Instructions to use bkh6722/bach-arb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bkh6722/bach-arb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bkh6722/bach-arb")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("bkh6722/bach-arb") model = AutoModelForCTC.from_pretrained("bkh6722/bach-arb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 1600
Browse files
config.json
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"num_feat_extract_layers": 7,
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"num_hidden_layers": 24,
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"num_negatives": 100,
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"pad_token_id":
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"proj_codevector_dim": 768,
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"torch_dtype": "float32",
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"transformers_version": "4.11.3",
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"use_weighted_layer_sum": false,
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"vocab_size":
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}
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"num_feat_extract_layers": 7,
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"num_hidden_layers": 24,
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"num_negatives": 100,
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"pad_token_id": 25,
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"proj_codevector_dim": 768,
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"torch_dtype": "float32",
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"transformers_version": "4.11.3",
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"use_weighted_layer_sum": false,
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"vocab_size": 28
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}
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pytorch_model.bin
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runs/May09_10-10-31_184ce95389f2/1652091093.0913694/events.out.tfevents.1652091093.184ce95389f2.4845.1
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runs/May09_10-10-31_184ce95389f2/events.out.tfevents.1652091093.184ce95389f2.4845.0
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training_args.bin
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