metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - mozilla-foundation/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: test
          args: fy-NL
        metrics:
          - name: Wer
            type: wer
            value: 0.15990775235054105
language:
  - fy

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: 0.1599

This model was developed together with golesheed for the course "Speech Recognition II" of the "MSc Voice Technology" program at Rijksuniversiteit Groningen - Campus Fryslân.

Intended uses & limitations

Intended use is for recognizing Frisian speech.

Limitations include not enough hyperparameter tuning, no LM rescoring, and using v12 of Common Voice instead of v13.

Training and evaluation data

Training and evaluation splits used are the ones available in the Common Voice dataset.

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