Instructions to use hts98/whisper-tiny-paper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hts98/whisper-tiny-paper with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hts98/whisper-tiny-paper")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("hts98/whisper-tiny-paper") model = AutoModelForSpeechSeq2Seq.from_pretrained("hts98/whisper-tiny-paper", device_map="auto") - Notebooks
- Google Colab
- Kaggle
update model card README.md
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README.md
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size:
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- eval_batch_size:
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 500
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- num_epochs:
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### Training results
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 48
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- eval_batch_size: 48
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 500
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- num_epochs: 7
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### Training results
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