Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Hindi
wav2vec2
hf-asr-leaderboard
robust-speech-event
Eval Results (legacy)
Instructions to use DrishtiSharma/wav2vec2-large-xls-r-300m-hi-wx1 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-wx1 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-wx1")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("DrishtiSharma/wav2vec2-large-xls-r-300m-hi-wx1") model = AutoModelForCTC.from_pretrained("DrishtiSharma/wav2vec2-large-xls-r-300m-hi-wx1") - Notebooks
- Google Colab
- Kaggle
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# wav2vec2-large-xls-r-300m-hi-wx1
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the
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It achieves the following results on the evaluation set:
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- Loss: 0.6552
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- Wer: 0.3200
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# wav2vec2-large-xls-r-300m-hi-wx1
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 -HI dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6552
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- Wer: 0.3200
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