metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - common_voice_12_0
metrics:
  - wer
model-index:
  - name: wav2vec2-large-xls-r-1b-frisian
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_12_0
          type: common_voice_12_0
          config: fy-NL
          split: validation
          args: fy-NL
        metrics:
          - name: Wer
            type: wer
            value: 0.15977951760699363

wav2vec2-large-xls-r-1b-frisian

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the common_voice_12_0 dataset. It achieves the following results on the evaluation set:

Loss: 0.2634
Wer: 0.1598

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: 8e-05
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
4.7284	2.1	250	2.9453	1.0
1.7496	4.2	500	0.5141	0.4771
0.8168	6.3	750	0.3220	0.3148
0.7403	8.4	1000	0.2988	0.2573
0.7298	10.5	1250	0.2794	0.2347
0.6303	12.61	1500	0.2577	0.2164
0.5201	14.71	1750	0.2746	0.2162
0.5189	16.81	2000	0.2543	0.2034
0.5054	18.91	2250	0.2847	0.2071
0.5112	21.01	2500	0.2772	0.1979
0.5105	23.11	2750	0.2633	0.1920
0.5032	25.21	3000	0.2667	0.1856
0.46	27.31	3250	0.2730	0.1852
0.4992	29.41	3500	0.2626	0.1782
0.4535	31.51	3750	0.2778	0.1749
0.4036	33.61	4000	0.2825	0.1747
0.3347	35.71	4250	0.2797	0.1708
0.2708	37.82	4500	0.2662	0.1712
0.1825	39.92	4750	0.2652	0.1648
0.1654	42.02	5000	0.2719	0.1628
0.1387	44.12	5250	0.2552	0.1607
0.1367	46.22	5500	0.2641	0.1591
0.1218	48.32	5750	0.2634	0.1598

Framework versions

Transformers 4.27.3
Pytorch 2.0.0+cu117
Datasets 2.10.1
Tokenizers 0.13.2