whisper-video-transcribe-workaround

name	whisper-video-transcribe-workaround
description	When Whisper (CLI or Python) gets stuck on video files during transcription — output dir stays empty, process runs at low CPU/memory with no progress — the workaround is to extract audio first with ffmpeg, then transcribe the WAV. Use this when Whisper hangs or times out on a video file.

Whisper Video Transcription Workaround

Problem

When running whisper video.mp4 directly, the process may:

• Appear to run (PID exists, CPU may be active)
• But output dir stays empty even after many minutes
• Eventually time out or get stuck at ~33-43% "downloading/decoding"

This happens because the Whisper CLI tries to decode the video container internally, and certain video streams (especially DASH/m3u8 segmented video from YouTube downloads) cause the decoder to stall.

Solution: Extract audio first, then transcribe

Step 1 — Extract audio with ffmpeg

ffmpeg -i "video.mp4" -vn -acodec pcm_s16le -ar 16000 -ac 1 /tmp/audio.wav -y

Flags explained:

• -vn — no video stream
• -acodec pcm_s16le — 16-bit PCM audio (Whisper prefers)
• -ar 16000 — 16kHz sample rate (Whisper's native rate)
• -ac 1 — mono (better for speech recognition)
• -y — overwrite without asking

Step 2 — Transcribe the WAV with faster-whisper

from faster_whisper import WhisperModel

model = WhisperModel("tiny", device="cpu", compute_type="int8")
segments, info = model.transcribe(
    "/tmp/audio.wav",
    language="zh",
    vad_filter=True,
    vad_parameters=dict(min_silence_duration_ms=800)
)
results = [{"start": round(s.start, 2), "end": round(s.end, 2), "text": s.text.strip()} for s in segments]

Model size guide for Chinese:

• tiny — fastest, ~3x realtime on CPU; acceptable accuracy
• medium — better accuracy but slower
• large-v3-turbo — best accuracy; much heavier

Step 3 — Save transcript to file

Always write to a JSON file rather than holding in memory:

import json
with open("/tmp/transcript.json", "w", encoding="utf-8") as f:
    json.dump({"segments": results, "language": info.language}, f, ensure_ascii=False, indent=2)

Verification checklist

• Audio WAV was created (ls -lh /tmp/audio.wav)
• faster-whisper started logging segment output
• JSON file written with non-empty segments array
• Transcript language matches expected language
• Total duration of last segment ≈ video duration

Why this works

ffmpeg's audio decoding is battle-tested and handles problematic streams gracefully. Whisper's audio decoder (even faster-whisper) can get confused by AV1 video streams in MP4 containers downloaded via yt-dlp (DASH format). Extracting to clean PCM WAV sidesteps the issue entirely.

Known limitations

• The tiny model makes more transcription errors than medium/large — review before publishing
• Very long audio (>1hr) may still be slow on CPU; consider splitting with ffmpeg -ss 0 -t 3600 -i audio.wav part1.wav