whisper-timestamped

First installation

Requirements:

• python3 (version higher or equal to 3.7, at least 3.9 is recommended)
• ffmpeg (see instructions for installation on the whisper repository)

You can install whisper-timestamped either by using pip:

pip3 install whisper-timestamped

or by cloning this repository and running installation:

git clone https://github.com/linto-ai/whisper-timestamped
cd whisper-timestamped/
python3 setup.py install

Additional packages that might be needed

If you want to plot alignment between audio timestamps and words (as in this section), you also need matplotlib:

pip3 install matplotlib

If you want to use VAD option (Voice Activity Detection before running Whisper model), you also need torchaudio and onnxruntime:

pip3 install onnxruntime torchaudio

If you want to use finetuned Whisper models from the Hugging Face Hub, you also need transformers:

pip3 install transformers

Docker

A docker image of about 9GB can be built using:

git clone https://github.com/linto-ai/whisper-timestamped
cd whisper-timestamped/
docker build -t whisper_timestamped:latest .

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Light installation for CPU

If you don't have a GPU (or don't want to use it), then you don't need to install the CUDA dependencies. You should then just install a light version of torch before installing whisper-timestamped, for instance as follows:

pip3 install \
     torch==1.13.1+cpu \
     torchaudio==0.13.1+cpu \
     -f https://download.pytorch.org/whl/torch_stable.html

A specific docker image of about 3.5GB can also be built using:

git clone https://github.com/linto-ai/whisper-timestamped
cd whisper-timestamped/
docker build -t whisper_timestamped_cpu:latest -f Dockerfile.cpu .