Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Hindi
wav2vec2
robust-speech-event
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8-b2 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-cv8-b2 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-cv8-b2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8-b2") model = AutoModelForCTC.from_pretrained("DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8-b2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2954c1251858148bb46ef46bc85f3597b956c5c937a61776edbd3f0f93697266
- Size of remote file:
- 1.26 GB
- SHA256:
- d2f16d3ec8027ffd8cbf937d5665101f1f4f001bfae2bbf7ccd9913ad4463359
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