WeST

Implement speech transcription based on LLM in just 300 lines of code.

CommonProductProgrammingSpeech RecognitionNatural Language Processing

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.

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.

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.

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.

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

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Bounce Rate

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WeST

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