metadata
license: mit
pipeline_tag: automatic-speech-recognition
language:
- en
- zh
- de
- es
- ru
- ko
- fr
- ja
- pt
- tr
- pl
- ca
- nl
- ar
- sv
- it
- id
- hi
- fi
- vi
- he
- uk
- el
- ms
- cs
- ro
- da
- hu
- ta
- 'no'
- th
- ur
- hr
- bg
- lt
- la
- mi
- ml
- cy
- sk
- te
- fa
- lv
- bn
- sr
- az
- sl
- kn
- et
- mk
- br
- eu
- is
- hy
- ne
- mn
- bs
- kk
- sq
- sw
- gl
- mr
- pa
- si
- km
- sn
- yo
- so
- af
- oc
- ka
- be
- tg
- sd
- gu
- am
- yi
- lo
- uz
- fo
- ht
- ps
- tk
- nn
- mt
- sa
- lb
- my
- bo
- tl
- mg
- as
- tt
- haw
- ln
- ha
- ba
- jw
- su
tags:
- executorch
- metal
- asr
- speech-to-text
- whisper
base_model: openai/whisper-small
Whisper Small — ExecuTorch Export
This repository contains an ExecuTorch .pte export of the OpenAI Whisper model openai/whisper-small for:
- Metal (macOS): bf16
Artifacts
| File | Description |
|---|---|
model.pte |
ExecuTorch program (main model) |
whisper_preprocessor.pte |
ExecuTorch program (audio preprocessor / mel spectrogram) |
tokenizer.json |
Tokenizer |
tokenizer_config.json |
Tokenizer configuration |
special_tokens_map.json |
Special tokens mapping |
Model Details
- Base Model:
openai/whisper-small - Backend: Metal (MPS)
- Precision: bf16
- Parameters: 244M
- Languages: Multilingual (99 languages)
How to Run (ExecuTorch)
Build the Whisper runner from an ExecuTorch checkout:
EXECUTORCH_BUILD_KERNELS_TORCHAO=1 TORCHAO_BUILD_EXPERIMENTAL_MPS=1 ./install_executorch.sh
make whisper-metal
Then run inference:
Metal (macOS)
./cmake-out/examples/models/whisper/whisper_runner \
--model_path /path/to/model.pte \
--preprocessor_path /path/to/whisper_preprocessor.pte \
--audio_path /path/to/audio.wav \
--tokenizer_path /path/to/tokenizer.json
Verification
The model was verified to transcribe test audio correctly and match the original PyTorch model output.
License / Attribution
- Upstream model license: MIT (see
openai/whisper-small). - Please attribute OpenAI when using these exported artifacts.