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AlexN
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xls-r-300m-pt

Automatic Speech Recognition
Transformers
PyTorch
Portuguese
wav2vec2
robust-speech-event
mozilla-foundation/common_voice_8_0
Generated from Trainer
hf-asr-leaderboard
Eval Results
Inference Endpoints
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update model card README.md

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  2. wandb/run-20220201_223624-2b1hcyq3/files/output.log +6 -0
  3. wandb/run-20220201_223624-2b1hcyq3/logs/debug-internal.log +7 -0
  4. wandb/run-20220201_223624-2b1hcyq3/run-2b1hcyq3.wandb +2 -2
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+ ---
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+ license: apache-2.0
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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: ''
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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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+ #
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+
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+ This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2290
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+ - Wer: 0.2382
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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: 0.0002
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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+ - seed: 42
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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: 1500
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+ - num_epochs: 15.0
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+ - mixed_precision_training: Native AMP
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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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+ | 3.0952 | 0.64 | 500 | 3.0982 | 1.0 |
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+ | 1.7975 | 1.29 | 1000 | 0.7887 | 0.5651 |
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+ | 1.4138 | 1.93 | 1500 | 0.5238 | 0.4389 |
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+ | 1.344 | 2.57 | 2000 | 0.4775 | 0.4318 |
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+ | 1.2737 | 3.21 | 2500 | 0.4648 | 0.4075 |
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+ | 1.2554 | 3.86 | 3000 | 0.4069 | 0.3678 |
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+ | 1.1996 | 4.5 | 3500 | 0.3914 | 0.3668 |
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+ | 1.1427 | 5.14 | 4000 | 0.3694 | 0.3572 |
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+ | 1.1372 | 5.78 | 4500 | 0.3568 | 0.3501 |
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+ | 1.0831 | 6.43 | 5000 | 0.3331 | 0.3253 |
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+ | 1.1074 | 7.07 | 5500 | 0.3332 | 0.3352 |
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+ | 1.0536 | 7.71 | 6000 | 0.3131 | 0.3152 |
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+ | 1.0248 | 8.35 | 6500 | 0.3024 | 0.3023 |
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+ | 1.0075 | 9.0 | 7000 | 0.2948 | 0.3028 |
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+ | 0.979 | 9.64 | 7500 | 0.2796 | 0.2853 |
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+ | 0.9594 | 10.28 | 8000 | 0.2719 | 0.2789 |
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+ | 0.9172 | 10.93 | 8500 | 0.2620 | 0.2695 |
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+ | 0.9047 | 11.57 | 9000 | 0.2537 | 0.2596 |
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+ | 0.8777 | 12.21 | 9500 | 0.2438 | 0.2525 |
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+ | 0.8629 | 12.85 | 10000 | 0.2409 | 0.2493 |
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+ | 0.8575 | 13.5 | 10500 | 0.2366 | 0.2440 |
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+ | 0.8361 | 14.14 | 11000 | 0.2317 | 0.2385 |
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+ | 0.8126 | 14.78 | 11500 | 0.2290 | 0.2382 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.17.0.dev0
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+ - Pytorch 1.10.2+cu102
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+ - Datasets 1.18.2.dev0
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+ - Tokenizers 0.11.0
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