tubescribe 0.3.6

CLI to transcribe YouTube audio via Whisper (local) or Gemini (cloud)

TubeScribe (ytx) — YouTube Transcriber (Whisper / Metal via whisper.cpp)

CLI that downloads YouTube audio and produces transcripts and captions using:

• Local Whisper (faster-whisper / CTranslate2)
• Whisper.cpp (Metal acceleration on Apple Silicon)

Repository: https://github.com/prateekjain24/TubeScribe

Managed with venv+pip (recommended) or uv, using the src layout.

Features

• One command: URL → audio → normalized WAV → transcript JSON + SRT captions
• Engines: whisper (faster-whisper) and whispercpp (Metal via whisper.cpp)
• Rich progress for download + transcription
• Deterministic JSON (orjson) and SRT line wrapping

Requirements

• Python >= 3.11
• FFmpeg installed and on PATH
  • Check: ffmpeg -version
  • macOS: brew install ffmpeg
  • Ubuntu/Debian: sudo apt-get update && sudo apt-get install -y ffmpeg
  • Fedora: sudo dnf install -y ffmpeg
  • Arch: sudo pacman -S ffmpeg
  • Windows: winget install Gyan.FFmpeg or choco install ffmpeg

Install (dev)

• Option A: venv + pip (recommended)
• cd ytx && python3 -m venv .venv && source .venv/bin/activate
  • python -m pip install -U pip setuptools wheel
  • python -m pip install -e .
  • ytx --help
• Option B: uv
  • cd ytx && uv sync
  • uv run ytx --help

Running locally without installing

• From repo root:
  • export PYTHONPATH="$(pwd)/ytx/src"
  • cd ytx && python3 -m ytx.cli --help
  • Example: python3 -m ytx.cli summarize-file 0jpcFxY_38k.json --write

Note: Avoid running the ytx console script from inside the ytx/ folder; Python may shadow the installed package. Use the module form or run from repo root.

Usage (CLI)

• Whisper (CPU by default):
  • ytx transcribe <url> --engine whisper --model small
• Whisper (larger model):
  • ytx transcribe <url> --engine whisper --model large-v3-turbo
• Gemini (best‑effort timestamps):
  • ytx transcribe <url> --engine gemini --timestamps chunked --fallback
• Chapters + summaries:
  • ytx transcribe <url> --by-chapter --parallel-chapters --chapter-overlap 2.0 --summarize-chapters --summarize
• Engine options and timestamp policy:
  • ytx transcribe <url> --engine-opts '{"utterances":true}' --timestamps native
• Output dir:
  • ytx transcribe <url> --output-dir ./artifacts
• Verbose logging:
  • ytx --verbose transcribe <url> --engine whisper
• Health check:
  • ytx health (ffmpeg, API key presence, network)
• Summarize an existing transcript JSON:
  • ytx summarize-file /path/to/<video_id>.json --write

Metal (Apple Silicon) via whisper.cpp

• Build whisper.cpp with Metal: make -j METAL=1
• Download a GGUF/GGML model (e.g., large-v3-turbo)
• Run with whisper.cpp engine by passing a model file path:
  • uv run ytx transcribe <url> --engine whispercpp --model /path/to/gguf-large-v3-turbo.bin
• Auto-prefer whisper.cpp when device=metal (if whisper.cpp binary is available):
  • Set env YTX_WHISPERCPP_BIN to the main binary path, and provide a model path as above
• Tuning (env or .env):
  • YTX_WHISPERCPP_NGL (GPU layers, default 35), YTX_WHISPERCPP_THREADS (CPU threads)

Outputs

• JSON (<video_id>.json): TranscriptDoc
  • keys: video_id, source_url, title, duration, language, engine, model, created_at, segments[], chapters?, summary?
  • segment: {id, start, end, text, confidence?} (seconds for time)
• SRT (<video_id>.srt): line-wrapped captions (2 lines max)
• Cache artifacts (under XDG cache root): meta.json, summary.json, transcript and captions.

Configuration (.env)

• Copy .env.example → .env, then adjust:
  • GEMINI_API_KEY (for Gemini)
  • YTX_ENGINE (default whisper), WHISPER_MODEL (e.g., large-v3-turbo)
  • YTX_WHISPERCPP_BIN and YTX_WHISPERCPP_MODEL_PATH for whisper.cpp
  • Optional: YTX_CACHE_DIR, YTX_OUTPUT_DIR, YTX_ENGINE_OPTS (JSON), and timeouts (YTX_NETWORK_TIMEOUT, etc.)

Restricted videos & cookies

• Some videos are age/region restricted or private. The downloader supports cookies, but CLI flags are not yet wired.
• Workarounds: run yt-dlp manually, or use the Python API (pass cookies_from_browser / cookies_file to downloader).
• Error messages suggest cookies usage when restrictions are detected.

Performance Tips

• faster‑whisper: compute_type=auto resolves to int8 on CPU, float16 on CUDA.
• Model sizing: start with small/medium; use large-v3(-turbo) for best quality.
• Metal (whisper.cpp): tune -ngl (30–40 typical on M‑series) and threads to maximize throughput.

Development

• Structure: code in src/ytx/, CLI in src/ytx/cli.py, engines in src/ytx/engines/, exporters in src/ytx/exporters/.
• Tests: pytest -q (add tests under ytx/tests/).
• Lint/format (if configured): ruff check . / ruff format ..

Roadmap

• Add VTT/TXT exporters, format selection (--formats json,srt,vtt,txt)
• OpenAI/Deepgram/ElevenLabs engines via shared cloud base
• More resilient chunking/alignment; diarization options where supported
• CI + tests; docs polish; performance tuning