Instructions to use ThomasFG/101.25-33.75 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ThomasFG/101.25-33.75 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ThomasFG/101.25-33.75")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("ThomasFG/101.25-33.75") model = AutoModelForSpeechSeq2Seq.from_pretrained("ThomasFG/101.25-33.75", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from ThomasFG/101.25-33.75: direct link, hf CLI and curl.
- Browser
- Download file 339 Bytes
-
https://huggingface.co/ThomasFG/101.25-33.75/resolve/76b98cf0a0fe1f717dec9d3b670776d141ced16d/preprocessor_config.json
- Command line
-
hf download hf://ThomasFG/101.25-33.75@76b98cf0a0fe1f717dec9d3b670776d141ced16d/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/ThomasFG/101.25-33.75/resolve/76b98cf0a0fe1f717dec9d3b670776d141ced16d/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 | |
| } | |