Multilingual Automatic Speech Recognition with word-level timestamps and confidence. Whisper is a set of multi-lingual, robust speech recognition models trained by OpenAI that achieve state-of-the-art results in many languages. Whisper models were trained to predict approximate timestamps on speech segments (most of the time with 1-second accuracy), but they cannot originally predict word timestamps. This repository proposes an implementation to predict word timestamps and provide a more accurate estimation of speech segments when transcribing with Whisper models. Besides, a confidence score is assigned to each word and each segment.

Features

  • The start/end estimation is more accurate
  • Documentation available
  • Confidence scores are assigned to each word
  • If possible (without beam search...), no additional inference steps are required to predict word timestamps (word alignment is done on the fly after each speech segment is decoded)
  • Special care has been taken regarding memory usage
  • Light installation for CPU
  • Plot of word alignment

Project Samples

Project Activity

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License

Affero GNU Public License

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Additional Project Details

Operating Systems

Linux, Mac, Windows

Programming Language

Python

Related Categories

Python Machine Learning Software, Python LLM Inference Tool

Registered

2024-08-14