Instructions to use hf-internal-testing/tiny-random-MoonshineForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MoonshineForConditionalGeneration with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hf-internal-testing/tiny-random-MoonshineForConditionalGeneration")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-MoonshineForConditionalGeneration") model = AutoModelForSpeechSeq2Seq.from_pretrained("hf-internal-testing/tiny-random-MoonshineForConditionalGeneration") - Notebooks
- Google Colab
- Kaggle
Upload processor
Browse files- preprocessor_config.json +10 -0
- special_tokens_map.json +1 -0
- tokenizer.json +0 -0
- tokenizer_config.json +0 -0
preprocessor_config.json
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{
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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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"processor_class": "Wav2Vec2Processor",
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"return_attention_mask": true,
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"sampling_rate": 16000
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tokenizer.json
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tokenizer_config.json
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