whisper-transcriber-telegram-bot

A local Whisper AI transcriber bot for Telegram, utilizing local GPU's or CPU for processing. No Whisper API access required -- just utilize whatever available hardware you have. The bot uses GPUtil to automatically select an available CUDA GPU, or reverts to CPU-only if none is found.

Runs on the latest Whisper v3 turbo model (released Sept 30, 2024) by default. Models can be selected both from config.ini and with the user command /model.

The program has been written in Python and it works on Python version 3.10+, tested up to Python 3.12.2.

Designed for transcribing audio from various media source, such as URLs supported by yt-dlp, or via Telegram's audio messages or over media file uploads on Telegram (.mp3, .wav, .ogg, .flac, etc.)

Sites supported by yt-dlp are listed here. Supported audio and video file uploads can be configured separately from config.ini. Compatible with all ffmpeg-supported media formats), with up to 20MB direct file transfer sizes as supported by Telegram in their bot API.

Can be safely installed and deployed with Docker by using the included Dockerfile.

Features

• 🚀 (New!) Now supporting OpenAI's turbo model of the Whisper v3 series
  • (Whisper-v3 turbo released on September 30, 2024)
  • 8x transcription speed (vs. real-time)
  • Nearly on par with the previous v3-large model with only 6GB VRAM usage
• 🎥 Downloads and processes media URLs from any source supported by yt-dlp
  • (can be configured to use cookies.txt in config.ini for better availability)
• 📲 Can receive Telegram audio messages as well as files, i.e. .mp3 and .wav for transcription
  • Direct video file uploads in supported media formats is also supported
  • (all other ffmpeg supported formats also configurable via config.ini)
• 🤖 Uses a local Whisper model from the openai-whisper package for transcription
  • (no API required, use your own PC & available CUDA GPU!)
• 🖥️ Automatically uses GPUtil to map out the best available CUDA-enabled local GPU
  • (auto-switching to CPU-only mode if no CUDA GPU is available)
• 📝 Transcribes audio using OpenAI's Whisper model (can be user-selected with /model)
  • (see openai/whisper for more info on Whisper)
• 📄 Returns transcription in text, SRT, and VTT formats
• 🔄 Handles concurrent transcription requests efficiently with async & task queuing
• 🕒 Features an asynchronous automatic queue system to manage multiple transcription requests seamlessly

Installation (non-Docker version)

To set up the Whisper Transcriber Telegram Bot, follow these steps:

1. Clone the repository:

   git clone https://github.com/FlyingFathead/whisper-transcriber-telegram-bot.git
   cd whisper-transcriber-telegram-bot
   

2. Install the required Python packages:

   pip install -r requirements.txt
   

3. Install ffmpeg (required for media processing):

   On Ubuntu or Debian tree Linux:

   sudo apt update && sudo apt install ffmpeg
   

   On Arch Linux:

   sudo pacman -S ffmpeg
   

   On macOS using Homebrew:

   brew install ffmpeg
   

   On Windows using Chocolatey:

   choco install ffmpeg
   

   On Windows using Scoop:

   scoop install ffmpeg
   

4. Set up your Telegram bot token either in config/bot_token.txt or as an environment variable TELEGRAM_BOT_TOKEN.

5. Run the bot:

   python src/main.py
   

Dockerized Installation

Prerequisites

• Docker installed on your machine
• If you want to run your Whisper transcripts GPU accelerated with CUDA (recommended), you'll need a Nvidia GPU and the NVIDIA Container Toolkit installed on the host machine that is running the Docker container

To enable GPU processing inside Docker files, install the NVIDIA Container Toolkit in i.e. Ubuntu (on the host machine) with these steps:

1. Add NVIDIA GPG Key and Repository: Use the following commands to configure the repository securely with the GPG key:

   curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg &&
   curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
   sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
   sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
   

2. Update the Package List: Run the following to refresh your package list:

   sudo apt-get update
   

3. Install the NVIDIA Container Toolkit: Install the toolkit using:

   sudo apt-get install -y nvidia-container-toolkit
   

4. Configure Docker to Use NVIDIA Runtime: Configure the NVIDIA runtime for Docker:

   sudo nvidia-ctk runtime configure --runtime=docker
   

5. Restart Docker: Restart the Docker service to apply the changes:

   sudo systemctl restart docker
   

6. Test the Setup: You can verify if the setup is working correctly by running a base CUDA container:

   sudo docker run --rm --runtime=nvidia --gpus all nvidia/cuda:11.6.2-base-ubuntu22.04 nvidia-smi
   

If everything is set up correctly, you should see your GPUs listed.

Dockerfile Install Option 1: Pull the prebuilt image from GHCR

Just grab the latest pre-built version with:

docker pull ghcr.io/flyingfathead/whisper-transcriber-telegram-bot:latest

Dockerfile Install Option 2: Build the Docker image yourself

If there's something wrong with GHCR's prebuilt image, you can also build the Docker image yourself.

1. Navigate to the root directory of the project where the Dockerfile is located.

2. Build the Docker image using the following command:

   docker build -t whisper-transcriber-telegram-bot .
   

   This command builds a Docker image named whisper-transcriber-telegram-bot based on the instructions in your Dockerfile.

Running the Bot Using Docker

To run the bot using Docker (may require sudo, depending on whether or not you're using a docker group or not):

docker run --gpus all --name whisper-transcriber-telegram-bot -d \
  -e TELEGRAM_BOT_TOKEN='YourTelegramBotToken' \
  -v whisper_cache:/root/.cache/whisper \
  ghcr.io/flyingfathead/whisper-transcriber-telegram-bot:latest

Replace 'YourTelegramBotToken' with your actual Telegram bot token. This command also mounts the config directory and the Whisper model cache directory to preserve settings and downloaded models across container restarts.

Getting the Telegram Bot API Token

1. If you haven't got an active Telegram Bot API token yet, set up a new Telegram bot by interacting with Telegram's @BotFather. Use Telegram's user lookup to search for the user, message it and run the necessary bot setup to get your API key.

2. After setting up your bot and receiving your Telegram Bot API token from @BotFather, either copy-paste the Telegram Bot API authentication token into a text file (config/bot_token.txt) or set the API token as an environment variable with the name TELEGRAM_BOT_TOKEN. The program will look for both during startup, and you can choose whichever you want.

Usage

After launching your bot successfully, you can interact with it via Telegram (send a message to @your_bot_name_Bot, or whatever your bot name is):

1. Send a video URL (for yt-dlp to download), a voice message or an audio file (i.e. .wav or .mp3 format) to the bot.
2. The bot will acknowledge the request and begin processing, notifying the user of the process.
3. Once processing is complete, the bot will send the transcription to you. By default, the transcription is sent as a message as well as .txt, .srt and .vtt files. Transcription delivery formats can be adjusted from the config.ini.

Commands

• /info to view current settings, uptime, GPU info and queue status
• /help and /about - get help on bot use, list version number, available models and commands, etc.
• /model - view the model in usedef process_url or change to another available model.
• /language - set the model's transcription language (auto = autodetect); if you know the language spoken in the audio, setting the transcription language manually with this command may improve both transcription speed and accuracy.

Changes

• v0.1708 - Direct video file uploads are now available
  • (to prevent abuse, they're disabled by default, see config.ini)
• v0.1707 - New config.ini option: add sites that require full video download
  • some media sites don't work well with yt-dlp's audio-only download method
  • there are now two new options in config.ini under [YTDLPSettings]:
  • download_original_video_for_domains_active = true (default)
  • download_original_video_domains = site1.com, site2.com, site3.com
  • at the moment it's used for media platforms that have had reported issues during testing
  • when active, a comma-separated list is used to check up on media sites that require their contents to be downloaded as the original video instead of audio-only
  • (the tradeoff is obviously download size and hence speed; the audio-only method is usually the fastest and should be preferred for most popular sites, hence only add problematic sites to the video-only list)
  • using worst available video quality (with audio in it) is usually recommended
  • video quality selection is in config.ini: use_worst_video_quality = true (default is true, set to false if it doesn't work for your setup)
  • again, the default setup in this version should work for most users
• v0.1706 - Disable asking for token if running inside Docker
  • by default, the app will ask for the token if it's not found, unless Dockerized
  • can be better for headless use case scenarios where you need the error message rather than a prompt for the bot token
  • Dockerfile now has RUNNING_IN_DOCKER environment variable set for detection
• v0.1705 - Dockerized pre-builds; thanks to jonmjr for assistance!
  • updated src/utils/bot_token.py to query for a bot token if it's not found from either env vars or from the file
  • can be useful when running the bot in Docker containers
  • this option can be set on/off in config.ini with AskForTokenIfNotFound = True (default is true)
• v0.1704 - Token logic / bot_token.py updates; added config.ini preferences for reading the bot token
  • preferenvforbottoken = True is now on by default to prefer the environment variable entry for the bot token.
  • set to false to prefer config/bot_token.txt over the environment variable
  • AllowBotTokenFallback = True to allow fallbacks (whether from the env var to bot_token.txt or the other way around)
  • set to false to strictly disallow fallback bot API token checking
  • improved error catching + exit logic when the token is not found
• v0.1703 - included and updated welcome message (/start)
• v0.17021 - updated model info in /model
• v0.1702 - prevent queue hang cases with new method
• v0.1701 - better exception catching when chunking long transcripts (due to Telegram's message limits) See issue
• v0.17 - (1. Oct 2024) Now supports OpenAI's brand new Whisper v3 turbo model
  • turbo is enabled by default
• v0.1658 - UpdateSettings setting added to config.ini to update your bot on startup (can be set to True or False), as i.e. yt-dlp is highly recommended to be kept up to date constantly. You can modify the command line string to whatever modules you want to check updates on during startup.
  • fixed a parsing bug in YouTube urls
  • bot now announces successful downloads
  • added a few emojis here and there for clarity (feel free to comment if you don't like them)
• v0.1657 - more verbose error messages from yt-dlp if the download failed
• v0.1656 - introduced safe_split_message to split transcription better and more reliably (edge cases etc) into chunks when longer transcripts are sent as messages
• v0.1655 - added diarization.py and resemblyzer_safety_check.py under src/utils/ for Resemblyzer diarization support
  • these are WIP; for future in-bot diarization implementations (requires pip install resemblyzer to be installed first in order to run)
  • the current resemblyzer pip version (resemblyzer==0.1.4) can be patched with resemblyzer_safety_check.py to ensure safe pickle/depickle as per up-to-date standards
  • diarization.py can be used as a standalone diarization module for testing (requires resemblyzer)
    • (try with i.e. python diarization.py inputfile.mp3 textfile.txt)
  • both will pave the way for future diarization options that will be implemented in the bot's functionalities in the future
• v0.1654 - yt-dlp can now be configured to use cookies (for i.e. YouTube downloads) in config.ini
• v0.1653 - even more exception and error catching, especially for YouTube URLs
• v0.1652 - maximum file size checks (20MB) as per to Telegram API
• v0.1651 - improved parsing for Youtube URLs, better error handling
• v0.165 - select allowed audio types if transcription from audio files is enabled
  • (default formats: mp3, wav, m4a, aac, flac, ogg, wma, aiff, can be expanded to any ffmpeg supported format)
• v0.1603 - error message to the user whenever cookies are needed for yt-dlp
• v0.1602 - adjustable transcription completion message (in use y/n, its contents) in config.ini
• v0.1601 - process order fixes for transcripts (send as msg <> file)
• v0.16 - added configurable cooldowns & rate limits, see config.ini:
  • under [RateLimitSettings]: cooldown_seconds, max_requests_per_minute
• v0.15 - added config.ini options sendasfiles and sendasmessages
  • can be set to true or false depending on your preferences
  • sendasmessages (when set to true) sends the transcripts as Telegram messages in chat
  • sendasfiles (when set to true) sends the transcripts as .stt, .vtt and .txt
  • small fixes to i.e. url handling (allowallsites checks; YouTube)
• v0.14.6 - fixed occasional queue hangs with sent audio files (wav/mp3)
• v0.14.5 - fixed following the "keep/don't keep audio files" config rule
• v0.14.4 - added the /info command for viewing current settings & queue status
• v0.14.3 - Whisper model language selection via /language command
• v0.14.2 - display duration & estimates
• v0.14.1 - small fixes to the file handler; more detailed exception catching
• v0.14 - now handles both Telegram's audio messages as well as audio files (.wav, .mp3)
• v0.13 - added GPUtil GPU mapping to figure out the best available CUDA GPU instance to use
  • (by default, uses a CUDA-enabled GPU on the system with the most free VRAM available)
• v0.12 - async handling & user model change fixes, improved error handling
• v0.11.1 - bot logic + layout changes, model list with /model (also in config.ini)
• v0.11 - bugfixes & rate limits for /model command changes for users
• v0.10 - /help & /about commands added for further assistance
  • config.ini now has a list of supported models that can be changed as needed
• v0.09 - users can now change the model Whisper model with /model command
• v0.08 - auto-retry TG connection on start-up connection failure
  • can be set in config.ini with RestartOnConnectionFailure
• v0.07.7 - log output from whisper to logging
• v0.07.6 - update interval for logging yt-dlp downloads now configurable from config.ini
• v0.07.5 - 10-second interval update for yt-dlp logging
• v0.07.4 - fixes for non-youtube urls
• v0.07.2 - job queues fine-tuned to be more informative
• v0.07.1 - job queues introduced
• v0.07 - transcript queuing, more precise transcript time estimates
• v0.06 - better handling of details for all video sources, transcription time estimates
• v0.05 - universal video description parsing (platform-agnostic)
• v0.04.1 - version number printouts and added utils
• v0.04 - expanded support for various media sources via yt-dlp, supported sites listed here
• v0.03 - better logging to console, Whisper model + keep audio y/n can now be set in config.ini
• v0.02 - add video information to the transcript text file
  • (see: config.ini => IncludeHeaderInTranscription = True)
• v0.01 - initial commit

Contributing

Contributions are welcome! If you have suggestions for improvements or bug fixes, please open an issue or submit a pull request.

License

Licensed under the MIT License. See the LICENSE file for more details.

Credits

• FlyingFathead - Project creator
• ChaosWhisperer - Contributions to the Whisper integration and documentation
• Thanks for additional code contributions: GRbit (Dockerization), jonmjr (more Dockerization)