metadata
library_name: transformers
language:
- pt
license: mit
base_model: microsoft/speechT5_tts
tags:
- generated_from_trainer
datasets:
- ylacombe/cml-tts
model-index:
- name: speechT5_tts-finetuned-cml-tts2
results: []
pipeline_tag: text-to-speech
speechT5_tts-finetuned-cml-tts2
This model is a fine-tuned version of microsoft/speechT5_tts on the cml-tts dataset. It achieves the following results on the evaluation set:
- Loss: 0.4595
Model description
SpeechT5 model trained for Audio course Unit 6 hands-on on Portugues language cml-tts2 dataset for 5 hours. Honestly it is not that good but definetly better then initial SpeechT5. More information here https://outleys.site/en/development/AI/hugface-audio-course-handson-unit-6-exercise/
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.99) and epsilon=1e-07 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 16000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.4819 | 0.0625 | 1000 | 0.5007 |
| 0.4364 | 0.125 | 2000 | 0.4965 |
| 0.4224 | 0.1875 | 3000 | 0.4841 |
| 0.4006 | 1.0473 | 4000 | 0.4782 |
| 0.3993 | 1.1098 | 5000 | 0.4728 |
| 0.3993 | 1.1723 | 6000 | 0.4687 |
| 0.389 | 2.032 | 7000 | 0.4684 |
| 0.3827 | 2.0945 | 8000 | 0.4665 |
| 0.3895 | 2.157 | 9000 | 0.4702 |
| 0.3829 | 3.0168 | 10000 | 0.4648 |
| 0.3717 | 3.0793 | 11000 | 0.4631 |
| 0.384 | 3.1418 | 12000 | 0.4627 |
| 0.3802 | 4.0015 | 13000 | 0.4601 |
| 0.3667 | 4.064 | 14000 | 0.4610 |
| 0.3757 | 4.1265 | 15000 | 0.4606 |
| 0.375 | 4.189 | 16000 | 0.4595 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3