wendy7756/AI-Video-Transcriber

Video Transcribe

Runs this repo's pipeline headlessly: platform subtitles when available (seconds), local Whisper as fallback, then optimize -> translate -> summarize.

Before running

The command must be run from the AI Video Transcriber repository root and needs the project virtualenv plus ffmpeg. Verify once per session:

ls venv/bin/python && command -v ffmpeg

• No venv: run ./install.sh, or python3 -m venv venv && venv/bin/pip install -r requirements.txt.
• No ffmpeg: install it first with brew install ffmpeg on macOS or sudo apt install ffmpeg on Debian/Ubuntu. Do not proceed without it; audio extraction will fail.

Run it

venv/bin/python transcribe.py "<URL or file path>" --json

Always pass --json: it puts machine-readable output on stdout and keeps progress chatter on stderr. Parse the JSON rather than scraping log lines.

Useful flags:

Flag	When to use
-l, --summary-language <code>	Summary language: en, zh, es, fr, de, it, pt, ru, ja, ko, ar. Default en
--no-llm	No API key is available, or the user only wants the raw transcript. Skips optimize/translate/summarize
--no-video	The user does not want the source video kept
--whisper-model small	Accuracy matters more than speed. tiny to large, default base
-o <dir>	Write the Markdown somewhere other than ./temp

The LLM steps need an OpenAI-compatible key via OPENAI_API_KEY and optionally OPENAI_BASE_URL. Without one the pipeline still transcribes but falls back to basic formatting; prefer --no-llm when the user only needs the transcript.

Provider settings:

export OPENAI_API_KEY="your_api_key_here"
export OPENAI_BASE_URL="https://openrouter.ai/api/v1"
export OPENAI_TRANSLATION_MODEL="gpt-4o"  # optional

For a one-off run, transcribe.py also accepts --api-key, --base-url, and --model. Prefer environment variables when possible so API keys are not written to shell history.

Reading the result

{
  "title": "...",
  "no_speech": false,
  "detected_language": "en",
  "files": { "raw": "...", "transcript": "...", "summary": "...", "translation": "..." },
  "media": { "path": "...", "kind": "video", "size_bytes": 2243691 }
}

Read the transcript and summary paths to get the content. translation is present only when the source language differs from the summary language.

Critical: no_speech

When "no_speech": true the source contains no speech. There is no transcript, summary, or translation. The pipeline deliberately skips the LLM so that nothing gets invented.

If you see this, tell the user the video has no speech. Do not guess at the content, infer it from the title, or describe what the video probably says.

Failure modes

• Exit 2: bad input, such as a missing file, unsupported extension, or empty text file. Report the message and fix the argument.
• Exit 1: download/transcode failure, usually an unreachable URL, region-blocked/private video, or missing ffmpeg. Report the actual error.
• First run downloads Whisper weights, so it can look stalled for a minute.
• Long videos are slow in Whisper mode. Warn the user before starting if no platform subtitles are available.