csml_word2vec / README.md
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metadata
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
  - generated_from_trainer
datasets:
  - audiofolder
metrics:
  - wer
model-index:
  - name: csml_word2vec
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: audiofolder
          type: audiofolder
          config: default
          split: None
          args: default
        metrics:
          - name: Wer
            type: wer
            value: 0.12267657992565056

csml_word2vec

This model is a fine-tuned version of facebook/wav2vec2-base on the audiofolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2183
  • Wer: 0.1227

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: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 8
  • optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 2000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
3.2282 1.0 100 3.1130 1.0
2.9562 2.0 200 2.9543 1.0
2.9212 3.0 300 2.9317 1.0
2.9119 4.0 400 2.9110 1.0
2.9099 5.0 500 2.9099 1.0
2.9027 6.0 600 2.9106 1.0
2.4303 7.0 700 1.9136 0.9615
0.7427 8.0 800 0.5675 0.4321
0.4794 9.0 900 0.4161 0.3183
0.3372 10.0 1000 0.3228 0.2441
0.2685 11.0 1100 0.2762 0.1916
0.2092 12.0 1200 0.2522 0.1654
0.1848 13.0 1300 0.2361 0.1564
0.1678 14.0 1400 0.2246 0.1383
0.1377 15.0 1500 0.2183 0.1337
0.122 16.0 1600 0.2206 0.1310
0.1125 17.0 1700 0.2205 0.1285
0.0938 18.0 1800 0.2219 0.1273
0.0994 19.0 1900 0.2209 0.1239
0.1 20.0 2000 0.2183 0.1227

Framework versions

  • Transformers 4.57.1
  • Pytorch 2.8.0+cu126
  • Datasets 4.0.0
  • Tokenizers 0.22.1