Coqui TTS (xTTS): Deep Learning Toolkit for Text-to-Speech Generation with Multiple Language Support and Voice Cloning Capabilities

General Introduction

Coqui TTS is an open source advanced text-to-speech (TTS) generation toolkit based on deep learning techniques. It has been battle-tested in both research and production environments, and provides a rich set of features and models to support text-to-speech conversion in multiple languages.Coqui TTS not only supports pre-trained models, but also provides tools to train new models and fine-tune existing ones for a variety of languages and application scenarios.

Demo: https://huggingface.co/spaces/coqui/xtts

Function List

• Multi-language support: Supports text-to-speech conversion in over 1100 languages.
• Pre-trained models: Provides a variety of pre-trained models that users can use directly.
• model training: Support for training new models and fine-tuning existing ones.
• sound cloning: Supports the voice cloning function, which allows you to generate a voice for a specific sound.
• Efficient training: Provide fast and efficient model training tools.
• Detailed log: Provide detailed training logs on the terminal and Tensorboard.
• Utilities: Provide tools for data set analysis and organization.

Using Help

Installation process

1. clone warehouse: First, clone the Coqui TTS GitHub repository.

   git clone https://github.com/coqui-ai/TTS.git
   cd TTS
   

2. **Installation of dependencies** : Use pip to install the required dependencies.

```bash
pip install -r requirements.txt

2. Installing TTS : Run the following command to install TTS.

python setup.py install

Usage

1. Loading pre-trained models : Text-to-speech conversion can be performed using pre-trained models.

from TTS.api import TTS
tts = TTS(model_name="tts_models/en/ljspeech/tacotron2-DDC", progress_bar=True)
tts.tts_to_file(text="Hello, world!", file_path="output.wav")

1. Training a new model : You can train new models based on your own dataset.

python TTS/bin/train_tts.py --config_path config.json --dataset_path /path/to/dataset

1. Fine-tuning of existing models : Existing models can be fine-tuned to fit specific application scenarios.

python TTS/bin/train_tts.py --config_path config.json --dataset_path /path/to/dataset --restore_path /path/to/pretrained/model

Detailed Operation Procedure

1. Data preparation : Prepare the training dataset and make sure that the data format meets the requirements.
2. configuration file : Edit Configuration File config.json, set the training parameters.
3. Start training : Run the training script to start model training.
4. Monitor training : Monitor the training process, view training logs and model performance through the terminal and Tensorboard.
5. Model Evaluation : After the training is completed, the model performance is evaluated and necessary adjustments and optimizations are made.

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