Overview
Whisper is a Transformer-based sequence-to-sequence model trained on diverse speech tasks. It enables high-quality multilingual speech recognition, translation, and language identification, and provides both CLI and Python APIs for integration.
Core Features
- Multilingual speech recognition and optional translation across multiple model sizes (tiny → large-v3).
- CLI and Python interfaces, pre-trained models, model cards, and example notebooks for quick onboarding.
- Portable implementation with support across common hardware and environments.
Use Cases
- Transcription and subtitle generation, cross-language speech translation, and voice data annotation.
- Media processing, meeting summarization, and voice-driven interfaces.
Technical Highlights
- Transformer sequence-to-sequence architecture with mel-spectrogram preprocessing and decoding utilities.
- MIT licensed, open-source codebase with extensive examples, benchmarks, and community support.