Automatic Speech Recognition
Transformers
Safetensors
Japanese
dual_ctc
feature-extraction
ctc
wavlm
japanese
hiragana
phoneme
custom_code
Instructions to use TylorShine/wavlm-base-plus-hiragana-ctc-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TylorShine/wavlm-base-plus-hiragana-ctc-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="TylorShine/wavlm-base-plus-hiragana-ctc-v2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TylorShine/wavlm-base-plus-hiragana-ctc-v2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 231 Bytes
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"backend": "tokenizers",
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"model_max_length": 1000000000000000019884624838656,
"pad_token": "<blank>",
"tokenizer_class": "TokenizersBackend",
"unk_token": "<blank>"
}
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