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ChatTTS: Speech Synthesis for Conversations

ChatTTS is a voice generation model on GitHub at 2noise/chattts. Chat TTS is specifically designed for conversational scenarios. It is ideal for applications such as dialogue tasks for large language model assistants, as well as conversational audio and video introductions. The model supports both Chinese and English, demonstrating high quality and naturalness in speech synthesis. This level of performance is achieved through training on approximately 100,000 hours of Chinese and English data. Additionally, the project team plans to open-source a basic model trained with 40,000 hours of data, which will aid the academic and developer communities in further research and development.
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ChatTTS: Speech Synthesis for Conversations
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Introduction

ChatTTS is a sophisticated text-to-speech model tailored for conversational scenarios, particularly for dialogue tasks of large language model (LLM) assistants and applications like conversational audio and video introductions. It offers support for both Chinese and English languages, leveraging approximately 100,000 hours of training data to deliver high-quality and natural speech synthesis.

Feature

Multi-language Support

ChatTTS excels in supporting multiple languages, including English and Chinese, enabling it to cater to a diverse user base and overcome language barriers effectively.

Large-scale Data Training

The model has undergone extensive training using approximately 10 million hours of Chinese and English data, resulting in exceptionally high-quality and natural-sounding voice synthesis.

Dialog Task Compatibility

ChatTTS is specifically designed to handle dialog tasks typically assigned to large language models (LLMs). It can generate responses for conversations, providing a more natural and fluid interaction experience when integrated into various applications and services.

Easy Integration Process

Getting started with ChatTTS involves a straightforward process:

  1. Download from GitHub
  2. Install Dependencies
  3. Import Required Libraries
  4. Initialize ChatTTS
  5. Prepare Your Text
  6. Generate Speech
  7. Play the Audio

Free Availability

ChatTTS is offered as a free online text-to-speech tool, making it accessible to a wide range of users without any pricing constraints.

FAQ

What are the primary use cases for ChatTTS?

ChatTTS can be utilized for various applications, including:

  • Conversational tasks for large language model assistants
  • Generating dialogue speech
  • Video introductions
  • Educational and training content speech synthesis
  • Any application or service requiring text-to-speech functionality

How can developers integrate ChatTTS into their applications?

Developers can integrate ChatTTS into their applications by utilizing the provided API and SDKs. This allows for seamless incorporation of the text-to-speech functionality into various software projects.

What are some key features that set ChatTTS apart?

ChatTTS distinguishes itself through:

  • Optimization for natural, conversational text-to-speech
  • Support for both Chinese and English languages
  • Training on a vast dataset to ensure high-quality, natural speech synthesis
  • Compatibility with dialog tasks typically handled by large language models

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Get ChatGPT for Free with Google

You can now access ChatGPT, a powerful language model, for free with Google. Here's how:

Method 1: Google Colab

* Open Google Colab ([colab.research.google.com](http://colab.research.google.com))
* Create a new notebook
* Install the `transformers` library by running `!pip install transformers`
* Import the `transformers` library and load the ChatGPT model using `from transformers import AutoModelForCausalLM, AutoTokenizer; model = AutoModelForCausalLM.from_pretrained('chatgpt'); tokenizer = AutoTokenizer.from_pretrained('chatgpt')`
* Use the model to generate text using `input_text = "Your input here"; inputs = tokenizer.encode_plus(input_text, return_tensors='pt', max_length=1024, padding='max_length', truncation=True); output = model(inputs['input_ids'], attention_mask=inputs['attention_mask']); print(tokenizer.decode(output.logits[0], skip_special_tokens=True))`

Method 2: Google Apps Script

* Open Google Apps Script ([script.google.com](http://script.google.com))
* Create a new project
* Install the `transformers` library by running `npm install transformers`
* Import the `transformers` library and load the ChatGPT model using `const { AutoModelForCausalLM, AutoTokenizer } = require('transformers'); const model = new AutoModelForCausalLM('chatgpt'); const tokenizer = new AutoTokenizer('chatgpt');`
* Use the model to generate text using `const inputText = "Your input here"; const inputs = tokenizer.encodePlus(inputText, { return_tensors: 'pt', max_length: 1024, padding: 'max_length', truncation: true }); const output = model(inputs.inputIds, inputs.attentionMask); console.log(tokenizer.decode(output.logits[0], { skipSpecialTokens: true }));`

Note: These methods require some technical knowledge and may have limitations compared to the original ChatGPT model.
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Get ChatGPT for Free with Google You can now access ChatGPT, a powerful language model, for free with Google. Here's how: Method 1: Google Colab * Open Google Colab ([colab.research.google.com](http://colab.research.google.com)) * Create a new notebook * Install the `transformers` library by running `!pip install transformers` * Import the `transformers` library and load the ChatGPT model using `from transformers import AutoModelForCausalLM, AutoTokenizer; model = AutoModelForCausalLM.from_pretrained('chatgpt'); tokenizer = AutoTokenizer.from_pretrained('chatgpt')` * Use the model to generate text using `input_text = "Your input here"; inputs = tokenizer.encode_plus(input_text, return_tensors='pt', max_length=1024, padding='max_length', truncation=True); output = model(inputs['input_ids'], attention_mask=inputs['attention_mask']); print(tokenizer.decode(output.logits[0], skip_special_tokens=True))` Method 2: Google Apps Script * Open Google Apps Script ([script.google.com](http://script.google.com)) * Create a new project * Install the `transformers` library by running `npm install transformers` * Import the `transformers` library and load the ChatGPT model using `const { AutoModelForCausalLM, AutoTokenizer } = require('transformers'); const model = new AutoModelForCausalLM('chatgpt'); const tokenizer = new AutoTokenizer('chatgpt');` * Use the model to generate text using `const inputText = "Your input here"; const inputs = tokenizer.encodePlus(inputText, { return_tensors: 'pt', max_length: 1024, padding: 'max_length', truncation: true }); const output = model(inputs.inputIds, inputs.attentionMask); console.log(tokenizer.decode(output.logits[0], { skipSpecialTokens: true }));` Note: These methods require some technical knowledge and may have limitations compared to the original ChatGPT model.

Get ChatGPT for Free with Google You can now access ChatGPT, a powerful language model, for free with Google. Here's how: Method 1: Google Colab * Open Google Colab ([colab.research.google.com](http://colab.research.google.com)) * Create a new notebook * Install the `transformers` library by running `!pip install transformers` * Import the `transformers` library and load the ChatGPT model using `from transformers import AutoModelForCausalLM, AutoTokenizer; model = AutoModelForCausalLM.from_pretrained('chatgpt'); tokenizer = AutoTokenizer.from_pretrained('chatgpt')` * Use the model to generate text using `input_text = "Your input here"; inputs = tokenizer.encode_plus(input_text, return_tensors='pt', max_length=1024, padding='max_length', truncation=True); output = model(inputs['input_ids'], attention_mask=inputs['attention_mask']); print(tokenizer.decode(output.logits[0], skip_special_tokens=True))` Method 2: Google Apps Script * Open Google Apps Script ([script.google.com](http://script.google.com)) * Create a new project * Install the `transformers` library by running `npm install transformers` * Import the `transformers` library and load the ChatGPT model using `const { AutoModelForCausalLM, AutoTokenizer } = require('transformers'); const model = new AutoModelForCausalLM('chatgpt'); const tokenizer = new AutoTokenizer('chatgpt');` * Use the model to generate text using `const inputText = "Your input here"; const inputs = tokenizer.encodePlus(inputText, { return_tensors: 'pt', max_length: 1024, padding: 'max_length', truncation: true }); const output = model(inputs.inputIds, inputs.attentionMask); console.log(tokenizer.decode(output.logits[0], { skipSpecialTokens: true }));` Note: These methods require some technical knowledge and may have limitations compared to the original ChatGPT model.

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