ChatTTS x OpenVoice
Enhance the authenticity of speech by utilizing ChatTTS for more natural voice generation, complemented with the voice timber simulation module from Openvoice for seamless tone transplantation.
Have a try on huggingface! https://huggingface.co/spaces/Hilley/ChatTTS-OpenVoice
Experimental LLM Integration
ChatTTS/experimental/llm.py provides an OpenAI-compatible LLM API wrapper for text pre-processing (e.g. text normalisation before TTS inference). Supported providers:
| Provider | base_url |
Prompt versions |
|---|---|---|
| Kimi (Moonshot AI) | https://api.moonshot.cn/v1 |
kimi |
| DeepSeek | https://api.deepseek.com/v1 |
deepseek, deepseek_TN |
| MiniMax | https://api.minimax.io/v1 |
minimax, minimax_TN |
MiniMax quick start (models: MiniMax-M2.7, MiniMax-M2.7-highspeed, MiniMax-M2.5, MiniMax-M2.5-highspeed; all with 204K context):
import os
from ChatTTS.experimental.llm import create_minimax_client
client = create_minimax_client(os.environ["MINIMAX_API_KEY"])
# Conversational reply (TTS-friendly tone, ≤100 chars)
reply = client.call("今天北京天气怎么样？", prompt_version="minimax")
# Text normalisation before TTS
normalized = client.call("We paid $123 for this desk.", prompt_version="minimax_TN")Get a MiniMax API key at https://www.minimaxi.com/
Notice:
We need to download the OpenVoice Checkpoint and save it into the ./OpenVoice/checkpoint folder.
OpenVoice Checkpoint: https://huggingface.co/myshell-ai/OpenVoice/tree/main/checkpoints
