Transcribe video or audio entirely on-device using yt-dlp audio extraction plus local mlx-whisper inference. Works offline, zero API cost, fastest on Apple Silicon M-series with MLX Metal acceleration.
Input Fit
- YouTube URLs.
- Bilibili URLs.
- Douyin URLs.
- Xiaoyuzhou podcast URLs.
- Local audio files such as MP3, M4A, WAV, AAC, FLAC, OGG, and OPUS.
- Local video files such as MP4, MOV, MKV, AVI, WEBM, FLV, and M4V.
Invocation
Call transcribe.py's sync transcribe(url_or_path: str) function with the original URL or local file path:
result = transcribe("https://www.youtube.com/watch?v=example")
Successful calls return:
{
"ok": True,
"raw_txt": "...",
"subtitle_srt": "1\n00:00:00,000 --> 00:00:03,100\n...\n",
"meta": {
"language": "en",
"duration_sec": 123.4,
"model": "mlx-community/whisper-large-v3-turbo",
"backend": "mlx-whisper",
},
"audio_path": "/tmp/autosearch-video-to-text-local-.../audio.mp3",
"source": "https://www.youtube.com/watch?v=example",
}
Failure Modes
- Missing
mlx-whisperpackage returnsreason: mlx_whisper_unavailable(install withpip install mlx-whisper, Apple Silicon only). - Unsupported URLs, network failures, and non-zero
yt-dlpexits returnreason: yt_dlp_failed. - Missing local
ffmpegfor video conversion returnsreason: ffmpeg_missing. - Model download failure (no network on first run) returns
reason: model_download_failed. - Other MLX / transcription runtime errors return
reason: mlx_whisper_runtime_error.
Downgrade chain: use video-to-text-groq as the free cloud fallback (requires GROQ_API_KEY), then video-to-text-openai as the paid cloud fallback.
Limits
- Requires Apple Silicon (M1 / M2 / M3 / M4). Not compatible with Intel Macs, Linux, or Windows.
- Requires
mlx-whisperpackage installed separately:pip install mlx-whisper(not a default autosearch dependency because it's macOS/ARM-only). - First run downloads the Whisper model to
~/.cache/huggingface/hub/(~3 GB for large-v3-turbo). - Default model:
mlx-community/whisper-large-v3-turbo. Override viaAUTOSEARCH_MLX_WHISPER_MODELenv var to any HuggingFace repo or local model path.
This tool does not produce a summary. It returns raw_txt and subtitles so the runtime AI can decide how to process, summarize, quote, or cite the transcript.
Quality Bar
- Evidence items have non-empty title and url.
- No crash on empty or malformed API response.
- Source channel field matches the channel name.