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--- |
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language: |
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- pt |
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tags: |
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- automatic-speech-recognition |
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- generated_from_trainer |
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- hf-asr-leaderboard |
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- mozilla-foundation/common_voice_7_0 |
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- pt |
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- robust-speech-event |
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datasets: |
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- mozilla-foundation/common_voice_7_0 |
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license: apache-2.0 |
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model-index: |
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- name: wav2vec2-xls-r-pt-cv7-from-bp400h |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Common Voice 7 |
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type: mozilla-foundation/common_voice_7_0 |
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args: pt |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 12.13 |
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- name: Test CER |
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type: cer |
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value: 3.68 |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Robust Speech Event - Dev Data |
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type: speech-recognition-community-v2/dev_data |
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args: sv |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 28.23 |
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- name: Test CER |
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type: cer |
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value: 12.58 |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Robust Speech Event - Dev Data |
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type: speech-recognition-community-v2/dev_data |
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args: pt |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 26.58 |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Robust Speech Event - Test Data |
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type: speech-recognition-community-v2/eval_data |
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args: pt |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 26.86 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# wav2vec2-xls-r-pt-cv7-from-bp400h |
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This model is a fine-tuned version of [lgris/bp_400h_xlsr2_300M](https://huggingface.co/lgris/bp_400h_xlsr2_300M) on the common_voice dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1535 |
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- Wer: 0.1254 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 100 |
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- training_steps: 5000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 0.4991 | 0.13 | 100 | 0.1774 | 0.1464 | |
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| 0.4655 | 0.26 | 200 | 0.1884 | 0.1568 | |
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| 0.4689 | 0.39 | 300 | 0.2282 | 0.1672 | |
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| 0.4662 | 0.52 | 400 | 0.1997 | 0.1584 | |
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| 0.4592 | 0.65 | 500 | 0.1989 | 0.1663 | |
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| 0.4533 | 0.78 | 600 | 0.2004 | 0.1698 | |
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| 0.4391 | 0.91 | 700 | 0.1888 | 0.1642 | |
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| 0.4655 | 1.04 | 800 | 0.1921 | 0.1624 | |
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| 0.4138 | 1.17 | 900 | 0.1950 | 0.1602 | |
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| 0.374 | 1.3 | 1000 | 0.2077 | 0.1658 | |
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| 0.4064 | 1.43 | 1100 | 0.1945 | 0.1596 | |
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| 0.3922 | 1.56 | 1200 | 0.2069 | 0.1665 | |
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| 0.4226 | 1.69 | 1300 | 0.1962 | 0.1573 | |
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| 0.3974 | 1.82 | 1400 | 0.1919 | 0.1553 | |
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| 0.3631 | 1.95 | 1500 | 0.1854 | 0.1573 | |
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| 0.3797 | 2.08 | 1600 | 0.1902 | 0.1550 | |
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| 0.3287 | 2.21 | 1700 | 0.1926 | 0.1598 | |
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| 0.3568 | 2.34 | 1800 | 0.1888 | 0.1534 | |
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| 0.3415 | 2.47 | 1900 | 0.1834 | 0.1502 | |
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| 0.3545 | 2.6 | 2000 | 0.1906 | 0.1560 | |
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| 0.3344 | 2.73 | 2100 | 0.1804 | 0.1524 | |
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| 0.3308 | 2.86 | 2200 | 0.1741 | 0.1485 | |
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| 0.344 | 2.99 | 2300 | 0.1787 | 0.1455 | |
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| 0.309 | 3.12 | 2400 | 0.1773 | 0.1448 | |
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| 0.312 | 3.25 | 2500 | 0.1738 | 0.1440 | |
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| 0.3066 | 3.38 | 2600 | 0.1727 | 0.1417 | |
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| 0.2999 | 3.51 | 2700 | 0.1692 | 0.1436 | |
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| 0.2985 | 3.64 | 2800 | 0.1732 | 0.1430 | |
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| 0.3058 | 3.77 | 2900 | 0.1754 | 0.1402 | |
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| 0.2943 | 3.9 | 3000 | 0.1691 | 0.1379 | |
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| 0.2813 | 4.03 | 3100 | 0.1754 | 0.1376 | |
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| 0.2733 | 4.16 | 3200 | 0.1639 | 0.1363 | |
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| 0.2592 | 4.29 | 3300 | 0.1675 | 0.1349 | |
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| 0.2697 | 4.42 | 3400 | 0.1618 | 0.1360 | |
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| 0.2538 | 4.55 | 3500 | 0.1658 | 0.1348 | |
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| 0.2746 | 4.67 | 3600 | 0.1674 | 0.1325 | |
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| 0.2655 | 4.8 | 3700 | 0.1655 | 0.1319 | |
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| 0.2745 | 4.93 | 3800 | 0.1665 | 0.1316 | |
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| 0.2617 | 5.06 | 3900 | 0.1600 | 0.1311 | |
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| 0.2674 | 5.19 | 4000 | 0.1623 | 0.1311 | |
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| 0.237 | 5.32 | 4100 | 0.1591 | 0.1315 | |
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| 0.2669 | 5.45 | 4200 | 0.1584 | 0.1295 | |
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| 0.2476 | 5.58 | 4300 | 0.1572 | 0.1285 | |
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| 0.2445 | 5.71 | 4400 | 0.1580 | 0.1271 | |
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| 0.2207 | 5.84 | 4500 | 0.1567 | 0.1269 | |
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| 0.2289 | 5.97 | 4600 | 0.1536 | 0.1260 | |
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| 0.2438 | 6.1 | 4700 | 0.1530 | 0.1260 | |
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| 0.227 | 6.23 | 4800 | 0.1544 | 0.1249 | |
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| 0.2256 | 6.36 | 4900 | 0.1543 | 0.1254 | |
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| 0.2184 | 6.49 | 5000 | 0.1535 | 0.1254 | |
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### Framework versions |
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- Transformers 4.15.0 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.18.0 |
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- Tokenizers 0.10.3 |
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