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
| { | |
| "backend": "tokenizers", | |
| "bos_token": "<blank>", | |
| "eos_token": "<blank>", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "<blank>", | |
| "tokenizer_class": "TokenizersBackend", | |
| "unk_token": "<blank>" | |
| } | |