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A Python component for streaming Whisper transcription and translation, with file simulation and a simple TCP server.

Added 2026-10-07
Replaces
GitHub

What you can build

  • Code a runner that feeds a 16 kHz mono WAV through selected chunk sizes and computational-awareness modes, then saves transcript emission timings to CSV.
  • Build a converter that reads the documented timestamped transcript lines and writes WebVTT cues for a separate caption player.
  • Create a local audio review page that turns transcript time ranges into controls for seeking to the corresponding passages in the source recording.
  • Use OnlineASRProcessor output to write timestamped transcript lines and session identifiers to SQLite, then export a chosen session as text.
  • Build a filter that replaces configured terms in timestamped transcript lines, saves a sanitized copy, and preserves the original transcript.

Try this prompt

Build a small Python transcript journal using Whisper Streaming's documented OnlineASRProcessor interface. Accept 16 kHz mono audio chunks, call process_iter and finish, and save each resulting timestamped transcript line with a session ID in a local SQLite database. Add a simple view to browse sessions and export one as text. Preserve prior records when a session is restarted, keep the transcription engine unchanged, and document the selected backend and dependencies.

Get started

  1. Install audio support

    The documented installation starts with librosa and soundfile. Choose a Whisper backend: faster-whisper is recommended, while whisper_timestamped, OpenAI API, and MLX have different requirements.

  2. Choose an input path

    For file simulation, use a 16 kHz mono WAV and select language, task, and chunk options documented for whisper_online.py. The TCP server example uses raw 16 kHz mono signed 16-bit little-endian audio.

  3. Use the Python processor

    For a custom pipeline, follow the README's OnlineASRProcessor example: pass audio chunks to insert_audio_chunk, call process_iter for updates, and call finish when the audio ends.

License & notes

MITMIT-licensed. Local backends require model downloads and suitable compute; the README recommends faster-whisper with GPU support. The OpenAI API backend requires paid API access, and repeated processing of audio fragments can make costs higher than expected. Running a server also requires hosting. No dollar amounts are quoted.

  • The README says Whisper Streaming is outdated and replaced by SimulStreaming; it also says maintenance of the back-dependency is not planned.
  • Backend requirements differ. Local inference needs suitable compute; OpenAI API use is paid, and repeated audio-fragment processing can increase costs.
  • File simulation expects a 16 kHz mono WAV. The TCP example expects raw 16 kHz mono signed 16-bit little-endian audio; other formats need adaptation.

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