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Llasa Training
Overview :
LLaSA_training is a speech synthesis training project based on LLaMA, aimed at enhancing the efficiency and performance of speech synthesis models by optimizing training and inference computational resources. This project leverages both open-source datasets and proprietary datasets for training, supports various configurations and training methods, and offers high flexibility and scalability. Its main advantages include efficient data processing capabilities, strong speech synthesis effects, and support for multiple languages. This project is suitable for researchers and developers in need of high-performance speech synthesis solutions, applicable to the development of intelligent voice assistants, speech broadcasting systems, and other scenarios.
Target Users :
This project is ideal for researchers and developers in need of high-performance speech synthesis solutions, particularly those focusing on speech synthesis technology, intelligent voice assistant development, and speech broadcasting systems. It aids users in quickly building and optimizing speech synthesis models, enhancing development efficiency and model performance.
Use Cases
Researchers utilize the LLaSA_training model to develop intelligent voice assistants, enhancing the voice interaction experience
Developers use the model trained with this project to create speech broadcasting features for online education platforms, improving teaching efficiency
Companies optimize customer service voice synthesis modules using the LLaSA_training model, enhancing customer satisfaction
Features
Supports training of LLaMA-based speech synthesis models, providing efficient computational optimization solutions
Compatible with various open-source datasets, such as LibriHeavy and Emilia, totaling 160,000 hours of data
Offers multiple training configuration files (e.g., ds_config_zero2.json and ds_config_zero3.json) to meet diverse training needs
Supports distributed training via the Slurm scheduling system, improving training efficiency
Allows for direct use of relevant models on Hugging Face, such as Llasa-3B, Llasa-1B, and Llasa-8B
How to Use
1. Clone the project repository to your local machine: `git clone https://github.com/zhenye234/LLaSA_training.git`
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2. Download necessary open-source datasets such as LibriHeavy and Emilia, or prepare your own dataset
3. Choose the appropriate configuration file based on your requirements (e.g., ds_config_zero2.json or ds_config_zero3.json)
4. Run the training script using the command `torchrun --nproc_per_node=8 train_tts.py config.json` or through the Slurm scheduling system
5. After training is complete, you can directly use the trained model for speech synthesis on Hugging Face
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