<div align="center">
# AI Video Transcriber
English | [ไธญๆ](README_ZH.md)
An AI-powered tool to transcribe, summarize, and archive videos and podcasts โ paste a URL from YouTube, TikTok, Bilibili, Apple Podcasts, SoundCloud, and 30+ platforms, **or upload a local file** (audio, video, or plain text).
</div>
## โจ Features
- ๐ฅ **Multi-Platform Support**: Works with YouTube, TikTok, Bilibili, Apple Podcasts, SoundCloud, and 30+ more
- โก **Subtitle-First Architecture**: For platforms with native subtitles (e.g. YouTube), transcripts are extracted instantly โ no audio download needed. Whisper is only a fallback, making the whole pipeline dramatically faster
- ๐ฌ **Original Video Download**: Keep the source video alongside the transcript. It downloads **in parallel** with transcription, previews inline in the results card, and saves with one click. On the Whisper path the audio is extracted from that same file, so the video is only fetched once. Toggle **Keep original video** off when you want text only
- ๐ **Local File Upload**: Drag-and-drop or pick a file โ `.txt` (treated as transcript text), `.mp3`, `.mp4`, `.m4a`, `.wav`, `.webm`, `.mkv`, `.ogg`, `.flac`. Media is normalized with FFmpeg for Whisper, then runs the same optimize โ translate โ summarize pipeline as URLs
- ๐ฃ๏ธ **Intelligent Transcription**: High-accuracy speech-to-text using Faster-Whisper when subtitles aren't available
- ๐ค **AI Text Optimization**: Automatic typo correction, sentence completion, and intelligent paragraphing
- ๐ **Multi-Language Summaries**: Generate intelligent summaries in 11 languages
- โ๏ธ **Conditional Translation**: Auto-translates the transcript when the summary language differs from the source language
- ๐ง **Bring Your Own Model**: Configure any OpenAI-compatible API endpoint (OpenAI, OpenRouter, local LLM, etc.) directly in the UI โ enter your API Base URL and API Key, then click **Fetch** to auto-discover available models
- ๐ก **Live Progress**: Server-sent events stream real-time status, with a badge showing whether the job took the subtitle or Whisper path
- ๐ฑ **Mobile-Friendly**: Responsive layout, dark UI
## ๐ Contents
- [Quick Start](#quick-start)
- [Usage Guide](#usage-guide)
- [Use It From Claude / Codex / Scripts](#agents)
- [API Reference](#api-reference)
- [Technical Architecture](#architecture)
- [Configuration Options](#configuration)
- [FAQ](#faq)
- [Supported Languages](#languages)
- [Performance Tips](#performance)
- [Contributing](#contributing)
<a id="quick-start"></a>
## ๐ Quick Start
### Prerequisites
- Python 3.8+
- FFmpeg (required for yt-dlp audio extraction, merging downloaded video, and normalizing uploaded media)
- An API key from any OpenAI-compatible provider (OpenAI, OpenRouter, etc.) โ configurable directly in the UI, no server-side env var needed
### Installation
<details open>
<summary><b>Method 1: Automatic Installation</b></summary>
```bash
git clone https://github.com/wendy7756/AI-Video-Transcriber.git
cd AI-Video-Transcriber
chmod +x install.sh
./install.sh
```
</details>
<details>
<summary><b>Method 2: Docker</b></summary>
```bash
git clone https://github.com/wendy7756/AI-Video-Transcriber.git
cd AI-Video-Transcriber
# Using Docker Compose (easiest)
cp .env.example .env
# Edit .env if you want server-side defaults (optional)
docker-compose up -d
# Or using Docker directly
docker build -t ai-video-transcriber .
docker run -p 8000:8000 --env-file .env ai-video-transcriber
```
The image uses **Python 3.12** (Debian Bookworm), upgrades `pip`/`setuptools`/`wheel`, then installs from `requirements.txt` โ same version constraints as a fresh local venv on a current Python.
> **Tip:** transcripts and downloaded videos live in `/app/temp` inside the container. Uncomment the `volumes` block in `docker-compose.yml` to persist them on the host.
</details>
<details>
<summary><b>Method 3: Manual Installation</b></summary>
**1. Install Python dependencies**
```bash
# macOS (PEP 668) strongly recommends a virtualenv
python3 -m venv venv
source venv/bin/activate
python -m pip install --upgrade pip
pip install -r requirements.txt
```
**2. Install FFmpeg**
```bash
brew install ffmpeg # macOS
sudo apt update && sudo apt install ffmpeg # Ubuntu/Debian
sudo yum install ffmpeg # CentOS/RHEL
```
**3. Configure environment variables** *(optional)*
```bash
# Only if you prefer server-side defaults โ otherwise configure in the UI
export OPENAI_API_KEY="your_api_key_here"
export OPENAI_BASE_URL="https://openrouter.ai/api/v1" # any OpenAI-compatible endpoint
```
</details>
### Start the Service
```bash
python3 start.py
```
Then open `http://localhost:8000`.
**Production mode (recommended for long videos)** โ disables hot-reload so the SSE connection stays stable across 30โ60+ minute tasks:
```bash
python3 start.py --prod
```
<details>
<summary>Run with explicit env (example)</summary>
```bash
source venv/bin/activate
export OPENAI_API_KEY=your_api_key_here # optional: server-side default
# export OPENAI_BASE_URL=https://openrouter.ai/api/v1 # optional: server-side default
python3 start.py --prod
```
</details>
<a id="usage-guide"></a>
## ๐ Usage Guide
**1. Choose your input โ URL or file**
- **Video / podcast URL**: paste a link from YouTube, Bilibili, or any supported platform
- **Local file**: drag a file onto the dashed upload area, or click to browse. The same **Transcribe** button starts the job. Uploads use the same API route as URLs (`POST /api/process-video` with a multipart `file`), which helps when a reverse proxy only allows that path
**2. Pick your options**
- **Summary Language** โ the output language for the summary
- **Keep original video** โ on by default. Downloads the source video (โค720p) so you can preview and save it with the results. Turn it off for text-only jobs to save bandwidth and disk
**3. (Optional) Configure your AI model** โ click **AI Settings** to expand
- Enter your **API Base URL** (e.g. `https://openrouter.ai/api/v1`) and **API Key**
- Click **Fetch** to auto-load available models, then select one โ or leave blank for the server default
- Credentials are stored in your browser's `localStorage`, never sent anywhere but your chosen provider
**4. Start processing** โ click **Transcribe**. For **URL** jobs a badge shows the active mode:
| Badge | Meaning |
|-------|---------|
| **โก Subtitle** (green) | Native subtitles found โ transcript extracted in seconds |
| **๐ Whisper** (cyan) | No subtitles available โ audio downloaded and transcribed |
For **local uploads**, media is normalized with FFmpeg then transcribed with Whisper. Plain **`.txt`** files skip download and Whisper entirely, going straight into the text pipeline.
**5. Review the results**
- **Transcript** and **AI Summary** tabs are always present; a **Translation** tab appears automatically when the transcript language differs from your summary language
- Each tab has its own **purple download icon** โ click it to save that file without switching tabs
- **Download original video** sits at the right of the tab row, next to an inline player showing the source file and its size
<a id="agents"></a>
## ๐ค Use It From Claude / Codex / Scripts
Besides the web UI there is a headless entry point, so agents and scripts can run the
same pipeline with no server and no browser.
### CLI
```bash
venv/bin/python transcribe.py "https://www.youtube.com/watch?v=VIDEO_ID" --json
venv/bin/python transcribe.py talk.mp4 -l zh --no-video
venv/bin/python transcribe.py notes.txt --no-llm # no API key needed
```
`--json` puts a machine-readable result on stdout and keeps progress on stderr.
Exit codes: `0` success, `2` bad input, `1` download/transcode failure.
| Flag | Meaning |
|------|---------|
| `-l, --summary-language` | Summary language (`en`, `zh`, `es`, `fr`, `de`, `it`, `pt`, `ru`, `ja`, `ko`, `ar`) |
| `--no-llm` | Transcript only โ skips optimize/translate/summarize, needs no API key |
| `--no-video` | Don't keep the original video |
| `--whisper-model` | `tiny` โฆ `large`, default `base` |
| `-o, --output-dir` | Where to write the Markdown, default `./temp` |
| `--json` / `-q` | Machine-readable output / silence progress |
#### AI provider settings for agents
For CLI/agent use, configure the OpenAI-compatible provider with environment
variables:
```bash
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 one-off CLI runs you can also pass `--api-key`, `--base-url`, and `--model`,
but environment variables are safer because API keys do not end up in shell history.
### Codex App plugin
The repo also ships a skills-only Codex plugin:
```text
.codex-plugin/plugin.json
skills/video-transcribe/SKILL.md
```
This repository root is the plugin root. Import or install this folder as a local
plugin in Codex App, then start a new task and select **AI Video Transcriber** from
Plugins. Shipping the plugin files does not install the plugin automatically; after
changing the plugin, refresh or reinstall it and start a new task so Codex reloads
the skill. The skill wraps the same CLI pipeline above, so the machine running
Codex still needs this repo's `venv` and `ffmpeg`.
Codex App does not provide a plugin-specific settings panel for this skills-only
plugin. To switch from OpenRouter to another OpenAI-compatible endpoint, update the
environment variables available to the Codex task, or ask Codex to run the CLI with
`--base-url` / `--model` for that specific run.
This plugin intentionally does not bundle the local stdio MCP server. If you want
Codex to call the `transcribe_video` MCP tool directly, register the MCP server
separately as shown below.
### Claude Code skill
The repo ships `.claude/skills/video-transcribe/SKILL.md`, so Claude Code picks it up
automatically when you work in this directory โ just ask it to transcribe a link. To
use it from anywhere, copy the folder to `~/.claude/skills/`.
### MCP server (Claude Code, Claude Desktop, Codex)
The project includes an optional stdio MCP server. It is not registered
automatically; add it once per client.
```bash
pip install "mcp>=2.0"
# From the repository root:
claude mcp add video-transcriber \
-e OPENAI_API_KEY=your_api_key_here \
-e OPENAI_BASE_URL=https://openrouter.ai/api/v1 \
-- "$(pwd)/venv/bin/python" "$(pwd)/mcp_server.py"
codex mcp add \
--env OPENAI_API_KEY=your_api_key_here \
--env OPENAI_BASE_URL=https://openrouter.ai/api/v1 \
video-transcriber -- "$(pwd)/venv/bin/python" "$(pwd)/mcp_server.py"
```
If you prefer editing Codex config directly, add this to `~/.codex/config.toml`:
```toml
[mcp_servers.video-transcriber]
command = "/abs/path/venv/bin/python"
args = ["/abs/path/mcp_server.py"]
[mcp_servers.video-transcriber.env]
OPENAI_API_KEY = "your_api_key_here"
OPENAI_BASE_URL = "https://openrouter.ai/api/v1"
```
For Claude Desktop, add the same command and args to your Claude Desktop MCP
configuration:
```json
{
"mcpServers": {
"video-transcriber": {
"command": "/abs/path/venv/bin/python",
"args": ["/abs/path/mcp_server.py"],
"env": {
"OPENAI_API_KEY": "your_api_key_here",
"OPENAI_BASE_URL": "https://openrouter.ai/api/v1"
}
}
}
}
```
To change providers later, update the MCP client's saved environment variables
or remove and re-add the MCP server with the new `OPENAI_BASE_URL` / model settings,
then restart or reload the client if it keeps MCP servers running.
Exposes one tool, `transcribe_video`, returning the transcript, summary, optional
translation, file paths, and a `no_speech` flag. Verify the wiring with
`venv/bin/python mcp_server.py --selftest`, then check client registration with
`claude mcp list` or `codex mcp list`.
> **`no_speech` matters:** when the source has no speech, the pipeline skips the LLM
> entirely and returns empty text. Agents should report that rather than guessing at
> the content โ feeding an empty transcript to an LLM produces confident fabrications.
<a id="api-reference"></a>
## ๐ API Reference
All endpoints are served from the same origin as the UI.
| Method | Endpoint | Purpose |
|--------|----------|---------|
| `POST` | `/api/process-video` | Start a job โ accepts either a URL or a multipart `file` |
| `POST` | `/api/process-upload` | Upload-only alias, identical behavior |
| `GET` | `/api/task-status/{task_id}` | Poll a job's current state |
| `GET` | `/api/task-stream/{task_id}` | SSE stream of live progress updates |
| `GET` | `/api/download/{filename}` | Download a result as an attachment (`.md` or media). Optional `?name=` sets a friendly filename |
| `GET` | `/api/media/{filename}` | Stream media inline for the player โ supports HTTP Range, so seeking works |
| `DELETE` | `/api/task/{task_id}` | Cancel a running job and drop its record |
| `POST` | `/api/models` | Proxy: list models from any OpenAI-compatible provider |
| `GET` | `/api/tasks/active` | Active job counters (debugging) |
<details>
<summary><b>Form fields for <code>POST /api/process-video</code></b></summary>
| Field | Type | Default | Description |
|-------|------|---------|-------------|
| `url` | string | `""` | Video/podcast URL. Omit when uploading a file |
| `file` | file | โ | Multipart upload. Takes precedence over `url` |
| `summary_language` | string | `zh` | Target summary language code |
| `download_video` | string | `1` | `0`/`false`/`no`/`off` disables the original video download |
| `api_key` | string | `""` | Per-request API key; falls back to `OPENAI_API_KEY` |
| `model_base_url` | string | `""` | Per-request OpenAI-compatible base URL |
| `model_id` | string | `""` | Model to use; blank means server default |
```bash
# Transcribe a URL, skipping the video download
curl -X POST http://localhost:8000/api/process-video \
-F "url=https://www.youtube.com/watch?v=VIDEO_ID" \
-F "summary_language=en" \
-F "download_video=0"
```
</details>
<a id="architecture"></a>
## ๐ ๏ธ Technical Architecture
**Backend** โ FastAPI ยท yt-dlp (download & subtitle extraction) ยท FFmpeg (audio extraction, video merge, upload normalization to mono 16 kHz) ยท Faster-Whisper (transcription) ยท OpenAI-compatible API (optimization, translation, summary)
**Frontend** โ vanilla HTML5/CSS3/ES6+ ยท Marked.js (Markdown rendering) ยท Font Awesome (icons) ยท SSE for live progress
### Processing Pipeline
```
URL โโโฌโโ probe subtitles โโfoundโโโ parse VTT/SRT โโโโโโโโโโโโโโ
โ โ
โ (in parallel, if "Keep original video" is on) โโโ optimize
โโโ download video (โค720p) โโโ extract audio โโโ Whisper โโโ โ translate*
โ summarize
File โโ normalize with FFmpeg โโโ Whisper โโโโโโโโโโโโโโโโโโโโโโโโ โ results
(.txt skips straight to the text pipeline) (* when languages differ)
```
### Project Structure
```
AI-Video-Transcriber/
โโโ backend/
โ โโโ main.py # FastAPI app, routes, task orchestration
โ โโโ pipeline.py # Pure helpers shared by web/CLI/MCP (incl. no-speech guard)
โ โโโ video_processor.py # yt-dlp: subtitles, audio, original video
โ โโโ transcriber.py # Faster-Whisper transcription
โ โโโ summarizer.py # Transcript optimization + summary
โ โโโ translator.py # Conditional translation
โ โโโ llm_sanitize.py # Post-process LLM output (strip boilerplate)
โโโ static/
โ โโโ index.html # UI markup + styles
โ โโโ app.js # Frontend logic, SSE, i18n
โโโ temp/ # Generated transcripts, summaries, media
โโโ Dockerfile
โโโ docker-compose.yml
โโโ .env.example
โโโ requirements.txt
โโโ install.sh
โโโ start.py # Startup script (--prod disables hot reload)
โโโ transcribe.py # Headless CLI (agents, scripts, cron)
โโโ mcp_server.py # MCP server exposing the transcribe_video tool
โโโ .codex-plugin/
โ โโโ plugin.json # Codex App plugin manifest
โโโ skills/
โ โโโ video-transcribe/ # Codex plugin skill wrapping the CLI
โโโ .claude/skills/
โโโ video-transcribe/ # Claude Code skill wrapping the CLI
```
<a id="configuration"></a>
## โ๏ธ Configuration Options
### Environment Variables
All are optional โ the app runs with defaults and accepts AI credentials from the UI.
| Variable | Description | Default |
|----------|-------------|---------|
| `OPENAI_API_KEY` | API key (server-side default) | โ |
| `OPENAI_BASE_URL` | OpenAI-compatible endpoint | provider default |
| `OPENAI_TRANSLATION_MODEL` | Model used for translation | `gpt-4o` |
| `WHISPER_MODEL_SIZE` | Whisper model size | `base` |
| `UPLOAD_MAX_MB` | Max upload size per file (MB) | `200` |
| `VIDEO_MAX_HEIGHT` | Max height for the original video download | `720` |
| `HOST` | Server address | `0.0.0.0` |
| `PORT` | Server port | `8000` |
| `PRODUCTION_MODE` | Set to `true` to disable hot reload (same as `--prod`) | โ |
### Whisper Model Size Options
| Model | Parameters | Speed | Memory |
|-------|------------|-------|--------|
| tiny | 39 M | Fast | ~150 MB |
| base | 74 M | Medium | ~250 MB |
| small | 244 M | Medium | ~750 MB |
| medium | 769 M | Slow | ~1.5 GB |
| large | 1550 M | Very slow | ~3 GB |
All sizes are multilingual; `tiny`โ`medium` also ship English-only variants.
<a id="faq"></a>
## ๐ง FAQ
<details>
<summary><b>Does keeping the original video slow things down?</b></summary>
Usually not much. The download runs concurrently with transcription and summarization and is only awaited at the very end, so it mostly hides behind work that was happening anyway. On the Whisper path it actually *saves* a download, because the audio is extracted from the video file instead of fetching the media twice.
Downloads are capped at 720p by default โ lower `VIDEO_MAX_HEIGHT` to save bandwidth, or turn the toggle off entirely for text-only jobs. If the video download fails, transcription still completes normally and the results simply omit the player.
</details>
<details>
<summary><b>Does <code>temp/</code> grow over time?</b></summary>
Yes. Transcripts, summaries, and downloaded videos are deliberately kept after a job finishes so you can still download them, and there is no automatic cleanup. Now that original videos are saved too, prune the directory periodically:
```bash
# Delete generated files older than 7 days
find temp -type f -mtime +7 ! -name 'tasks.json' -delete
```
</details>
<details>
<summary><b>Why is transcription slow?</b></summary>
Speed depends on video length, Whisper model size, and hardware. Use a smaller model (`tiny` or `base`) to speed it up. Note that videos with native subtitles skip Whisper entirely and finish in seconds.
</details>
<details>
<summary><b>Which video platforms are supported?</b></summary>
Everything yt-dlp supports โ including YouTube, TikTok, Facebook, Instagram, X/Twitter, Bilibili, Youku, iQiyi, Tencent Video, Apple Podcasts, and SoundCloud.
</details>
<details>
<summary><b>What local file types and size limits apply?</b></summary>
Allowed extensions: `.txt`, `.mp3`, `.mp4`, `.m4a`, `.wav`, `.webm`, `.mkv`, `.ogg`, `.flac`. Default limit is **200 MB** per file โ override with `UPLOAD_MAX_MB`.
</details>
<details>
<summary><b>The AI features are unavailable โ what now?</b></summary>
They need an API key from any OpenAI-compatible provider. Enter it in the **AI Settings** panel in the UI (no restart needed), or set `OPENAI_API_KEY` for a server-side default. Without a key, transcription still works โ Whisper runs locally โ but optimization, translation, and summaries fall back to basic formatting.
</details>
<details>
<summary><b>I get HTTP 500 errors. Why?</b></summary>
Usually environment configuration rather than a code bug. Check that you:
- Activated the virtualenv: `source venv/bin/activate`
- Installed deps inside it: `pip install -r requirements.txt`
- Configured an API key in **AI Settings**, or set `OPENAI_API_KEY`
- Installed FFmpeg: `brew install ffmpeg` / `sudo apt install ffmpeg`
- Freed port 8000, or changed `PORT`
</details>
<details>
<summary><b>How do I deploy with Docker?</b></summary>
**Prerequisites:** install Docker Desktop from https://www.docker.com/products/docker-desktop/ and make sure the service is running.
```bash
git clone https://github.com/wendy7756/AI-Video-Transcriber.git
cd AI-Video-Transcriber
cp .env.example .env # edit for server-side defaults (optional)
docker-compose up -d # recommended
# Or manually
docker build -t ai-video-transcriber .
docker run -p 8000:8000 --env-file .env ai-video-transcriber
```
**Common issues**
- **Port conflict** โ remap with `-p 8001:8000`
- **Permission denied** โ confirm Docker Desktop is running
- **Build fails** โ check disk space (~2 GB free) and network
- **Container won't start** โ inspect `docker logs <container_id>`
**Useful commands**
```bash
docker ps # running containers
docker logs ai-video-transcriber-ai-video-transcriber-1 # logs
docker-compose down # stop
docker-compose build --no-cache # rebuild
```
</details>
<details>
<summary><b>What are the memory requirements?</b></summary>
**Docker:** ~128 MB idle, 500 MBโ2 GB while processing, ~1.6 GB image. 4 GB+ RAM recommended.
**Traditional:** ~50โ100 MB for the FastAPI server, plus the Whisper model (see the table above), plus roughly 500 MB peak for processing.
```bash
# Reduce memory usage
WHISPER_MODEL_SIZE=tiny
# Limit container memory
docker run -m 1g -p 8000:8000 --env-file .env ai-video-transcriber
# Monitor
docker stats ai-video-transcriber-ai-video-transcriber-1
```
</details>
<details>
<summary><b>Network connection errors or timeouts?</b></summary>
Symptoms include "Unable to extract" or timeouts on download, API connection/DNS failures, and slow Docker pulls.
1. **Switch VPN/proxy** to a different server
2. **Check network stability**
3. **Wait 30โ60 seconds** after changing network settings before retrying
4. **Verify custom endpoints** are reachable from your network
5. **Restart Docker Desktop** if container networking fails
```bash
curl -I https://www.youtube.com/ # platform access
curl -I https://openrouter.ai # AI provider
docker pull hello-world # Docker Hub
```
</details>
<a id="languages"></a>
## ๐ฏ Supported Languages
**Transcription** โ 100+ languages via Whisper, with automatic language detection.
**Summaries & Translation** โ English, Chinese (Simplified), Japanese, Korean, Spanish, French, German, Italian, Portuguese, Russian, Arabic.
**Interface** โ English and Chinese, switchable from the top-right.
<a id="performance"></a>
## ๐ Performance Tips
**Hardware**
- Minimum: 4 GB RAM, dual-core CPU
- Recommended: 8 GB RAM, quad-core CPU
- Ideal: 16 GB RAM, multi-core CPU, SSD
**Processing time estimates**
| Video Length | Subtitle Mode | Whisper Mode | Notes |
|--------------|---------------|--------------|-------|
| 1 minute | ~5 s | 30 sโ1 min | Subtitle mode needs no audio download |
| 5 minutes | ~10 s | 2โ5 min | YouTube auto-captions trigger subtitle mode |
| 15 minutes | ~15 s | 5โ15 min | Most YouTube videos support subtitle mode |
| 30+ minutes | ~20 s | 15โ60 min | Podcasts/audio-only always use Whisper |
Times assume **Keep original video** is off. With it on, subtitle-mode jobs are bounded by the video download rather than the transcript โ the download overlaps the AI steps, so the added wall-clock is usually well under the download time itself.
<a id="contributing"></a>
## ๐ค Contributing
Issues and Pull Requests are welcome!
1. Fork the project
2. Create a feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
## Acknowledgments
- [yt-dlp](https://github.com/yt-dlp/yt-dlp) โ powerful video downloading tool
- [Faster-Whisper](https://github.com/guillaumekln/faster-whisper) โ efficient Whisper implementation
- [FastAPI](https://fastapi.tiangolo.com/) โ modern Python web framework
- [OpenAI](https://openai.com/) โ intelligent text processing API
## ๐ Contact
For questions or suggestions, please open an Issue or contact Wendy.
---
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