Instructions to use okuparinen/SKN_300m_simple with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use okuparinen/SKN_300m_simple with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="okuparinen/SKN_300m_simple")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("okuparinen/SKN_300m_simple") model = AutoModelForCTC.from_pretrained("okuparinen/SKN_300m_simple", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload preprocessor
Browse files- preprocessor_config.json +9 -0
- vocab.json +1 -0
preprocessor_config.json
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{
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"do_normalize": true,
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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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vocab.json
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{"a": 1, "b": 2, "c": 3, "d": 4, "e": 5, "f": 6, "g": 7, "h": 8, "i": 9, "j": 10, "k": 11, "l": 12, "m": 13, "n": 14, "o": 15, "p": 16, "r": 17, "s": 18, "t": 19, "u": 20, "v": 21, "w": 22, "y": 23, "z": 24, "\u00e4": 25, "\u00e5": 26, "\u00f6": 27, "\u014b": 28, "|": 0, "[UNK]": 29, "[PAD]": 30}
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