Instructions to use Priyanship/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Priyanship/output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Priyanship/output")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Priyanship/output") model = AutoModelForCTC.from_pretrained("Priyanship/output") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 4000
Browse files
config.json
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"_name_or_path": "/scratch/elec/puhe/p/palp3/MUCS/mucs_language_segregated_data/
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"activation_dropout": 0.0,
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"adapter_attn_dim": null,
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"adapter_kernel_size": 3,
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"_name_or_path": "/scratch/elec/puhe/p/palp3/MUCS/mucs_language_segregated_data/trainwithtags_warmup1500_s300_shuff100/output",
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"activation_dropout": 0.0,
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"adapter_attn_dim": null,
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"adapter_kernel_size": 3,
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indicwav2vec_trainwtags_MUCS_warmup1500_s300shuff100_2217719.out
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model.safetensors
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predictionswtags_indicw2v_ad0_3_hd_02_featd_0_2_lr6e-4_warmup1500_s300_shuff100_v2.txt
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training_args.bin
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