Automatic Speech Recognition
Transformers
PyTorch
Hindi
wav2vec2
hf-asr-leaderboard
model_for_talk
mozilla-foundation/common_voice_7_0
robust-speech-event
Eval Results (legacy)
Instructions to use Harveenchadha/hindi_large_wav2vec2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Harveenchadha/hindi_large_wav2vec2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Harveenchadha/hindi_large_wav2vec2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Harveenchadha/hindi_large_wav2vec2") model = AutoModelForCTC.from_pretrained("Harveenchadha/hindi_large_wav2vec2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 75b5ce93de3d89c30733e2179ed8d38a618534224b24a232ea38052037e2b76d
- Size of remote file:
- 1.26 GB
- SHA256:
- 25fc7f824579e265234adfa7c96d7e1baf4ced6bfb97f8426016795521ef642b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.