metadata

library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
  - generated_from_trainer
datasets:
  - fleurs
metrics:
  - wer
model-index:
  - name: wav2vec2-xls-r-Wolof-10-hours-Google-Fleurs-dataset
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: fleurs
          type: fleurs
          config: wo_sn
          split: None
          args: wo_sn
        metrics:
          - name: Wer
            type: wer
            value: 0.442296823782073

wav2vec2-xls-r-Wolof-10-hours-Google-Fleurs-dataset

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

Loss: 1.2082
Wer: 0.4423
Cer: 0.1524

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_ratio: 0.1
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
6.6995	2.6144	200	3.0428	1.0	1.0
3.0391	5.2288	400	3.0117	1.0	1.0
3.0035	7.8431	600	2.9794	1.0	1.0
2.0946	10.4575	800	0.9827	0.7357	0.2560
0.8407	13.0719	1000	0.7398	0.5189	0.1848
0.5774	15.6863	1200	0.7214	0.4926	0.1745
0.4229	18.3007	1400	0.6996	0.4852	0.1707
0.3332	20.9150	1600	0.7950	0.4878	0.1708
0.2488	23.5294	1800	0.8972	0.4645	0.1624
0.2043	26.1438	2000	0.9122	0.4576	0.1609
0.1699	28.7582	2200	1.0064	0.4777	0.1672
0.1472	31.3725	2400	1.0141	0.4554	0.1581
0.1251	33.9869	2600	1.0362	0.4553	0.1580
0.1152	36.6013	2800	1.1312	0.4490	0.1554
0.0986	39.2157	3000	1.1552	0.4499	0.1555
0.0905	41.8301	3200	1.1811	0.4463	0.1547
0.0879	44.4444	3400	1.1849	0.4513	0.1551
0.0793	47.0588	3600	1.2074	0.4422	0.1527
0.0802	49.6732	3800	1.2082	0.4423	0.1524

Framework versions

Transformers 4.44.2
Pytorch 2.1.0+cu118
Datasets 2.17.0
Tokenizers 0.19.1