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: 519 Bytes
cb9ad2c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | 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 |