whisper-timestamped
A Python library that extends OpenAI's Whisper to provide accurate word-level timestamps and confidence scores for multilingual speech recognition.
What is whisper-timestamped?
whisper-timestamped is a Python library that enhances OpenAI's Whisper speech recognition model by adding precise word-level timestamps and confidence scores to transcriptions. It solves the problem of aligning transcribed text accurately to audio timelines, which is essential for creating subtitles, analyzing speech patterns, or editing video content. The library uses Dynamic Time Warping on Whisper's internal attention weights to infer word boundaries without requiring additional language-specific models.
Developers and researchers working on applications that require precise audio-text alignment, such as subtitle generation, video editing tools, speech analytics platforms, or any project leveraging Whisper for transcription with timing metadata.
It provides a robust, efficient, and language-agnostic method for word-level timestamping directly integrated with Whisper, avoiding the overhead and limitations of alternative approaches like wav2vec models. The inclusion of confidence scores and optional Voice Activity Detection further enhances transcription reliability.
Overview
Multilingual Automatic Speech Recognition with word-level timestamps and confidence
Use Cases
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