Delete infer/infer_cli.py

infer/infer_cli.py

@@ -1,226 +0,0 @@
-import argparse
-import codecs
-import os
-import re
-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 infer.utils_infer import (
-    infer_process,
-    load_model,
-    load_vocoder,
-    preprocess_ref_audio_text,
-    remove_silence_for_generated_wav,
-)
-from model import DiT, UNetT
-
-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",
-    help="Configuration file. Default=infer/examples/basic/basic.toml",
-    default=os.path.join(files("f5_tts").joinpath("infer/examples/basic"), "basic.toml"),
-)
-parser.add_argument(
-    "-m",
-    "--model",
-    help="F5-TTS | E2-TTS",
-)
-parser.add_argument(
-    "-p",
-    "--ckpt_file",
-    help="The Checkpoint .pt",
-)
-parser.add_argument(
-    "-v",
-    "--vocab_file",
-    help="The vocab .txt",
-)
-parser.add_argument("-r", "--ref_audio", type=str, help="Reference audio file < 15 seconds.")
-parser.add_argument("-s", "--ref_text", type=str, default="666", help="Subtitle for the reference audio.")
-parser.add_argument(
-    "-t",
-    "--gen_text",
-    type=str,
-    help="Text to generate.",
-)
-parser.add_argument(
-    "-f",
-    "--gen_file",
-    type=str,
-    help="File with text to generate. Ignores --gen_text",
-)
-parser.add_argument(
-    "-o",
-    "--output_dir",
-    type=str,
-    help="Path to output folder..",
-)
-parser.add_argument(
-    "-w",
-    "--output_file",
-    type=str,
-    help="Filename of output file..",
-)
-parser.add_argument(
-    "--remove_silence",
-    help="Remove silence.",
-)
-parser.add_argument("--vocoder_name", type=str, default="vocos", choices=["vocos", "bigvgan"], help="vocoder name")
-parser.add_argument(
-    "--load_vocoder_from_local",
-    action="store_true",
-    help="load vocoder from local. Default: ../checkpoints/charactr/vocos-mel-24khz",
-)
-parser.add_argument(
-    "--speed",
-    type=float,
-    default=1.0,
-    help="Adjust the speed of the audio generation (default: 1.0)",
-)
-args = parser.parse_args()
-
-config = tomli.load(open(args.config, "rb"))
-
-ref_audio = args.ref_audio if args.ref_audio else config["ref_audio"]
-ref_text = args.ref_text if args.ref_text != "666" else config["ref_text"]
-gen_text = args.gen_text if args.gen_text else config["gen_text"]
-gen_file = args.gen_file if args.gen_file else config["gen_file"]
-
-# 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}"))
-
-if gen_file:
-    gen_text = codecs.open(gen_file, "r", "utf-8").read()
-output_dir = args.output_dir if args.output_dir else config["output_dir"]
-output_file = args.output_file if args.output_file else config["output_file"]
-model = args.model if args.model else config["model"]
-ckpt_file = args.ckpt_file if args.ckpt_file else ""
-vocab_file = args.vocab_file if args.vocab_file else ""
-remove_silence = args.remove_silence if args.remove_silence else config["remove_silence"]
-speed = args.speed
-
-wave_path = Path(output_dir) / output_file
-# spectrogram_path = Path(output_dir) / "infer_cli_out.png"
-
-vocoder_name = args.vocoder_name
-mel_spec_type = args.vocoder_name
-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=mel_spec_type, is_local=args.load_vocoder_from_local, local_path=vocoder_local_path)
-
-
-# load models
-if model == "F5-TTS":
-    model_cls = DiT
-    model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
-    if ckpt_file == "":
-        if vocoder_name == "vocos":
-            repo_name = "F5-TTS"
-            exp_name = "F5TTS_Base"
-            ckpt_step = 1200000
-            ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors"))
-            # ckpt_file = f"ckpts/{exp_name}/model_{ckpt_step}.pt"  # .pt | .safetensors; local path
-        elif vocoder_name == "bigvgan":
-            repo_name = "F5-TTS"
-            exp_name = "F5TTS_Base_bigvgan"
-            ckpt_step = 1250000
-            ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.pt"))
-
-elif model == "E2-TTS":
-    assert vocoder_name == "vocos", "E2-TTS only supports vocoder vocos"
-    model_cls = UNetT
-    model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
-    if ckpt_file == "":
-        repo_name = "E2-TTS"
-        exp_name = "E2TTS_Base"
-        ckpt_step = 1200000
-        ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors"))
-        # ckpt_file = f"ckpts/{exp_name}/model_{ckpt_step}.pt"  # .pt | .safetensors; local path
-
-
-print(f"Using {model}...")
-ema_model = load_model(model_cls, model_cfg, ckpt_file, mel_spec_type=mel_spec_type, vocab_file=vocab_file)
-
-
-def main_process(ref_audio, ref_text, text_gen, model_obj, mel_spec_type, remove_silence, speed):
-    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:
-        voices[voice]["ref_audio"], voices[voice]["ref_text"] = preprocess_ref_audio_text(
-            voices[voice]["ref_audio"], voices[voice]["ref_text"]
-        )
-        print("Voice:", voice)
-        print("Ref_audio:", voices[voice]["ref_audio"])
-        print("Ref_text:", voices[voice]["ref_text"])
-
-    generated_audio_segments = []
-    reg1 = r"(?=\[\w+\])"
-    chunks = re.split(reg1, text_gen)
-    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)
-        gen_text = text.strip()
-        ref_audio = voices[voice]["ref_audio"]
-        ref_text = voices[voice]["ref_text"]
-        print(f"Voice: {voice}")
-        audio, final_sample_rate, spectragram = infer_process(
-            ref_audio, ref_text, gen_text, model_obj, vocoder, mel_spec_type=mel_spec_type, speed=speed
-        )
-        generated_audio_segments.append(audio)
-
-    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)
-
-
-def main():
-    main_process(ref_audio, ref_text, gen_text, ema_model, mel_spec_type, remove_silence, speed)
-
-
-if __name__ == "__main__":
-    main()