Automatic Speech Recognition
Transformers
ONNX
Transformers.js
English
whisper
speech-recognition
encoder-decoder
webgpu
wasm
browser
rtx-5090
Instructions to use anmol-unitmole/streaming-speech-recognition-whisper-encoder-decoder-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anmol-unitmole/streaming-speech-recognition-whisper-encoder-decoder-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="anmol-unitmole/streaming-speech-recognition-whisper-encoder-decoder-model")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("anmol-unitmole/streaming-speech-recognition-whisper-encoder-decoder-model") model = AutoModelForSpeechSeq2Seq.from_pretrained("anmol-unitmole/streaming-speech-recognition-whisper-encoder-decoder-model", device_map="auto") - Transformers.js
How to use anmol-unitmole/streaming-speech-recognition-whisper-encoder-decoder-model with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('automatic-speech-recognition', 'anmol-unitmole/streaming-speech-recognition-whisper-encoder-decoder-model'); - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 07fcffbc39989ed87893e2c6e8ccec758755700cb2e5ad33e142f1a9929d88b4
- Size of remote file:
- 29.9 MB
- SHA256:
- 92cb522649c93f106fa01651e7009fae5aa323e30ddda33029eb5509f5a61169
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