Instructions to use CWKSC/whisper-small-onnx-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CWKSC/whisper-small-onnx-quantized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="CWKSC/whisper-small-onnx-quantized")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("CWKSC/whisper-small-onnx-quantized") model = AutoModelForSpeechSeq2Seq.from_pretrained("CWKSC/whisper-small-onnx-quantized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from CWKSC/whisper-small-onnx-quantized: direct link, hf CLI and curl.
- Browser
- Download file 339 Bytes
-
https://huggingface.co/CWKSC/whisper-small-onnx-quantized/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://CWKSC/whisper-small-onnx-quantized/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/CWKSC/whisper-small-onnx-quantized/resolve/main/preprocessor_config.json
339 Bytes
| { | |
| "chunk_length": 30, | |
| "feature_extractor_type": "WhisperFeatureExtractor", | |
| "feature_size": 80, | |
| "hop_length": 160, | |
| "n_fft": 400, | |
| "n_samples": 480000, | |
| "nb_max_frames": 3000, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "processor_class": "WhisperProcessor", | |
| "return_attention_mask": false, | |
| "sampling_rate": 16000 | |
| } | |