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

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Portuguese
wav2vec2
Generated from Trainer
hf-asr-leaderboard
mozilla-foundation/common_voice_7_0
robust-speech-event
Eval Results
Inference Endpoints
Model card Files Metrics Community
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+ ---
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - common_voice
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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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+ ---
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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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+
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+ # wav2vec2-xls-r-pt-cv7-from-bp400h
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+
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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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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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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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+
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+
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+ ### Framework versions
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+
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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