Instructions to use rashmi035/wav2vec2-large-mms-1b-hindi_2-VS-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rashmi035/wav2vec2-large-mms-1b-hindi_2-VS-code with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rashmi035/wav2vec2-large-mms-1b-hindi_2-VS-code")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("rashmi035/wav2vec2-large-mms-1b-hindi_2-VS-code") model = AutoModelForCTC.from_pretrained("rashmi035/wav2vec2-large-mms-1b-hindi_2-VS-code") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ef70e0aeb8e34954f9ae522418b33f3080e6be5c15d477ecde41c884ed500093
- Size of remote file:
- 4.03 kB
- SHA256:
- 40300cb9c874e3fbd416a0e062bbe936f8a48f484d04f30867c5eb31a9f544fd
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