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--- |
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language: |
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- ar |
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license: mit |
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tags: |
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- ara |
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- generated_from_trainer |
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datasets: |
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- SDA_CLEAN_NAJDI |
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model-index: |
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- name: SpeechT5 TTS |
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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 TTS |
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This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the SDA dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4853 |
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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-05 |
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- train_batch_size: 16 |
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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: 32 |
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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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- training_steps: 40000 |
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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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| 0.5703 | 1.49 | 1000 | 0.5289 | |
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| 0.541 | 2.98 | 2000 | 0.5131 | |
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| 0.5487 | 4.46 | 3000 | 0.5059 | |
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| 0.5232 | 5.95 | 4000 | 0.5011 | |
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| 0.5295 | 7.44 | 5000 | 0.4979 | |
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| 0.5257 | 8.93 | 6000 | 0.4970 | |
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| 0.5091 | 10.42 | 7000 | 0.4905 | |
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| 0.5141 | 11.9 | 8000 | 0.4893 | |
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| 0.5033 | 13.39 | 9000 | 0.4865 | |
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| 0.507 | 14.88 | 10000 | 0.4850 | |
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| 0.502 | 16.37 | 11000 | 0.4830 | |
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| 0.497 | 17.86 | 12000 | 0.4823 | |
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| 0.4974 | 19.35 | 13000 | 0.4801 | |
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| 0.4993 | 20.83 | 14000 | 0.4794 | |
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| 0.496 | 22.32 | 15000 | 0.4814 | |
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| 0.4845 | 23.81 | 16000 | 0.4780 | |
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| 0.4977 | 25.3 | 17000 | 0.4775 | |
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| 0.4888 | 26.79 | 18000 | 0.4780 | |
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| 0.4773 | 28.27 | 19000 | 0.4792 | |
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| 0.4914 | 29.76 | 20000 | 0.4817 | |
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| 0.4864 | 31.25 | 21000 | 0.4775 | |
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| 0.486 | 32.74 | 22000 | 0.4773 | |
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| 0.4884 | 34.23 | 23000 | 0.4835 | |
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| 0.4856 | 35.71 | 24000 | 0.4788 | |
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| 0.4814 | 37.2 | 25000 | 0.4811 | |
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| 0.4831 | 38.69 | 26000 | 0.4814 | |
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| 0.4732 | 40.18 | 27000 | 0.4816 | |
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| 0.4846 | 41.67 | 28000 | 0.4812 | |
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| 0.4731 | 43.15 | 29000 | 0.4843 | |
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| 0.4772 | 44.64 | 30000 | 0.4830 | |
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| 0.4793 | 46.13 | 31000 | 0.4834 | |
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| 0.4736 | 47.62 | 32000 | 0.4834 | |
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| 0.4798 | 49.11 | 33000 | 0.4826 | |
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| 0.4744 | 50.6 | 34000 | 0.4841 | |
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| 0.4784 | 52.08 | 35000 | 0.4844 | |
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| 0.4743 | 53.57 | 36000 | 0.4851 | |
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| 0.4779 | 55.06 | 37000 | 0.4854 | |
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| 0.4719 | 56.55 | 38000 | 0.4854 | |
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| 0.4825 | 58.04 | 39000 | 0.4856 | |
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| 0.4805 | 59.52 | 40000 | 0.4853 | |
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### Framework versions |
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- Transformers 4.30.0.dev0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.13.0 |
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- Tokenizers 0.13.3 |
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