---
language:
- ar
license: mit
tags:
- ara
- generated_from_trainer
datasets:
- SDA_CLEAN_NAJDI
model-index:
- name: SpeechT5 TTS
  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

This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the SDA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4853

## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: 40000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.5703        | 1.49  | 1000  | 0.5289          |
| 0.541         | 2.98  | 2000  | 0.5131          |
| 0.5487        | 4.46  | 3000  | 0.5059          |
| 0.5232        | 5.95  | 4000  | 0.5011          |
| 0.5295        | 7.44  | 5000  | 0.4979          |
| 0.5257        | 8.93  | 6000  | 0.4970          |
| 0.5091        | 10.42 | 7000  | 0.4905          |
| 0.5141        | 11.9  | 8000  | 0.4893          |
| 0.5033        | 13.39 | 9000  | 0.4865          |
| 0.507         | 14.88 | 10000 | 0.4850          |
| 0.502         | 16.37 | 11000 | 0.4830          |
| 0.497         | 17.86 | 12000 | 0.4823          |
| 0.4974        | 19.35 | 13000 | 0.4801          |
| 0.4993        | 20.83 | 14000 | 0.4794          |
| 0.496         | 22.32 | 15000 | 0.4814          |
| 0.4845        | 23.81 | 16000 | 0.4780          |
| 0.4977        | 25.3  | 17000 | 0.4775          |
| 0.4888        | 26.79 | 18000 | 0.4780          |
| 0.4773        | 28.27 | 19000 | 0.4792          |
| 0.4914        | 29.76 | 20000 | 0.4817          |
| 0.4864        | 31.25 | 21000 | 0.4775          |
| 0.486         | 32.74 | 22000 | 0.4773          |
| 0.4884        | 34.23 | 23000 | 0.4835          |
| 0.4856        | 35.71 | 24000 | 0.4788          |
| 0.4814        | 37.2  | 25000 | 0.4811          |
| 0.4831        | 38.69 | 26000 | 0.4814          |
| 0.4732        | 40.18 | 27000 | 0.4816          |
| 0.4846        | 41.67 | 28000 | 0.4812          |
| 0.4731        | 43.15 | 29000 | 0.4843          |
| 0.4772        | 44.64 | 30000 | 0.4830          |
| 0.4793        | 46.13 | 31000 | 0.4834          |
| 0.4736        | 47.62 | 32000 | 0.4834          |
| 0.4798        | 49.11 | 33000 | 0.4826          |
| 0.4744        | 50.6  | 34000 | 0.4841          |
| 0.4784        | 52.08 | 35000 | 0.4844          |
| 0.4743        | 53.57 | 36000 | 0.4851          |
| 0.4779        | 55.06 | 37000 | 0.4854          |
| 0.4719        | 56.55 | 38000 | 0.4854          |
| 0.4825        | 58.04 | 39000 | 0.4856          |
| 0.4805        | 59.52 | 40000 | 0.4853          |


### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.0
- Tokenizers 0.13.3