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--- |
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library_name: transformers |
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license: mit |
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base_model: microsoft/speecht5_tts |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: speecht5_mehdi_new_as_try111 |
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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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# speecht5_mehdi_new_as_try111 |
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This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5467 |
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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: 1e-06 |
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- train_batch_size: 4 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 32 |
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- optimizer: Use adamw_torch 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: 100 |
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- training_steps: 3000 |
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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 | |
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|:-------------:|:-------:|:----:|:---------------:| |
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| 1.0666 | 1.7778 | 100 | 0.8627 | |
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| 0.8711 | 3.5556 | 200 | 0.7175 | |
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| 0.8314 | 5.3333 | 300 | 0.6804 | |
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| 0.8011 | 7.1111 | 400 | 0.6603 | |
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| 0.7676 | 8.8889 | 500 | 0.6402 | |
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| 0.7335 | 10.6667 | 600 | 0.6158 | |
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| 0.7093 | 12.4444 | 700 | 0.5889 | |
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| 0.676 | 14.2222 | 800 | 0.5793 | |
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| 0.6617 | 16.0 | 900 | 0.5743 | |
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| 0.664 | 17.7778 | 1000 | 0.5711 | |
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| 0.6516 | 19.5556 | 1100 | 0.5664 | |
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| 0.6478 | 21.3333 | 1200 | 0.5609 | |
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| 0.6445 | 23.1111 | 1300 | 0.5590 | |
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| 0.642 | 24.8889 | 1400 | 0.5601 | |
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| 0.6341 | 26.6667 | 1500 | 0.5585 | |
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| 0.6415 | 28.4444 | 1600 | 0.5584 | |
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| 0.6373 | 30.2222 | 1700 | 0.5533 | |
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| 0.6257 | 32.0 | 1800 | 0.5508 | |
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| 0.6311 | 33.7778 | 1900 | 0.5516 | |
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| 0.6201 | 35.5556 | 2000 | 0.5487 | |
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| 0.6257 | 37.3333 | 2100 | 0.5496 | |
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| 0.6304 | 39.1111 | 2200 | 0.5494 | |
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| 0.6177 | 40.8889 | 2300 | 0.5473 | |
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| 0.6235 | 42.6667 | 2400 | 0.5463 | |
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| 0.6202 | 44.4444 | 2500 | 0.5475 | |
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| 0.6191 | 46.2222 | 2600 | 0.5464 | |
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| 0.6188 | 48.0 | 2700 | 0.5442 | |
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| 0.6034 | 49.7778 | 2800 | 0.5452 | |
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| 0.6132 | 51.5556 | 2900 | 0.5453 | |
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| 0.6205 | 53.3333 | 3000 | 0.5467 | |
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### Framework versions |
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- Transformers 4.46.3 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.20.3 |
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