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---
language:
- pt
license: mit
base_model: microsoft/speecht5_tts
tags:
- text-to-speech
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
model-index:
- name: speecht5_pt_full
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_pt_full
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the mozilla-foundation/common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5021
> *DISCLAIMER*: This model is trained for the sole purpose of finishing the HuggingFace [Audio course](https://huggingface.co/learn/audio-course/chapter0/introduction). It doesn't have any usability and outputs pure noise. If you have an idea of how to improve the model, feel free to create a post in the Community tab of this model. Thank you!
## 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: 300
- training_steps: 2000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8882 | 0.18 | 100 | 0.7494 |
| 0.7726 | 0.36 | 200 | 0.6657 |
| 0.642 | 0.54 | 300 | 0.5767 |
| 0.6042 | 0.71 | 400 | 0.5545 |
| 0.5972 | 0.89 | 500 | 0.5342 |
| 0.5832 | 1.07 | 600 | 0.5337 |
| 0.5851 | 1.25 | 700 | 0.5291 |
| 0.5744 | 1.43 | 800 | 0.5245 |
| 0.5638 | 1.61 | 900 | 0.5186 |
| 0.5562 | 1.78 | 1000 | 0.5174 |
| 0.56 | 1.96 | 1100 | 0.5133 |
| 0.5446 | 2.14 | 1200 | 0.5113 |
| 0.5556 | 2.32 | 1300 | 0.5099 |
| 0.5457 | 2.5 | 1400 | 0.5071 |
| 0.5504 | 2.68 | 1500 | 0.5087 |
| 0.5497 | 2.85 | 1600 | 0.5039 |
| 0.545 | 3.03 | 1700 | 0.5034 |
| 0.5503 | 3.21 | 1800 | 0.5051 |
| 0.5621 | 3.39 | 1900 | 0.5040 |
| 0.5347 | 3.57 | 2000 | 0.5021 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|