ChatTTS is a text-to-speech model designed specifically for dialogue scenarios such as LLM assistant.
You can refer to this video on Bilibili for the detailed description.
[!Important] This repo is for academic purposes only.
It is intended for educational and research use, and should not be used for any commercial or legal purposes. The authors do not guarantee the accuracy, completeness, or reliability of the information. The information and data used in this repo, are for academic and research purposes only. The data obtained from publicly available sources, and the authors do not claim any ownership or copyright over the data.
ChatTTS is a powerful text-to-speech system. However, it is very important to utilize this technology responsibly and ethically. To limit the use of ChatTTS, we added a small amount of high-frequency noise during the training of the 40,000-hour model, and compressed the audio quality as much as possible using MP3 format, to prevent malicious actors from potentially using it for criminal purposes. At the same time, we have internally trained a detection model and plan to open-source it in the future.
GitHub issues/PRs are always welcomed.
For formal inquiries about the model and roadmap, please contact us at open-source@2noise.com.
Join by clicking here.
Will be uploaded to pypi soon according to https://github.com/2noise/ChatTTS/issues/269
pip install git+https://github.com/2noise/ChatTTS
git clone https://github.com/2noise/ChatTTS
cd ChatTTS
pip install --upgrade -r requirements.txt
conda create -n chattts
conda activate chattts
pip install -r requirements.txt
[!Note] The installation process is very slow.
pip install git+https://github.com/NVIDIA/TransformerEngine.git@stable
Make sure you are under the project root directory when you execute these commands below.
python examples/web/webui.py
It will save audio to
./output_audio_n.mp3
python examples/cmd/run.py "Your text 1." "Your text 2."
import ChatTTS
import torch
import torchaudio
chat = ChatTTS.Chat()
chat.load(compile=False) # Set to True for better performance
texts = ["PUT YOUR 1st TEXT HERE", "PUT YOUR 2nd TEXT HERE"]
wavs = chat.infer(texts)
torchaudio.save("output1.wav", torch.from_numpy(wavs[0]), 24000)
###################################
# Sample a speaker from Gaussian.
rand_spk = chat.sample_random_speaker()
print(rand_spk) # save it for later timbre recovery
params_infer_code = ChatTTS.Chat.InferCodeParams(
spk_emb = rand_spk, # add sampled speaker
temperature = .3, # using custom temperature
top_P = 0.7, # top P decode
top_K = 20, # top K decode
)
###################################
# For sentence level manual control.
# use oral_(0-9), laugh_(0-2), break_(0-7)
# to generate special token in text to synthesize.
params_refine_text = ChatTTS.Chat.RefineTextParams(
prompt='[oral_2][laugh_0][break_6]',
)
wavs = chat.infer(
texts,
params_refine_text=params_refine_text,
params_infer_code=params_infer_code,
)
###################################
# For word level manual control.
text = 'What is [uv_break]your favorite english food?[laugh][lbreak]'
wavs = chat.infer(text, skip_refine_text=True, params_refine_text=params_refine_text, params_infer_code=params_infer_code)
torchaudio.save("output2.wav", torch.from_numpy(wavs[0]), 24000)
**male speaker** | **female speaker** |
[male speaker](https://github.com/2noise/ChatTTS/assets/130631963/e0f51251-db7f-4d39-a0e9-3e095bb65de1) | [female speaker](https://github.com/2noise/ChatTTS/assets/130631963/f5dcdd01-1091-47c5-8241-c4f6aaaa8bbd) |
For a 30-second audio clip, at least 4GB of GPU memory is required. For the 4090 GPU, it can generate audio corresponding to approximately 7 semantic tokens per second. The Real-Time Factor (RTF) is around 0.3.
This is a problem that typically occurs with autoregressive models (for bark and valle). It's generally difficult to avoid. One can try multiple samples to find a suitable result.
In the current released model, the only token-level control units are [laugh]
, [uv_break]
, and [lbreak]
. In future versions, we may open-source models with additional emotional control capabilities.