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_base_wav2vec2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Harveenchadha/hindi_base_wav2vec2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Harveenchadha/hindi_base_wav2vec2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Harveenchadha/hindi_base_wav2vec2") model = AutoModelForCTC.from_pretrained("Harveenchadha/hindi_base_wav2vec2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 82eccc4b3d89ed6958a4a27086fde43e75f3140c4fe5032f433effa274aeb0fe
- Size of remote file:
- 378 MB
- SHA256:
- cbbc85954164d5518955c075419fc056cb9b494fe48393e8d6ad8fbae386e0c8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.