FireRedTTS-1S: An Upgraded Streamable Foundation Text-to-Speech System
Paper
•
2503.20499
•
Published
https://github.com/FireRedTeam/FireRedTTS.git
cd FireRedTTS
# 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
cd fireredtts
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
# acoustic llm decoder
tts = FireRedTTS(
config_path="configs/config_24k.json",
pretrained_path=<pretrained_models_dir>,
)
"""
# flow matching decoder
tts = FireRedTTS(
config_path="configs/config_24k_flow.json",
pretrained_path=<pretrained_models_dir>,
)
"""
#same language
# For the test-hard evaluation, we enabled the use_tn=True configuration setting.
rec_wavs = tts.synthesize(
prompt_wav="examples/prompt_1.wav",
prompt_text="对,所以说你现在的话,这个账单的话,你既然说能处理,那你就想办法处理掉。",
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",
use_tn=True
)
rec_wavs = rec_wavs.detach().cpu()
out_wav_path = os.path.join("./example.wav")
torchaudio.save(out_wav_path, rec_wavs, 24000)