---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: EGY1.5K
  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. -->

# EGY1.5K

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: 0.4590

## 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: 4
- total_train_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5688        | 1.48  | 100  | 0.5112          |
| 0.5301        | 2.96  | 200  | 0.4908          |
| 0.5023        | 4.44  | 300  | 0.4707          |
| 0.524         | 5.93  | 400  | 0.4797          |
| 0.5001        | 7.41  | 500  | 0.4712          |
| 0.4774        | 8.89  | 600  | 0.4690          |
| 0.4793        | 10.37 | 700  | 0.4627          |
| 0.4666        | 11.85 | 800  | 0.4657          |
| 0.4649        | 13.33 | 900  | 0.4599          |
| 0.4659        | 14.81 | 1000 | 0.4616          |
| 0.4557        | 16.3  | 1100 | 0.4532          |
| 0.4516        | 17.78 | 1200 | 0.4535          |
| 0.4489        | 19.26 | 1300 | 0.4572          |
| 0.4431        | 20.74 | 1400 | 0.4504          |
| 0.4488        | 22.22 | 1500 | 0.4543          |
| 0.4452        | 23.7  | 1600 | 0.4557          |
| 0.4386        | 25.19 | 1700 | 0.4549          |
| 0.4297        | 26.67 | 1800 | 0.4487          |
| 0.4327        | 28.15 | 1900 | 0.4559          |
| 0.425         | 29.63 | 2000 | 0.4572          |
| 0.4251        | 31.11 | 2100 | 0.4531          |
| 0.4295        | 32.59 | 2200 | 0.4500          |
| 0.4258        | 34.07 | 2300 | 0.4561          |
| 0.4222        | 35.56 | 2400 | 0.4550          |
| 0.4119        | 37.04 | 2500 | 0.4569          |
| 0.4208        | 38.52 | 2600 | 0.4573          |
| 0.4145        | 40.0  | 2700 | 0.4568          |
| 0.4215        | 41.48 | 2800 | 0.4585          |
| 0.4141        | 42.96 | 2900 | 0.4594          |
| 0.4136        | 44.44 | 3000 | 0.4590          |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.3.0+cu118
- Datasets 3.0.0
- Tokenizers 0.15.2