ChatTTS: a speech generation model that mimics the voice of a real human speaker

General Introduction

ChatTTS is a generative speech model designed for conversational scenarios. It generates natural and expressive speech, supports multiple languages and multiple speakers, and is suitable for interactive conversations. The model outperforms most open-source speech synthesis models by predicting and controlling fine-grained rhythmic features such as laughter, pauses, and interjections.ChatTTS provides pre-trained models to support further research and development, primarily for academic purposes.

Function List

• Multi-language support: Chinese and English are supported, and more languages will be expanded in the future.
• Multi-talker support: The ability to generate multiple speakers' voices makes it suitable for interactive conversations.
• Fine-grained rhythmic control: Rhythmic features such as laughter, pauses and interjections can be predicted and controlled.
• Pre-trained models: Provides 40,000 hours of pre-trained models to support further research and development.
• open source: The code is open source on GitHub for academic and research use.

Using Help

Installation process

1. Cloning Project Code::

   git clone https://github.com/2noise/ChatTTS.git
   

2. Installation of dependencies::

   cd ChatTTS
   pip install -r requirements.txt
   

3. Download pre-trained model: Download the pre-trained model from HuggingFace or ModelScope and place it in the specified directory.

Usage

1. Loading Models::

   from chattts import ChatTTS
   model = ChatTTS.load_model('path/to/pretrained/model')
   

2. Generate Speech::

   text = "Hello and welcome to ChatTTS!"
   audio = model.synthesize(text)
   

3. Saving audio files::

   with open('output.wav', 'wb') as f.
       f.write(audio)
   

Detailed Function Operation

• text input: Supports mixed Chinese and English text input.
• Rhythmic control: Rhyme features such as laughter, pauses and interjections are controlled by setting parameters.
• tone control: The generated tone can be controlled by a preset tone seed value or tone code.
• emotional control: Control the emotional characteristics of the generated speech by setting the emotion volatility and relevance parameters.
• streaming output: Supports long audio generation and split-role reading for complex dialog scenarios.

sample code (computing)

from chattts import ChatTTS

# loading model
model = ChatTTS.load_model('path/to/pretrained/model')

# Setting Text and Rhyme Parameters
text = "Hello and welcome to ChatTTS!"
params = {
    'laugh': True,
    'pause': True,
    'interjection': True
}

# Generate Voice
audio = model.synthesize(text, params)

# Save Audio Files
with open('output.wav', 'wb') as f.
    f.write(audio)

ChatTTS Client

Quick Experience

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Functionality Expansion

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