FireRedTTS
An Open-Sourced LLM-empowered Foundation TTS System
Stars: 313
FireRedTTS is a foundation text-to-speech framework designed for industry-level generative speech applications. It offers a rich-punctuation model with expanded punctuation coverage and enhanced audio production consistency. The tool provides pre-trained checkpoints, inference code, and an interactive demo space. Users can clone the repository, create a conda environment, download required model files, and utilize the tool for synthesizing speech in various languages. FireRedTTS aims to enhance stability and provide controllable human-like speech generation capabilities.
README:
👉🏻 FireRedTTS Paper 👈🏻
👉🏻 FireRedTTS Demos 👈🏻
- [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
-
[ ] 2024/09
- [x] Release the pre-trained checkpoints and inference code.
- [ ] Release testing set.
-
[ ] 2024/10
- [x] Release rich punctuation version.
- [ ] Release finetuned checkpoints for controllable human-like speech generation.
- 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 the required model files from Model_Lists and place them in the folder pretrained_models
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)
- 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.
-
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.
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