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
| { | |
| "model_id": "YOUR_USERNAME/whisper-tiny-en-browser-asr-onnx", | |
| "source": "models\\merged\\tiny_browser_rtx5090", | |
| "onnx_source": "models\\onnx\\tiny_browser_rtx5090", | |
| "quantized_source": "models\\onnx\\tiny_browser_rtx5090_q8", | |
| "default_dtype": "fp32", | |
| "status": "ready_for_hub_upload", | |
| "onnx_files": [ | |
| "decoder_model.onnx", | |
| "decoder_model_merged.onnx", | |
| "decoder_model_merged_quantized.onnx", | |
| "decoder_model_quantized.onnx", | |
| "decoder_with_past_model.onnx", | |
| "decoder_with_past_model_quantized.onnx", | |
| "encoder_model.onnx", | |
| "encoder_model_quantized.onnx" | |
| ] | |
| } |