---
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
---

<!-- 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-finetuned-cml-tts2

This model is a fine-tuned version of [microsoft/speechT5_tts](https://huggingface.co/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