metadata

license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
  - generated_from_trainer
datasets:
  - common_voice_11_0
metrics:
  - wer
model-index:
  - name: wav2vec2-large-xlsr-sw
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_11_0
          type: common_voice_11_0
          config: sw
          split: test
          args: sw
        metrics:
          - name: Wer
            type: wer
            value: 0.3230712635221355

wav2vec2-large-xlsr-sw

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice_11_0 dataset. It achieves the following results on the evaluation set:

Loss: 0.4334
Wer: 0.3231

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_steps: 500
num_epochs: 30
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
5.3405	0.88	400	2.8882	1.0000
1.083	1.77	800	0.5223	0.5849
0.4721	2.65	1200	0.3921	0.4667
0.3793	3.54	1600	0.3725	0.4257
0.3264	4.42	2000	0.3646	0.4179
0.294	5.31	2400	0.3542	0.4104
0.2623	6.19	2800	0.3576	0.3892
0.2408	7.08	3200	0.3516	0.3876
0.2229	7.96	3600	0.3580	0.3877
0.206	8.85	4000	0.3466	0.3683
0.1991	9.73	4400	0.3306	0.3783
0.1863	10.62	4800	0.3605	0.3707
0.1743	11.5	5200	0.3483	0.3703
0.1678	12.39	5600	0.3645	0.3618
0.1547	13.27	6000	0.3671	0.3589
0.152	14.16	6400	0.3733	0.3568
0.144	15.04	6800	0.3684	0.3486
0.136	15.93	7200	0.3558	0.3493
0.1262	16.81	7600	0.3748	0.3486
0.1222	17.7	8000	0.3774	0.3466
0.1164	18.58	8400	0.3840	0.3427
0.1108	19.47	8800	0.3988	0.3438
0.1072	20.35	9200	0.4020	0.3384
0.1008	21.24	9600	0.4013	0.3375
0.0982	22.12	10000	0.4162	0.3361
0.0951	23.01	10400	0.4107	0.3346
0.0923	23.89	10800	0.4248	0.3337
0.0866	24.78	11200	0.4151	0.3295
0.0875	25.66	11600	0.4211	0.3310
0.0813	26.55	12000	0.4303	0.3290
0.0775	27.43	12400	0.4334	0.3249
0.0759	28.32	12800	0.4312	0.3240
0.0758	29.2	13200	0.4334	0.3231

Framework versions

Transformers 4.37.0.dev0
Pytorch 2.1.1+cu121
Datasets 2.16.1
Tokenizers 0.15.0