Automatic Speech Recognition
Transformers
PyTorch
Oriya
wav2vec2
hf-asr-leaderboard
model_for_talk
mozilla-foundation/common_voice_7_0
robust-speech-event
Eval Results (legacy)
Instructions to use Harveenchadha/odia_large_wav2vec2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Harveenchadha/odia_large_wav2vec2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Harveenchadha/odia_large_wav2vec2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Harveenchadha/odia_large_wav2vec2") model = AutoModelForCTC.from_pretrained("Harveenchadha/odia_large_wav2vec2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c24ddf01e5f3e9c9f2914a01dff33ec727fb7e636aadcb47b9402ab39e60c1d0
- Size of remote file:
- 1.26 GB
- SHA256:
- 422eac4eb5a418e9c1da84e9b0dd1834d162664c716c96232a568a20d9b7d97c
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