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 30
Browse files
pytorch_model.bin
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runs/Dec13_12-46-08_d7f040c448a8/events.out.tfevents.1670935580.d7f040c448a8.26499.0
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train.log
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eval_steps_per_second = 0.119
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eval_wer = 52.1978
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{'loss': 2.5622, 'learning_rate': 7e-05, 'epoch': 0.1}
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eval_steps_per_second = 0.119
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eval_wer = 52.1978
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{'loss': 2.5622, 'learning_rate': 7e-05, 'epoch': 0.1}
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{'eval_loss': 1.5401501655578613, 'eval_wer': 94.5054945054945, 'eval_runtime': 62.3188, 'eval_samples_per_second': 1.027, 'eval_steps_per_second': 0.032, 'epoch': 0.1}
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{'loss': 1.3719, 'learning_rate': 9.222222222222223e-05, 'epoch': 0.2}
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{'eval_loss': 1.001212239265442, 'eval_wer': 75.27472527472527, 'eval_runtime': 18.9902, 'eval_samples_per_second': 3.37, 'eval_steps_per_second': 0.105, 'epoch': 0.2}
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{'loss': 0.9898, 'learning_rate': 8.111111111111112e-05, 'epoch': 0.3}
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