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:
- 1cc4cbbea33e21b338249ec1677f93187f2bab484ab1b0be4046cea4a8b6af48
- Size of remote file:
- 118 MB
- SHA256:
- 8ffec635a987cc1e6e59fe828b3c98bacf83ddfbcb1661b068e7c3a94a654c9d
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