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Dev372
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Medical_tiny_en_1_1v

Automatic Speech Recognition
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English
whisper
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Eval Results (legacy)
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How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("automatic-speech-recognition", model="Dev372/Medical_tiny_en_1_1v")
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq

processor = AutoProcessor.from_pretrained("Dev372/Medical_tiny_en_1_1v")
model = AutoModelForSpeechSeq2Seq.from_pretrained("Dev372/Medical_tiny_en_1_1v")
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English Whisper Model

This model is a fine-tuned version of openai/whisper-tiny.en on the Medical dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1566
  • Wer: 6.5482

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1e-05
  • train_batch_size: 18
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 1100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
1.8857 0.1554 55 1.6694 13.1520
1.3264 0.3107 110 1.0577 11.8358
0.9159 0.4661 165 0.8809 10.3857
0.8292 0.6215 220 0.7654 9.8893
0.641 0.7768 275 0.6364 9.2557
0.5445 0.9322 330 0.4931 8.6417
0.4072 1.0876 385 0.3397 8.2759
0.2378 1.2429 440 0.2414 8.1322
0.2109 1.3983 495 0.2116 7.6684
0.1641 1.5537 550 0.1940 7.6423
0.1498 1.7090 605 0.1819 7.1198
0.1445 1.8644 660 0.1752 6.8095
0.1349 2.0198 715 0.1679 6.7181
0.1032 2.1751 770 0.1661 6.7344
0.0898 2.3305 825 0.1632 6.8291
0.1032 2.4859 880 0.1606 6.7278
0.0845 2.6412 935 0.1592 6.7083
0.0958 2.7966 990 0.1578 6.5743
0.097 2.9520 1045 0.1570 6.5515
0.0689 3.1073 1100 0.1566 6.5482

Framework versions

  • Transformers 4.43.2
  • Pytorch 2.1.2
  • Datasets 2.20.0
  • Tokenizers 0.19.1
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Dataset used to train Dev372/Medical_tiny_en_1_1v

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