Hugging Face's logo

clinical-assistance
/
whisper-large-v3-clinical-assistance

Automatic Speech Recognition
Transformers
Safetensors
Spanish
whisper
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
whisper-large-v3-clinical-assistance / README.md
Mezosky's picture
Mezosky
End of training
28b19a7 verified
preview code
|
raw history blame contribute delete
No virus
2.95 kB
metadata
language:
  - es
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
  - generated_from_trainer
datasets:
  - Mezosky/es_clinical_assistance_10k
metrics:
  - wer
model-index:
  - name: Whisper Chilean Spanish Large v3
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Mezosky/es_clinical_assistance_10k
          type: Mezosky/es_clinical_assistance_10k
        metrics:
          - name: Wer
            type: wer
            value: 6.935235697300322

Whisper Chilean Spanish Large v3

This model is a fine-tuned version of openai/whisper-large-v3 on the Mezosky/es_clinical_assistance_10k dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0961
  • Wer: 6.9352

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: 16
  • 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: 2000

Training results

Training Loss Epoch Step Validation Loss Wer
0.2816 0.17 100 0.2250 11.2827
0.1505 0.34 200 0.1479 9.8196
0.1293 0.51 300 0.1350 72.1192
0.1221 0.69 400 0.1292 9.6825
0.141 0.86 500 0.1194 53.0899
0.0922 1.03 600 0.1150 12.0380
0.0773 1.2 700 0.1079 12.8661
0.0745 1.37 800 0.1036 67.3017
0.0699 1.54 900 0.1016 8.2697
0.0917 1.72 1000 0.0956 8.6334
0.0716 1.89 1100 0.0968 7.7997
0.0441 2.06 1200 0.0946 8.3760
0.0377 2.23 1300 0.0963 7.6178
0.0417 2.4 1400 0.0951 7.5703
0.0409 2.57 1500 0.0926 7.2681
0.0356 2.74 1600 0.0912 6.8933
0.0361 2.92 1700 0.0918 7.0835
0.0215 3.09 1800 0.0938 6.9548
0.018 3.26 1900 0.0960 6.6415
0.0196 3.43 2000 0.0961 6.9352

Framework versions

  • Transformers 4.39.3
  • Pytorch 2.2.2+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2