Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Hindi
wav2vec2
mozilla-foundation/common_voice_8_0
Generated from Trainer
robust-speech-event
model_for_talk
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8 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 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")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8") model = AutoModelForCTC.from_pretrained("DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 80dd25e
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README.md
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python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8 --dataset speech-recognition-community-v2/dev_data --config hi --split validation --chunk_length_s 10 --stride_length_s 1
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The following hyperparameters were used during training:
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python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8 --dataset speech-recognition-community-v2/dev_data --config hi --split validation --chunk_length_s 10 --stride_length_s 1
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Note: Hindi language not found in speech-recognition-community-v2/dev_data
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### Training hyperparameters
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The following hyperparameters were used during training:
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