Instructions to use fkHug/modelFromWav2vec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fkHug/modelFromWav2vec with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="fkHug/modelFromWav2vec")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("fkHug/modelFromWav2vec") model = AutoModelForCTC.from_pretrained("fkHug/modelFromWav2vec", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update from
Browse files- models/config.json → config.json +0 -0
- models/preprocessor_config.json → preprocessor_config.json +0 -0
- models/pytorch_model.bin → pytorch_model.bin +0 -0
- models/scheduler.pt → scheduler.pt +0 -0
- vocab_dict/special_tokens_map.json → special_tokens_map.json +0 -0
- vocab_dict/tokenizer_config.json → tokenizer_config.json +0 -0
- models/trainer_state.json → trainer_state.json +0 -0
- models/training_args.bin → training_args.bin +0 -0
- vocab_dict/vocab.json → vocab.json +0 -0
models/config.json → config.json
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models/preprocessor_config.json → preprocessor_config.json
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models/pytorch_model.bin → pytorch_model.bin
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models/scheduler.pt → scheduler.pt
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vocab_dict/special_tokens_map.json → special_tokens_map.json
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vocab_dict/tokenizer_config.json → tokenizer_config.json
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models/trainer_state.json → trainer_state.json
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models/training_args.bin → training_args.bin
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vocab_dict/vocab.json → vocab.json
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