README.md

Whisper model generation and code samples

Environment

Create a conda environment with the following packages.

conda create -n whisper
pip install onnx
pip install torch
pip install transformers
pip install optimum[onnx]
pip install onnxruntime
pip install onnxruntime-extensions

Use nightly packages

pip install ort-nightly==1.16.0.dev20230701001 --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT-Nightly/pypi/simple/

Install from conda spec

Or install from the saved environment whisper.yml

PyTorch HuggingFace

Run from pytorch

HuggingFace Optimum

Run from optimum

Generate all in one model with Olive

Install Olive from source:

python -m pip install git+https://github.com/microsoft/Olive.git

Configs for each of the model variants can be found in ./models

cd models/whisper-tiny
python prepare_whisper_configs.py --model_name openai/whisper-tiny.en
python -m olive.workflows.run --config whisper_cpu_int8.json --setup
python -m olive.workflows.run --config whisper_cpu_int8.json

In the models folder, you will find a folder called CandidateModels/cpu-cpu/BestCandidateModel_1, which contains the model model.onnx. Copy the model into your application directory.

Run with ONNX Runtime

cd cpu
python transcribe.py

Export model without Olive

python -m onnxruntime.transformers.models.whisper.convert_to_onnx -m openai/whisper-base.en --output whisper -e

Run script to generate composite model

curl https://raw.githubusercontent.com/microsoft/onnxruntime-extensions/main/test/data/1272-141231-0002.mp3 > 1272-141231-0002.mp3 
curl https://raw.githubusercontent.com/microsoft/onnxruntime-extensions/main/tutorials/whisper_e2e.py > whisper_e2e.py
python whisper_e2e.py -a 1272-141231-0002.mp3 -m whisper/openai/whisper-base.en_beamsearch.onnx

Produces

 whisper-base.en_all.onnx.data