Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
Transformers.js
English
whisper
audio
Eval Results
Instructions to use distil-whisper/distil-large-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use distil-whisper/distil-large-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="distil-whisper/distil-large-v2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("distil-whisper/distil-large-v2") model = AutoModelForSpeechSeq2Seq.from_pretrained("distil-whisper/distil-large-v2") - Transformers.js
How to use distil-whisper/distil-large-v2 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-v2'); - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 03936e69828155bfb2f039a649602581912cb45803962851ef189d4eedcc5b38
- Size of remote file:
- 1.51 GB
- SHA256:
- de43a5c27f30b9de4095ff5e0158c3ed30aa0783fe3be68172f705ba9a3014c9
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