Instructions to use waxal-benchmarking/whisper-tiny-sid-Oreoluwa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use waxal-benchmarking/whisper-tiny-sid-Oreoluwa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="waxal-benchmarking/whisper-tiny-sid-Oreoluwa")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("waxal-benchmarking/whisper-tiny-sid-Oreoluwa") model = AutoModelForSpeechSeq2Seq.from_pretrained("waxal-benchmarking/whisper-tiny-sid-Oreoluwa") - Notebooks
- Google Colab
- Kaggle
| { | |
| "feature_extractor": { | |
| "chunk_length": 30, | |
| "dither": 0.0, | |
| "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, | |
| "return_attention_mask": false, | |
| "sampling_rate": 16000 | |
| }, | |
| "processor_class": "WhisperProcessor" | |
| } | |