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, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("onnx-internal-testing/tiny-random-GraniteSpeechForConditionalGeneration") model = AutoModelForMultimodalLM.from_pretrained("onnx-internal-testing/tiny-random-GraniteSpeechForConditionalGeneration") - Notebooks
- Google Colab
- Kaggle
Update processor_config.json
Browse files- processor_config.json +2 -2
processor_config.json
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@@ -8,8 +8,8 @@
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"sample_rate": 16000,
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"win_length": 400
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},
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"projector_downsample_rate":
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"projector_window_size":
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"sampling_rate": 16000
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},
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"audio_token": "<|audio|>",
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"sample_rate": 16000,
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"win_length": 400
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},
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"projector_downsample_rate": 1,
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"projector_window_size": 3,
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"sampling_rate": 16000
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},
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"audio_token": "<|audio|>",
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