Automatic Speech Recognition
Transformers
PyTorch
wav2vec2
mozilla-foundation/common_voice_8_0
Generated from Trainer
pa-IN
robust-speech-event
model_for_talk
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use DrishtiSharma/wav2vec2-xls-r-300m-pa-IN-r5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DrishtiSharma/wav2vec2-xls-r-300m-pa-IN-r5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="DrishtiSharma/wav2vec2-xls-r-300m-pa-IN-r5")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("DrishtiSharma/wav2vec2-xls-r-300m-pa-IN-r5") model = AutoModelForCTC.from_pretrained("DrishtiSharma/wav2vec2-xls-r-300m-pa-IN-r5") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aecdd32e1d6582de2544d4202ae1626a93587218e13d973d6ac29347c5996e88
- Size of remote file:
- 1.26 GB
- SHA256:
- 7347dc3496ed06a5b73bd290084026f2bdf6ef7455c6775e31d637bda0a31f11
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.