Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Hindi
wav2vec2
hf-asr-leaderboard
robust-speech-event
Eval Results (legacy)
Instructions to use DrishtiSharma/wav2vec2-large-xls-r-300m-hi-wx1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DrishtiSharma/wav2vec2-large-xls-r-300m-hi-wx1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="DrishtiSharma/wav2vec2-large-xls-r-300m-hi-wx1")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("DrishtiSharma/wav2vec2-large-xls-r-300m-hi-wx1") model = AutoModelForCTC.from_pretrained("DrishtiSharma/wav2vec2-large-xls-r-300m-hi-wx1") - Notebooks
- Google Colab
- Kaggle
wav2vec2-large-xls-r-300m-hi-wx1 / runs /Feb07_00-32-28_e3fe0fc9090f /1644194055.7944305 /events.out.tfevents.1644194055.e3fe0fc9090f.95.3
- Xet hash:
- 041c80e3249dd306f52623245efbff669c048e05d3818b48d6c26090fbbf01f0
- Size of remote file:
- 4.81 kB
- SHA256:
- 88ef1f3b21d6aad455921db1095a8da99c1bab14b4a36b138f8b22f23c3f7f55
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