metadata

title: Faster Whisper Webui
emoji: 🚀
colorFrom: indigo
colorTo: blue
sdk: gradio
sdk_version: 3.23.0
app_file: app.py
pinned: false
license: apache-2.0

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Running Locally

To run this program locally, first install Python 3.9+ and Git. Then install Pytorch 10.1+ and all the other dependencies:

pip install -r requirements.txt

You can find detailed instructions for how to install this on Windows 10/11 here (PDF).

Finally, run the full version (no audio length restrictions) of the app with parallel CPU/GPU enabled:

python app.py --input_audio_max_duration -1 --server_name 127.0.0.1 --auto_parallel True

You can also run the CLI interface, which is similar to Whisper's own CLI but also supports the following additional arguments:

python cli.py \
[--vad {none,silero-vad,silero-vad-skip-gaps,silero-vad-expand-into-gaps,periodic-vad}] \
[--vad_merge_window VAD_MERGE_WINDOW] \
[--vad_max_merge_size VAD_MAX_MERGE_SIZE] \
[--vad_padding VAD_PADDING] \
[--vad_prompt_window VAD_PROMPT_WINDOW]
[--vad_cpu_cores NUMBER_OF_CORES]
[--vad_parallel_devices COMMA_DELIMITED_DEVICES]
[--auto_parallel BOOLEAN]

In addition, you may also use URL's in addition to file paths as input.

python cli.py --model large --vad silero-vad --language Japanese "https://www.youtube.com/watch?v=4cICErqqRSM"

Rather than supplying arguments to app.py or cli.py, you can also use the configuration file config.json5. See that file for more information. If you want to use a different configuration file, you can use the WHISPER_WEBUI_CONFIG environment variable to specify the path to another file.

Multiple Files

You can upload multiple files either through the "Upload files" option, or as a playlist on YouTube. Each audio file will then be processed in turn, and the resulting SRT/VTT/Transcript will be made available in the "Download" section. When more than one file is processed, the UI will also generate a "All_Output" zip file containing all the text output files.

Diarization

To detect different speakers in the audio, you can use the whisper-diarization application, or check "Diarization" in the options.

Download the JSON file after running Whisper on an audio file, and then run app.py in the whisper-diarization repository with the audio file and the JSON file as arguments.

Translation

To translate the transcript to English, set the task to "Translate". You can also use ChatGPT for this task via my translate-gpt CLI application.

Whisper Implementation

You can choose between using whisper or faster-whisper. Faster Whisper as a drop-in replacement for the default Whisper which achieves up to a 4x speedup and 2x reduction in memory usage.

You can install the requirements for a specific Whisper implementation in requirements-fasterWhisper.txt or requirements-whisper.txt:

pip install -r requirements-fasterWhisper.txt

And then run the App or the CLI with the --whisper_implementation faster-whisper flag:

python app.py --whisper_implementation faster-whisper --input_audio_max_duration -1 --server_name 127.0.0.1 --server_port 7860 --auto_parallel True

You can also select the whisper implementation in config.json5:

{
    "whisper_implementation": "faster-whisper"
}

GPU Acceleration

In order to use GPU acceleration with Faster Whisper, both CUDA 11.2 and cuDNN 8 must be installed. You may want to install it in a virtual environment like Anaconda.

Google Colab

You can also run this Web UI directly on Google Colab, if you haven't got a GPU powerful enough to run the larger models.

See the colab documentation for more information.

Parallel Execution

You can also run both the Web-UI or the CLI on multiple GPUs in parallel, using the vad_parallel_devices option. This takes a comma-delimited list of device IDs (0, 1, etc.) that Whisper should be distributed to and run on concurrently:

python cli.py --model large --vad silero-vad --language Japanese \
--vad_parallel_devices 0,1 "https://www.youtube.com/watch?v=4cICErqqRSM"

Note that this requires a VAD to function properly, otherwise only the first GPU will be used. Though you could use period-vad to avoid taking the hit of running Silero-Vad, at a slight cost to accuracy.

This is achieved by creating N child processes (where N is the number of selected devices), where Whisper is run concurrently. In app.py, you can also set the vad_process_timeout option. This configures the number of seconds until a process is killed due to inactivity, freeing RAM and video memory. The default value is 30 minutes.

python app.py --input_audio_max_duration -1 --vad_parallel_devices 0,1 --vad_process_timeout 3600

To execute the Silero VAD itself in parallel, use the vad_cpu_cores option:

python app.py --input_audio_max_duration -1 --vad_parallel_devices 0,1 --vad_process_timeout 3600 --vad_cpu_cores 4

You may also use vad_process_timeout with a single device (--vad_parallel_devices 0), if you prefer to always free video memory after a period of time.

Auto Parallel

You can also set auto_parallel to True. This will set vad_parallel_devices to use all the GPU devices on the system, and vad_cpu_cores to be equal to the number of cores (up to 8):

python app.py --input_audio_max_duration -1 --auto_parallel True

Docker

To run it in Docker, first install Docker and optionally the NVIDIA Container Toolkit in order to use the GPU. Then either use the GitLab hosted container below, or check out this repository and build an image:

sudo docker build -t whisper-webui:1 .

You can then start the WebUI with GPU support like so:

sudo docker run -d --gpus=all -p 7860:7860 whisper-webui:1

Leave out "--gpus=all" if you don't have access to a GPU with enough memory, and are fine with running it on the CPU only:

sudo docker run -d -p 7860:7860 whisper-webui:1

GitLab Docker Registry

This Docker container is also hosted on GitLab:

sudo docker run -d --gpus=all -p 7860:7860 registry.gitlab.com/aadnk/whisper-webui:latest

Custom Arguments

You can also pass custom arguments to app.py in the Docker container, for instance to be able to use all the GPUs in parallel (replace administrator with your user):

sudo docker run -d --gpus all -p 7860:7860 \
--mount type=bind,source=/home/administrator/.cache/whisper,target=/root/.cache/whisper \
--mount type=bind,source=/home/administrator/.cache/huggingface,target=/root/.cache/huggingface \
--restart=on-failure:15 registry.gitlab.com/aadnk/whisper-webui:latest \
app.py --input_audio_max_duration -1 --server_name 0.0.0.0 --auto_parallel True \
--default_vad silero-vad --default_model_name large

You can also call cli.py the same way:

sudo docker run --gpus all \
--mount type=bind,source=/home/administrator/.cache/whisper,target=/root/.cache/whisper \
--mount type=bind,source=/home/administrator/.cache/huggingface,target=/root/.cache/huggingface \
--mount type=bind,source=${PWD},target=/app/data \
registry.gitlab.com/aadnk/whisper-webui:latest \
cli.py --model large --auto_parallel True --vad silero-vad \
--output_dir /app/data /app/data/YOUR-FILE-HERE.mp4

Caching

Note that the models themselves are currently not included in the Docker images, and will be downloaded on the demand. To avoid this, bind the directory /root/.cache/whisper to some directory on the host (for instance /home/administrator/.cache/whisper), where you can (optionally) prepopulate the directory with the different Whisper models.

sudo docker run -d --gpus=all -p 7860:7860 \
--mount type=bind,source=/home/administrator/.cache/whisper,target=/root/.cache/whisper \
registry.gitlab.com/aadnk/whisper-webui:latest