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--- |
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language: |
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- es |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- facebook/voxpopuli |
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pipeline_tag: text-to-speech |
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base_model: microsoft/speecht5_tts |
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model-index: |
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- name: speec T5 ES - Peter Gelderbloem |
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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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# speec T5 ES - Peter Gelderbloem |
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This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the Vox Populi ES dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4400 |
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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: 4 |
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- eval_batch_size: 4 |
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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: 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: 4000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.6709 | 0.14 | 100 | 0.6107 | |
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| 0.624 | 0.28 | 200 | 0.5773 | |
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| 0.5921 | 0.43 | 300 | 0.5397 | |
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| 0.5504 | 0.57 | 400 | 0.4941 | |
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| 0.5289 | 0.71 | 500 | 0.4807 | |
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| 0.5236 | 0.85 | 600 | 0.4732 | |
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| 0.5194 | 0.99 | 700 | 0.4672 | |
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| 0.5095 | 1.13 | 800 | 0.4650 | |
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| 0.508 | 1.28 | 900 | 0.4608 | |
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| 0.5029 | 1.42 | 1000 | 0.4580 | |
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| 0.5029 | 1.56 | 1100 | 0.4569 | |
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| 0.4952 | 1.7 | 1200 | 0.4540 | |
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| 0.4939 | 1.84 | 1300 | 0.4540 | |
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| 0.4924 | 1.99 | 1400 | 0.4523 | |
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| 0.4902 | 2.13 | 1500 | 0.4519 | |
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| 0.492 | 2.27 | 1600 | 0.4491 | |
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| 0.4896 | 2.41 | 1700 | 0.4497 | |
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| 0.4874 | 2.55 | 1800 | 0.4482 | |
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| 0.4889 | 2.7 | 1900 | 0.4473 | |
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| 0.4888 | 2.84 | 2000 | 0.4471 | |
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| 0.4902 | 2.98 | 2100 | 0.4461 | |
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| 0.4818 | 3.12 | 2200 | 0.4457 | |
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| 0.4854 | 3.26 | 2300 | 0.4451 | |
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| 0.4886 | 3.4 | 2400 | 0.4429 | |
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| 0.4799 | 3.55 | 2500 | 0.4429 | |
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| 0.4806 | 3.69 | 2600 | 0.4429 | |
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| 0.4777 | 3.83 | 2700 | 0.4416 | |
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| 0.4787 | 3.98 | 2800 | 0.4423 | |
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| 0.4823 | 4.12 | 2900 | 0.4419 | |
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| 0.4822 | 4.26 | 3000 | 0.4409 | |
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| 0.4796 | 4.4 | 3100 | 0.4404 | |
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| 0.4805 | 4.54 | 3200 | 0.4418 | |
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| 0.4795 | 4.69 | 3300 | 0.4409 | |
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| 0.4784 | 4.83 | 3400 | 0.4411 | |
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| 0.4812 | 4.97 | 3500 | 0.4406 | |
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| 0.478 | 5.11 | 3600 | 0.4399 | |
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| 0.4766 | 5.25 | 3700 | 0.4396 | |
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| 0.4821 | 5.39 | 3800 | 0.4407 | |
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| 0.48 | 5.54 | 3900 | 0.4396 | |
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| 0.4795 | 5.68 | 4000 | 0.4400 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 1.12.1+cu116 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
