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
File size: 409 Bytes
8a4e40f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"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"
}
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