nn.Linear(dim, intermediate_dim)  # pointwise/1x1 convs, implemented with linear layers
+        self.act = nn.GELU()
+        self.grn = GRN(intermediate_dim)
+        self.pwconv2 = nn.Linear(intermediate_dim, dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        residual = x
+        x = x.transpose(1, 2)  # b n d -> b d n
+        x = self.dwconv(x)
+        x = x.transpose(1, 2)  # b d n -> b n d
+        x = self.norm(x)
+        x = self.pwconv1(x)
+        x = self.act(x)
+        x = self.grn(x)
+        x = self.pwconv2(x)
+        return residual + x
+
+
+# RMSNorm
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim: int, eps: float):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+        self.native_rms_norm = float(torch.__version__[:3]) >= 2.4
+
+    def forward(self, x):
+        if self.native_rms_norm:
+            if self.weight.dtype in [torch.float16, torch.bfloat16]:
+                x = x.to(self.weight.dtype)
+            x = F.rms_norm(x, normalized_shape=(x.shape[-1],), weight=self.weight, eps=self.eps)
+        else:
+            variance = x.to(torch.float32).pow(2).mean(-1, keepdim=True)
+            x = x * torch.rsqrt(variance + self.eps)
+            if self.weight.dtype in [torch.float16, torch.bfloat16]:
+                x = x.to(self.weight.dtype)
+            x = x * self.weight
+
+        return x
+
+
+# AdaLayerNorm
+# return with modulated x for attn input, and params for later mlp modulation
+
+
+class AdaLayerNorm(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+
+        self.silu = nn.SiLU()
+        self.linear = nn.Linear(dim, dim * 6)
+
+        self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+
+    def forward(self, x, emb=None):
+        emb = self.linear(self.silu(emb))
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = torch.chunk(emb, 6, dim=1)
+
+        x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None]
+        return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
+
+
+# AdaLayerNorm for final layer
+# return only with modulated x for attn input, cuz no more mlp modulation
+
+
+class AdaLayerNorm_Final(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+
+        self.silu = nn.SiLU()
+        self.linear = nn.Linear(dim, dim * 2)
+
+        self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+
+    def forward(self, x, emb):
+        emb = self.linear(self.silu(emb))
+        scale, shift = torch.chunk(emb, 2, dim=1)
+
+        x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :]
+        return x
+
+
+# FeedForward
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, dim_out=None, mult=4, dropout=0.0, approximate: str = "none"):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        dim_out = dim_out if dim_out is not None else dim
+
+        activation = nn.GELU(approximate=approximate)
+        project_in = nn.Sequential(nn.Linear(dim, inner_dim), activation)
+        self.ff = nn.Sequential(project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out))
+
+    def forward(self, x):
+        return self.ff(x)
+
+
+# Attention with possible joint part
+# modified from diffusers/src/diffusers/models/attention_processor.py
+
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        processor: JointAttnProcessor | AttnProcessor,
+        dim: int,
+        heads: int = 8,
+        dim_head: int = 64,
+        dropout: float = 0.0,
+        context_dim: Optional[int] = None,  # if not None -> joint attention
+        context_pre_only: bool = False,
+        qk_norm: Optional[str] = None,
+    ):
+        super().__init__()
+
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("Attention equires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+        self.processor = processor
+
+        self.dim = dim
+        self.heads = heads
+        self.inner_dim = dim_head * heads
+        self.dropout = dropout
+
+        self.context_dim = context_dim
+        self.context_pre_only = context_pre_only
+
+        self.to_q = nn.Linear(dim, self.inner_dim)
+        self.to_k = nn.Linear(dim, self.inner_dim)
+        self.to_v = nn.Linear(dim, self.inner_dim)
+
+        if qk_norm is None:
+            self.q_norm = None
+            self.k_norm = None
+        elif qk_norm == "rms_norm":
+            self.q_norm = RMSNorm(dim_head, eps=1e-6)
+            self.k_norm = RMSNorm(dim_head, eps=1e-6)
+        else:
+            raise ValueError(f"Unimplemented qk_norm: {qk_norm}")
+
+        if self.context_dim is not None:
+            self.to_q_c = nn.Linear(context_dim, self.inner_dim)
+            self.to_k_c = nn.Linear(context_dim, self.inner_dim)
+            self.to_v_c = nn.Linear(context_dim, self.inner_dim)
+            if qk_norm is None:
+                self.c_q_norm = None
+                self.c_k_norm = None
+            elif qk_norm == "rms_norm":
+                self.c_q_norm = RMSNorm(dim_head, eps=1e-6)
+                self.c_k_norm = RMSNorm(dim_head, eps=1e-6)
+
+        self.to_out = nn.ModuleList([])
+        self.to_out.append(nn.Linear(self.inner_dim, dim))
+        self.to_out.append(nn.Dropout(dropout))
+
+        if self.context_dim is not None and not self.context_pre_only:
+            self.to_out_c = nn.Linear(self.inner_dim, context_dim)
+
+    def forward(
+        self,
+        x: float["b n d"],  # noised input x
+        c: float["b n d"] = None,  # context c
+        mask: bool["b n"] | None = None,
+        rope=None,  # rotary position embedding for x
+        c_rope=None,  # rotary position embedding for c
+    ) -> torch.Tensor:
+        if c is not None:
+            return self.processor(self, x, c=c, mask=mask, rope=rope, c_rope=c_rope)
+        else:
+            return self.processor(self, x, mask=mask, rope=rope)
+
+
+# Attention processor
+
+if is_package_available("flash_attn"):
+    from flash_attn.bert_padding import pad_input, unpad_input
+    from flash_attn import flash_attn_varlen_func, flash_attn_func
+
+
+class AttnProcessor:
+    def __init__(
+        self,
+        pe_attn_head: int | None = None,  # number of attention head to apply rope, None for all
+        attn_backend: str = "torch",  # "torch" or "flash_attn"
+        attn_mask_enabled: bool = True,
+    ):
+        if attn_backend == "flash_attn":
+            assert is_package_available("flash_attn"), "Please install flash-attn first."
+
+        self.pe_attn_head = pe_attn_head
+        self.attn_backend = attn_backend
+        self.attn_mask_enabled = attn_mask_enabled
+
+    def __call__(
+        self,
+        attn: Attention,
+        x: float["b n d"],  # noised input x
+        mask: bool["b n"] | None = None,
+        rope=None,  # rotary position embedding
+    ) -> torch.FloatTensor:
+        batch_size = x.shape[0]
+
+        # `sample` projections
+        query = attn.to_q(x)
+        key = attn.to_k(x)
+        value = attn.to_v(x)
+
+        # attention
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # qk norm
+        if attn.q_norm is not None:
+            query = attn.q_norm(query)
+        if attn.k_norm is not None:
+            key = attn.k_norm(key)
+
+        # apply rotary position embedding
+        if rope is not None:
+            freqs, xpos_scale = rope
+            q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0)
+
+            if self.pe_attn_head is not None:
+                pn = self.pe_attn_head
+                query[:, :pn, :, :] = apply_rotary_pos_emb(query[:, :pn, :, :], freqs, q_xpos_scale)
+                key[:, :pn, :, :] = apply_rotary_pos_emb(key[:, :pn, :, :], freqs, k_xpos_scale)
+            else:
+                query = apply_rotary_pos_emb(query, freqs, q_xpos_scale)
+                key = apply_rotary_pos_emb(key, freqs, k_xpos_scale)
+
+        if self.attn_backend == "torch":
+            # mask. e.g. inference got a batch with different target durations, mask out the padding
+            if self.attn_mask_enabled and mask is not None:
+                attn_mask = mask
+                attn_mask = attn_mask.unsqueeze(1).unsqueeze(1)  # 'b n -> b 1 1 n'
+                attn_mask = attn_mask.expand(batch_size, attn.heads, query.shape[-2], key.shape[-2])
+            else:
+                attn_mask = None
+            x = F.scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=0.0, is_causal=False)
+            x = x.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+
+        elif self.attn_backend == "flash_attn":
+            query = query.transpose(1, 2)  # [b, h, n, d] -> [b, n, h, d]
+            key = key.transpose(1, 2)
+            value = value.transpose(1, 2)
+            if self.attn_mask_enabled and mask is not None:
+                query, indices, q_cu_seqlens, q_max_seqlen_in_batch, _ = unpad_input(query, mask)
+                key, _, k_cu_seqlens, k_max_seqlen_in_batch, _ = unpad_input(key, mask)
+                value, _, _, _, _ = unpad_input(value, mask)
+                x = flash_attn_varlen_func(
+                    query,
+                    key,
+                    value,
+                    q_cu_seqlens,
+                    k_cu_seqlens,
+                    q_max_seqlen_in_batch,
+                    k_max_seqlen_in_batch,
+                )
+                x = pad_input(x, indices, batch_size, q_max_seqlen_in_batch)
+                x = x.reshape(batch_size, -1, attn.heads * head_dim)
+            else:
+                x = flash_attn_func(query, key, value, dropout_p=0.0, causal=False)
+                x = x.reshape(batch_size, -1, attn.heads * head_dim)
+
+        x = x.to(query.dtype)
+
+        # linear proj
+        x = attn.to_out[0](x)
+        # dropout
+        x = attn.to_out[1](x)
+
+        if mask is not None:
+            mask = mask.unsqueeze(-1)
+            x = x.masked_fill(~mask, 0.0)
+
+        return x
+
+
+# Joint Attention processor for MM-DiT
+# modified from diffusers/src/diffusers/models/attention_processor.py
+
+
+class JointAttnProcessor:
+    def __init__(self):
+        pass
+
+    def __call__(
+        self,
+        attn: Attention,
+        x: float["b n d"],  # noised input x
+        c: float["b nt d"] = None,  # context c, here text
+        mask: bool["b n"] | None = None,
+        rope=None,  # rotary position embedding for x
+        c_rope=None,  # rotary position embedding for c
+    ) -> torch.FloatTensor:
+        residual = x
+
+        batch_size = c.shape[0]
+
+        # `sample` projections
+        query = attn.to_q(x)
+        key = attn.to_k(x)
+        value = attn.to_v(x)
+
+        # `context` projections
+        c_query = attn.to_q_c(c)
+        c_key = attn.to_k_c(c)
+        c_value = attn.to_v_c(c)
+
+        # attention
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        c_query = c_query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        c_key = c_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        c_value = c_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # qk norm
+        if attn.q_norm is not None:
+            query = attn.q_norm(query)
+        if attn.k_norm is not None:
+            key = attn.k_norm(key)
+        if attn.c_q_norm is not None:
+            c_query = attn.c_q_norm(c_query)
+        if attn.c_k_norm is not None:
+            c_key = attn.c_k_norm(c_key)
+
+        # apply rope for context and noised input independently
+        if rope is not None:
+            freqs, xpos_scale = rope
+            q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0)
+            query = apply_rotary_pos_emb(query, freqs, q_xpos_scale)
+            key = apply_rotary_pos_emb(key, freqs, k_xpos_scale)
+        if c_rope is not None:
+            freqs, xpos_scale = c_rope
+            q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0)
+            c_query = apply_rotary_pos_emb(c_query, freqs, q_xpos_scale)
+            c_key = apply_rotary_pos_emb(c_key, freqs, k_xpos_scale)
+
+        # joint attention
+        query = torch.cat([query, c_query], dim=2)
+        key = torch.cat([key, c_key], dim=2)
+        value = torch.cat([value, c_value], dim=2)
+
+        # mask. e.g. inference got a batch with different target durations, mask out the padding
+        if mask is not None:
+            attn_mask = F.pad(mask, (0, c.shape[1]), value=True)  # no mask for c (text)
+            attn_mask = attn_mask.unsqueeze(1).unsqueeze(1)  # 'b n -> b 1 1 n'
+            attn_mask = attn_mask.expand(batch_size, attn.heads, query.shape[-2], key.shape[-2])
+        else:
+            attn_mask = None
+
+        x = F.scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=0.0, is_causal=False)
+        x = x.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        x = x.to(query.dtype)
+
+        # Split the attention outputs.
+        x, c = (
+            x[:, : residual.shape[1]],
+            x[:, residual.shape[1] :],
+        )
+
+        # linear proj
+        x = attn.to_out[0](x)
+        # dropout
+        x = attn.to_out[1](x)
+        if not attn.context_pre_only:
+            c = attn.to_out_c(c)
+
+        if mask is not None:
+            mask = mask.unsqueeze(-1)
+            x = x.masked_fill(~mask, 0.0)
+            # c = c.masked_fill(~mask, 0.)  # no mask for c (text)
+
+        return x, c
+
+
+# DiT Block
+
+
+class DiTBlock(nn.Module):
+    def __init__(
+        self,
+        dim,
+        heads,
+        dim_head,
+        ff_mult=4,
+        dropout=0.1,
+        qk_norm=None,
+        pe_attn_head=None,
+        attn_backend="torch",  # "torch" or "flash_attn"
+        attn_mask_enabled=True,
+    ):
+        super().__init__()
+
+        self.attn_norm = AdaLayerNorm(dim)
+        self.attn = Attention(
+            processor=AttnProcessor(
+                pe_attn_head=pe_attn_head,
+                attn_backend=attn_backend,
+                attn_mask_enabled=attn_mask_enabled,
+            ),
+            dim=dim,
+            heads=heads,
+            dim_head=dim_head,
+            dropout=dropout,
+            qk_norm=qk_norm,
+        )
+
+        self.ff_norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh")
+
+    def forward(self, x, t, mask=None, rope=None):  # x: noised input, t: time embedding
+        # pre-norm & modulation for attention input
+        norm, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.attn_norm(x, emb=t)
+
+        # attention
+        attn_output = self.attn(x=norm, mask=mask, rope=rope)
+
+        # process attention output for input x
+        x = x + gate_msa.unsqueeze(1) * attn_output
+
+        norm = self.ff_norm(x) * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+        ff_output = self.ff(norm)
+        x = x + gate_mlp.unsqueeze(1) * ff_output
+
+        return x
+
+
+# MMDiT Block https://arxiv.org/abs/2403.03206
+
+
+class MMDiTBlock(nn.Module):
+    r"""
+    modified from diffusers/src/diffusers/models/attention.py
+
+    notes.
+    _c: context related. text, cond, etc. (left part in sd3 fig2.b)
+    _x: noised input related. (right part)
+    context_pre_only: last layer only do prenorm + modulation cuz no more ffn
+    """
+
+    def __init__(
+        self, dim, heads, dim_head, ff_mult=4, dropout=0.1, context_dim=None, context_pre_only=False, qk_norm=None
+    ):
+        super().__init__()
+        if context_dim is None:
+            context_dim = dim
+        self.context_pre_only = context_pre_only
+
+        self.attn_norm_c = AdaLayerNorm_Final(context_dim) if context_pre_only else AdaLayerNorm(context_dim)
+        self.attn_norm_x = AdaLayerNorm(dim)
+        self.attn = Attention(
+            processor=JointAttnProcessor(),
+            dim=dim,
+            heads=heads,
+            dim_head=dim_head,
+            dropout=dropout,
+            context_dim=context_dim,
+            context_pre_only=context_pre_only,
+            qk_norm=qk_norm,
+        )
+
+        if not context_pre_only:
+            self.ff_norm_c = nn.LayerNorm(context_dim, elementwise_affine=False, eps=1e-6)
+            self.ff_c = FeedForward(dim=context_dim, mult=ff_mult, dropout=dropout, approximate="tanh")
+        else:
+            self.ff_norm_c = None
+            self.ff_c = None
+        self.ff_norm_x = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff_x = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh")
+
+    def forward(self, x, c, t, mask=None, rope=None, c_rope=None):  # x: noised input, c: context, t: time embedding
+        # pre-norm & modulation for attention input
+        if self.context_pre_only:
+            norm_c = self.attn_norm_c(c, t)
+        else:
+            norm_c, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.attn_norm_c(c, emb=t)
+        norm_x, x_gate_msa, x_shift_mlp, x_scale_mlp, x_gate_mlp = self.attn_norm_x(x, emb=t)
+
+        # attention
+        x_attn_output, c_attn_output = self.attn(x=norm_x, c=norm_c, mask=mask, rope=rope, c_rope=c_rope)
+
+        # process attention output for context c
+        if self.context_pre_only:
+            c = None
+        else:  # if not last layer
+            c = c + c_gate_msa.unsqueeze(1) * c_attn_output
+
+            norm_c = self.ff_norm_c(c) * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]
+            c_ff_output = self.ff_c(norm_c)
+            c = c + c_gate_mlp.unsqueeze(1) * c_ff_output
+
+        # process attention output for input x
+        x = x + x_gate_msa.unsqueeze(1) * x_attn_output
+
+        norm_x = self.ff_norm_x(x) * (1 + x_scale_mlp[:, None]) + x_shift_mlp[:, None]
+        x_ff_output = self.ff_x(norm_x)
+        x = x + x_gate_mlp.unsqueeze(1) * x_ff_output
+
+        return c, x
+
+
+# time step conditioning embedding
+
+
+class TimestepEmbedding(nn.Module):
+    def __init__(self, dim, freq_embed_dim=256):
+        super().__init__()
+        self.time_embed = SinusPositionEmbedding(freq_embed_dim)
+        self.time_mlp = nn.Sequential(nn.Linear(freq_embed_dim, dim), nn.SiLU(), nn.Linear(dim, dim))
+
+    def forward(self, timestep: float["b"]):
+        time_hidden = self.time_embed(timestep)
+        time_hidden = time_hidden.to(timestep.dtype)
+        time = self.time_mlp(time_hidden)  # b d
+        return time

src/f5_tts/model/trainer.py

@@ -0,0 +1,439 @@
+from __future__ import annotations
+
+import gc
+import math
+import os
+
+import torch
+import torchaudio
+import wandb
+from accelerate import Accelerator
+from accelerate.utils import DistributedDataParallelKwargs
+from ema_pytorch import EMA
+from torch.optim import AdamW
+from torch.optim.lr_scheduler import LinearLR, SequentialLR
+from torch.utils.data import DataLoader, Dataset, SequentialSampler
+from tqdm import tqdm
+
+from f5_tts.model import CFM
+from f5_tts.model.dataset import DynamicBatchSampler, collate_fn
+from f5_tts.model.utils import default, exists
+
+
+# trainer
+
+
+class Trainer:
+    def __init__(
+        self,
+        model: CFM,
+        epochs,
+        learning_rate,
+        num_warmup_updates=20000,
+        save_per_updates=1000,
+        keep_last_n_checkpoints: int = -1,  # -1 to keep all, 0 to not save intermediate, > 0 to keep last N checkpoints
+        checkpoint_path=None,
+        batch_size_per_gpu=32,
+        batch_size_type: str = "sample",
+        max_samples=32,
+        grad_accumulation_steps=1,
+        max_grad_norm=1.0,
+        noise_scheduler: str | None = None,
+        duration_predictor: torch.nn.Module | None = None,
+        logger: str | None = "wandb",  # "wandb" | "tensorboard" | None
+        wandb_project="test_f5-tts",
+        wandb_run_name="test_run",
+        wandb_resume_id: str = None,
+        log_samples: bool = False,
+        last_per_updates=None,
+        accelerate_kwargs: dict = dict(),
+        ema_kwargs: dict = dict(),
+        bnb_optimizer: bool = False,
+        mel_spec_type: str = "vocos",  # "vocos" | "bigvgan"
+        is_local_vocoder: bool = False,  # use local path vocoder
+        local_vocoder_path: str = "",  # local vocoder path
+        model_cfg_dict: dict = dict(),  # training config
+    ):
+        ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
+
+        if logger == "wandb" and not wandb.api.api_key:
+            logger = None
+        self.log_samples = log_samples
+
+        self.accelerator = Accelerator(
+            log_with=logger if logger == "wandb" else None,
+            kwargs_handlers=[ddp_kwargs],
+            gradient_accumulation_steps=grad_accumulation_steps,
+            **accelerate_kwargs,
+        )
+
+        self.logger = logger
+        if self.logger == "wandb":
+            if exists(wandb_resume_id):
+                init_kwargs = {"wandb": {"resume": "allow", "name": wandb_run_name, "id": wandb_resume_id}}
+            else:
+                init_kwargs = {"wandb": {"resume": "allow", "name": wandb_run_name}}
+
+            if not model_cfg_dict:
+                model_cfg_dict = {
+                    "epochs": epochs,
+                    "learning_rate": learning_rate,
+                    "num_warmup_updates": num_warmup_updates,
+                    "batch_size_per_gpu": batch_size_per_gpu,
+                    "batch_size_type": batch_size_type,
+                    "max_samples": max_samples,
+                    "grad_accumulation_steps": grad_accumulation_steps,
+                    "max_grad_norm": max_grad_norm,
+                    "noise_scheduler": noise_scheduler,
+                }
+            model_cfg_dict["gpus"] = self.accelerator.num_processes
+            self.accelerator.init_trackers(
+                project_name=wandb_project,
+                init_kwargs=init_kwargs,
+                config=model_cfg_dict,
+            )
+
+        elif self.logger == "tensorboard":
+            from torch.utils.tensorboard import SummaryWriter
+
+            self.writer = SummaryWriter(log_dir=f"runs/{wandb_run_name}")
+
+        self.model = model
+
+        if self.is_main:
+            self.ema_model = EMA(model, include_online_model=False, **ema_kwargs)
+            self.ema_model.to(self.accelerator.device)
+
+            print(f"Using logger: {logger}")
+            if grad_accumulation_steps > 1:
+                print(
+                    "Gradient accumulation checkpointing with per_updates now, old logic per_steps used with before f992c4e"
+                )
+
+        self.epochs = epochs
+        self.num_warmup_updates = num_warmup_updates
+        self.save_per_updates = save_per_updates
+        self.keep_last_n_checkpoints = keep_last_n_checkpoints
+        self.last_per_updates = default(last_per_updates, save_per_updates)
+        self.checkpoint_path = default(checkpoint_path, "ckpts/test_f5-tts")
+
+        self.batch_size_per_gpu = batch_size_per_gpu
+        self.batch_size_type = batch_size_type
+        self.max_samples = max_samples
+        self.grad_accumulation_steps = grad_accumulation_steps
+        self.max_grad_norm = max_grad_norm
+
+        # mel vocoder config
+        self.vocoder_name = mel_spec_type
+        self.is_local_vocoder = is_local_vocoder
+        self.local_vocoder_path = local_vocoder_path
+
+        self.noise_scheduler = noise_scheduler
+
+        self.duration_predictor = duration_predictor
+
+        if bnb_optimizer:
+            import bitsandbytes as bnb
+
+            self.optimizer = bnb.optim.AdamW8bit(model.parameters(), lr=learning_rate)
+        else:
+            self.optimizer = AdamW(model.parameters(), lr=learning_rate)
+        self.model, self.optimizer = self.accelerator.prepare(self.model, self.optimizer)
+
+    @property
+    def is_main(self):
+        return self.accelerator.is_main_process
+
+    def save_checkpoint(self, update, last=False):
+        self.accelerator.wait_for_everyone()
+        if self.is_main:
+            checkpoint = dict(
+                model_state_dict=self.accelerator.unwrap_model(self.model).state_dict(),
+                optimizer_state_dict=self.optimizer.state_dict(),
+                ema_model_state_dict=self.ema_model.state_dict(),
+                scheduler_state_dict=self.scheduler.state_dict(),
+                update=update,
+            )
+            if not os.path.exists(self.checkpoint_path):
+                os.makedirs(self.checkpoint_path)
+            if last:
+                self.accelerator.save(checkpoint, f"{self.checkpoint_path}/model_last.pt")
+                print(f"Saved last checkpoint at update {update}")
+            else:
+                if self.keep_last_n_checkpoints == 0:
+                    return
+                self.accelerator.save(checkpoint, f"{self.checkpoint_path}/model_{update}.pt")
+                if self.keep_last_n_checkpoints > 0:
+                    # Updated logic to exclude pretrained model from rotation
+                    checkpoints = [
+                        f
+                        for f in os.listdir(self.checkpoint_path)
+                        if f.startswith("model_")
+                        and not f.startswith("pretrained_")  # Exclude pretrained models
+                        and f.endswith(".pt")
+                        and f != "model_last.pt"
+                    ]
+                    checkpoints.sort(key=lambda x: int(x.split("_")[1].split(".")[0]))
+                    while len(checkpoints) > self.keep_last_n_checkpoints:
+                        oldest_checkpoint = checkpoints.pop(0)
+                        os.remove(os.path.join(self.checkpoint_path, oldest_checkpoint))
+                        print(f"Removed old checkpoint: {oldest_checkpoint}")
+
+    def load_checkpoint(self):
+        if (
+            not exists(self.checkpoint_path)
+            or not os.path.exists(self.checkpoint_path)
+            or not any(filename.endswith((".pt", ".safetensors")) for filename in os.listdir(self.checkpoint_path))
+        ):
+            return 0
+
+        self.accelerator.wait_for_everyone()
+        if "model_last.pt" in os.listdir(self.checkpoint_path):
+            latest_checkpoint = "model_last.pt"
+        else:
+            # Updated to consider pretrained models for loading but prioritize training checkpoints
+            all_checkpoints = [
+                f
+                for f in os.listdir(self.checkpoint_path)
+                if (f.startswith("model_") or f.startswith("pretrained_")) and f.endswith((".pt", ".safetensors"))
+            ]
+
+            # First try to find regular training checkpoints
+            training_checkpoints = [f for f in all_checkpoints if f.startswith("model_") and f != "model_last.pt"]
+            if training_checkpoints:
+                latest_checkpoint = sorted(
+                    training_checkpoints,
+                    key=lambda x: int("".join(filter(str.isdigit, x))),
+                )[-1]
+            else:
+                # If no training checkpoints, use pretrained model
+                latest_checkpoint = next(f for f in all_checkpoints if f.startswith("pretrained_"))
+
+        if latest_checkpoint.endswith(".safetensors"):  # always a pretrained checkpoint
+            from safetensors.torch import load_file
+
+            checkpoint = load_file(f"{self.checkpoint_path}/{latest_checkpoint}", device="cpu")
+            checkpoint = {"ema_model_state_dict": checkpoint}
+        elif latest_checkpoint.endswith(".pt"):
+            # checkpoint = torch.load(f"{self.checkpoint_path}/{latest_checkpoint}", map_location=self.accelerator.device)  # rather use accelerator.load_state ಥ_ಥ
+            checkpoint = torch.load(
+                f"{self.checkpoint_path}/{latest_checkpoint}", weights_only=True, map_location="cpu"
+            )
+
+        # patch for backward compatibility, 305e3ea
+        for key in ["ema_model.mel_spec.mel_stft.mel_scale.fb", "ema_model.mel_spec.mel_stft.spectrogram.window"]:
+            if key in checkpoint["ema_model_state_dict"]:
+                del checkpoint["ema_model_state_dict"][key]
+
+        if self.is_main:
+            self.ema_model.load_state_dict(checkpoint["ema_model_state_dict"])
+
+        if "update" in checkpoint or "step" in checkpoint:
+            # patch for backward compatibility, with before f992c4e
+            if "step" in checkpoint:
+                checkpoint["update"] = checkpoint["step"] // self.grad_accumulation_steps
+                if self.grad_accumulation_steps > 1 and self.is_main:
+                    print(
+                        "F5-TTS WARNING: Loading checkpoint saved with per_steps logic (before f992c4e), will convert to per_updates according to grad_accumulation_steps setting, may have unexpected behaviour."
+                    )
+            # patch for backward compatibility, 305e3ea
+            for key in ["mel_spec.mel_stft.mel_scale.fb", "mel_spec.mel_stft.spectrogram.window"]:
+                if key in checkpoint["model_state_dict"]:
+                    del checkpoint["model_state_dict"][key]
+
+            self.accelerator.unwrap_model(self.model).load_state_dict(checkpoint["model_state_dict"])
+            self.optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
+            if self.scheduler:
+                self.scheduler.load_state_dict(checkpoint["scheduler_state_dict"])
+            update = checkpoint["update"]
+        else:
+            checkpoint["model_state_dict"] = {
+                k.replace("ema_model.", ""): v
+                for k, v in checkpoint["ema_model_state_dict"].items()
+                if k not in ["initted", "update", "step"]
+            }
+            self.accelerator.unwrap_model(self.model).load_state_dict(checkpoint["model_state_dict"])
+            update = 0
+
+        del checkpoint
+        gc.collect()
+        return update
+
+    def train(self, train_dataset: Dataset, num_workers=16, resumable_with_seed: int = None):
+        if self.log_samples:
+            from f5_tts.infer.utils_infer import cfg_strength, load_vocoder, nfe_step, sway_sampling_coef
+
+            vocoder = load_vocoder(
+                vocoder_name=self.vocoder_name, is_local=self.is_local_vocoder, local_path=self.local_vocoder_path
+            )
+            target_sample_rate = self.accelerator.unwrap_model(self.model).mel_spec.target_sample_rate
+            log_samples_path = f"{self.checkpoint_path}/samples"
+            os.makedirs(log_samples_path, exist_ok=True)
+
+        if exists(resumable_with_seed):
+            generator = torch.Generator()
+            generator.manual_seed(resumable_with_seed)
+        else:
+            generator = None
+
+        if self.batch_size_type == "sample":
+            train_dataloader = DataLoader(
+                train_dataset,
+                collate_fn=collate_fn,
+                num_workers=num_workers,
+                pin_memory=True,
+                persistent_workers=True,
+                batch_size=self.batch_size_per_gpu,
+                shuffle=True,
+                generator=generator,
+            )
+        elif self.batch_size_type == "frame":
+            self.accelerator.even_batches = False
+            sampler = SequentialSampler(train_dataset)
+            batch_sampler = DynamicBatchSampler(
+                sampler,
+                self.batch_size_per_gpu,
+                max_samples=self.max_samples,
+                random_seed=resumable_with_seed,  # This enables reproducible shuffling
+                drop_residual=False,
+            )
+            train_dataloader = DataLoader(
+                train_dataset,
+                collate_fn=collate_fn,
+                num_workers=num_workers,
+                pin_memory=True,
+                persistent_workers=True,
+                batch_sampler=batch_sampler,
+            )
+        else:
+            raise ValueError(f"batch_size_type must be either 'sample' or 'frame', but received {self.batch_size_type}")
+
+        #  accelerator.prepare() dispatches batches to devices;
+        #  which means the length of dataloader calculated before, should consider the number of devices
+        warmup_updates = (
+            self.num_warmup_updates * self.accelerator.num_processes
+        )  # consider a fixed warmup steps while using accelerate multi-gpu ddp
+        # otherwise by default with split_batches=False, warmup steps change with num_processes
+        total_updates = math.ceil(len(train_dataloader) / self.grad_accumulation_steps) * self.epochs
+        decay_updates = total_updates - warmup_updates
+        warmup_scheduler = LinearLR(self.optimizer, start_factor=1e-8, end_factor=1.0, total_iters=warmup_updates)
+        decay_scheduler = LinearLR(self.optimizer, start_factor=1.0, end_factor=1e-8, total_iters=decay_updates)
+        self.scheduler = SequentialLR(
+            self.optimizer, schedulers=[warmup_scheduler, decay_scheduler], milestones=[warmup_updates]
+        )
+        train_dataloader, self.scheduler = self.accelerator.prepare(
+            train_dataloader, self.scheduler
+        )  # actual multi_gpu updates = single_gpu updates / gpu nums
+        start_update = self.load_checkpoint()
+        global_update = start_update
+
+        if exists(resumable_with_seed):
+            orig_epoch_step = len(train_dataloader)
+            start_step = start_update * self.grad_accumulation_steps
+            skipped_epoch = int(start_step // orig_epoch_step)
+            skipped_batch = start_step % orig_epoch_step
+            skipped_dataloader = self.accelerator.skip_first_batches(train_dataloader, num_batches=skipped_batch)
+        else:
+            skipped_epoch = 0
+
+        for epoch in range(skipped_epoch, self.epochs):
+            self.model.train()
+            if exists(resumable_with_seed) and epoch == skipped_epoch:
+                progress_bar_initial = math.ceil(skipped_batch / self.grad_accumulation_steps)
+                current_dataloader = skipped_dataloader
+            else:
+                progress_bar_initial = 0
+                current_dataloader = train_dataloader
+
+            # Set epoch for the batch sampler if it exists
+            if hasattr(train_dataloader, "batch_sampler") and hasattr(train_dataloader.batch_sampler, "set_epoch"):
+                train_dataloader.batch_sampler.set_epoch(epoch)
+
+            progress_bar = tqdm(
+                range(math.ceil(len(train_dataloader) / self.grad_accumulation_steps)),
+                desc=f"Epoch {epoch + 1}/{self.epochs}",
+                unit="update",
+                disable=not self.accelerator.is_local_main_process,
+                initial=progress_bar_initial,
+            )
+
+            for batch in current_dataloader:
+                with self.accelerator.accumulate(self.model):
+                    text_inputs = batch["text"]
+                    mel_spec = batch["mel"].permute(0, 2, 1)
+                    mel_lengths = batch["mel_lengths"]
+
+                    # TODO. add duration predictor training
+                    if self.duration_predictor is not None and self.accelerator.is_local_main_process:
+                        dur_loss = self.duration_predictor(mel_spec, lens=batch.get("durations"))
+                        self.accelerator.log({"duration loss": dur_loss.item()}, step=global_update)
+
+                    loss, cond, pred = self.model(
+                        mel_spec, text=text_inputs, lens=mel_lengths, noise_scheduler=self.noise_scheduler
+                    )
+                    self.accelerator.backward(loss)
+
+                    if self.max_grad_norm > 0 and self.accelerator.sync_gradients:
+                        self.accelerator.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)
+
+                    self.optimizer.step()
+                    self.scheduler.step()
+                    self.optimizer.zero_grad()
+
+                if self.accelerator.sync_gradients:
+                    if self.is_main:
+                        self.ema_model.update()
+
+                    global_update += 1
+                    progress_bar.update(1)
+                    progress_bar.set_postfix(update=str(global_update), loss=loss.item())
+
+                if self.accelerator.is_local_main_process:
+                    self.accelerator.log(
+                        {"loss": loss.item(), "lr": self.scheduler.get_last_lr()[0]}, step=global_update
+                    )
+                    if self.logger == "tensorboard":
+                        self.writer.add_scalar("loss", loss.item(), global_update)
+                        self.writer.add_scalar("lr", self.scheduler.get_last_lr()[0], global_update)
+
+                if global_update % self.last_per_updates == 0 and self.accelerator.sync_gradients:
+                    self.save_checkpoint(global_update, last=True)
+
+                if global_update % self.save_per_updates == 0 and self.accelerator.sync_gradients:
+                    self.save_checkpoint(global_update)
+
+                    if self.log_samples and self.accelerator.is_local_main_process:
+                        ref_audio_len = mel_lengths[0]
+                        infer_text = [
+                            text_inputs[0] + ([" "] if isinstance(text_inputs[0], list) else " ") + text_inputs[0]
+                        ]
+                        with torch.inference_mode():
+                            generated, _ = self.accelerator.unwrap_model(self.model).sample(
+                                cond=mel_spec[0][:ref_audio_len].unsqueeze(0),
+                                text=infer_text,
+                                duration=ref_audio_len * 2,
+                                steps=nfe_step,
+                                cfg_strength=cfg_strength,
+                                sway_sampling_coef=sway_sampling_coef,
+                            )
+                            generated = generated.to(torch.float32)
+                            gen_mel_spec = generated[:, ref_audio_len:, :].permute(0, 2, 1).to(self.accelerator.device)
+                            ref_mel_spec = batch["mel"][0].unsqueeze(0)
+                            if self.vocoder_name == "vocos":
+                                gen_audio = vocoder.decode(gen_mel_spec).cpu()
+                                ref_audio = vocoder.decode(ref_mel_spec).cpu()
+                            elif self.vocoder_name == "bigvgan":
+                                gen_audio = vocoder(gen_mel_spec).squeeze(0).cpu()
+                                ref_audio = vocoder(ref_mel_spec).squeeze(0).cpu()
+
+                        torchaudio.save(
+                            f"{log_samples_path}/update_{global_update}_gen.wav", gen_audio, target_sample_rate
+                        )
+                        torchaudio.save(
+                            f"{log_samples_path}/update_{global_update}_ref.wav", ref_audio, target_sample_rate
+                        )
+                        self.model.train()
+
+        self.save_checkpoint(global_update, last=True)
+
+        self.accelerator.end_training()

src/f5_tts/model/utils.py

@@ -0,0 +1,220 @@
+from __future__ import annotations
+
+import os
+import random
+from collections import defaultdict
+from importlib.resources import files
+
+import jieba
+import torch
+from pypinyin import Style, lazy_pinyin
+from torch.nn.utils.rnn import pad_sequence
+
+
+# seed everything
+
+
+def seed_everything(seed=0):
+    random.seed(seed)
+    os.environ["PYTHONHASHSEED"] = str(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+
+
+# helpers
+
+
+def exists(v):
+    return v is not None
+
+
+def default(v, d):
+    return v if exists(v) else d
+
+
+def is_package_available(package_name: str) -> bool:
+    try:
+        import importlib
+
+        package_exists = importlib.util.find_spec(package_name) is not None
+        return package_exists
+    except Exception:
+        return False
+
+
+# tensor helpers
+
+
+def lens_to_mask(t: int["b"], length: int | None = None) -> bool["b n"]:  # noqa: F722 F821
+    if not exists(length):
+        length = t.amax()
+
+    seq = torch.arange(length, device=t.device)
+    return seq[None, :] < t[:, None]
+
+
+def mask_from_start_end_indices(seq_len: int["b"], start: int["b"], end: int["b"]):  # noqa: F722 F821
+    max_seq_len = seq_len.max().item()
+    seq = torch.arange(max_seq_len, device=start.device).long()
+    start_mask = seq[None, :] >= start[:, None]
+    end_mask = seq[None, :] < end[:, None]
+    return start_mask & end_mask
+
+
+def mask_from_frac_lengths(seq_len: int["b"], frac_lengths: float["b"]):  # noqa: F722 F821
+    lengths = (frac_lengths * seq_len).long()
+    max_start = seq_len - lengths
+
+    rand = torch.rand_like(frac_lengths)
+    start = (max_start * rand).long().clamp(min=0)
+    end = start + lengths
+
+    return mask_from_start_end_indices(seq_len, start, end)
+
+
+def maybe_masked_mean(t: float["b n d"], mask: bool["b n"] = None) -> float["b d"]:  # noqa: F722
+    if not exists(mask):
+        return t.mean(dim=1)
+
+    t = torch.where(mask[:, :, None], t, torch.tensor(0.0, device=t.device))
+    num = t.sum(dim=1)
+    den = mask.float().sum(dim=1)
+
+    return num / den.clamp(min=1.0)
+
+
+# simple utf-8 tokenizer, since paper went character based
+def list_str_to_tensor(text: list[str], padding_value=-1) -> int["b nt"]:  # noqa: F722
+    list_tensors = [torch.tensor([*bytes(t, "UTF-8")]) for t in text]  # ByT5 style
+    text = pad_sequence(list_tensors, padding_value=padding_value, batch_first=True)
+    return text
+
+
+# char tokenizer, based on custom dataset's extracted .txt file
+def list_str_to_idx(
+    text: list[str] | list[list[str]],
+    vocab_char_map: dict[str, int],  # {char: idx}
+    padding_value=-1,
+) -> int["b nt"]:  # noqa: F722
+    list_idx_tensors = [torch.tensor([vocab_char_map.get(c, 0) for c in t]) for t in text]  # pinyin or char style
+    text = pad_sequence(list_idx_tensors, padding_value=padding_value, batch_first=True)
+    return text
+
+
+# Get tokenizer
+
+
+def get_tokenizer(dataset_name, tokenizer: str = "pinyin"):
+    """
+    tokenizer   - "pinyin" do g2p for only chinese characters, need .txt vocab_file
+                - "char" for char-wise tokenizer, need .txt vocab_file
+                - "byte" for utf-8 tokenizer
+                - "custom" if you're directly passing in a path to the vocab.txt you want to use
+    vocab_size  - if use "pinyin", all available pinyin types, common alphabets (also those with accent) and symbols
+                - if use "char", derived from unfiltered character & symbol counts of custom dataset
+                - if use "byte", set to 256 (unicode byte range)
+    """
+    if tokenizer in ["pinyin", "char"]:
+        tokenizer_path = os.path.join(files("f5_tts").joinpath("../../data"), f"{dataset_name}_{tokenizer}/vocab.txt")
+        with open(tokenizer_path, "r", encoding="utf-8") as f:
+            vocab_char_map = {}
+            for i, char in enumerate(f):
+                vocab_char_map[char[:-1]] = i
+        vocab_size = len(vocab_char_map)
+        assert vocab_char_map[" "] == 0, "make sure space is of idx 0 in vocab.txt, cuz 0 is used for unknown char"
+
+    elif tokenizer == "byte":
+        vocab_char_map = None
+        vocab_size = 256
+
+    elif tokenizer == "custom":
+        with open(dataset_name, "r", encoding="utf-8") as f:
+            vocab_char_map = {}
+            for i, char in enumerate(f):
+                vocab_char_map[char[:-1]] = i
+        vocab_size = len(vocab_char_map)
+
+    return vocab_char_map, vocab_size
+
+
+# convert char to pinyin
+
+
+def convert_char_to_pinyin(text_list, polyphone=True):
+    if jieba.dt.initialized is False:
+        jieba.default_logger.setLevel(50)  # CRITICAL
+        jieba.initialize()
+
+    final_text_list = []
+    custom_trans = str.maketrans(
+        {";": ",", "“": '"', "”": '"', "‘": "'", "’": "'"}
+    )  # add custom trans here, to address oov
+
+    def is_chinese(c):
+        return (
+            "\u3100" <= c <= "\u9fff"  # common chinese characters
+        )
+
+    for text in text_list:
+        char_list = []
+        text = text.translate(custom_trans)
+        for seg in jieba.cut(text):
+            seg_byte_len = len(bytes(seg, "UTF-8"))
+            if seg_byte_len == len(seg):  # if pure alphabets and symbols
+                if char_list and seg_byte_len > 1 and char_list[-1] not in " :'\"":
+                    char_list.append(" ")
+                char_list.extend(seg)
+            elif polyphone and seg_byte_len == 3 * len(seg):  # if pure east asian characters
+                seg_ = lazy_pinyin(seg, style=Style.TONE3, tone_sandhi=True)
+                for i, c in enumerate(seg):
+                    if is_chinese(c):
+                        char_list.append(" ")
+                    char_list.append(seg_[i])
+            else:  # if mixed characters, alphabets and symbols
+                for c in seg:
+                    if ord(c) < 256:
+                        char_list.extend(c)
+                    elif is_chinese(c):
+                        char_list.append(" ")
+                        char_list.extend(lazy_pinyin(c, style=Style.TONE3, tone_sandhi=True))
+                    else:
+                        char_list.append(c)
+        final_text_list.append(char_list)
+
+    return final_text_list
+
+
+# filter func for dirty data with many repetitions
+
+
+def repetition_found(text, length=2, tolerance=10):
+    pattern_count = defaultdict(int)
+    for i in range(len(text) - length + 1):
+        pattern = text[i : i + length]
+        pattern_count[pattern] += 1
+    for pattern, count in pattern_count.items():
+        if count > tolerance:
+            return True
+    return False
+
+
+# get the empirically pruned step for sampling
+
+
+def get_epss_timesteps(n, device, dtype):
+    dt = 1 / 32
+    predefined_timesteps = {
+        5: [0, 2, 4, 8, 16, 32],
+        6: [0, 2, 4, 6, 8, 16, 32],
+        7: [0, 2, 4, 6, 8, 16, 24, 32],
+        10: [0, 2, 4, 6, 8, 12, 16, 20, 24, 28, 32],
+        12: [0, 2, 4, 6, 8, 10, 12, 14, 16, 20, 24, 28, 32],
+        16: [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 16, 20, 24, 28, 32],
+    }
+    t = predefined_timesteps.get(n, [])
+    if not t:
+        return torch.linspace(0, 1, n + 1, device=device, dtype=dtype)
+    return dt * torch.tensor(t, device=device, dtype=dtype)

src/f5_tts/runtime/triton_trtllm/Dockerfile.server

@@ -0,0 +1,3 @@
+FROM nvcr.io/nvidia/tritonserver:24.12-py3
+RUN pip install tritonclient[grpc] tensorrt-llm==0.16.0 torchaudio==2.5.1 jieba pypinyin librosa vocos
+WORKDIR /workspace

src/f5_tts/runtime/triton_trtllm/README.md

@@ -0,0 +1,69 @@
+## Triton Inference Serving Best Practice for F5-TTS
+
+### Quick Start
+Directly launch the service using docker compose.
+```sh
+# TODO: support F5TTS_v1_Base
+MODEL=F5TTS_Base docker compose up
+```
+
+### Build Image
+Build the docker image from scratch. 
+```sh
+docker build . -f Dockerfile.server -t soar97/triton-f5-tts:24.12
+```
+
+### Create Docker Container
+```sh
+your_mount_dir=/mnt:/mnt
+docker run -it --name "f5-server" --gpus all --net host -v $your_mount_dir --shm-size=2g soar97/triton-f5-tts:24.12
+```
+
+### Export Models to TensorRT-LLM and Launch Server
+Inside docker container, we would follow the official guide of TensorRT-LLM to build qwen and whisper TensorRT-LLM engines. See [here](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/whisper).
+```sh
+bash run.sh 0 4 F5TTS_Base
+```
+
+### HTTP Client
+```sh
+python3 client_http.py
+```
+
+### Benchmark using Client-Server Mode
+```sh
+num_task=2
+python3 client_grpc.py --num-tasks $num_task --huggingface-dataset yuekai/seed_tts --split-name wenetspeech4tts
+```
+
+### Benchmark using Offline TRT-LLM Mode
+```sh
+batch_size=1
+split_name=wenetspeech4tts
+backend_type=trt
+log_dir=./log_benchmark_batch_size_${batch_size}_${split_name}_${backend_type}
+rm -r $log_dir
+ln -s model_repo_f5_tts/f5_tts/1/f5_tts_trtllm.py ./
+torchrun --nproc_per_node=1 \
+benchmark.py --output-dir $log_dir \
+--batch-size $batch_size \
+--enable-warmup \
+--split-name $split_name \
+--model-path $F5_TTS_HF_DOWNLOAD_PATH/$model/model_1200000.pt \
+--vocab-file $F5_TTS_HF_DOWNLOAD_PATH/$model/vocab.txt \
+--vocoder-trt-engine-path $vocoder_trt_engine_path \
+--backend-type $backend_type \
+--tllm-model-dir $F5_TTS_TRT_LLM_ENGINE_PATH || exit 1
+```
+
+### Benchmark Results
+Decoding on a single L20 GPU, using 26 different prompt_audio & target_text pairs, 16 NFE.
+
+| Model               | Concurrency    | Avg Latency | RTF    | Mode            |
+|---------------------|----------------|-------------|--------|-----------------|
+| F5-TTS Base (Vocos) | 2              | 253 ms      | 0.0394 | Client-Server   |
+| F5-TTS Base (Vocos) | 1 (Batch_size) | -           | 0.0402 | Offline TRT-LLM |
+| F5-TTS Base (Vocos) | 1 (Batch_size) | -           | 0.1467 | Offline Pytorch |
+
+### Credits
+1. [F5-TTS-TRTLLM](https://github.com/Bigfishering/f5-tts-trtllm)

src/f5_tts/runtime/triton_trtllm/benchmark.py

@@ -0,0 +1,560 @@
+# Copyright (c) 2024 Tsinghua Univ. (authors: Xingchen Song)
+#               2025               （authors: Yuekai Zhang）
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Modified from https://github.com/xingchensong/S3Tokenizer/blob/main/s3tokenizer/cli.py
+""" Example Usage
+torchrun --nproc_per_node=1 \
+benchmark.py --output-dir $log_dir \
+--batch-size $batch_size \
+--enable-warmup \
+--split-name $split_name \
+--model-path $F5_TTS_HF_DOWNLOAD_PATH/$model/model_1200000.pt \
+--vocab-file $F5_TTS_HF_DOWNLOAD_PATH/$model/vocab.txt \
+--vocoder-trt-engine-path $vocoder_trt_engine_path \
+--backend-type $backend_type \
+--tllm-model-dir $F5_TTS_TRT_LLM_ENGINE_PATH || exit 1
+"""
+
+import argparse
+import json
+import os
+import time
+from typing import Dict, List, Union
+
+import datasets
+import jieba
+import tensorrt as trt
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+import torchaudio
+from datasets import load_dataset
+from f5_tts_trtllm import F5TTS
+from huggingface_hub import hf_hub_download
+from pypinyin import Style, lazy_pinyin
+from tensorrt_llm._utils import trt_dtype_to_torch
+from tensorrt_llm.logger import logger
+from tensorrt_llm.runtime.session import Session, TensorInfo
+from torch.nn.utils.rnn import pad_sequence
+from torch.utils.data import DataLoader, DistributedSampler
+from tqdm import tqdm
+from vocos import Vocos
+
+
+torch.manual_seed(0)
+
+
+def get_args():
+    parser = argparse.ArgumentParser(description="extract speech code")
+    parser.add_argument(
+        "--split-name",
+        type=str,
+        default="wenetspeech4tts",
+        choices=["wenetspeech4tts", "test_zh", "test_en", "test_hard"],
+        help="huggingface dataset split name",
+    )
+    parser.add_argument("--output-dir", required=True, type=str, help="dir to save result")
+    parser.add_argument(
+        "--vocab-file",
+        required=True,
+        type=str,
+        help="vocab file",
+    )
+    parser.add_argument(
+        "--model-path",
+        required=True,
+        type=str,
+        help="model path, to load text embedding",
+    )
+    parser.add_argument(
+        "--tllm-model-dir",
+        required=True,
+        type=str,
+        help="tllm model dir",
+    )
+    parser.add_argument(
+        "--batch-size",
+        required=True,
+        type=int,
+        help="batch size (per-device) for inference",
+    )
+    parser.add_argument("--num-workers", type=int, default=0, help="workers for dataloader")
+    parser.add_argument("--prefetch", type=int, default=None, help="prefetch for dataloader")
+    parser.add_argument(
+        "--vocoder",
+        default="vocos",
+        type=str,
+        help="vocoder name",
+    )
+    parser.add_argument(
+        "--vocoder-trt-engine-path",
+        default=None,
+        type=str,
+        help="vocoder trt engine path",
+    )
+    parser.add_argument("--enable-warmup", action="store_true")
+    parser.add_argument("--remove-input-padding", action="store_true")
+    parser.add_argument("--use-perf", action="store_true", help="use nvtx to record performance")
+    parser.add_argument("--backend-type", type=str, default="triton", choices=["trt", "pytorch"], help="backend type")
+    args = parser.parse_args()
+    return args
+
+
+def padded_mel_batch(ref_mels, max_seq_len):
+    padded_ref_mels = []
+    for mel in ref_mels:
+        # pad along the last dimension
+        padded_ref_mel = F.pad(mel, (0, 0, 0, max_seq_len - mel.shape[0]), value=0)
+        padded_ref_mels.append(padded_ref_mel)
+    padded_ref_mels = torch.stack(padded_ref_mels)
+    return padded_ref_mels
+
+
+def data_collator(batch, vocab_char_map, device="cuda", use_perf=False):
+    if use_perf:
+        torch.cuda.nvtx.range_push("data_collator")
+    target_sample_rate = 24000
+    target_rms = 0.1
+    ids, ref_mel_list, ref_mel_len_list, estimated_reference_target_mel_len, reference_target_texts_list = (
+        [],
+        [],
+        [],
+        [],
+        [],
+    )
+    for i, item in enumerate(batch):
+        item_id, prompt_text, target_text = (
+            item["id"],
+            item["prompt_text"],
+            item["target_text"],
+        )
+        ids.append(item_id)
+        reference_target_texts_list.append(prompt_text + target_text)
+
+        ref_audio_org, ref_sr = (
+            item["prompt_audio"]["array"],
+            item["prompt_audio"]["sampling_rate"],
+        )
+        ref_audio_org = torch.from_numpy(ref_audio_org).unsqueeze(0).float()
+        ref_rms = torch.sqrt(torch.mean(torch.square(ref_audio_org)))
+        if ref_rms < target_rms:
+            ref_audio_org = ref_audio_org * target_rms / ref_rms
+
+        if ref_sr != target_sample_rate:
+            resampler = torchaudio.transforms.Resample(ref_sr, target_sample_rate)
+            ref_audio = resampler(ref_audio_org)
+        else:
+            ref_audio = ref_audio_org
+
+        if use_perf:
+            torch.cuda.nvtx.range_push(f"mel_spectrogram {i}")
+        ref_mel = mel_spectrogram(ref_audio, vocoder="vocos", device="cuda")
+        if use_perf:
+            torch.cuda.nvtx.range_pop()
+        ref_mel = ref_mel.squeeze()
+        ref_mel_len = ref_mel.shape[0]
+        assert ref_mel.shape[1] == 100
+
+        ref_mel_list.append(ref_mel)
+        ref_mel_len_list.append(ref_mel_len)
+
+        estimated_reference_target_mel_len.append(
+            int(ref_mel.shape[0] * (1 + len(target_text.encode("utf-8")) / len(prompt_text.encode("utf-8"))))
+        )
+
+    max_seq_len = max(estimated_reference_target_mel_len)
+    ref_mel_batch = padded_mel_batch(ref_mel_list, max_seq_len)
+    ref_mel_len_batch = torch.LongTensor(ref_mel_len_list)
+
+    pinyin_list = convert_char_to_pinyin(reference_target_texts_list, polyphone=True)
+    text_pad_sequence = list_str_to_idx(pinyin_list, vocab_char_map)
+
+    for i, item in enumerate(text_pad_sequence):
+        text_pad_sequence[i] = F.pad(
+            item, (0, estimated_reference_target_mel_len[i] - len(item)), mode="constant", value=-1
+        )
+        text_pad_sequence[i] += 1  # WAR: 0 is reserved for padding token, hard coding in F5-TTS
+    text_pad_sequence = pad_sequence(text_pad_sequence, padding_value=-1, batch_first=True).to(device)
+    text_pad_sequence = F.pad(
+        text_pad_sequence, (0, max_seq_len - text_pad_sequence.shape[1]), mode="constant", value=-1
+    )
+    if use_perf:
+        torch.cuda.nvtx.range_pop()
+    return {
+        "ids": ids,
+        "ref_mel_batch": ref_mel_batch,
+        "ref_mel_len_batch": ref_mel_len_batch,
+        "text_pad_sequence": text_pad_sequence,
+        "estimated_reference_target_mel_len": estimated_reference_target_mel_len,
+    }
+
+
+def init_distributed():
+    world_size = int(os.environ.get("WORLD_SIZE", 1))
+    local_rank = int(os.environ.get("LOCAL_RANK", 0))
+    rank = int(os.environ.get("RANK", 0))
+    print(
+        "Inference on multiple gpus, this gpu {}".format(local_rank)
+        + ", rank {}, world_size {}".format(rank, world_size)
+    )
+    torch.cuda.set_device(local_rank)
+    # Initialize process group with explicit device IDs
+    dist.init_process_group(
+        "nccl",
+    )
+    return world_size, local_rank, rank
+
+
+def get_tokenizer(vocab_file_path: str):
+    """
+    tokenizer   - "pinyin" do g2p for only chinese characters, need .txt vocab_file
+                - "char" for char-wise tokenizer, need .txt vocab_file
+                - "byte" for utf-8 tokenizer
+                - "custom" if you're directly passing in a path to the vocab.txt you want to use
+    vocab_size  - if use "pinyin", all available pinyin types, common alphabets (also those with accent) and symbols
+                - if use "char", derived from unfiltered character & symbol counts of custom dataset
+                - if use "byte", set to 256 (unicode byte range)
+    """
+    with open(vocab_file_path, "r", encoding="utf-8") as f:
+        vocab_char_map = {}
+        for i, char in enumerate(f):
+            vocab_char_map[char[:-1]] = i
+    vocab_size = len(vocab_char_map)
+    return vocab_char_map, vocab_size
+
+
+def convert_char_to_pinyin(reference_target_texts_list, polyphone=True):
+    final_reference_target_texts_list = []
+    custom_trans = str.maketrans(
+        {";": ",", "“": '"', "”": '"', "‘": "'", "’": "'"}
+    )  # add custom trans here, to address oov
+
+    def is_chinese(c):
+        return "\u3100" <= c <= "\u9fff"  # common chinese characters
+
+    for text in reference_target_texts_list:
+        char_list = []
+        text = text.translate(custom_trans)
+        for seg in jieba.cut(text):
+            seg_byte_len = len(bytes(seg, "UTF-8"))
+            if seg_byte_len == len(seg):  # if pure alphabets and symbols
+                if char_list and seg_byte_len > 1 and char_list[-1] not in " :'\"":
+                    char_list.append(" ")
+                char_list.extend(seg)
+            elif polyphone and seg_byte_len == 3 * len(seg):  # if pure east asian characters
+                seg_ = lazy_pinyin(seg, style=Style.TONE3, tone_sandhi=True)
+                for i, c in enumerate(seg):
+                    if is_chinese(c):
+                        char_list.append(" ")
+                    char_list.append(seg_[i])
+            else:  # if mixed characters, alphabets and symbols
+                for c in seg:
+                    if ord(c) < 256:
+                        char_list.extend(c)
+                    elif is_chinese(c):
+                        char_list.append(" ")
+                        char_list.extend(lazy_pinyin(c, style=Style.TONE3, tone_sandhi=True))
+                    else:
+                        char_list.append(c)
+        final_reference_target_texts_list.append(char_list)
+
+    return final_reference_target_texts_list
+
+
+def list_str_to_idx(
+    text: Union[List[str], List[List[str]]],
+    vocab_char_map: Dict[str, int],  # {char: idx}
+    padding_value=-1,
+):
+    list_idx_tensors = [torch.tensor([vocab_char_map.get(c, 0) for c in t]) for t in text]  # pinyin or char style
+    # text = pad_sequence(list_idx_tensors, padding_value=padding_value, batch_first=True)
+    return list_idx_tensors
+
+
+def load_vocoder(
+    vocoder_name="vocos", is_local=False, local_path="", device="cuda", hf_cache_dir=None, vocoder_trt_engine_path=None
+):
+    if vocoder_name == "vocos":
+        if vocoder_trt_engine_path is not None:
+            vocoder = VocosTensorRT(engine_path=vocoder_trt_engine_path)
+        else:
+            # vocoder = Vocos.from_pretrained("charactr/vocos-mel-24khz").to(device)
+            if is_local:
+                print(f"Load vocos from local path {local_path}")
+                config_path = f"{local_path}/config.yaml"
+                model_path = f"{local_path}/pytorch_model.bin"
+            else:
+                print("Download Vocos from huggingface charactr/vocos-mel-24khz")
+                repo_id = "charactr/vocos-mel-24khz"
+                config_path = hf_hub_download(repo_id=repo_id, cache_dir=hf_cache_dir, filename="config.yaml")
+                model_path = hf_hub_download(repo_id=repo_id, cache_dir=hf_cache_dir, filename="pytorch_model.bin")
+            vocoder = Vocos.from_hparams(config_path)
+            state_dict = torch.load(model_path, map_location="cpu", weights_only=True)
+            from vocos.feature_extractors import EncodecFeatures
+
+            if isinstance(vocoder.feature_extractor, EncodecFeatures):
+                encodec_parameters = {
+                    "feature_extractor.encodec." + key: value
+                    for key, value in vocoder.feature_extractor.encodec.state_dict().items()
+                }
+                state_dict.update(encodec_parameters)
+            vocoder.load_state_dict(state_dict)
+            vocoder = vocoder.eval().to(device)
+    elif vocoder_name == "bigvgan":
+        raise NotImplementedError("BigVGAN is not implemented yet")
+    return vocoder
+
+
+def mel_spectrogram(waveform, vocoder="vocos", device="cuda"):
+    if vocoder == "vocos":
+        mel_stft = torchaudio.transforms.MelSpectrogram(
+            sample_rate=24000,
+            n_fft=1024,
+            win_length=1024,
+            hop_length=256,
+            n_mels=100,
+            power=1,
+            center=True,
+            normalized=False,
+            norm=None,
+        ).to(device)
+    mel = mel_stft(waveform.to(device))
+    mel = mel.clamp(min=1e-5).log()
+    return mel.transpose(1, 2)
+
+
+class VocosTensorRT:
+    def __init__(self, engine_path="./vocos_vocoder.plan", stream=None):
+        TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
+        trt.init_libnvinfer_plugins(TRT_LOGGER, namespace="")
+        logger.info(f"Loading vae engine from {engine_path}")
+        self.engine_path = engine_path
+        with open(engine_path, "rb") as f:
+            engine_buffer = f.read()
+        self.session = Session.from_serialized_engine(engine_buffer)
+        self.stream = stream if stream is not None else torch.cuda.current_stream().cuda_stream
+
+    def decode(self, mels):
+        mels = mels.contiguous()
+        inputs = {"mel": mels}
+        output_info = self.session.infer_shapes([TensorInfo("mel", trt.DataType.FLOAT, mels.shape)])
+        outputs = {
+            t.name: torch.empty(tuple(t.shape), dtype=trt_dtype_to_torch(t.dtype), device="cuda") for t in output_info
+        }
+        ok = self.session.run(inputs, outputs, self.stream)
+
+        assert ok, "Runtime execution failed for vae session"
+
+        samples = outputs["waveform"]
+        return samples
+
+
+def main():
+    args = get_args()
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    assert torch.cuda.is_available()
+    world_size, local_rank, rank = init_distributed()
+    device = torch.device(f"cuda:{local_rank}")
+
+    vocab_char_map, vocab_size = get_tokenizer(args.vocab_file)
+
+    tllm_model_dir = args.tllm_model_dir
+    config_file = os.path.join(tllm_model_dir, "config.json")
+    with open(config_file) as f:
+        config = json.load(f)
+    if args.backend_type == "trt":
+        model = F5TTS(
+            config, debug_mode=False, tllm_model_dir=tllm_model_dir, model_path=args.model_path, vocab_size=vocab_size
+        )
+    elif args.backend_type == "pytorch":
+        import sys
+
+        sys.path.append(f"{os.path.dirname(os.path.abspath(__file__))}/../../../../src/")
+        from f5_tts.infer.utils_infer import load_model
+        from f5_tts.model import DiT
+
+        F5TTS_model_cfg = dict(
+            dim=1024,
+            depth=22,
+            heads=16,
+            ff_mult=2,
+            text_dim=512,
+            conv_layers=4,
+            pe_attn_head=1,
+            text_mask_padding=False,
+        )
+        model = load_model(DiT, F5TTS_model_cfg, args.model_path)
+
+    vocoder = load_vocoder(
+        vocoder_name=args.vocoder, device=device, vocoder_trt_engine_path=args.vocoder_trt_engine_path
+    )
+
+    dataset = load_dataset(
+        "yuekai/seed_tts",
+        split=args.split_name,
+        trust_remote_code=True,
+    )
+
+    def add_estimated_duration(example):
+        prompt_audio_len = example["prompt_audio"]["array"].shape[0]
+        scale_factor = 1 + len(example["target_text"]) / len(example["prompt_text"])
+        estimated_duration = prompt_audio_len * scale_factor
+        example["estimated_duration"] = estimated_duration / example["prompt_audio"]["sampling_rate"]
+        return example
+
+    dataset = dataset.map(add_estimated_duration)
+    dataset = dataset.sort("estimated_duration", reverse=True)
+    if args.use_perf:
+        # dataset_list = [dataset.select(range(1)) for i in range(16)]  # seq_len 1000
+        dataset_list_short = [dataset.select([24]) for i in range(8)]  # seq_len 719
+        # dataset_list_long = [dataset.select([23]) for i in range(8)] # seq_len 2002
+        # dataset = datasets.concatenate_datasets(dataset_list_short + dataset_list_long)
+        dataset = datasets.concatenate_datasets(dataset_list_short)
+    if world_size > 1:
+        sampler = DistributedSampler(dataset, num_replicas=world_size, rank=rank)
+    else:
+        # This would disable shuffling
+        sampler = None
+
+    dataloader = DataLoader(
+        dataset,
+        batch_size=args.batch_size,
+        sampler=sampler,
+        shuffle=False,
+        num_workers=args.num_workers,
+        prefetch_factor=args.prefetch,
+        collate_fn=lambda x: data_collator(x, vocab_char_map, use_perf=args.use_perf),
+    )
+
+    total_steps = len(dataset)
+
+    if args.enable_warmup:
+        for batch in dataloader:
+            ref_mels, ref_mel_lens = batch["ref_mel_batch"].to(device), batch["ref_mel_len_batch"].to(device)
+            text_pad_seq = batch["text_pad_sequence"].to(device)
+            total_mel_lens = batch["estimated_reference_target_mel_len"]
+            if args.backend_type == "trt":
+                _ = model.sample(
+                    text_pad_seq, ref_mels, ref_mel_lens, total_mel_lens, remove_input_padding=args.remove_input_padding
+                )
+            elif args.backend_type == "pytorch":
+                with torch.inference_mode():
+                    text_pad_seq -= 1
+                    text_pad_seq[text_pad_seq == -2] = -1
+                    total_mel_lens = torch.tensor(total_mel_lens, device=device)
+                    generated, _ = model.sample(
+                        cond=ref_mels,
+                        text=text_pad_seq,
+                        duration=total_mel_lens,
+                        steps=16,
+                        cfg_strength=2.0,
+                        sway_sampling_coef=-1,
+                    )
+
+    if rank == 0:
+        progress_bar = tqdm(total=total_steps, desc="Processing", unit="wavs")
+
+    decoding_time = 0
+    vocoder_time = 0
+    total_duration = 0
+    if args.use_perf:
+        torch.cuda.cudart().cudaProfilerStart()
+    total_decoding_time = time.time()
+    for batch in dataloader:
+        if args.use_perf:
+            torch.cuda.nvtx.range_push("data sample")
+        ref_mels, ref_mel_lens = batch["ref_mel_batch"].to(device), batch["ref_mel_len_batch"].to(device)
+        text_pad_seq = batch["text_pad_sequence"].to(device)
+        total_mel_lens = batch["estimated_reference_target_mel_len"]
+
+        if args.use_perf:
+            torch.cuda.nvtx.range_pop()
+        if args.backend_type == "trt":
+            generated, cost_time = model.sample(
+                text_pad_seq,
+                ref_mels,
+                ref_mel_lens,
+                total_mel_lens,
+                remove_input_padding=args.remove_input_padding,
+                use_perf=args.use_perf,
+            )
+        elif args.backend_type == "pytorch":
+            total_mel_lens = torch.tensor(total_mel_lens, device=device)
+            with torch.inference_mode():
+                start_time = time.time()
+                text_pad_seq -= 1
+                text_pad_seq[text_pad_seq == -2] = -1
+                generated, _ = model.sample(
+                    cond=ref_mels,
+                    text=text_pad_seq,
+                    duration=total_mel_lens,
+                    lens=ref_mel_lens,
+                    steps=16,
+                    cfg_strength=2.0,
+                    sway_sampling_coef=-1,
+                )
+                cost_time = time.time() - start_time
+        decoding_time += cost_time
+        vocoder_start_time = time.time()
+        for i, gen in enumerate(generated):
+            gen = gen[ref_mel_lens[i] : total_mel_lens[i], :].unsqueeze(0)
+            gen_mel_spec = gen.permute(0, 2, 1).to(torch.float32)
+            if args.vocoder == "vocos":
+                if args.use_perf:
+                    torch.cuda.nvtx.range_push("vocoder decode")
+                generated_wave = vocoder.decode(gen_mel_spec).cpu()
+                if args.use_perf:
+                    torch.cuda.nvtx.range_pop()
+            else:
+                generated_wave = vocoder(gen_mel_spec).squeeze(0).cpu()
+            target_rms = 0.1
+            target_sample_rate = 24_000
+            # if ref_rms_list[i] < target_rms:
+            #     generated_wave = generated_wave * ref_rms_list[i] / target_rms
+            rms = torch.sqrt(torch.mean(torch.square(generated_wave)))
+            if rms < target_rms:
+                generated_wave = generated_wave * target_rms / rms
+            utt = batch["ids"][i]
+            torchaudio.save(
+                f"{args.output_dir}/{utt}.wav",
+                generated_wave,
+                target_sample_rate,
+            )
+            total_duration += generated_wave.shape[1] / target_sample_rate
+        vocoder_time += time.time() - vocoder_start_time
+        if rank == 0:
+            progress_bar.update(world_size * len(batch["ids"]))
+    total_decoding_time = time.time() - total_decoding_time
+    if rank == 0:
+        progress_bar.close()
+    rtf = total_decoding_time / total_duration
+    s = f"RTF: {rtf:.4f}\n"
+    s += f"total_duration: {total_duration:.3f} seconds\n"
+    s += f"({total_duration / 3600:.2f} hours)\n"
+    s += f"DiT time: {decoding_time:.3f} seconds ({decoding_time / 3600:.2f} hours)\n"
+    s += f"Vocoder time: {vocoder_time:.3f} seconds ({vocoder_time / 3600:.2f} hours)\n"
+    s += f"total decoding time: {total_decoding_time:.3f} seconds ({total_decoding_time / 3600:.2f} hours)\n"
+    s += f"batch size: {args.batch_size}\n"
+    print(s)
+
+    with open(f"{args.output_dir}/rtf.txt", "w") as f:
+        f.write(s)
+
+    dist.barrier()
+    dist.destroy_process_group()
+
+
+if __name__ == "__main__":
+    main()

src/f5_tts/runtime/triton_trtllm/client_grpc.py

@@ -0,0 +1,470 @@
+#!/usr/bin/env python3
+# Copyright      2022  Xiaomi Corp.        (authors: Fangjun Kuang)
+#                2023  Nvidia              (authors: Yuekai Zhang)
+#                2023  Recurrent.ai        (authors: Songtao Shi)
+# See LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+This script supports to load dataset from huggingface and sends it to the server
+for decoding, in parallel.
+
+Usage:
+num_task=2
+
+# For offline F5-TTS
+python3 client_grpc.py \
+    --server-addr localhost \
+    --model-name f5_tts \
+    --num-tasks $num_task \
+    --huggingface-dataset yuekai/seed_tts \
+    --split-name test_zh \
+    --log-dir ./log_concurrent_tasks_${num_task}
+
+# For offline Spark-TTS-0.5B
+python3 client_grpc.py \
+    --server-addr localhost \
+    --model-name spark_tts \
+    --num-tasks $num_task \
+    --huggingface-dataset yuekai/seed_tts \
+    --split-name wenetspeech4tts \
+    --log-dir ./log_concurrent_tasks_${num_task}
+"""
+
+import argparse
+import asyncio
+import json
+import os
+import time
+import types
+from pathlib import Path
+
+import numpy as np
+import soundfile as sf
+import tritonclient
+import tritonclient.grpc.aio as grpcclient
+from tritonclient.utils import np_to_triton_dtype
+
+
+def write_triton_stats(stats, summary_file):
+    with open(summary_file, "w") as summary_f:
+        model_stats = stats["model_stats"]
+        # write a note, the log is from triton_client.get_inference_statistics(), to better human readability
+        summary_f.write(
+            "The log is parsing from triton_client.get_inference_statistics(), to better human readability. \n"
+        )
+        summary_f.write("To learn more about the log, please refer to: \n")
+        summary_f.write("1. https://github.com/triton-inference-server/server/blob/main/docs/user_guide/metrics.md \n")
+        summary_f.write("2. https://github.com/triton-inference-server/server/issues/5374 \n\n")
+        summary_f.write(
+            "To better improve throughput, we always would like let requests wait in the queue for a while, and then execute them with a larger batch size. \n"
+        )
+        summary_f.write(
+            "However, there is a trade-off between the increased queue time and the increased batch size. \n"
+        )
+        summary_f.write(
+            "You may change 'max_queue_delay_microseconds' and 'preferred_batch_size' in the model configuration file to achieve this. \n"
+        )
+        summary_f.write(
+            "See https://github.com/triton-inference-server/server/blob/main/docs/user_guide/model_configuration.md#delayed-batching for more details. \n\n"
+        )
+        for model_state in model_stats:
+            if "last_inference" not in model_state:
+                continue
+            summary_f.write(f"model name is {model_state['name']} \n")
+            model_inference_stats = model_state["inference_stats"]
+            total_queue_time_s = int(model_inference_stats["queue"]["ns"]) / 1e9
+            total_infer_time_s = int(model_inference_stats["compute_infer"]["ns"]) / 1e9
+            total_input_time_s = int(model_inference_stats["compute_input"]["ns"]) / 1e9
+            total_output_time_s = int(model_inference_stats["compute_output"]["ns"]) / 1e9
+            summary_f.write(
+                f"queue time {total_queue_time_s:<5.2f} s, compute infer time {total_infer_time_s:<5.2f} s, compute input time {total_input_time_s:<5.2f} s, compute output time {total_output_time_s:<5.2f} s \n"  # noqa
+            )
+            model_batch_stats = model_state["batch_stats"]
+            for batch in model_batch_stats:
+                batch_size = int(batch["batch_size"])
+                compute_input = batch["compute_input"]
+                compute_output = batch["compute_output"]
+                compute_infer = batch["compute_infer"]
+                batch_count = int(compute_infer["count"])
+                assert compute_infer["count"] == compute_output["count"] == compute_input["count"]
+                compute_infer_time_ms = int(compute_infer["ns"]) / 1e6
+                compute_input_time_ms = int(compute_input["ns"]) / 1e6
+                compute_output_time_ms = int(compute_output["ns"]) / 1e6
+                summary_f.write(
+                    f"execuate inference with batch_size {batch_size:<2} total {batch_count:<5} times, total_infer_time {compute_infer_time_ms:<9.2f} ms, avg_infer_time {compute_infer_time_ms:<9.2f}/{batch_count:<5}={compute_infer_time_ms / batch_count:.2f} ms, avg_infer_time_per_sample {compute_infer_time_ms:<9.2f}/{batch_count:<5}/{batch_size}={compute_infer_time_ms / batch_count / batch_size:.2f} ms \n"  # noqa
+                )
+                summary_f.write(
+                    f"input {compute_input_time_ms:<9.2f} ms, avg {compute_input_time_ms / batch_count:.2f} ms, "  # noqa
+                )
+                summary_f.write(
+                    f"output {compute_output_time_ms:<9.2f} ms, avg {compute_output_time_ms / batch_count:.2f} ms \n"  # noqa
+                )
+
+
+def get_args():
+    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+
+    parser.add_argument(
+        "--server-addr",
+        type=str,
+        default="localhost",
+        help="Address of the server",
+    )
+
+    parser.add_argument(
+        "--server-port",
+        type=int,
+        default=8001,
+        help="Grpc port of the triton server, default is 8001",
+    )
+
+    parser.add_argument(
+        "--reference-audio",
+        type=str,
+        default=None,
+        help="Path to a single audio file. It can't be specified at the same time with --manifest-dir",
+    )
+
+    parser.add_argument(
+        "--reference-text",
+        type=str,
+        default="",
+        help="",
+    )
+
+    parser.add_argument(
+        "--target-text",
+        type=str,
+        default="",
+        help="",
+    )
+
+    parser.add_argument(
+        "--huggingface-dataset",
+        type=str,
+        default="yuekai/seed_tts",
+        help="dataset name in huggingface dataset hub",
+    )
+
+    parser.add_argument(
+        "--split-name",
+        type=str,
+        default="wenetspeech4tts",
+        choices=["wenetspeech4tts", "test_zh", "test_en", "test_hard"],
+        help="dataset split name, default is 'test'",
+    )
+
+    parser.add_argument(
+        "--manifest-path",
+        type=str,
+        default=None,
+        help="Path to the manifest dir which includes wav.scp trans.txt files.",
+    )
+
+    parser.add_argument(
+        "--model-name",
+        type=str,
+        default="f5_tts",
+        choices=["f5_tts", "spark_tts"],
+        help="triton model_repo module name to request: transducer for k2, attention_rescoring for wenet offline, streaming_wenet for wenet streaming, infer_pipeline for paraformer large offline",
+    )
+
+    parser.add_argument(
+        "--num-tasks",
+        type=int,
+        default=1,
+        help="Number of concurrent tasks for sending",
+    )
+
+    parser.add_argument(
+        "--log-interval",
+        type=int,
+        default=5,
+        help="Controls how frequently we print the log.",
+    )
+
+    parser.add_argument(
+        "--compute-wer",
+        action="store_true",
+        default=False,
+        help="""True to compute WER.
+        """,
+    )
+
+    parser.add_argument(
+        "--log-dir",
+        type=str,
+        required=False,
+        default="./tmp",
+        help="log directory",
+    )
+
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=1,
+        help="Inference batch_size per request for offline mode.",
+    )
+
+    return parser.parse_args()
+
+
+def load_audio(wav_path, target_sample_rate=24000):
+    assert target_sample_rate == 24000, "hard coding in server"
+    if isinstance(wav_path, dict):
+        waveform = wav_path["array"]
+        sample_rate = wav_path["sampling_rate"]
+    else:
+        waveform, sample_rate = sf.read(wav_path)
+    if sample_rate != target_sample_rate:
+        from scipy.signal import resample
+
+        num_samples = int(len(waveform) * (target_sample_rate / sample_rate))
+        waveform = resample(waveform, num_samples)
+    return waveform, target_sample_rate
+
+
+async def send(
+    manifest_item_list: list,
+    name: str,
+    triton_client: tritonclient.grpc.aio.InferenceServerClient,
+    protocol_client: types.ModuleType,
+    log_interval: int,
+    model_name: str,
+    padding_duration: int = None,
+    audio_save_dir: str = "./",
+    save_sample_rate: int = 24000,
+):
+    total_duration = 0.0
+    latency_data = []
+    task_id = int(name[5:])
+
+    print(f"manifest_item_list: {manifest_item_list}")
+    for i, item in enumerate(manifest_item_list):
+        if i % log_interval == 0:
+            print(f"{name}: {i}/{len(manifest_item_list)}")
+        waveform, sample_rate = load_audio(item["audio_filepath"], target_sample_rate=24000)
+        duration = len(waveform) / sample_rate
+        lengths = np.array([[len(waveform)]], dtype=np.int32)
+
+        reference_text, target_text = item["reference_text"], item["target_text"]
+
+        estimated_target_duration = duration / len(reference_text) * len(target_text)
+
+        if padding_duration:
+            # padding to nearset 10 seconds
+            samples = np.zeros(
+                (
+                    1,
+                    padding_duration
+                    * sample_rate
+                    * ((int(estimated_target_duration + duration) // padding_duration) + 1),
+                ),
+                dtype=np.float32,
+            )
+
+            samples[0, : len(waveform)] = waveform
+        else:
+            samples = waveform
+
+        samples = samples.reshape(1, -1).astype(np.float32)
+
+        inputs = [
+            protocol_client.InferInput("reference_wav", samples.shape, np_to_triton_dtype(samples.dtype)),
+            protocol_client.InferInput("reference_wav_len", lengths.shape, np_to_triton_dtype(lengths.dtype)),
+            protocol_client.InferInput("reference_text", [1, 1], "BYTES"),
+            protocol_client.InferInput("target_text", [1, 1], "BYTES"),
+        ]
+        inputs[0].set_data_from_numpy(samples)
+        inputs[1].set_data_from_numpy(lengths)
+
+        input_data_numpy = np.array([reference_text], dtype=object)
+        input_data_numpy = input_data_numpy.reshape((1, 1))
+        inputs[2].set_data_from_numpy(input_data_numpy)
+
+        input_data_numpy = np.array([target_text], dtype=object)
+        input_data_numpy = input_data_numpy.reshape((1, 1))
+        inputs[3].set_data_from_numpy(input_data_numpy)
+
+        outputs = [protocol_client.InferRequestedOutput("waveform")]
+
+        sequence_id = 100000000 + i + task_id * 10
+        start = time.time()
+        response = await triton_client.infer(model_name, inputs, request_id=str(sequence_id), outputs=outputs)
+
+        audio = response.as_numpy("waveform").reshape(-1)
+
+        end = time.time() - start
+
+        audio_save_path = os.path.join(audio_save_dir, f"{item['target_audio_path']}.wav")
+        sf.write(audio_save_path, audio, save_sample_rate, "PCM_16")
+
+        actual_duration = len(audio) / save_sample_rate
+        latency_data.append((end, actual_duration))
+        total_duration += actual_duration
+
+    return total_duration, latency_data
+
+
+def load_manifests(manifest_path):
+    with open(manifest_path, "r") as f:
+        manifest_list = []
+        for line in f:
+            assert len(line.strip().split("|")) == 4
+            utt, prompt_text, prompt_wav, gt_text = line.strip().split("|")
+            utt = Path(utt).stem
+            # gt_wav = os.path.join(os.path.dirname(manifest_path), "wavs", utt + ".wav")
+            if not os.path.isabs(prompt_wav):
+                prompt_wav = os.path.join(os.path.dirname(manifest_path), prompt_wav)
+            manifest_list.append(
+                {
+                    "audio_filepath": prompt_wav,
+                    "reference_text": prompt_text,
+                    "target_text": gt_text,
+                    "target_audio_path": utt,
+                }
+            )
+    return manifest_list
+
+
+def split_data(data, k):
+    n = len(data)
+    if n < k:
+        print(f"Warning: the length of the input list ({n}) is less than k ({k}). Setting k to {n}.")
+        k = n
+
+    quotient = n // k
+    remainder = n % k
+
+    result = []
+    start = 0
+    for i in range(k):
+        if i < remainder:
+            end = start + quotient + 1
+        else:
+            end = start + quotient
+
+        result.append(data[start:end])
+        start = end
+
+    return result
+
+
+async def main():
+    args = get_args()
+    url = f"{args.server_addr}:{args.server_port}"
+
+    triton_client = grpcclient.InferenceServerClient(url=url, verbose=False)
+    protocol_client = grpcclient
+
+    if args.reference_audio:
+        args.num_tasks = 1
+        args.log_interval = 1
+        manifest_item_list = [
+            {
+                "reference_text": args.reference_text,
+                "target_text": args.target_text,
+                "audio_filepath": args.reference_audio,
+                "target_audio_path": "test",
+            }
+        ]
+    elif args.huggingface_dataset:
+        import datasets
+
+        dataset = datasets.load_dataset(
+            args.huggingface_dataset,
+            split=args.split_name,
+            trust_remote_code=True,
+        )
+        manifest_item_list = []
+        for i in range(len(dataset)):
+            manifest_item_list.append(
+                {
+                    "audio_filepath": dataset[i]["prompt_audio"],
+                    "reference_text": dataset[i]["prompt_text"],
+                    "target_audio_path": dataset[i]["id"],
+                    "target_text": dataset[i]["target_text"],
+                }
+            )
+    else:
+        manifest_item_list = load_manifests(args.manifest_path)
+
+    args.num_tasks = min(args.num_tasks, len(manifest_item_list))
+    manifest_item_list = split_data(manifest_item_list, args.num_tasks)
+
+    os.makedirs(args.log_dir, exist_ok=True)
+    tasks = []
+    start_time = time.time()
+    for i in range(args.num_tasks):
+        task = asyncio.create_task(
+            send(
+                manifest_item_list[i],
+                name=f"task-{i}",
+                triton_client=triton_client,
+                protocol_client=protocol_client,
+                log_interval=args.log_interval,
+                model_name=args.model_name,
+                audio_save_dir=args.log_dir,
+                padding_duration=1,
+                save_sample_rate=24000,
+            )
+        )
+        tasks.append(task)
+
+    ans_list = await asyncio.gather(*tasks)
+
+    end_time = time.time()
+    elapsed = end_time - start_time
+
+    total_duration = 0.0
+    latency_data = []
+    for ans in ans_list:
+        total_duration += ans[0]
+        latency_data += ans[1]
+
+    rtf = elapsed / total_duration
+
+    s = f"RTF: {rtf:.4f}\n"
+    s += f"total_duration: {total_duration:.3f} seconds\n"
+    s += f"({total_duration / 3600:.2f} hours)\n"
+    s += f"processing time: {elapsed:.3f} seconds ({elapsed / 3600:.2f} hours)\n"
+
+    latency_list = [chunk_end for (chunk_end, chunk_duration) in latency_data]
+    latency_ms = sum(latency_list) / float(len(latency_list)) * 1000.0
+    latency_variance = np.var(latency_list, dtype=np.float64) * 1000.0
+    s += f"latency_variance: {latency_variance:.2f}\n"
+    s += f"latency_50_percentile_ms: {np.percentile(latency_list, 50) * 1000.0:.2f}\n"
+    s += f"latency_90_percentile_ms: {np.percentile(latency_list, 90) * 1000.0:.2f}\n"
+    s += f"latency_95_percentile_ms: {np.percentile(latency_list, 95) * 1000.0:.2f}\n"
+    s += f"latency_99_percentile_ms: {np.percentile(latency_list, 99) * 1000.0:.2f}\n"
+    s += f"average_latency_ms: {latency_ms:.2f}\n"
+
+    print(s)
+    if args.manifest_path:
+        name = Path(args.manifest_path).stem
+    elif args.split_name:
+        name = args.split_name
+    with open(f"{args.log_dir}/rtf-{name}.txt", "w") as f:
+        f.write(s)
+
+    stats = await triton_client.get_inference_statistics(model_name="", as_json=True)
+    write_triton_stats(stats, f"{args.log_dir}/stats_summary-{name}.txt")
+
+    metadata = await triton_client.get_model_config(model_name=args.model_name, as_json=True)
+    with open(f"{args.log_dir}/model_config-{name}.json", "w") as f:
+        json.dump(metadata, f, indent=4)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

src/f5_tts/runtime/triton_trtllm/client_http.py

@@ -0,0 +1,143 @@
+# Copyright 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#  * Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#  * Redistributions in binary form must reproduce the above copyright
+#    notice, this list of conditions and the following disclaimer in the
+#    documentation and/or other materials provided with the distribution.
+#  * Neither the name of NVIDIA CORPORATION nor the names of its
+#    contributors may be used to endorse or promote products derived
+#    from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+# PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+import argparse
+
+import numpy as np
+import requests
+import soundfile as sf
+
+
+def get_args():
+    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+
+    parser.add_argument(
+        "--server-url",
+        type=str,
+        default="localhost:8000",
+        help="Address of the server",
+    )
+
+    parser.add_argument(
+        "--reference-audio",
+        type=str,
+        default="../../infer/examples/basic/basic_ref_en.wav",
+        help="Path to a single audio file. It can't be specified at the same time with --manifest-dir",
+    )
+
+    parser.add_argument(
+        "--reference-text",
+        type=str,
+        default="Some call me nature, others call me mother nature.",
+        help="",
+    )
+
+    parser.add_argument(
+        "--target-text",
+        type=str,
+        default="I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring.",
+        help="",
+    )
+
+    parser.add_argument(
+        "--model-name",
+        type=str,
+        default="f5_tts",
+        choices=["f5_tts", "spark_tts"],
+        help="triton model_repo module name to request",
+    )
+
+    parser.add_argument(
+        "--output-audio",
+        type=str,
+        default="output.wav",
+        help="Path to save the output audio",
+    )
+    return parser.parse_args()
+
+
+def prepare_request(
+    samples,
+    reference_text,
+    target_text,
+    sample_rate=24000,
+    audio_save_dir: str = "./",
+):
+    assert len(samples.shape) == 1, "samples should be 1D"
+    lengths = np.array([[len(samples)]], dtype=np.int32)
+    samples = samples.reshape(1, -1).astype(np.float32)
+
+    data = {
+        "inputs": [
+            {"name": "reference_wav", "shape": samples.shape, "datatype": "FP32", "data": samples.tolist()},
+            {
+                "name": "reference_wav_len",
+                "shape": lengths.shape,
+                "datatype": "INT32",
+                "data": lengths.tolist(),
+            },
+            {"name": "reference_text", "shape": [1, 1], "datatype": "BYTES", "data": [reference_text]},
+            {"name": "target_text", "shape": [1, 1], "datatype": "BYTES", "data": [target_text]},
+        ]
+    }
+
+    return data
+
+
+def load_audio(wav_path, target_sample_rate=24000):
+    assert target_sample_rate == 24000, "hard coding in server"
+    if isinstance(wav_path, dict):
+        samples = wav_path["array"]
+        sample_rate = wav_path["sampling_rate"]
+    else:
+        samples, sample_rate = sf.read(wav_path)
+    if sample_rate != target_sample_rate:
+        from scipy.signal import resample
+
+        num_samples = int(len(samples) * (target_sample_rate / sample_rate))
+        samples = resample(samples, num_samples)
+    return samples, target_sample_rate
+
+
+if __name__ == "__main__":
+    args = get_args()
+    server_url = args.server_url
+    if not server_url.startswith(("http://", "https://")):
+        server_url = f"http://{server_url}"
+
+    url = f"{server_url}/v2/models/{args.model_name}/infer"
+    samples, sr = load_audio(args.reference_audio)
+    assert sr == 24000, "sample rate hardcoded in server"
+
+    samples = np.array(samples, dtype=np.float32)
+    data = prepare_request(samples, args.reference_text, args.target_text)
+
+    rsp = requests.post(
+        url, headers={"Content-Type": "application/json"}, json=data, verify=False, params={"request_id": "0"}
+    )
+    result = rsp.json()
+    audio = result["outputs"][0]["data"]
+    audio = np.array(audio, dtype=np.float32)
+    sf.write(args.output_audio, audio, 24000, "PCM_16")

src/f5_tts/runtime/triton_trtllm/docker-compose.yml

@@ -0,0 +1,20 @@
+services:
+  tts:
+    image: soar97/triton-f5-tts:24.12
+    shm_size: '1gb'
+    ports:
+      - "8000:8000"
+      - "8001:8001"
+      - "8002:8002"
+    environment:
+      - PYTHONIOENCODING=utf-8
+      - MODEL_ID=${MODEL_ID}
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              device_ids: ['0']
+              capabilities: [gpu]
+    command: >
+      /bin/bash -c "pip install vocos && rm -rf F5-TTS && git clone https://github.com/SWivid/F5-TTS.git && cd F5-TTS/src/f5_tts/runtime/triton_trtllm/ && bash run.sh 0 4 $MODEL"

src/f5_tts/runtime/triton_trtllm/model_repo_f5_tts/f5_tts/1/f5_tts_trtllm.py

@@ -0,0 +1,430 @@
+import math
+import os
+import time
+from functools import wraps
+from typing import List, Optional
+
+import tensorrt as trt
+import tensorrt_llm
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tensorrt_llm._utils import str_dtype_to_torch, trt_dtype_to_torch
+from tensorrt_llm.logger import logger
+from tensorrt_llm.runtime.session import Session
+
+
+def remove_tensor_padding(input_tensor, input_tensor_lengths=None):
+    # Audio tensor case: batch, seq_len, feature_len
+    # position_ids case: batch, seq_len
+    assert input_tensor_lengths is not None, "input_tensor_lengths must be provided for 3D input_tensor"
+
+    # Initialize a list to collect valid sequences
+    valid_sequences = []
+
+    for i in range(input_tensor.shape[0]):
+        valid_length = input_tensor_lengths[i]
+        valid_sequences.append(input_tensor[i, :valid_length])
+
+    # Concatenate all valid sequences along the batch dimension
+    output_tensor = torch.cat(valid_sequences, dim=0).contiguous()
+    return output_tensor
+
+
+class TextEmbedding(nn.Module):
+    def __init__(self, text_num_embeds, text_dim, conv_layers=0, conv_mult=2, precompute_max_pos=4096):
+        super().__init__()
+        self.text_embed = nn.Embedding(text_num_embeds + 1, text_dim)  # use 0 as filler token
+        self.register_buffer("freqs_cis", precompute_freqs_cis(text_dim, precompute_max_pos), persistent=False)
+        self.text_blocks = nn.Sequential(*[ConvNeXtV2Block(text_dim, text_dim * conv_mult) for _ in range(conv_layers)])
+
+    def forward(self, text):
+        # only keep tensors with value not -1
+        text_mask = text != -1
+        text_pad_cut_off_index = text_mask.sum(dim=1).max()
+
+        text = text[:, :text_pad_cut_off_index]
+        text = self.text_embed(text)
+        text = text + self.freqs_cis[: text.shape[1], :]
+        for block in self.text_blocks:
+            text = block(text)
+        # padding text to the original length
+        # text shape: B,seq_len,C
+        # pad at the second dimension
+        text = F.pad(text, (0, 0, 0, text_mask.shape[1] - text.shape[1], 0, 0), value=0)
+        return text
+
+
+class GRN(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.gamma = nn.Parameter(torch.zeros(1, 1, dim))
+        self.beta = nn.Parameter(torch.zeros(1, 1, dim))
+
+    def forward(self, x):
+        Gx = torch.norm(x, p=2, dim=1, keepdim=True)
+        Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6)
+        return self.gamma * (x * Nx) + self.beta + x
+
+
+class ConvNeXtV2Block(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        intermediate_dim: int,
+        dilation: int = 1,
+    ):
+        super().__init__()
+        padding = (dilation * (7 - 1)) // 2
+        self.dwconv = nn.Conv1d(
+            dim, dim, kernel_size=7, padding=padding, groups=dim, dilation=dilation
+        )  # depthwise conv
+        self.norm = nn.LayerNorm(dim, eps=1e-6)
+        self.pwconv1 = nn.Linear(dim, intermediate_dim)  # pointwise/1x1 convs, implemented with linear layers
+        self.act = nn.GELU()
+        self.grn = GRN(intermediate_dim)
+        self.pwconv2 = nn.Linear(intermediate_dim, dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        residual = x
+        x = x.transpose(1, 2)  # b n d -> b d n
+        x = self.dwconv(x)
+        x = x.transpose(1, 2)  # b d n -> b n d
+        x = self.norm(x)
+        x = self.pwconv1(x)
+        x = self.act(x)
+        x = self.grn(x)
+        x = self.pwconv2(x)
+        return residual + x
+
+
+def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0, theta_rescale_factor=1.0):
+    # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
+    # has some connection to NTK literature
+    # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
+    # https://github.com/lucidrains/rotary-embedding-torch/blob/main/rotary_embedding_torch/rotary_embedding_torch.py
+    theta *= theta_rescale_factor ** (dim / (dim - 2))
+    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+    t = torch.arange(end, device=freqs.device)  # type: ignore
+    freqs = torch.outer(t, freqs).float()  # type: ignore
+    freqs_cos = torch.cos(freqs)  # real part
+    freqs_sin = torch.sin(freqs)  # imaginary part
+    return torch.cat([freqs_cos, freqs_sin], dim=-1)
+
+
+def load_checkpoint(ckpt_path, use_ema=True):
+    checkpoint = torch.load(ckpt_path, weights_only=True)
+    if use_ema:
+        checkpoint["model_state_dict"] = {
+            k.replace("ema_model.", ""): v
+            for k, v in checkpoint["ema_model_state_dict"].items()
+            if k not in ["initted", "step"]
+        }
+    dict_state = checkpoint["model_state_dict"]
+    text_embed_dict = {}
+    for key in dict_state.keys():
+        # transformer.text_embed.text_embed.weight -> text_embed.weight
+        if "text_embed" in key:
+            text_embed_dict[key.replace("transformer.text_embed.", "")] = dict_state[key]
+    return text_embed_dict
+
+
+class F5TTS(object):
+    def __init__(
+        self,
+        config,
+        debug_mode=True,
+        stream: Optional[torch.cuda.Stream] = None,
+        tllm_model_dir: Optional[str] = None,
+        model_path: Optional[str] = None,
+        vocab_size: Optional[int] = None,
+    ):
+        self.dtype = config["pretrained_config"]["dtype"]
+
+        rank = tensorrt_llm.mpi_rank()
+        world_size = config["pretrained_config"]["mapping"]["world_size"]
+        cp_size = config["pretrained_config"]["mapping"]["cp_size"]
+        tp_size = config["pretrained_config"]["mapping"]["tp_size"]
+        pp_size = config["pretrained_config"]["mapping"]["pp_size"]
+        assert pp_size == 1
+        self.mapping = tensorrt_llm.Mapping(
+            world_size=world_size, rank=rank, cp_size=cp_size, tp_size=tp_size, pp_size=1, gpus_per_node=1
+        )
+
+        local_rank = rank % self.mapping.gpus_per_node
+        self.device = torch.device(f"cuda:{local_rank}")
+
+        torch.cuda.set_device(self.device)
+
+        self.stream = stream
+        if self.stream is None:
+            self.stream = torch.cuda.Stream(self.device)
+        torch.cuda.set_stream(self.stream)
+
+        engine_file = os.path.join(tllm_model_dir, f"rank{rank}.engine")
+        logger.info(f"Loading engine from {engine_file}")
+        with open(engine_file, "rb") as f:
+            engine_buffer = f.read()
+
+        assert engine_buffer is not None
+
+        self.session = Session.from_serialized_engine(engine_buffer)
+
+        self.debug_mode = debug_mode
+
+        self.inputs = {}
+        self.outputs = {}
+        self.buffer_allocated = False
+
+        expected_tensor_names = ["noise", "cond", "time", "rope_cos", "rope_sin", "input_lengths", "denoised"]
+
+        found_tensor_names = [self.session.engine.get_tensor_name(i) for i in range(self.session.engine.num_io_tensors)]
+        if not self.debug_mode and set(expected_tensor_names) != set(found_tensor_names):
+            logger.error(
+                f"The following expected tensors are not found: {set(expected_tensor_names).difference(set(found_tensor_names))}"
+            )
+            logger.error(
+                f"Those tensors in engine are not expected: {set(found_tensor_names).difference(set(expected_tensor_names))}"
+            )
+            logger.error(f"Expected tensor names: {expected_tensor_names}")
+            logger.error(f"Found tensor names: {found_tensor_names}")
+            raise RuntimeError("Tensor names in engine are not the same as expected.")
+        if self.debug_mode:
+            self.debug_tensors = list(set(found_tensor_names) - set(expected_tensor_names))
+
+        self.max_mel_len = 4096
+        self.text_embedding = TextEmbedding(
+            text_num_embeds=vocab_size, text_dim=512, conv_layers=4, precompute_max_pos=self.max_mel_len
+        ).to(self.device)
+        self.text_embedding.load_state_dict(load_checkpoint(model_path), strict=True)
+
+        self.target_audio_sample_rate = 24000
+        self.target_rms = 0.15  # target rms for audio
+        self.n_fft = 1024
+        self.win_length = 1024
+        self.hop_length = 256
+        self.n_mel_channels = 100
+        # self.max_mel_len = 3000
+        self.head_dim = 64
+        self.base_rescale_factor = 1.0
+        self.interpolation_factor = 1.0
+        base = 10000.0 * self.base_rescale_factor ** (self.head_dim / (self.head_dim - 2))
+        inv_freq = 1.0 / (base ** (torch.arange(0, self.head_dim, 2).float() / self.head_dim))
+        freqs = torch.outer(torch.arange(self.max_mel_len, dtype=torch.float32), inv_freq) / self.interpolation_factor
+        self.freqs = freqs.repeat_interleave(2, dim=-1).unsqueeze(0)
+        self.rope_cos = self.freqs.cos().half()
+        self.rope_sin = self.freqs.sin().half()
+        self.nfe_steps = 16
+        t = torch.linspace(0, 1, self.nfe_steps + 1, dtype=torch.float32)
+        time_step = t + (-1.0) * (torch.cos(torch.pi * 0.5 * t) - 1 + t)
+        delta_t = torch.diff(time_step)
+        # WAR: hard coding 256 here
+        tmp_dim = 256
+        time_expand = torch.zeros((1, self.nfe_steps, tmp_dim), dtype=torch.float32)
+        half_dim = tmp_dim // 2
+        emb_factor = math.log(10000) / (half_dim - 1)
+        emb_factor = 1000.0 * torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb_factor)
+        for i in range(self.nfe_steps):
+            emb = time_step[i] * emb_factor
+            time_expand[:, i, :] = torch.cat((emb.sin(), emb.cos()), dim=-1)
+        self.time_expand = time_expand.to(self.device)
+        self.delta_t = torch.cat((delta_t, delta_t), dim=0).contiguous().to(self.device)
+
+    def _tensor_dtype(self, name):
+        # return torch dtype given tensor name for convenience
+        dtype = trt_dtype_to_torch(self.session.engine.get_tensor_dtype(name))
+        return dtype
+
+    def _setup(self, batch_size, seq_len):
+        for i in range(self.session.engine.num_io_tensors):
+            name = self.session.engine.get_tensor_name(i)
+            if self.session.engine.get_tensor_mode(name) == trt.TensorIOMode.OUTPUT:
+                shape = list(self.session.engine.get_tensor_shape(name))
+                shape[0] = batch_size
+                shape[1] = seq_len
+                self.outputs[name] = torch.empty(shape, dtype=self._tensor_dtype(name), device=self.device)
+
+        self.buffer_allocated = True
+
+    def cuda_stream_guard(func):
+        """Sync external stream and set current stream to the one bound to the session. Reset on exit."""
+
+        @wraps(func)
+        def wrapper(self, *args, **kwargs):
+            external_stream = torch.cuda.current_stream()
+            if external_stream != self.stream:
+                external_stream.synchronize()
+                torch.cuda.set_stream(self.stream)
+            ret = func(self, *args, **kwargs)
+            if external_stream != self.stream:
+                self.stream.synchronize()
+                torch.cuda.set_stream(external_stream)
+            return ret
+
+        return wrapper
+
+    @cuda_stream_guard
+    def forward(
+        self,
+        noise: torch.Tensor,
+        cond: torch.Tensor,
+        time_expand: torch.Tensor,
+        rope_cos: torch.Tensor,
+        rope_sin: torch.Tensor,
+        input_lengths: torch.Tensor,
+        delta_t: torch.Tensor,
+        use_perf: bool = False,
+    ):
+        if use_perf:
+            torch.cuda.nvtx.range_push("flow matching")
+        cfg_strength = 2.0
+        batch_size = noise.shape[0]
+        half_batch = batch_size // 2
+        noise_half = noise[:half_batch]  # Store the initial half of noise
+
+        input_type = str_dtype_to_torch(self.dtype)
+
+        # Keep a copy of the initial tensors
+        cond = cond.to(input_type)
+        rope_cos = rope_cos.to(input_type)
+        rope_sin = rope_sin.to(input_type)
+        input_lengths = input_lengths.to(str_dtype_to_torch("int32"))
+
+        # Instead of iteratively updating noise within a single model context,
+        # we'll do a single forward pass for each iteration with fresh context setup
+        for i in range(self.nfe_steps):
+            # Re-setup the buffers for clean execution
+            self._setup(batch_size, noise.shape[1])
+            if not self.buffer_allocated:
+                raise RuntimeError("Buffer not allocated, please call setup first!")
+
+            # Re-create combined noises for this iteration
+            current_noise = torch.cat([noise_half, noise_half], dim=0).to(input_type)
+
+            # Get time step for this iteration
+            current_time = time_expand[:, i].to(input_type)
+
+            # Create fresh input dictionary for this iteration
+            current_inputs = {
+                "noise": current_noise,
+                "cond": cond,
+                "time": current_time,
+                "rope_cos": rope_cos,
+                "rope_sin": rope_sin,
+                "input_lengths": input_lengths,
+            }
+
+            # Update inputs and set shapes
+            self.inputs.clear()  # Clear previous inputs
+            self.inputs.update(**current_inputs)
+            self.session.set_shapes(self.inputs)
+
+            if use_perf:
+                torch.cuda.nvtx.range_push(f"execute {i}")
+            ok = self.session.run(self.inputs, self.outputs, self.stream.cuda_stream)
+            assert ok, "Failed to execute model"
+            # self.session.context.execute_async_v3(self.stream.cuda_stream)
+            if use_perf:
+                torch.cuda.nvtx.range_pop()
+            # Process results
+            t_scale = delta_t[i].unsqueeze(0).to(input_type)
+
+            # Extract predictions
+            pred_cond = self.outputs["denoised"][:half_batch]
+            pred_uncond = self.outputs["denoised"][half_batch:]
+
+            # Apply classifier-free guidance with safeguards
+            guidance = pred_cond + (pred_cond - pred_uncond) * cfg_strength
+            # Calculate update for noise
+            noise_half = noise_half + guidance * t_scale
+        if use_perf:
+            torch.cuda.nvtx.range_pop()
+        return noise_half
+
+    def sample(
+        self,
+        text_pad_sequence: torch.Tensor,
+        ref_mel_batch: torch.Tensor,
+        ref_mel_len_batch: torch.Tensor,
+        estimated_reference_target_mel_len: List[int],
+        remove_input_padding: bool = False,
+        use_perf: bool = False,
+    ):
+        if use_perf:
+            torch.cuda.nvtx.range_push("text embedding")
+        batch = text_pad_sequence.shape[0]
+        max_seq_len = ref_mel_batch.shape[1]
+
+        text_pad_sequence_drop = torch.cat(
+            (text_pad_sequence, torch.zeros((1, text_pad_sequence.shape[1]), dtype=torch.int32).to(self.device)), dim=0
+        )
+
+        text_embedding_drop_list = []
+        for i in range(batch + 1):
+            text_embedding_drop_list.append(self.text_embedding(text_pad_sequence_drop[i].unsqueeze(0).to(self.device)))
+        text_embedding_drop_condition = torch.cat(text_embedding_drop_list, dim=0)
+
+        text_embedding = text_embedding_drop_condition[:-1]
+        # text_embedding_drop B,T,C batch should be the same
+        text_embedding_drop = text_embedding_drop_condition[-1].unsqueeze(0).repeat(batch, 1, 1)
+
+        noise = torch.randn_like(ref_mel_batch).to(self.device)
+        rope_cos = self.rope_cos[:, :max_seq_len, :].float().repeat(batch, 1, 1)
+        rope_sin = self.rope_sin[:, :max_seq_len, :].float().repeat(batch, 1, 1)
+
+        cat_mel_text = torch.cat((ref_mel_batch, text_embedding), dim=-1)
+        cat_mel_text_drop = torch.cat(
+            (
+                torch.zeros((batch, max_seq_len, self.n_mel_channels), dtype=torch.float32).to(self.device),
+                text_embedding_drop,
+            ),
+            dim=-1,
+        )
+
+        time_expand = self.time_expand.repeat(2 * batch, 1, 1).contiguous()
+
+        # Convert estimated_reference_target_mel_len to tensor
+        input_lengths = torch.tensor(estimated_reference_target_mel_len, dtype=torch.int32)
+
+        # combine above along the batch dimension
+        inputs = {
+            "noise": torch.cat((noise, noise), dim=0).contiguous(),
+            "cond": torch.cat((cat_mel_text, cat_mel_text_drop), dim=0).contiguous(),
+            "time_expand": time_expand,
+            "rope_cos": torch.cat((rope_cos, rope_cos), dim=0).contiguous(),
+            "rope_sin": torch.cat((rope_sin, rope_sin), dim=0).contiguous(),
+            "input_lengths": torch.cat((input_lengths, input_lengths), dim=0).contiguous(),
+            "delta_t": self.delta_t,
+        }
+        if use_perf and remove_input_padding:
+            torch.cuda.nvtx.range_push("remove input padding")
+        if remove_input_padding:
+            max_seq_len = inputs["cond"].shape[1]
+            inputs["noise"] = remove_tensor_padding(inputs["noise"], inputs["input_lengths"])
+            inputs["cond"] = remove_tensor_padding(inputs["cond"], inputs["input_lengths"])
+            # for time_expand, convert from B,D to B,T,D by repeat
+            inputs["time_expand"] = inputs["time_expand"].unsqueeze(1).repeat(1, max_seq_len, 1, 1)
+            inputs["time_expand"] = remove_tensor_padding(inputs["time_expand"], inputs["input_lengths"])
+            inputs["rope_cos"] = remove_tensor_padding(inputs["rope_cos"], inputs["input_lengths"])
+            inputs["rope_sin"] = remove_tensor_padding(inputs["rope_sin"], inputs["input_lengths"])
+        if use_perf and remove_input_padding:
+            torch.cuda.nvtx.range_pop()
+        for key in inputs:
+            inputs[key] = inputs[key].to(self.device)
+        if use_perf:
+            torch.cuda.nvtx.range_pop()
+        start_time = time.time()
+        denoised = self.forward(**inputs, use_perf=use_perf)
+        cost_time = time.time() - start_time
+        if use_perf and remove_input_padding:
+            torch.cuda.nvtx.range_push("remove input padding output")
+        if remove_input_padding:
+            denoised_list = []
+            start_idx = 0
+            for i in range(batch):
+                denoised_list.append(denoised[start_idx : start_idx + inputs["input_lengths"][i]])
+                start_idx += inputs["input_lengths"][i]
+            if use_perf and remove_input_padding:
+                torch.cuda.nvtx.range_pop()
+            return denoised_list, cost_time
+        return denoised, cost_time

src/f5_tts/runtime/triton_trtllm/model_repo_f5_tts/f5_tts/1/model.py

@@ -0,0 +1,278 @@
+# Copyright 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#  * Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#  * Redistributions in binary form must reproduce the above copyright
+#    notice, this list of conditions and the following disclaimer in the
+#    documentation and/or other materials provided with the distribution.
+#  * Neither the name of NVIDIA CORPORATION nor the names of its
+#    contributors may be used to endorse or promote products derived
+#    from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+# PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+import json
+import os
+
+import jieba
+import torch
+import torch.nn.functional as F
+import torchaudio
+import triton_python_backend_utils as pb_utils
+from f5_tts_trtllm import F5TTS
+from pypinyin import Style, lazy_pinyin
+from torch.nn.utils.rnn import pad_sequence
+from torch.utils.dlpack import from_dlpack, to_dlpack
+
+
+def get_tokenizer(vocab_file_path: str):
+    """
+    tokenizer   - "pinyin" do g2p for only chinese characters, need .txt vocab_file
+                - "char" for char-wise tokenizer, need .txt vocab_file
+                - "byte" for utf-8 tokenizer
+                - "custom" if you're directly passing in a path to the vocab.txt you want to use
+    vocab_size  - if use "pinyin", all available pinyin types, common alphabets (also those with accent) and symbols
+                - if use "char", derived from unfiltered character & symbol counts of custom dataset
+                - if use "byte", set to 256 (unicode byte range)
+    """
+    with open(vocab_file_path, "r", encoding="utf-8") as f:
+        vocab_char_map = {}
+        for i, char in enumerate(f):
+            vocab_char_map[char[:-1]] = i
+    vocab_size = len(vocab_char_map)
+    return vocab_char_map, vocab_size
+
+
+def convert_char_to_pinyin(reference_target_texts_list, polyphone=True):
+    final_reference_target_texts_list = []
+    custom_trans = str.maketrans(
+        {";": ",", "“": '"', "”": '"', "‘": "'", "’": "'"}
+    )  # add custom trans here, to address oov
+
+    def is_chinese(c):
+        return "\u3100" <= c <= "\u9fff"  # common chinese characters
+
+    for text in reference_target_texts_list:
+        char_list = []
+        text = text.translate(custom_trans)
+        for seg in jieba.cut(text):
+            seg_byte_len = len(bytes(seg, "UTF-8"))
+            if seg_byte_len == len(seg):  # if pure alphabets and symbols
+                if char_list and seg_byte_len > 1 and char_list[-1] not in " :'\"":
+                    char_list.append(" ")
+                char_list.extend(seg)
+            elif polyphone and seg_byte_len == 3 * len(seg):  # if pure east asian characters
+                seg_ = lazy_pinyin(seg, style=Style.TONE3, tone_sandhi=True)
+                for i, c in enumerate(seg):
+                    if is_chinese(c):
+                        char_list.append(" ")
+                    char_list.append(seg_[i])
+            else:  # if mixed characters, alphabets and symbols
+                for c in seg:
+                    if ord(c) < 256:
+                        char_list.extend(c)
+                    elif is_chinese(c):
+                        char_list.append(" ")
+                        char_list.extend(lazy_pinyin(c, style=Style.TONE3, tone_sandhi=True))
+                    else:
+                        char_list.append(c)
+        final_reference_target_texts_list.append(char_list)
+
+    return final_reference_target_texts_list
+
+
+def list_str_to_idx(
+    text: list[str] | list[list[str]],
+    vocab_char_map: dict[str, int],  # {char: idx}
+    padding_value=-1,
+):  # noqa: F722
+    list_idx_tensors = [torch.tensor([vocab_char_map.get(c, 0) for c in t]) for t in text]  # pinyin or char style
+    return list_idx_tensors
+
+
+class TritonPythonModel:
+    def initialize(self, args):
+        self.use_perf = True
+        self.device = torch.device("cuda")
+        self.target_audio_sample_rate = 24000
+        self.target_rms = 0.15  # target rms for audio
+        self.n_fft = 1024
+        self.win_length = 1024
+        self.hop_length = 256
+        self.n_mel_channels = 100
+        self.max_mel_len = 3000
+        self.head_dim = 64
+
+        parameters = json.loads(args["model_config"])["parameters"]
+        for key, value in parameters.items():
+            parameters[key] = value["string_value"]
+
+        self.vocab_char_map, self.vocab_size = get_tokenizer(parameters["vocab_file"])
+        self.reference_sample_rate = int(parameters["reference_audio_sample_rate"])
+        self.resampler = torchaudio.transforms.Resample(self.reference_sample_rate, self.target_audio_sample_rate)
+
+        self.tllm_model_dir = parameters["tllm_model_dir"]
+        config_file = os.path.join(self.tllm_model_dir, "config.json")
+        with open(config_file) as f:
+            config = json.load(f)
+        self.model = F5TTS(
+            config,
+            debug_mode=False,
+            tllm_model_dir=self.tllm_model_dir,
+            model_path=parameters["model_path"],
+            vocab_size=self.vocab_size,
+        )
+
+        self.vocoder = parameters["vocoder"]
+        assert self.vocoder in ["vocos", "bigvgan"]
+        if self.vocoder == "vocos":
+            self.mel_stft = torchaudio.transforms.MelSpectrogram(
+                sample_rate=self.target_audio_sample_rate,
+                n_fft=self.n_fft,
+                win_length=self.win_length,
+                hop_length=self.hop_length,
+                n_mels=self.n_mel_channels,
+                power=1,
+                center=True,
+                normalized=False,
+                norm=None,
+            ).to(self.device)
+            self.compute_mel_fn = self.get_vocos_mel_spectrogram
+        elif self.vocoder == "bigvgan":
+            self.compute_mel_fn = self.get_bigvgan_mel_spectrogram
+
+    def get_vocos_mel_spectrogram(self, waveform):
+        mel = self.mel_stft(waveform)
+        mel = mel.clamp(min=1e-5).log()
+        return mel.transpose(1, 2)
+
+    def forward_vocoder(self, mel):
+        mel = mel.to(torch.float32).contiguous().cpu()
+        input_tensor_0 = pb_utils.Tensor.from_dlpack("mel", to_dlpack(mel))
+
+        inference_request = pb_utils.InferenceRequest(
+            model_name="vocoder", requested_output_names=["waveform"], inputs=[input_tensor_0]
+        )
+        inference_response = inference_request.exec()
+        if inference_response.has_error():
+            raise pb_utils.TritonModelException(inference_response.error().message())
+        else:
+            waveform = pb_utils.get_output_tensor_by_name(inference_response, "waveform")
+            waveform = torch.utils.dlpack.from_dlpack(waveform.to_dlpack()).cpu()
+
+            return waveform
+
+    def execute(self, requests):
+        (
+            reference_text_list,
+            target_text_list,
+            reference_target_texts_list,
+            estimated_reference_target_mel_len,
+            reference_mel_len,
+        ) = [], [], [], [], []
+        mel_features_list = []
+        if self.use_perf:
+            torch.cuda.nvtx.range_push("preprocess")
+        for request in requests:
+            wav_tensor = pb_utils.get_input_tensor_by_name(request, "reference_wav")
+            wav_lens = pb_utils.get_input_tensor_by_name(request, "reference_wav_len")
+
+            reference_text = pb_utils.get_input_tensor_by_name(request, "reference_text").as_numpy()
+            reference_text = reference_text[0][0].decode("utf-8")
+            reference_text_list.append(reference_text)
+            target_text = pb_utils.get_input_tensor_by_name(request, "target_text").as_numpy()
+            target_text = target_text[0][0].decode("utf-8")
+            target_text_list.append(target_text)
+
+            text = reference_text + target_text
+            reference_target_texts_list.append(text)
+
+            wav = from_dlpack(wav_tensor.to_dlpack())
+            wav_len = from_dlpack(wav_lens.to_dlpack())
+            wav_len = wav_len.squeeze()
+            assert wav.shape[0] == 1, "Only support batch size 1 for now."
+            wav = wav[:, :wav_len]
+
+            ref_rms = torch.sqrt(torch.mean(torch.square(wav)))
+            if ref_rms < self.target_rms:
+                wav = wav * self.target_rms / ref_rms
+            if self.reference_sample_rate != self.target_audio_sample_rate:
+                wav = self.resampler(wav)
+            wav = wav.to(self.device)
+            if self.use_perf:
+                torch.cuda.nvtx.range_push("compute_mel")
+            mel_features = self.compute_mel_fn(wav)
+            if self.use_perf:
+                torch.cuda.nvtx.range_pop()
+            mel_features_list.append(mel_features)
+
+            reference_mel_len.append(mel_features.shape[1])
+            estimated_reference_target_mel_len.append(
+                int(
+                    mel_features.shape[1] * (1 + len(target_text.encode("utf-8")) / len(reference_text.encode("utf-8")))
+                )
+            )
+
+        max_seq_len = min(max(estimated_reference_target_mel_len), self.max_mel_len)
+
+        batch = len(requests)
+        mel_features = torch.zeros((batch, max_seq_len, self.n_mel_channels), dtype=torch.float16).to(self.device)
+        for i, mel in enumerate(mel_features_list):
+            mel_features[i, : mel.shape[1], :] = mel
+
+        reference_mel_len_tensor = torch.LongTensor(reference_mel_len).to(self.device)
+
+        pinyin_list = convert_char_to_pinyin(reference_target_texts_list, polyphone=True)
+        text_pad_sequence = list_str_to_idx(pinyin_list, self.vocab_char_map)
+
+        for i, item in enumerate(text_pad_sequence):
+            text_pad_sequence[i] = F.pad(
+                item, (0, estimated_reference_target_mel_len[i] - len(item)), mode="constant", value=-1
+            )
+            text_pad_sequence[i] += 1  # WAR: 0 is reserved for padding token, hard coding in F5-TTS
+        text_pad_sequence = pad_sequence(text_pad_sequence, padding_value=-1, batch_first=True).to(self.device)
+        text_pad_sequence = F.pad(
+            text_pad_sequence, (0, max_seq_len - text_pad_sequence.shape[1]), mode="constant", value=-1
+        )
+        if self.use_perf:
+            torch.cuda.nvtx.range_pop()
+
+        denoised, cost_time = self.model.sample(
+            text_pad_sequence,
+            mel_features,
+            reference_mel_len_tensor,
+            estimated_reference_target_mel_len,
+            remove_input_padding=False,
+            use_perf=self.use_perf,
+        )
+        if self.use_perf:
+            torch.cuda.nvtx.range_push("vocoder")
+
+        responses = []
+        for i in range(batch):
+            ref_me_len = reference_mel_len[i]
+            estimated_mel_len = estimated_reference_target_mel_len[i]
+            denoised_one_item = denoised[i, ref_me_len:estimated_mel_len, :].unsqueeze(0).transpose(1, 2)
+            audio = self.forward_vocoder(denoised_one_item)
+            rms = torch.sqrt(torch.mean(torch.square(audio)))
+            if rms < self.target_rms:
+                audio = audio * self.target_rms / rms
+
+            audio = pb_utils.Tensor.from_dlpack("waveform", to_dlpack(audio))
+            inference_response = pb_utils.InferenceResponse(output_tensors=[audio])
+            responses.append(inference_response)
+        if self.use_perf:
+            torch.cuda.nvtx.range_pop()
+        return responses

src/f5_tts/runtime/triton_trtllm/model_repo_f5_tts/f5_tts/config.pbtxt

@@ -0,0 +1,81 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+name: "f5_tts"
+backend: "python"
+max_batch_size: 4
+dynamic_batching {
+    max_queue_delay_microseconds: 1000
+}
+parameters [
+  {
+    key: "vocab_file"
+    value: { string_value: "${vocab}"}
+  },
+  {
+   key: "model_path", 
+   value: {string_value:"${model}"}
+  },
+  {
+   key: "tllm_model_dir", 
+   value: {string_value:"${trtllm}"}
+  },
+  {
+   key: "reference_audio_sample_rate", 
+   value: {string_value:"24000"}
+  },
+  {
+   key: "vocoder", 
+   value: {string_value:"${vocoder}"}
+  }
+]
+
+input [
+  {
+    name: "reference_wav"
+    data_type: TYPE_FP32
+    dims: [-1]
+    optional: True
+  },
+  {
+    name: "reference_wav_len"
+    data_type: TYPE_INT32
+    dims: [1]
+    optional: True
+  },
+  {
+    name: "reference_text"
+    data_type: TYPE_STRING
+    dims: [1]
+  },
+  {
+    name: "target_text"
+    data_type: TYPE_STRING
+    dims: [1]
+  }
+]
+output [
+  {
+    name: "waveform"
+    data_type: TYPE_FP32
+    dims: [ -1 ]
+  }
+]
+
+instance_group [
+  {
+    count: 1
+    kind: KIND_GPU
+  }
+]