WeST
W
West
Overview :
WeST is an open-source speech recognition transcription model that achieves speech-to-text conversion in a concise format of 300 lines of code, based on a large language model (LLM). It includes a large language model, a speech encoder, and a projector, with only the projector being trainable. The development of WeST is inspired by SLAM-ASR and LLaMA 3.1, aiming to deliver efficient speech recognition capabilities through simplified code.
Target Users :
WeST primarily targets developers and data scientists, especially professionals interested in the fields of speech recognition and natural language processing. Its simplicity and ease of use make it an ideal choice for rapid prototyping and academic research.
Total Visits: 474.6M
Top Region: US(19.34%)
Website Views : 49.1K
Use Cases
Developers quickly build prototypes of voice assistants using WeST.
Researchers conduct experiments and write papers on speech recognition technology with WeST.
Educational institutions use WeST as a teaching tool to demonstrate how speech recognition works.
Features
Integrates interchangeable large language models like LLaMA or QWen.
Uses speech encoders, such as Whisper, to encode speech signals.
Supports jsonl format configuration for custom training and testing data.
Provides detailed configuration options for training parameters, including learning rate, weight decay, etc.
Supports Deepspeed configuration to optimize the model training process.
Features concise code that is easy to understand and extend.
How to Use
1. Prepare training and testing datasets, ensuring they meet the jsonl format requirements.
2. Set up the Python environment and install necessary dependencies according to project requirements.
3. Configure training parameters, including learning rate, weight decay, and saving strategy.
4. Set up Deepspeed if necessary to optimize the training process.
5. Run the training script to initiate model training.
6. Use the trained model for speech recognition and transcription tasks.
7. Analyze the transcription results and adjust model parameters as needed to improve accuracy.
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