FastWhisper
This is an optimized implementation of OpenAI's Whisper using a greedy decode
for multilingual transcription. It supports all sizes of the Whisper model (from tiny to large).
This codebase exports the models into TorchScript, ONNX, and TensorRT formats.
Getting Started
Docker, docker-compose and nvidia-container-toolkit is required to be installed.
Simply run bash run.sh; then you can access a simple UI at http://localhost:7860/.
Please note the initial setup can be quite slow and requires significant memory. Additionally, the TensorRT export will require an Nvidia GPU.
By default, the model selects tiny model to be exported to the optimized frameworks.
This can be adjusted by changing th MODEL_NAME in run.sh.
Please note the larger models will take much longer and use more memory!
The medium size took 4 hours and 40GB+ of memory on my system!
Model Performance
With my system with an AMD Ryzen Threadripper PRO 3975WX and an Nvidia RTX A6000, the following inference time on a ~5 second audio clip:
| Model Framework (Model) | tiny |
medium |
|---|---|---|
| PyTorch (Original) | 52.9 ms | 327 ms |
| PyTorch (Modded) | 41.6 ms | 261 ms |
| TorchScript (Modded) | 32.7 ms | 209 ms |
| ONNX (Modded) | 16.8 ms | 142 ms |
| TensorRT (Modded) | 8.1 ms | 60 ms |
Note the PyTorch (Original) model is using a Beam Search while the PyTorch (Modded) model is using a Greedy Search for decoding.
Note, the first few inference times will be quite long while the model "warms-up".
Disclaimer
The accelerated models should be validated for accuracy against the original model before being used. Limited testing has been done. Use at your own risk.
Sources
Credit to https://github.com/evanarlian/whisper-torchscript/ for creating a first cut of a scriptable model.