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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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model-index: |
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- name: wav2vec-base-Millad_TIMIT |
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results: [] |
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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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# wav2vec-base-Millad_TIMIT |
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3772 |
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- Wer: 0.6859 |
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- Cer: 0.3217 |
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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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- 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: 5000 |
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- num_epochs: 60 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| |
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| No log | 2.36 | 2000 | 2.6233 | 1.0130 | 0.6241 | |
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| No log | 4.73 | 4000 | 2.2206 | 0.9535 | 0.5032 | |
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| No log | 7.09 | 6000 | 2.3036 | 0.9368 | 0.5063 | |
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| 1.235 | 9.46 | 8000 | 1.9932 | 0.9275 | 0.5032 | |
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| 1.235 | 11.82 | 10000 | 2.0207 | 0.8922 | 0.4498 | |
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| 1.235 | 14.18 | 12000 | 1.6171 | 0.7993 | 0.3976 | |
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| 1.235 | 16.55 | 14000 | 1.6729 | 0.8309 | 0.4209 | |
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| 0.2779 | 18.91 | 16000 | 1.7043 | 0.8141 | 0.4340 | |
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| 0.2779 | 21.28 | 18000 | 1.7426 | 0.7658 | 0.3960 | |
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| 0.2779 | 23.64 | 20000 | 1.5230 | 0.7361 | 0.3830 | |
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| 0.2779 | 26.0 | 22000 | 1.4286 | 0.7658 | 0.3794 | |
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| 0.1929 | 28.37 | 24000 | 1.4450 | 0.7379 | 0.3644 | |
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| 0.1929 | 30.73 | 26000 | 1.5922 | 0.7491 | 0.3826 | |
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| 0.1929 | 33.1 | 28000 | 1.4443 | 0.7454 | 0.3617 | |
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| 0.1929 | 35.46 | 30000 | 1.5450 | 0.7268 | 0.3621 | |
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| 0.1394 | 37.83 | 32000 | 1.9268 | 0.7491 | 0.3763 | |
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| 0.1394 | 40.19 | 34000 | 1.7094 | 0.7342 | 0.3783 | |
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| 0.1394 | 42.55 | 36000 | 1.4024 | 0.7082 | 0.3494 | |
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| 0.1394 | 44.92 | 38000 | 1.4467 | 0.6840 | 0.3395 | |
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| 0.104 | 47.28 | 40000 | 1.4145 | 0.6933 | 0.3407 | |
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| 0.104 | 49.65 | 42000 | 1.3901 | 0.6970 | 0.3403 | |
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| 0.104 | 52.01 | 44000 | 1.3589 | 0.6636 | 0.3348 | |
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| 0.104 | 54.37 | 46000 | 1.3716 | 0.6952 | 0.3340 | |
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| 0.0781 | 56.74 | 48000 | 1.4025 | 0.6896 | 0.3312 | |
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| 0.0781 | 59.1 | 50000 | 1.3772 | 0.6859 | 0.3217 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 1.12.0+cu113 |
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- Datasets 1.18.3 |
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- Tokenizers 0.12.1 |
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