Automatic Speech Recognition
Transformers
PyTorch
JAX
Japanese
wav2vec2
audio
speech
Eval Results (legacy)
Instructions to use NTQAI/wav2vec2-large-japanese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NTQAI/wav2vec2-large-japanese with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NTQAI/wav2vec2-large-japanese")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("NTQAI/wav2vec2-large-japanese") model = AutoModelForCTC.from_pretrained("NTQAI/wav2vec2-large-japanese", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f0fd86ebe6f22599b4e39c3ec223a73416017678a4313a6ed9d184a7da8e1e18
- Size of remote file:
- 1.27 GB
- SHA256:
- 27543abe272644c9a3e68ba4de2151c4549f15bbb95ac7d233bd76793788b762
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