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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - timit_asr
model-index:
  - name: wav2vec2-base_phoneme-timit_english_timit-4k_001
    results: []
language:
  - en
metrics:
  - wer
library_name: transformers
pipeline_tag: automatic-speech-recognition

wav2vec2-base_phoneme-timit_english_timit-4k_001

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

  • Loss: 0.6361
  • Per: 0.1195

Model description

The wav2vec 2.0 base model is pre-trained on 960 hours of the LibriSpeech dataset.

  • 12 Transformer blocks (Each block: 768 dimensions & 8 attention heads)

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: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 5000
  • training_steps: 10000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Per
5.2193 3.46 1000 3.5945 0.9617
1.5174 6.92 2000 0.5574 0.1665
0.5246 10.38 3000 0.4228 0.1503
0.3915 13.84 4000 0.4276 0.1512
0.3293 17.3 5000 0.4656 0.1517
0.2757 20.76 6000 0.4719 0.1486
0.209 24.22 7000 0.5314 0.1478
0.1589 27.68 8000 0.6102 0.1484
0.1207 31.14 9000 0.6449 0.1484
0.0951 34.6 10000 0.6579 0.1471

Framework versions

  • Transformers 4.28.1
  • Pytorch 2.0.1
  • Datasets 2.18.0
  • Tokenizers 0.13.3