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metadata
library_name: transformers
language:
  - en
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: Whisper-squeezeformer-NSQU-whisper
    results: []

Whisper-squeezeformer-NSQU-whisper

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

  • Loss: 0.1322
  • Wer: 5.6642

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: 20
  • 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: 2500
  • training_steps: 50000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
4.8718 1.0 2500 3.8609 111.8590
2.5628 2.0 5000 0.2978 15.6193
0.1698 3.0 7500 0.2218 11.0906
0.0867 4.0 10000 0.2011 10.1891
0.1697 5.0 12500 0.1641 8.9851
0.0993 6.0 15000 0.1553 7.8039
0.0651 7.0 17500 0.1555 7.2448
0.0468 8.0 20000 0.1569 7.1497
0.2168 9.0 22500 0.1509 7.0507
0.1467 10.0 25000 0.1494 6.9671
0.1113 11.0 27500 0.1493 6.7597
0.0914 12.0 30000 0.1511 6.8035
0.1946 13.0 32500 0.1391 6.4212
0.1425 14.0 35000 0.1369 5.8753
0.1145 15.0 37500 0.1368 5.7536
0.1776 16.0 40000 0.1302 5.5995
0.1416 17.0 42500 0.1298 5.6204
0.1239 18.0 45000 0.1297 5.6204
0.3373 19.0 47500 0.1353 5.7403
0.2785 20.0 50000 0.1322 5.6642

Framework versions

  • Transformers 4.45.2
  • Pytorch 2.4.0
  • Datasets 3.1.0
  • Tokenizers 0.20.0