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wav2vec2-large-xls-r-300m-common_voice-tr-ft

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Turkish
wav2vec2
common_voice
Generated from Trainer
xls_r_repro_common_voice_tr
Inference Endpoints
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metadata
language:
  - tr
license: apache-2.0
tags:
  - automatic-speech-recognition
  - common_voice
  - generated_from_trainer
datasets:
  - common_voice
model-index:
  - name: wav2vec2-large-xls-r-300m-common_voice-tr-ft
    results: []

wav2vec2-large-xls-r-300m-common_voice-tr-ft

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3644
  • Wer: 0.3394
  • Cer: 0.0811

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: 0.0003
  • train_batch_size: 4
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 8
  • total_train_batch_size: 32
  • total_eval_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 100.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
1.6613 4.59 500 0.8079 0.8542 0.2504
1.3496 9.17 1000 0.4729 0.5968 0.1518
1.1003 13.76 1500 0.4106 0.5225 0.1357
1.1532 18.35 2000 0.3957 0.4978 0.1256
1.0305 22.94 2500 0.3764 0.5008 0.1291
0.8303 27.52 3000 0.3826 0.5113 0.1292
0.9115 32.11 3500 0.3819 0.4324 0.1070
0.8193 36.7 4000 0.3694 0.4223 0.1036
0.8948 41.28 4500 0.3714 0.4100 0.1005
0.774 45.87 5000 0.3558 0.3923 0.0971
0.8194 50.46 5500 0.3729 0.4603 0.1180
0.8616 55.05 6000 0.3616 0.3908 0.0963
0.7901 59.63 6500 0.3575 0.3837 0.0952
0.778 64.22 7000 0.3732 0.3790 0.0928
0.7238 68.81 7500 0.3674 0.3734 0.0904
0.6985 73.39 8000 0.3627 0.3615 0.0863
0.5889 77.98 8500 0.3705 0.3548 0.0858
0.5447 82.57 9000 0.3678 0.3534 0.0854
0.4763 87.16 9500 0.3627 0.3509 0.0840
0.3544 91.74 10000 0.3690 0.3495 0.0834
0.4879 96.33 10500 0.3683 0.3418 0.0820

Framework versions

  • Transformers 4.13.0.dev0
  • Pytorch 1.9.0+cu111
  • Datasets 1.15.2.dev0
  • Tokenizers 0.10.3