FireRedTTS: A Foundation Text-To-Speech Framework for Industry-Level Generative Speech Applications

👉🏻 FireRedTTS Paper 👈🏻

👉🏻 FireRedTTS Demos 👈🏻

👉🏻 FireRedTTS Space (Interactive Demo) 👈🏻

News

• [2024/10/17] 🔥 We release new rich-punctuation model, offering expanded punctuation coverage and enhanced audio production consistency. In addition, we have strengthened the capabilities of the text front-end and enhanced the stability of synthesis.
• [2024/09/26] 🔥 Our model is already available on huggingface space，try it through the interactive interface.
• [2024/09/20] 🔥 We release the pre-trained checkpoints and inference code.
• [2024/09/06] 🔥 We release the technical report and project page

Roadmap

• 2024/09

  • Release the pre-trained checkpoints and inference code.
  • Release testing set.

• 2024/10

  • Release rich punctuation version.
  • Release finetuned checkpoints for controllable human-like speech generation.

Usage

Clone and install

• Clone the repo

https://github.com/FireRedTeam/FireRedTTS.git
cd FireRedTTS

• Create conda env

# step1.create env
conda create --name redtts python=3.10

# stpe2.install torch （pytorch should match the cuda-version on your machine）
# CUDA 11.8
conda install pytorch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 pytorch-cuda=11.8 -c pytorch -c nvidia
# CUDA 12.1
conda install pytorch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 pytorch-cuda=12.1 -c pytorch -c nvidia

# step3.install fireredtts form source
pip install -e . 

# step4.install other requirements
pip install -r requirements.txt

Download models

Download the required model files from Model_Lists and place them in the folder pretrained_models

Basic Usage

import os
import torchaudio
from fireredtts.fireredtts import FireRedTTS

tts = FireRedTTS(
    config_path="configs/config_24k.json",
    pretrained_path=<pretrained_models_dir>,
)

#same language
rec_wavs = tts.synthesize(
        prompt_wav="examples/prompt_1.wav",
        text="小红书，是中国大陆的网络购物和社交平台，成立于二零一三年六月。",
        lang="zh",
)

rec_wavs = rec_wavs.detach().cpu()
out_wav_path = os.path.join("./example.wav")
torchaudio.save(out_wav_path, rec_wavs, 24000)

Tips

• Removing the long silence (>1s) in the middle of prompt_wav may bring better stability. If there are too many long silences in your prompt_wav and it causes stability problems, it is recommended to use our tool(tools/process_prompts.py) to remove the silence.

Acknowledgements

• Tortoise-tts and XTTS-v2 offer invaluable insights for constructing an autoregressive-style system.

• Matcha-TTS and CosyVoice demonstrate the excellent ability of flow-matching in converting audio code to mel.

• BigVGAN-v2, utilized for vocoding.

• We referred to whisper’s text tokenizer solution.