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WhisperX: Automatic Speech Recognition with Word-level Timestamps (& Diarization)
WhisperX is an advanced Automatic Speech Recognition (ASR) tool based on OpenAI's Whisper model, enhanced with accurate word-level timestamps and speaker diarization capabilities. It provides precise transcription and speaker identification for audio files.
Python
Added on 2025年5月8日15,586
Stars1,670
ForksPython
LanguageProject Introduction
Summary
WhisperX is an open-source project building upon the powerful Whisper ASR model to offer enhanced features critical for many real-world applications: accurate word timestamps and speaker diarization. It provides a more comprehensive and useful transcription output.
Problem Solved
Standard ASR often lacks precise timing for individual words and the ability to distinguish speakers, making it difficult to use for tasks like subtitling or meeting analysis. WhisperX solves this by adding accurate timestamping and diarization on top of a robust ASR model.
Core Features
High Accuracy ASR
Leverages OpenAI's Whisper model for highly accurate speech-to-text conversion across multiple languages.
Word-Level Timestamps
Aligns the ASR output to the original audio to provide precise start and end times for every word.
Speaker Diarization
Identifies and separates different speakers within the audio, assigning segments to specific individuals.
Performance Optimizations
Optimized for faster processing compared to standard Whisper implementations on certain hardware.
Tech Stack
Python
PyTorch
Hugging Face Transformers
OpenAI Whisper
SpeechBrain (for diarization)
ffmpeg
Use Cases
WhisperX's combination of accurate ASR, precise timestamping, and diarization makes it suitable for a variety of applications:
Automatic Subtitle Generation
Details
Generate highly accurate and time-aligned transcripts for videos, allowing seamless creation of subtitles.
User Value
Significantly reduces the manual effort and time required for subtitling, improving accessibility and reach.
Meeting and Interview Transcription & Analysis
Details
Process meeting recordings or interviews to produce transcripts where each utterance is attributed to the correct speaker with precise timing.
User Value
Enables easier review, searching, and analysis of conversational audio, improving productivity and record-keeping.
Audio Content Indexing and Search
Details
Analyze large audio datasets, extract speech segments associated with specific topics or speakers based on timestamps and diarization.
User Value
Facilitates the creation of searchable audio archives and enables sophisticated data mining from spoken content.
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