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
| from transformers import WavLMConfig | |
| class DualCTCConfig(WavLMConfig): | |
| model_type = "dual_ctc" | |
| def __init__( | |
| self, | |
| kana_vocab_size=84, | |
| phoneme_vocab_size=44, | |
| kana_ctc_layer=11, | |
| phoneme_ctc_layer=6, | |
| **kwargs | |
| ): | |
| super().__init__(**kwargs) | |
| self.kana_vocab_size = kana_vocab_size | |
| self.phoneme_vocab_size = phoneme_vocab_size | |
| self.kana_ctc_layer = kana_ctc_layer | |
| self.phoneme_ctc_layer = phoneme_ctc_layer |