metadata

language:
  - br
license: apache-2.0
tags:
  - generated_from_trainer
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_7_0
metrics:
  - wer
model-index:
  - name: wav2vec2-large-xls-r-300m-br-d2
    results:
      - task:
          type: automatic-speech-recognition
          name: Speech Recognition
        dataset:
          type: mozilla-foundation/common_voice_8_0
          name: Common Voice 7
          args: br
        metrics:
          - type: wer
            value: []
            name: Test WER
          - name: Test CER
            type: cer
            value: []

wav2vec2-large-xls-r-300m-br-d2

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

Loss: 1.1257
Wer: 0.4631

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: 0.00034
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: 750
num_epochs: 50
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
14.0379	0.68	100	5.6808	1.0
3.9145	1.35	200	3.1970	1.0
3.0293	2.03	300	2.9513	1.0
2.0927	2.7	400	1.4545	0.8887
1.1556	3.38	500	1.0966	0.7564
0.9628	4.05	600	0.9808	0.7364
0.7869	4.73	700	1.0488	0.7355
0.703	5.41	800	0.9500	0.6881
0.6657	6.08	900	0.9309	0.6259
0.5663	6.76	1000	0.9133	0.6357
0.496	7.43	1100	0.9890	0.6028
0.4748	8.11	1200	0.9469	0.5894
0.4135	8.78	1300	0.9270	0.6045
0.3579	9.46	1400	0.8818	0.5708
0.353	10.14	1500	0.9244	0.5781
0.334	10.81	1600	0.9009	0.5638
0.2917	11.49	1700	1.0132	0.5828
0.29	12.16	1800	0.9696	0.5668
0.2691	12.84	1900	0.9811	0.5455
0.25	13.51	2000	0.9951	0.5624
0.2467	14.19	2100	0.9653	0.5573
0.2242	14.86	2200	0.9714	0.5378
0.2066	15.54	2300	0.9829	0.5394
0.2075	16.22	2400	1.0547	0.5520
0.1923	16.89	2500	1.0014	0.5397
0.1919	17.57	2600	0.9978	0.5477
0.1908	18.24	2700	1.1064	0.5397
0.157	18.92	2800	1.0629	0.5238
0.159	19.59	2900	1.0642	0.5321
0.1652	20.27	3000	1.0207	0.5328
0.141	20.95	3100	0.9948	0.5312
0.1417	21.62	3200	1.0338	0.5328
0.1514	22.3	3300	1.0513	0.5313
0.1365	22.97	3400	1.0357	0.5291
0.1319	23.65	3500	1.0587	0.5167
0.1298	24.32	3600	1.0636	0.5236
0.1245	25.0	3700	1.1367	0.5280
0.1114	25.68	3800	1.0633	0.5200
0.1088	26.35	3900	1.0495	0.5210
0.1175	27.03	4000	1.0897	0.5095
0.1043	27.7	4100	1.0580	0.5309
0.0951	28.38	4200	1.0448	0.5067
0.1011	29.05	4300	1.0665	0.5137
0.0889	29.73	4400	1.0579	0.5026
0.0833	30.41	4500	1.0740	0.5037
0.0889	31.08	4600	1.0933	0.5083
0.0784	31.76	4700	1.0715	0.5089
0.0767	32.43	4800	1.0658	0.5049
0.0769	33.11	4900	1.1118	0.4979
0.0722	33.78	5000	1.1413	0.4986
0.0709	34.46	5100	1.0706	0.4885
0.0664	35.14	5200	1.1217	0.4884
0.0648	35.81	5300	1.1298	0.4941
0.0657	36.49	5400	1.1330	0.4920
0.0582	37.16	5500	1.0598	0.4835
0.0602	37.84	5600	1.1097	0.4943
0.0598	38.51	5700	1.0976	0.4876
0.0547	39.19	5800	1.0734	0.4825
0.0561	39.86	5900	1.0926	0.4850
0.0516	40.54	6000	1.1579	0.4751
0.0478	41.22	6100	1.1384	0.4706
0.0396	41.89	6200	1.1462	0.4739
0.0472	42.57	6300	1.1277	0.4732
0.0447	43.24	6400	1.1517	0.4752
0.0423	43.92	6500	1.1219	0.4784
0.0426	44.59	6600	1.1311	0.4724
0.0391	45.27	6700	1.1135	0.4692
0.0362	45.95	6800	1.0878	0.4645
0.0329	46.62	6900	1.1137	0.4668
0.0356	47.3	7000	1.1233	0.4687
0.0328	47.97	7100	1.1238	0.4653
0.0323	48.65	7200	1.1307	0.4646
0.0325	49.32	7300	1.1242	0.4645
0.03	50.0	7400	1.1257	0.4631

Framework versions

Transformers 4.16.2
Pytorch 1.10.0+cu111
Datasets 1.18.3
Tokenizers 0.11.0