metadata

license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
  - generated_from_trainer
datasets:
  - common_voice_13_0
metrics:
  - wer
model-index:
  - name: wav2vec2-large-xls-r-300m-breton-colab
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_13_0
          type: common_voice_13_0
          config: br
          split: test
          args: br
        metrics:
          - name: Wer
            type: wer
            value: 0.4936988936988937

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

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_13_0 dataset. It achieves the following results on the evaluation set:

Loss: 1.2211
Wer: 0.4937

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: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
5.3288	1.34	400	1.7076	0.9809
1.2014	2.69	800	1.0803	0.7733
0.7687	4.03	1200	0.9806	0.6642
0.5539	5.38	1600	0.9914	0.6301
0.4456	6.72	2000	0.9797	0.6265
0.3586	8.07	2400	1.0354	0.5803
0.2922	9.41	2800	0.9996	0.5821
0.2628	10.76	3200	1.0250	0.5708
0.2284	12.1	3600	1.0865	0.5722
0.1908	13.45	4000	1.0674	0.5450
0.1732	14.79	4400	1.1775	0.5614
0.153	16.13	4800	1.1542	0.5435
0.14	17.48	5200	1.1807	0.5449
0.1302	18.82	5600	1.1679	0.5376
0.1142	20.17	6000	1.1441	0.5276
0.104	21.51	6400	1.2243	0.5355
0.0882	22.86	6800	1.1837	0.5316
0.0807	24.2	7200	1.1986	0.5132
0.0744	25.55	7600	1.2182	0.5108
0.0646	26.89	8000	1.2116	0.5047
0.0551	28.24	8400	1.2009	0.4948
0.0503	29.58	8800	1.2211	0.4937

Framework versions

Transformers 4.32.1
Pytorch 2.0.1+cu118
Datasets 2.14.4
Tokenizers 0.13.3