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whisper-small-multilingual-spoken-ner-end2end-v2

Automatic Speech Recognition
Transformers
Safetensors
whisper
Generated from Trainer
Eval Results
Inference Endpoints
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whisper-small-multilingual-spoken-ner-end2end-v2 / README.md
Quentin Meeus
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metadata
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - facebook/voxpopuli
metrics:
  - wer
model-index:
  - name: WhisperForSpokenNER-end2end
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: facebook/voxpopuli de+es+fr+nl
          type: facebook/voxpopuli
          config: de+es+fr_nl
          split: None
        metrics:
          - name: Wer
            type: wer
            value: 0.08582479210984335

WhisperForSpokenNER-end2end

This model is a fine-tuned version of openai/whisper-small on the facebook/voxpopuli de+es+fr+nl dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2755
  • Combined Wer: 0.1491
  • F1 Score: 0.7163
  • Label F1: 0.8200
  • Wer: 0.0858

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: 0.0001
  • train_batch_size: 64
  • eval_batch_size: 64
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 500
  • training_steps: 5000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Combined Wer F1 Score Label F1 Wer
0.3252 0.1 500 0.3396 0.1918 0.6148 0.7578 0.1193
0.2729 0.2 1000 0.3158 0.1730 0.6449 0.7907 0.1058
0.2369 0.3 1500 0.2971 0.1736 0.6917 0.8083 0.1067
0.1967 0.4 2000 0.2823 0.1634 0.6915 0.8095 0.0999
0.1623 0.5 2500 0.2804 0.1693 0.7088 0.8249 0.1052
0.1146 1.02 3000 0.2820 0.1593 0.7012 0.8106 0.0951
0.0938 1.12 3500 0.2792 0.1500 0.7205 0.8238 0.0875
0.1001 1.22 4000 0.2750 0.1549 0.7072 0.8061 0.0928
0.0848 1.32 4500 0.2741 0.1471 0.7243 0.8318 0.0860
0.0649 1.42 5000 0.2745 0.1468 0.7304 0.8350 0.0858

Framework versions

  • Transformers 4.37.0.dev0
  • Pytorch 2.1.0
  • Datasets 2.14.6
  • Tokenizers 0.14.1