Automatic Speech Recognition
Transformers
PyTorch
Marathi
wav2vec2
speech_to_text
audio
speech
xlsr-fine-tuning-week
Eval Results (legacy)
Instructions to use Tejas2000/Wav2Vec_Deploy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tejas2000/Wav2Vec_Deploy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Tejas2000/Wav2Vec_Deploy")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Tejas2000/Wav2Vec_Deploy") model = AutoModelForCTC.from_pretrained("Tejas2000/Wav2Vec_Deploy") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6c9f61d279743a668014b96383a21bf572408b30d537662fda362bdcb6962bc6
- Size of remote file:
- 1.26 GB
- SHA256:
- 8446330fbc3dfc074420fe9ce779d33cbbdad59f2be086c47fb28e0e08673ad7
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