Instructions to use Bedru/Whisper-lora_model-aml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bedru/Whisper-lora_model-aml with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Bedru/Whisper-lora_model-aml", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Download preprocessor_config.json from Bedru/Whisper-lora_model-aml: direct link, hf CLI and curl.
- Browser
- Download file 357 Bytes
-
https://huggingface.co/Bedru/Whisper-lora_model-aml/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://Bedru/Whisper-lora_model-aml/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/Bedru/Whisper-lora_model-aml/resolve/main/preprocessor_config.json
357 Bytes
| { | |
| "chunk_length": 30, | |
| "dither": 0.0, | |
| "feature_extractor_type": "WhisperFeatureExtractor", | |
| "feature_size": 128, | |
| "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 | |
| } | |