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Ukrainian STT model (with Language Model)

Have a look on the better model with more parameters: https://huggingface.co/Yehor/wav2vec2-xls-r-1b-uk-with-lm

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - UK dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3015
  • Wer: 0.3377
  • Cer: 0.0708

The above results present evaluation without the language model.

Follow our community in Telegram: https://t.me/speech_recognition_uk

Model description

On 100 test example the model shows the following results:

Without LM:

  • WER: 0.2647058823529412
  • CER: 0.046974185357596274

With LM:

  • WER: 0.1568627450980392
  • CER: 0.028988573846804908

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0003
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 20
  • total_train_batch_size: 160
  • 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
3.0255 7.93 500 2.5514 0.9921 0.9047
1.3809 15.86 1000 0.4065 0.5361 0.1201
1.2355 23.8 1500 0.3474 0.4618 0.1033
1.1956 31.74 2000 0.3617 0.4580 0.1005
1.1416 39.67 2500 0.3182 0.4074 0.0891
1.0996 47.61 3000 0.3166 0.3985 0.0875
1.0427 55.55 3500 0.3116 0.3835 0.0828
0.9961 63.49 4000 0.3137 0.3757 0.0807
0.9575 71.42 4500 0.2992 0.3632 0.0771
0.9154 79.36 5000 0.3015 0.3502 0.0740
0.8994 87.3 5500 0.3004 0.3425 0.0723
0.871 95.24 6000 0.3016 0.3394 0.0713

Framework versions

  • Transformers 4.16.0.dev0
  • Pytorch 1.10.1+cu102
  • Datasets 1.18.1.dev0
  • Tokenizers 0.11.0
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Dataset used to train Yehor/wav2vec2-xls-r-300m-uk-with-lm

Evaluation results