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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: facebook/wav2vec2-large-xlsr-53 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- audiofolder |
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metrics: |
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- wer |
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model-index: |
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- name: w2v2-lmk_updated |
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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: audiofolder |
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type: audiofolder |
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config: default |
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split: test |
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args: default |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.5749128919860628 |
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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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# w2v2-lmk_updated |
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This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the audiofolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0281 |
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- Wer: 0.5749 |
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- Cer: 0.2049 |
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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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 300 |
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- num_epochs: 100 |
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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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| 9.4495 | 6.25 | 100 | 4.3525 | 1.0 | 1.0 | |
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| 3.1633 | 12.5 | 200 | 2.9339 | 1.0 | 1.0 | |
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| 2.964 | 18.75 | 300 | 2.8446 | 1.0 | 1.0 | |
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| 2.8489 | 25.0 | 400 | 2.6748 | 1.0 | 1.0 | |
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| 2.409 | 31.25 | 500 | 1.9297 | 1.0 | 0.6824 | |
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| 1.6654 | 37.5 | 600 | 1.3226 | 0.9024 | 0.3686 | |
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| 1.2589 | 43.75 | 700 | 1.0957 | 0.7666 | 0.2749 | |
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| 1.0072 | 50.0 | 800 | 1.0241 | 0.6585 | 0.2445 | |
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| 0.818 | 56.25 | 900 | 1.0104 | 0.6132 | 0.2239 | |
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| 0.7072 | 62.5 | 1000 | 0.9941 | 0.5923 | 0.2155 | |
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| 0.5998 | 68.75 | 1100 | 1.0503 | 0.5749 | 0.2079 | |
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| 0.5677 | 75.0 | 1200 | 1.0376 | 0.5784 | 0.2049 | |
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| 0.5125 | 81.25 | 1300 | 1.0549 | 0.5819 | 0.2133 | |
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| 0.4983 | 87.5 | 1400 | 1.0222 | 0.5505 | 0.1988 | |
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| 0.4859 | 93.75 | 1500 | 1.0176 | 0.5575 | 0.1995 | |
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| 0.4436 | 100.0 | 1600 | 1.0281 | 0.5749 | 0.2049 | |
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### Framework versions |
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- Transformers 4.57.1 |
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- Pytorch 2.8.0+cu128 |
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- Datasets 3.0.0 |
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- Tokenizers 0.22.1 |
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