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--- |
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library_name: transformers |
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language: |
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- pt |
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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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datasets: |
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- ylacombe/cml-tts |
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model-index: |
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- name: speechT5_tts-finetuned-cml-tts2 |
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results: [] |
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pipeline_tag: text-to-speech |
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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-finetuned-cml-tts2 |
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This model is a fine-tuned version of [microsoft/speechT5_tts](https://huggingface.co/microsoft/speechT5_tts) on the cml-tts dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4595 |
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## Model description |
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SpeechT5 model trained for Audio course Unit 6 hands-on on Portugues language cml-tts2 dataset for 5 hours. |
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Honestly it is not that good but definetly better then initial SpeechT5. |
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More information here https://outleys.site/en/development/AI/hugface-audio-course-handson-unit-6-exercise/ |
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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: 5e-05 |
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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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- optimizer: Use adamw_torch with betas=(0.9,0.99) and epsilon=1e-07 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- training_steps: 16000 |
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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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| 0.4819 | 0.0625 | 1000 | 0.5007 | |
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| 0.4364 | 0.125 | 2000 | 0.4965 | |
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| 0.4224 | 0.1875 | 3000 | 0.4841 | |
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| 0.4006 | 1.0473 | 4000 | 0.4782 | |
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| 0.3993 | 1.1098 | 5000 | 0.4728 | |
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| 0.3993 | 1.1723 | 6000 | 0.4687 | |
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| 0.389 | 2.032 | 7000 | 0.4684 | |
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| 0.3827 | 2.0945 | 8000 | 0.4665 | |
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| 0.3895 | 2.157 | 9000 | 0.4702 | |
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| 0.3829 | 3.0168 | 10000 | 0.4648 | |
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| 0.3717 | 3.0793 | 11000 | 0.4631 | |
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| 0.384 | 3.1418 | 12000 | 0.4627 | |
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| 0.3802 | 4.0015 | 13000 | 0.4601 | |
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| 0.3667 | 4.064 | 14000 | 0.4610 | |
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| 0.3757 | 4.1265 | 15000 | 0.4606 | |
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| 0.375 | 4.189 | 16000 | 0.4595 | |
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### Framework versions |
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- Transformers 4.46.2 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
