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Dev372
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Medical_tiny_en_1_1v_check_train

Automatic Speech Recognition
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English
whisper
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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_check_train")
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq

processor = AutoProcessor.from_pretrained("Dev372/Medical_tiny_en_1_1v_check_train")
model = AutoModelForSpeechSeq2Seq.from_pretrained("Dev372/Medical_tiny_en_1_1v_check_train")
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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: 0.1509
  • Wer: 6.5548

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: 1500
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
1.3263 0.2825 100 1.1474 12.0219
0.8292 0.5650 200 0.8086 9.9840
0.5971 0.8475 300 0.5736 9.0597
0.2888 1.1299 400 0.3038 8.2465
0.172 1.4124 500 0.2112 7.5835
0.1499 1.6949 600 0.1839 7.0773
0.1347 1.9774 700 0.1693 6.6691
0.0977 2.2599 800 0.1650 6.7834
0.0966 2.5424 900 0.1578 7.0381
0.0877 2.8249 1000 0.1542 6.6462
0.0587 3.1073 1100 0.1539 6.5090
0.0642 3.3898 1200 0.1531 6.5646
0.0597 3.6723 1300 0.1518 6.5090
0.0754 3.9548 1400 0.1511 6.5254
0.0506 4.2373 1500 0.1509 6.5548

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_check_train

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