Allows reading recordings from mounted Google Drive folders so they can be transcribed without making a separate local video copy.
Allows transcribing YouTube videos by URL, automatically downloading the video, reusing it on subsequent runs, and producing transcripts with screenshots and timestamps.
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
Transcribe videos in their original language and match the speech with screenshots.
Demo · Features · Installation · MCP · YouTube · Models · Documentation · License
Read the transcript · HTML · PDF · Review uncertain speech
Left: the input video. Right: the actual HTML output with speech, timestamps, and a matching screenshot. The demo recording uses synthesized narration.
Related MCP server: loop-tts
Transcribe lectures, presentations, and screen recordings.
Keep English, Korean, Spanish, and other supported languages as spoken.
Link speech to screenshots taken around screen changes.
Export Markdown, plain HTML, PDF, JSON, SRT, and WebVTT.
Resume interrupted work or process several videos at once.
Read recordings from mounted Google Drive or OneDrive folders.
Review through MCP or your agent's terminal; keep suggestions separate from the original transcript.
Run locally on Linux, Windows, or macOS, in Docker, or through a shared server.
Speech recognition can miss or mishear words. Review important passages before relying on them.
Install FFmpeg, then clone the project. You need Git and Python 3.10 or later.
git clone https://github.com/ts06068/please-transcribe.git
cd please-transcribe
python -m venv .venv
# Linux/macOS:
. .venv/bin/activate
# Windows PowerShell instead:
# .venv\Scripts\Activate.ps1
python -m pip install -e '.[media,cpu,url]'
please-transcribe doctor --backend faster-whisper
please-transcribe run video.mp4 --work work/session --backend faster-whisperThe first run downloads the model. Original files are saved in work/session/exports/;
automatically polished files go in exports/polished/ after agent review.
Repeat the command to resume; use a new folder for another recording.
PDF needs extra setup. For Docker or a shared server, follow the installation guide.
Open the repository in Codex, Claude Code, or another agent with terminal access and ask:
Read AGENTS.md and docs/INSTALLATION.md. Install please-transcribe for local CPU use in a virtual environment, install the required media tools, and run doctor.
See installation prompts for shared servers and other setups.
After the installation above, add the MCP extra:
python -m pip install -e '.[mcp]'Register this stdio server in your MCP client's settings, using absolute paths:
{
"command": "/path/to/please-transcribe/.venv/bin/please-transcribe",
"args": ["mcp", "--root", "/path/to/please-transcribe/work"]
}On Windows, use .venv\Scripts\please-transcribe.exe as the executable path.
The surrounding settings format depends on your client.
Ask your agent to run the transcription, then call polish_session to inspect
unclear speech and submit_polish to return its review. Corrections and exports
are applied automatically. See MCP setup.
run includes polishing by default. Codex, Claude Code, or another active agent
continues through the review without manual approval commands. Unattended CLI
use needs a configured harness; otherwise it
returns an agent handoff. Use --polish off for baseline-only processing.
In the corrected HTML, hover over a marked phrase to see the raw text. PDF uses
yellow highlights with raw-text comments. Originals stay intact; corrected files
are in exports/polished/. See polishing details.
Give your agent a YouTube link, or run:
please-transcribe run 'https://www.youtube.com/watch?v=VIDEO_ID' --work work/youtubeThe video is downloaded automatically and reused on the next run. Local YouTube intake needs Node.js 22+ or Deno 2.3+; both Docker images include Node.js. See URL setup for details.
Configure a remote with rclone config, then mount it read-only in one terminal:
please-transcribe cloud mount gdrive:Recordings ~/mnt/recordingsIn another terminal, pass the mounted video path to run. No separate local video
copy is needed; audio, screenshots, and results still use a work directory.
See Google Drive and OneDrive setup for mount drivers,
Windows examples, and use from a container.
The default model is OpenAI Whisper large-v3 (about 1.55 billion parameters).
For local transcription, use --model to choose another compatible model.
Setting | Default or option |
Local engine | faster-whisper/CTranslate2, CPU, INT8. |
Optional MLX engine |
|
Published demo | Remote large-v3, verified OpenAI weights converted to MLX FP16, English. |
Audio | Mono, 16 kHz, PCM16 WAV. |
Timing | Segment and word timestamps from the engine. |
Agents use screenshots during review; the initial transcript comes from audio. The optional speech-detection audit flags possible omissions. HTML uses basic browser elements and embedded screenshots, with no CSS or JavaScript.
See the full specification for model sources, decoder settings, timestamps, and what each output records. The automated server installer currently targets macOS; local CPU use and remote clients support all three platforms.
Installation: local, Docker, shared server, and agent setup.
Workflows: languages, batches, review, Python API, and MCP.
Cloud recordings: Google Drive and OneDrive mounts.
Agent guide: requests you can give your agent.
Contributing: development setup, tests, and commit conventions.
Security: data handling and reporting vulnerabilities.
Built with Whisper, faster-whisper, MLX Whisper, and FFmpeg. README structure follows Markdownify, listed in awesome-readme.
MIT. Dependencies and model weights have their own licenses.
This server cannot be deployed
Subtitle engine for AI agents: one upload gives a written track and a translation, timings exact.
Transcribe audio & video to text for AI agents: 100+ languages, speaker labels, webhooks.
YouTube transcripts for AI agents: captions with timestamps, languages, TXT, SRT and VTT export.
Transcribe audio and video, and translate subtitles into 50+ languages.
