---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: EGPABG
  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. -->

# EGPABG

This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5020

## 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: 0.0001
- 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: 100
- training_steps: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.6281        | 0.2540 | 100  | 0.5862          |
| 0.6048        | 0.5079 | 200  | 0.5734          |
| 0.5792        | 0.7619 | 300  | 0.5444          |
| 0.57          | 1.0159 | 400  | 0.5418          |
| 0.5657        | 1.2698 | 500  | 0.5281          |
| 0.5625        | 1.5238 | 600  | 0.5301          |
| 0.5585        | 1.7778 | 700  | 0.5245          |
| 0.5559        | 2.0317 | 800  | 0.5263          |
| 0.5572        | 2.2857 | 900  | 0.5263          |
| 0.5527        | 2.5397 | 1000 | 0.5161          |
| 0.5524        | 2.7937 | 1100 | 0.5190          |
| 0.544         | 3.0476 | 1200 | 0.5154          |
| 0.542         | 3.3016 | 1300 | 0.5203          |
| 0.5425        | 3.5556 | 1400 | 0.5163          |
| 0.5413        | 3.8095 | 1500 | 0.5099          |
| 0.5328        | 4.0635 | 1600 | 0.5149          |
| 0.5406        | 4.3175 | 1700 | 0.5107          |
| 0.5347        | 4.5714 | 1800 | 0.5079          |
| 0.5362        | 4.8254 | 1900 | 0.5068          |
| 0.5355        | 5.0794 | 2000 | 0.5050          |
| 0.5312        | 5.3333 | 2100 | 0.5061          |
| 0.5282        | 5.5873 | 2200 | 0.5081          |
| 0.5298        | 5.8413 | 2300 | 0.5029          |
| 0.5288        | 6.0952 | 2400 | 0.5028          |
| 0.5371        | 6.3492 | 2500 | 0.5023          |
| 0.525         | 6.6032 | 2600 | 0.5022          |
| 0.5281        | 6.8571 | 2700 | 0.5039          |
| 0.5223        | 7.1111 | 2800 | 0.5023          |
| 0.5231        | 7.3651 | 2900 | 0.5022          |
| 0.5243        | 7.6190 | 3000 | 0.5020          |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1