Instructions to use deepspeechvision/wav2vec2_hindi_asr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepspeechvision/wav2vec2_hindi_asr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="deepspeechvision/wav2vec2_hindi_asr")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("deepspeechvision/wav2vec2_hindi_asr") model = AutoModelForCTC.from_pretrained("deepspeechvision/wav2vec2_hindi_asr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
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by librarian-bot - opened
README.md
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- generated_from_trainer
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datasets:
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- common_voice
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model-index:
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- name: wav2vec2_hindi_asr
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results: []
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- generated_from_trainer
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datasets:
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- common_voice
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base_model: facebook/wav2vec2-xls-r-300m
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model-index:
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- name: wav2vec2_hindi_asr
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results: []
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