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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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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-common_voice-tr-demo |
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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 |
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type: common_voice |
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config: tr |
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split: test |
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args: tr |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.49443366356858337 |
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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-common_voice-tr-demo |
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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 common_voice dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5314 |
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- Wer: 0.4944 |
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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: 0.0002 |
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- train_batch_size: 32 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 64 |
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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: 500 |
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- num_epochs: 20.0 |
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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 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| No log | 1.83 | 100 | 4.1084 | 1.0 | |
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| No log | 3.67 | 200 | 3.1519 | 1.0 | |
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| No log | 5.5 | 300 | 1.9348 | 0.9799 | |
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| No log | 7.34 | 400 | 0.7185 | 0.7490 | |
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| 3.6165 | 9.17 | 500 | 0.6041 | 0.6368 | |
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| 3.6165 | 11.01 | 600 | 0.5610 | 0.5771 | |
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| 3.6165 | 12.84 | 700 | 0.5292 | 0.5398 | |
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| 3.6165 | 14.68 | 800 | 0.5242 | 0.5083 | |
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| 3.6165 | 16.51 | 900 | 0.5443 | 0.5037 | |
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| 0.1894 | 18.35 | 1000 | 0.5314 | 0.4944 | |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 2.0.1 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.2 |
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