Instructions to use Talha/urdumodel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Talha/urdumodel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Talha/urdumodel")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Talha/urdumodel") model = AutoModelForCTC.from_pretrained("Talha/urdumodel") - Notebooks
- Google Colab
- Kaggle
newurdumodel
Browse files- preprocessor_config.json +1 -0
- pytorch_model.bin +1 -1
preprocessor_config.json
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"feature_size": 1,
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"padding_side": "right",
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"padding_value": 0.0,
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"return_attention_mask": true,
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"sampling_rate": 16000
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}
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"feature_size": 1,
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"padding_side": "right",
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"padding_value": 0.0,
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"processor_class": "Wav2Vec2Processor",
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"return_attention_mask": true,
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"sampling_rate": 16000
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 1262116017
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version https://git-lfs.github.com/spec/v1
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oid sha256:eec111fc7a3720dea437874cbb7bc746296cdf68279c9b0ecfe18808d4502c85
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size 1262116017
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