Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Belarusian
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use ales/whisper-tiny-be-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ales/whisper-tiny-be-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ales/whisper-tiny-be-test")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("ales/whisper-tiny-be-test") model = AutoModelForSpeechSeq2Seq.from_pretrained("ales/whisper-tiny-be-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 50
Browse files
pytorch_model.bin
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runs/Dec12_19-28-45_13ea4557542f/events.out.tfevents.1670873355.13ea4557542f.8478.0
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train.log
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eval_steps_per_second = 0.098
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eval_wer = 75.2747
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{'loss': 0.9934, 'learning_rate': 7.68421052631579e-05, 'epoch': 0.3}
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eval_steps_per_second = 0.098
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eval_wer = 75.2747
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{'loss': 0.9934, 'learning_rate': 7.68421052631579e-05, 'epoch': 0.3}
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{'eval_loss': 0.811407208442688, 'eval_wer': 72.16117216117216, 'eval_runtime': 17.9554, 'eval_samples_per_second': 3.564, 'eval_steps_per_second': 0.111, 'epoch': 0.3}
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{'loss': 0.9568, 'learning_rate': 6.631578947368421e-05, 'epoch': 0.4}
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{'eval_loss': 0.7814445495605469, 'eval_wer': 72.7106227106227, 'eval_runtime': 44.1521, 'eval_samples_per_second': 1.45, 'eval_steps_per_second': 0.045, 'epoch': 0.4}
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{'loss': 0.6856, 'learning_rate': 5.5789473684210526e-05, 'epoch': 0.5}
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