Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Marathi
wav2vec2
mozilla-foundation/common_voice_8_0
Generated from Trainer
robust-speech-event
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use DrishtiSharma/wav2vec2-large-xls-r-300m-mr-v2 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-mr-v2 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-mr-v2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("DrishtiSharma/wav2vec2-large-xls-r-300m-mr-v2") model = AutoModelForCTC.from_pretrained("DrishtiSharma/wav2vec2-large-xls-r-300m-mr-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c3b93be6d057fc2bd20e59f3a5a53d46613f883a80bcd3dc3ff13bd06d351cee
- Size of remote file:
- 1.26 GB
- SHA256:
- 11923685f1550f9d9f3aa4d9814a12dbf8ee9a7e9503fd7bdbd3fa43b9942781
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