---
language:
- ti
license: mit
base_model: microsoft/speecht5_tts
tags:
- tigrinya, tts
- generated_from_trainer
datasets:
- tigregna_parallel
model-index:
- name: TTS_tigregna
  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. -->

# TTS_tigregna

This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the tigregna_20_hr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3608

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 30000

### Training results

| Training Loss | Epoch  | Step  | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 0.4347        | 11.33  | 1000  | 0.3954          |
| 0.4138        | 22.66  | 2000  | 0.3801          |
| 0.4055        | 33.99  | 3000  | 0.3721          |
| 0.3997        | 45.33  | 4000  | 0.3683          |
| 0.3941        | 56.66  | 5000  | 0.3643          |
| 0.3879        | 67.99  | 6000  | 0.3631          |
| 0.3826        | 79.32  | 7000  | 0.3619          |
| 0.3846        | 90.65  | 8000  | 0.3607          |
| 0.3779        | 101.98 | 9000  | 0.3599          |
| 0.3756        | 113.31 | 10000 | 0.3603          |
| 0.3758        | 124.65 | 11000 | 0.3596          |
| 0.3729        | 135.98 | 12000 | 0.3586          |
| 0.3742        | 147.31 | 13000 | 0.3610          |
| 0.3714        | 158.64 | 14000 | 0.3583          |
| 0.3712        | 169.97 | 15000 | 0.3601          |
| 0.3689        | 181.3  | 16000 | 0.3608          |
| 0.3706        | 192.63 | 17000 | 0.3607          |
| 0.3676        | 203.97 | 18000 | 0.3594          |
| 0.367         | 215.3  | 19000 | 0.3595          |
| 0.3627        | 226.63 | 20000 | 0.3593          |
| 0.3623        | 237.96 | 21000 | 0.3601          |
| 0.3641        | 249.29 | 22000 | 0.3599          |
| 0.365         | 260.62 | 23000 | 0.3604          |
| 0.3621        | 271.95 | 24000 | 0.3607          |
| 0.3644        | 283.29 | 25000 | 0.3603          |
| 0.3678        | 294.62 | 26000 | 0.3607          |
| 0.3642        | 305.95 | 27000 | 0.3610          |
| 0.3624        | 317.28 | 28000 | 0.3615          |
| 0.3621        | 328.61 | 29000 | 0.3615          |
| 0.3615        | 339.94 | 30000 | 0.3608          |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2