Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
English
wav2vec2
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use Classroom-workshop/assignment1-omar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Classroom-workshop/assignment1-omar with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Classroom-workshop/assignment1-omar")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Classroom-workshop/assignment1-omar") model = AutoModelForCTC.from_pretrained("Classroom-workshop/assignment1-omar", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Upload vocab.json
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Upload tokenizer_config.json
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Upload tf_model.h5 with git-lfs
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Upload special_tokens_map.json
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Upload preprocessor_config.json
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Upload feature_extractor_config.json
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Upload config.json
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Upload README.md
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initial commit
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