Project Title

whisperX — Advanced Speech Recognition with Word-level Timestamps and Speaker Diarization

Overview

WhisperX is an open-source Python project that offers fast automatic speech recognition (ASR) with word-level timestamps and speaker diarization. It leverages the Whisper model by OpenAI and enhances it with forced phoneme alignment and voice-activity-based batching for improved accuracy and speed. This project stands out for its real-time transcription capabilities and its ability to handle multispeaker ASR.

Key Features

Batched inference for 70x real-time transcription using Whisper large-v2
Utilizes the faster-whisper backend, requiring less than 8GB GPU memory
Accurate word-level timestamps via wav2vec2 alignment
Multispeaker ASR with speaker diarization from pyannote-audio
Voice Activity Detection (VAD) preprocessing to reduce hallucination and enable efficient batching

Use Cases

Researchers and developers needing high-speed, accurate transcriptions for audio analysis
Applications in call centers for real-time transcription and speaker identification
Use in multimedia content creation for automated captioning and subtitling

Advantages

Real-time transcription at 70x speed, significantly faster than standard ASR tools
Improved timestamp accuracy at the word level, enhancing the usability of transcriptions
Open-source and community-driven, allowing for continuous improvement and customization

Limitations / Considerations

Requires a GPU with at least 8GB memory for optimal performance with large-v2 models
The project's complexity might pose a steep learning curve for new users
May have limitations in handling extremely noisy environments or non-native speaker accents

Mozilla DeepSpeech: An open-source speech-to-text engine with a focus on offline use, differing in its approach to ASR without the need for cloud services.
Kaldi: A widely-used open-source speech recognition toolkit that offers a range of features but may not match whisperX's speed.
wav2vec2: A model for unsupervised pre-training of wav2vec 2.0, which whisperX uses for phoneme-based ASR, but it is not an end-to-end solution like whisperX.

Basic Information

GitHub: https://github.com/m-bain/whisperX
Stars: 17,103
License: Unknown
Last Commit: 2025-08-04

📊 Project Information

Project Name: whisperX
GitHub URL: https://github.com/m-bain/whisperX
Programming Language: Python
⭐ Stars: 17,103
🍴 Forks: 1,808
📅 Created: 2022-12-09
🔄 Last Updated: 2025-08-04

🏷️ Project Topics

Topics: [, ", a, s, r, ", ,, , ", s, p, e, e, c, h, ", ,, , ", s, p, e, e, c, h, -, r, e, c, o, g, n, i, t, i, o, n, ", ,, , ", s, p, e, e, c, h, -, t, o, -, t, e, x, t, ", ,, , ", w, h, i, s, p, e, r, ", ]

📚 Documentation

This article is automatically generated by AI based on GitHub project information and README content analysis

whisperX

Project Description