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Open-source tools & agent skills to build the things you’re paying for.

WhisperX

Project24.4K

Transcribe audio with word-level timestamps and optional speaker labels.

Added 2026-10-07
Replaces
GitHub

What you can build

  • Build an audio player that seeks to each aligned segment, accepts corrected words, and saves an edited transcript while preserving timestamps.
  • Build a local SQLite index from transcript text, speaker IDs, and timestamps; accept search terms and return matching excerpts with audio positions.
  • Match transcript segments against a supplied course-topic vocabulary and output a topic index with the corresponding segment timestamps.
  • Process diarized segments to calculate each speaker's speaking time and turn count, then export a summary table for a recorded discussion.
  • Apply a supplied list of sensitive words or phrases to transcript segments and output a redacted text record that retains the original timing.

Try this prompt

Build a small local transcript-review tool in Python using WhisperX. Accept an audio file, transcribe and align it, then show timestamped segments beside audio playback. Let a reviewer correct segment text and save the original and edited results, speaker IDs when available, and timestamps to a JSON file. Preserve the source audio, make model and device choices explicit, and report setup requirements and known alignment or diarization limitations. Do not assume a cloud API.

Get started

  1. Choose the runtime

    Install WhisperX from PyPI. For GPU acceleration, install CUDA toolkit 12.8 first; the README also documents CPU use.

  2. Check audio dependencies

    The README notes that ffmpeg and other dependencies may be needed and points to OpenAI Whisper's setup guidance.

  3. Enable diarization if needed

    Provide a Hugging Face read token and accept the speaker-diarization-community-1 model agreement; otherwise, use transcription and alignment without diarization.

License & notes

BSD-2-ClauseWhisperX is BSD-2-Clause licensed; retain its required notices and disclaimer when redistributing. Running it requires compute and storage for audio and downloaded models; GPU use requires compatible hardware and CUDA, while CPU use is documented. Diarization requires a Hugging Face access token and acceptance of the speaker-diarization-community-1 model agreement. Model and dependency terms may differ from WhisperX's license. No dollar amounts are quoted.

  • Diarization is imperfect, overlapping speech is not handled particularly well, and some words cannot be aligned when their characters are absent from the alignment model's dictionary.
  • Alignment models are language-specific; available language support depends on the documented models. GPU acceleration requires compatible hardware and CUDA, while model downloads and compute consume resources.
  • WhisperX uses dependencies and models with separate terms; diarization requires a Hugging Face token and acceptance of the community model agreement. Review those terms in addition to the BSD-2-Clause license.

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