结合 fastapi，在本地做一个服务。输入文本，生成音频并保存到本地。
pip3 install requirements.txt
uvicorn main:app --host "0.0.0.0" --port 8000curl -X POST -H 'content-type: application/json' -d\
'{"text":"朋友你好啊，今天天气怎么样 ？", "output_path": "abc.wav", "seed":232}' \
http://localhost:8000/tts说明：
text：要合成的文本output_path：合成音频的保存路径seed：音色种子，不同的种子会产生不同的音色，默认为 697（测试的一个比较好的音色）
docker run -d --name chattts -p8000:8000 -v /tmp/audio:/audio ghcr.io/ultrasev/chattts:latest注：
/tmp/audio，本地存储音频文件的路径/audio，docker 里面存储音频文件的路径，这两个都可以更改。
在使用时应该指定 output_path = /audio/abc.wav，以便把音频写到 /audio 下面。
curl -X POST -H 'content-type: application/json' -d\
'{"text":"朋友你好啊，今天天气怎么样 ？", "output_path": "/audio/abc.wav", "seed":232}' \
http://localhost:8000/tts- pip3 install chattts-fork
- chattts "哈哈" -o test.wav
- 支持了 seed 固定音色
chattts "哈哈" -o test.wav --seed 222
ChatTTS is a text-to-speech model designed specifically for dialogue scenario such as LLM assistant. It supports both English and Chinese languages. Our model is trained with 100,000+ hours composed of chinese and english. The open-source version on HuggingFace is a 40,000 hours pre trained model without SFT.
For formal inquiries about model and roadmap, please contact us at open-source@2noise.com. You could join our QQ group: 808364215 for discussion. Adding github issues is always welcomed.
- Conversational TTS: ChatTTS is optimized for dialogue-based tasks, enabling natural and expressive speech synthesis. It supports multiple speakers, facilitating interactive conversations.
- Fine-grained Control: The model could predict and control fine-grained prosodic features, including laughter, pauses, and interjections.
- Better Prosody: ChatTTS surpasses most of open-source TTS models in terms of prosody. We provide pretrained models to support further research and development.
For the detailed description of the model, you can refer to video on Bilibili
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.
import ChatTTS
from IPython.display import Audio
chat = ChatTTS.Chat()
chat.load_models()
texts = ["<PUT YOUR TEXT HERE>",]
wavs = chat.infer(texts, use_decoder=True)
Audio(wavs[0], rate=24_000, autoplay=True)###################################
# Sample a speaker from Gaussian.
import torch
std, mean = torch.load('ChatTTS/asset/spk_stat.pt').chunk(2)
rand_spk = torch.randn(768) * std + mean
params_infer_code = {
'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 = {
'prompt': '[oral_2][laugh_0][break_6]'
}
wav = chat.infer("<PUT YOUR TEXT HERE>", 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]'
wav = chat.infer(text, skip_refine_text=True, params_infer_code=params_infer_code)inputs_en = """
chat T T S is a text to speech model designed for dialogue applications.
[uv_break]it supports mixed language input [uv_break]and offers multi speaker
capabilities with precise control over prosodic elements [laugh]like like
[uv_break]laughter[laugh], [uv_break]pauses, [uv_break]and intonation.
[uv_break]it delivers natural and expressive speech,[uv_break]so please
[uv_break] use the project responsibly at your own risk.[uv_break]
""".replace('\n', '') # English is still experimental.
params_refine_text = {
'prompt': '[oral_2][laugh_0][break_4]'
}
audio_array_cn = chat.infer(inputs_cn, params_refine_text=params_refine_text)
audio_array_en = chat.infer(inputs_en, params_refine_text=params_refine_text)intro_en_m.webm
intro_en_f.webm
- Open-source the 40k hour base model and spk_stats file
- Open-source VQ encoder and Lora training code
- Streaming audio generation without refining the text*
- Open-source the 40k hour version with multi-emotion control
- ChatTTS.cpp maybe? (PR or new repo are welcomed.)
For a 30-second audio clip, at least 4GB of GPU memory is required. For the 4090D GPU, it can generate audio corresponding to approximately 7 semantic tokens per second. The Real-Time Factor (RTF) is around 0.65.
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.
- bark, XTTSv2 and valle demostrate a remarkable TTS result by a autoregressive-style system.
- fish-speech reveals capability of GVQ as audio tokenizer for LLM modeling.
- vocos which is used as a pretrained vocoder.
- wlu-audio lab for early algorithm experiments.