Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Serbian
wav2vec2
Generated from Trainer
hf-asr-leaderboard
model_for_talk
mozilla-foundation/common_voice_8_0
robust-speech-event
Eval Results (legacy)
Instructions to use DrishtiSharma/wav2vec2-large-xls-r-300m-sr-v4 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-sr-v4 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-sr-v4")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("DrishtiSharma/wav2vec2-large-xls-r-300m-sr-v4") model = AutoModelForCTC.from_pretrained("DrishtiSharma/wav2vec2-large-xls-r-300m-sr-v4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9c705cd286e929ece1fd4e947f9859d208785a03c040e1963a4ca2a984f7871b
- Size of remote file:
- 3.06 kB
- SHA256:
- e00355fbe118fb6dfa8d688790c38d80b45f257bfc0a8f07b3bb82aaa136c368
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