guillaumekln/faster-whisper

Features

• Speech-to-Text Engines - Provides an optimized engine for converting spoken audio recordings into written text.
• Audio Model Optimization - Optimizes audio models via lower precision formats to improve hardware execution speed and memory requirements.
• CTranslate2 Deployment - Runs audio transcription models through the CTranslate2 engine for higher performance than standard implementations.
• Automatic Speech Recognition - Implements a high-performance system for converting spoken audio into written text.
• Model Quantization Tools - Provides utilities to reduce numerical precision of audio model parameters for improved inference performance.
• Weight Quantization - Implements techniques to compress model weights into lower-precision integer formats for faster inference and reduced memory use.
• Whisper-Based Engines - Implements an optimized version of the Whisper model using CTranslate2 for faster, memory-efficient transcription.
• Transformer Inference Engines - Functions as a high-performance engine optimized for executing transformer-based speech models.
• Batch Transcription - Provides parallel processing of audio segments to maximize transcription throughput and reduce latency.
• Model Format Converters - Transforms PyTorch checkpoints into a proprietary format compatible with the CTranslate2 runtime.
• Voice Activity Detection - Identifies speech boundaries to filter out silent or non-speech segments before transcription.
• Voice Activity Detection - Identify and strip silent or non-speech sections from audio files using a voice activity detection model.
• Model Serving Engines - Optimized C++ inference engine for speech recognition models.
• Model Variants - Optimized reimplementation using CTranslate2 for increased speed.

Star history