Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Assamese
wav2vec2
mozilla-foundation/common_voice_8_0
Generated from Trainer
robust-speech-event
model_for_talk
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use DrishtiSharma/wav2vec2-large-xls-r-300m-as-v9 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-as-v9 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-as-v9")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("DrishtiSharma/wav2vec2-large-xls-r-300m-as-v9") model = AutoModelForCTC.from_pretrained("DrishtiSharma/wav2vec2-large-xls-r-300m-as-v9") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0549396bbc209dcf891568d0ea526810dee3d95b319f7a97b3c5faf2367674d0
- Size of remote file:
- 1.26 GB
- SHA256:
- fdbd6ff6219e50c341a276195002bcdf956e6d15a85dea922a67e8cefa841c57
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.