|
--- |
|
language: |
|
- pt |
|
license: mit |
|
base_model: microsoft/speecht5_tts |
|
tags: |
|
- tts |
|
- generated_from_trainer |
|
datasets: |
|
- multilingual_librispeech |
|
model-index: |
|
- name: SpeechT5 TTS Portuguese |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# SpeechT5 TTS Portuguese |
|
|
|
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the MultilingualLibrispeech dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.3452 |
|
|
|
## Model description |
|
|
|
More information needed |
|
|
|
## 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: 1e-05 |
|
- train_batch_size: 4 |
|
- eval_batch_size: 2 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 8 |
|
- total_train_batch_size: 32 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_steps: 500 |
|
- training_steps: 10000 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:-----:|:-----:|:---------------:| |
|
| 0.4091 | 1.68 | 1000 | 0.3728 | |
|
| 0.3906 | 3.35 | 2000 | 0.3598 | |
|
| 0.3899 | 5.03 | 3000 | 0.3543 | |
|
| 0.3842 | 6.71 | 4000 | 0.3518 | |
|
| 0.376 | 8.38 | 5000 | 0.3492 | |
|
| 0.3745 | 10.06 | 6000 | 0.3474 | |
|
| 0.3773 | 11.74 | 7000 | 0.3473 | |
|
| 0.3774 | 13.41 | 8000 | 0.3461 | |
|
| 0.3719 | 15.09 | 9000 | 0.3454 | |
|
| 0.3712 | 16.76 | 10000 | 0.3452 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.38.0.dev0 |
|
- Pytorch 2.1.0+cu121 |
|
- Datasets 2.16.1 |
|
- Tokenizers 0.15.0 |
|
|
