Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
Transformers.js
English
whisper
audio
Eval Results
Instructions to use distil-whisper/distil-medium.en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use distil-whisper/distil-medium.en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="distil-whisper/distil-medium.en")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("distil-whisper/distil-medium.en") model = AutoModelForSpeechSeq2Seq.from_pretrained("distil-whisper/distil-medium.en", device_map="auto") - Transformers.js
How to use distil-whisper/distil-medium.en with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('automatic-speech-recognition', 'distil-whisper/distil-medium.en'); - Notebooks
- Google Colab
- Kaggle
Download original-model.fp32.bin from distil-whisper/distil-medium.en: direct link, hf CLI and curl.
- Browser
- Download file 1.58 GB
-
https://huggingface.co/distil-whisper/distil-medium.en/resolve/main/original-model.fp32.bin
- Command line
-
hf download hf://distil-whisper/distil-medium.en/original-model.fp32.bin
-
curl -L -o original-model.fp32.bin https://huggingface.co/distil-whisper/distil-medium.en/resolve/main/original-model.fp32.bin
1.58 GB
- Xet hash:
- 1c216dcc2277fcc00eb3fe5b62d0a661907a4fd02bc7571f3f406ca8bf9177e2
- Size of remote file:
- 1.58 GB
- SHA256:
- 904e2f6df7d1e8546ae7c2e3e5c4c3503f71e503a031fe4b988a3e200914f014
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