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:
- 002a773dd4f181dd77a928ed38a914f4af78283793d929814c705cf6423ac9c8
- Size of remote file:
- 3.86 GB
- SHA256:
- 3b53ac6096ff357220fa9faab5b68edb34298eb7fdcd1f7c5a50698ecdddd1d0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.