metadata

license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
  - generated_from_trainer
datasets:
  - common_voice_17_0
metrics:
  - wer
model-index:
  - name: xls-r-300-cv17-polish-adap-cs
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_17_0
          type: common_voice_17_0
          config: pl
          split: validation
          args: pl
        metrics:
          - name: Wer
            type: wer
            value: 0.3181674482322567

xls-r-300-cv17-polish-adap-cs

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:

Loss: 0.4585
Wer: 0.3182
Cer: 0.0713

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: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 50
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
3.5986	1.6	100	3.9654	0.9986	0.9660
3.2886	3.2	200	3.4889	1.0	1.0
3.1683	4.8	300	3.1937	0.9946	0.9735
2.7362	6.4	400	2.6853	1.0	0.8424
0.6269	8.0	500	0.5183	0.5745	0.1381
0.2661	9.6	600	0.4218	0.4551	0.1048
0.1646	11.2	700	0.4160	0.4211	0.0985
0.1197	12.8	800	0.4793	0.4578	0.1072
0.1925	14.4	900	0.4402	0.4283	0.0969
0.1132	16.0	1000	0.4253	0.3909	0.0906
0.0851	17.6	1100	0.4609	0.3951	0.0921
0.0799	19.2	1200	0.4453	0.3944	0.0907
0.0657	20.8	1300	0.4681	0.3846	0.0887
0.1188	22.4	1400	0.4575	0.3785	0.0873
0.1088	24.0	1500	0.4649	0.3824	0.0882
0.0698	25.6	1600	0.4496	0.3611	0.0817
0.0575	27.2	1700	0.4459	0.3585	0.0822
0.0705	28.8	1800	0.4542	0.3608	0.0820
0.0524	30.4	1900	0.4785	0.3549	0.0814
0.0338	32.0	2000	0.4566	0.3521	0.0801
0.0357	33.6	2100	0.4597	0.3472	0.0783
0.0477	35.2	2200	0.4626	0.3451	0.0788
0.0478	36.8	2300	0.4730	0.3375	0.0765
0.0568	38.4	2400	0.4713	0.3333	0.0749
0.0217	40.0	2500	0.4701	0.3324	0.0755
0.0404	41.6	2600	0.4585	0.3278	0.0740
0.0118	43.2	2700	0.4656	0.3259	0.0736
0.0374	44.8	2800	0.4625	0.3249	0.0731
0.0417	46.4	2900	0.4599	0.3206	0.0721
0.0378	48.0	3000	0.4614	0.3195	0.0717
0.0381	49.6	3100	0.4585	0.3182	0.0713

Framework versions

Transformers 4.42.0.dev0
Pytorch 2.3.1+cu121
Datasets 2.19.2
Tokenizers 0.19.1