metadata

language:
  - pt
tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - mozilla-foundation/common_voice_7_0
  - pt
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_7_0
license: apache-2.0
model-index:
  - name: wav2vec2-xls-r-pt-cv7-from-bp400h
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7
          type: mozilla-foundation/common_voice_7_0
          args: pt
        metrics:
          - name: Test WER
            type: wer
            value: 12.13
          - name: Test CER
            type: cer
            value: 3.68
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: sv
        metrics:
          - name: Test WER
            type: wer
            value: 28.23
          - name: Test CER
            type: cer
            value: 12.58
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: pt
        metrics:
          - name: Test WER
            type: wer
            value: 26.58
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Test Data
          type: speech-recognition-community-v2/eval_data
          args: pt
        metrics:
          - name: Test WER
            type: wer
            value: 26.86

wav2vec2-xls-r-pt-cv7-from-bp400h

This model is a fine-tuned version of lgris/bp_400h_xlsr2_300M on the common_voice dataset. It achieves the following results on the evaluation set:

Loss: 0.1535
Wer: 0.1254

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: 0.0001
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
training_steps: 5000

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.4991	0.13	100	0.1774	0.1464
0.4655	0.26	200	0.1884	0.1568
0.4689	0.39	300	0.2282	0.1672
0.4662	0.52	400	0.1997	0.1584
0.4592	0.65	500	0.1989	0.1663
0.4533	0.78	600	0.2004	0.1698
0.4391	0.91	700	0.1888	0.1642
0.4655	1.04	800	0.1921	0.1624
0.4138	1.17	900	0.1950	0.1602
0.374	1.3	1000	0.2077	0.1658
0.4064	1.43	1100	0.1945	0.1596
0.3922	1.56	1200	0.2069	0.1665
0.4226	1.69	1300	0.1962	0.1573
0.3974	1.82	1400	0.1919	0.1553
0.3631	1.95	1500	0.1854	0.1573
0.3797	2.08	1600	0.1902	0.1550
0.3287	2.21	1700	0.1926	0.1598
0.3568	2.34	1800	0.1888	0.1534
0.3415	2.47	1900	0.1834	0.1502
0.3545	2.6	2000	0.1906	0.1560
0.3344	2.73	2100	0.1804	0.1524
0.3308	2.86	2200	0.1741	0.1485
0.344	2.99	2300	0.1787	0.1455
0.309	3.12	2400	0.1773	0.1448
0.312	3.25	2500	0.1738	0.1440
0.3066	3.38	2600	0.1727	0.1417
0.2999	3.51	2700	0.1692	0.1436
0.2985	3.64	2800	0.1732	0.1430
0.3058	3.77	2900	0.1754	0.1402
0.2943	3.9	3000	0.1691	0.1379
0.2813	4.03	3100	0.1754	0.1376
0.2733	4.16	3200	0.1639	0.1363
0.2592	4.29	3300	0.1675	0.1349
0.2697	4.42	3400	0.1618	0.1360
0.2538	4.55	3500	0.1658	0.1348
0.2746	4.67	3600	0.1674	0.1325
0.2655	4.8	3700	0.1655	0.1319
0.2745	4.93	3800	0.1665	0.1316
0.2617	5.06	3900	0.1600	0.1311
0.2674	5.19	4000	0.1623	0.1311
0.237	5.32	4100	0.1591	0.1315
0.2669	5.45	4200	0.1584	0.1295
0.2476	5.58	4300	0.1572	0.1285
0.2445	5.71	4400	0.1580	0.1271
0.2207	5.84	4500	0.1567	0.1269
0.2289	5.97	4600	0.1536	0.1260
0.2438	6.1	4700	0.1530	0.1260
0.227	6.23	4800	0.1544	0.1249
0.2256	6.36	4900	0.1543	0.1254
0.2184	6.49	5000	0.1535	0.1254

Framework versions

Transformers 4.15.0
Pytorch 1.10.0+cu111
Datasets 1.18.0
Tokenizers 0.10.3