Instructions to use onnx-internal-testing/tiny-random-GraniteSpeechForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use onnx-internal-testing/tiny-random-GraniteSpeechForConditionalGeneration with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="onnx-internal-testing/tiny-random-GraniteSpeechForConditionalGeneration")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("onnx-internal-testing/tiny-random-GraniteSpeechForConditionalGeneration") model = AutoModelForSpeechSeq2Seq.from_pretrained("onnx-internal-testing/tiny-random-GraniteSpeechForConditionalGeneration") - Notebooks
- Google Colab
- Kaggle
File size: 407 Bytes
35b5a66 d5090b3 35b5a66 08cefe3 35b5a66 774f713 35b5a66 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | {
"n_mels": 8,
"n_fft": 512,
"win_length": 400,
"hop_length": 160,
"feature_extractor_type": "GraniteSpeechFeatureExtractor",
"melspec_kwargs": {
"hop_length": 160,
"n_fft": 512,
"n_mels": 8,
"sample_rate": 16000,
"win_length": 400
},
"processor_class": "GraniteSpeechProcessor",
"projector_downsample_rate": 1,
"projector_window_size": 3,
"sampling_rate": 16000
}
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