Automatic Speech Recognition
Transformers
JAX
TensorBoard
ONNX
Safetensors
Transformers.js
English
whisper
audio
Eval Results
Instructions to use distil-whisper/distil-large-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use distil-whisper/distil-large-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="distil-whisper/distil-large-v3")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("distil-whisper/distil-large-v3") model = AutoModelForSpeechSeq2Seq.from_pretrained("distil-whisper/distil-large-v3", device_map="auto") - Transformers.js
How to use distil-whisper/distil-large-v3 with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('automatic-speech-recognition', 'distil-whisper/distil-large-v3'); - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8d8ae86c92956ccb42d6b2e84355896158444cc6e36d18bf36a1400d56bc16b5
- Size of remote file:
- 645 MB
- SHA256:
- a31efd268a09c46df913d4ca4e3799b02b44aecabe945341d821a619bc652437
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.