metadata

license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
  - generated_from_trainer
datasets:
  - common_voice
metrics:
  - wer
model-index:
  - name: wav2vec2-large-xls-r-300m-euskera-colab
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice
          type: common_voice
          config: eu
          split: test
          args: eu
        metrics:
          - name: Wer
            type: wer
            value: 0.17522160918337998

wav2vec2-large-xls-r-300m-euskera-colab

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

Loss: 0.1873
Wer: 0.1752

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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
2.5827	0.76	600	0.3985	0.5778
0.2963	1.51	1200	0.2492	0.4142
0.2122	2.27	1800	0.2132	0.3133
0.1686	3.03	2400	0.2078	0.2851
0.1373	3.79	3000	0.1910	0.2750
0.1254	4.54	3600	0.1850	0.2619
0.1137	5.3	4200	0.1874	0.2503
0.1008	6.06	4800	0.1857	0.2554
0.0934	6.81	5400	0.1844	0.2404
0.0876	7.57	6000	0.2001	0.2375
0.0801	8.33	6600	0.2036	0.2512
0.0732	9.09	7200	0.1921	0.2301
0.069	9.84	7800	0.1821	0.2330
0.0628	10.6	8400	0.1915	0.2249
0.0619	11.36	9000	0.1881	0.2113
0.0549	12.11	9600	0.1920	0.2076
0.0524	12.87	10200	0.1901	0.2079
0.0492	13.63	10800	0.1767	0.2020
0.0445	14.38	11400	0.1852	0.1933
0.0427	15.14	12000	0.1995	0.1994
0.0398	15.9	12600	0.1922	0.1932
0.037	16.66	13200	0.1956	0.1920
0.0353	17.41	13800	0.1990	0.1909
0.0327	18.17	14400	0.1906	0.1784
0.0311	18.93	15000	0.1847	0.1765
0.0295	19.68	15600	0.1873	0.1752

Framework versions

Transformers 4.32.1
Pytorch 2.0.1+cu118
Datasets 2.14.4
Tokenizers 0.13.3