Instructions to use eustlb/moonshine-streaming-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eustlb/moonshine-streaming-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="eustlb/moonshine-streaming-tiny")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("eustlb/moonshine-streaming-tiny") model = AutoModelForSpeechSeq2Seq.from_pretrained("eustlb/moonshine-streaming-tiny") - Notebooks
- Google Colab
- Kaggle
Upload processor
Browse files- processor_config.json +12 -0
- tokenizer.json +0 -0
- tokenizer_config.json +7 -0
processor_config.json
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{
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"feature_extractor": {
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"do_normalize": false,
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"feature_extractor_type": "Wav2Vec2FeatureExtractor",
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"feature_size": 1,
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"padding_side": "right",
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"padding_value": 0.0,
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"return_attention_mask": true,
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"sampling_rate": 16000
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},
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"processor_class": "MoonshineStreamingProcessor"
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}
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tokenizer.json
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tokenizer_config.json
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{
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"backend": "tokenizers",
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"is_local": false,
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"model_max_length": 1000000000000000019884624838656,
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"processor_class": "MoonshineStreamingProcessor",
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"tokenizer_class": "TokenizersBackend"
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}
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