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

faster-whisper

Project25.7K

Build Python speech-to-text workflows using Whisper models and CTranslate2.

Added 2026-10-07
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What you can build

  • Build a folder ingester that stores each recording’s segment text and timestamps in a local index, then returns matching files and time ranges for a query.
  • Take recordings and word-timestamp results as input; generate a report listing each occurrence of a selected word with its start and end times.
  • Run the same recordings with two documented silence thresholds; output a report comparing segment counts, timestamps, and transcript text for review.
  • Process audio files and group their detected languages and probabilities into a review queue, with low-confidence detections marked for human checking.
  • Run a fixed audio set through a converted compatible Whisper model and a second model; output aligned transcript differences and detected-language results.

Try this prompt

Build a small Python tool that accepts a folder of audio recordings, transcribes each with faster-whisper, and creates a searchable local catalog of segment text and timestamps. Add a search input that returns matching file names and time ranges. Consume each segment generator fully, preserve original recordings, and report per-file errors without stopping the batch. Document CPU requirements and optional GPU dependencies. Keep the implementation focused; do not add meeting summaries or editing features.

Get started

  1. Check Python and hardware requirements

    Use Python 3.9 or newer. CPU inference is supported; GPU inference requires NVIDIA cuBLAS for CUDA 12 and cuDNN 9, with compatibility notes in the README.

  2. Install and load the package

    Follow the repository README’s PyPI installation instructions. Its Python example loads a named model with WhisperModel and transcribes an audio file.

  3. Select documented transcription options

    Use the documented options for batching, VAD, language, or word-level timestamps. Named models download their weights when loaded; compatible Transformers Whisper models can also be converted.

License & notes

MITThe repository is MIT licensed; commercial use is permitted subject to retaining its copyright and license notice. Python 3.9 or newer is required. Named models download weights from Hugging Face for local inference, using your storage and compute. GPU inference requires compatible NVIDIA libraries. The documented workflow does not require a paid API; hosting an application you build has separate infrastructure costs.

  • This is a transcription library, not a finished user interface, transcript editor, diarization system, or meeting-summary product.
  • Inference uses local compute and model storage; GPU use requires compatible NVIDIA libraries. Speed and memory use vary with hardware, model, settings, and audio.
  • VAD filters silence, not speech content. Review transcripts and the effects of VAD settings for your recordings.

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