README.md

---
language:
- lt
license: mit
tags:
- generated_from_trainer
- text-to-speech
datasets:
- voxpopuli
base_model: microsoft/speecht5_tts
model-index:
- name: speecht5_finetuned_voxpopuli_lt
  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_finetuned_voxpopuli

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

- validation Loss: 0.5676
- training loss: 0.38
- 
## Model description

text-to-speech

## Intended uses & limitations

text to speech, stst models

## Training and evaluation data

finetuning using the voxpopuli dataset for the Lithuanian language, 
in this case there were few speakers and few examples, so the training gives us 0.56 validation loss
and 0.38 of training loss,   This means the model may not generalize well to new data it hasn't seen before. 
To avoid overfitting, you can try some regularization techniques, such as dropout, batch normalization, 
or model size reduction. 

### 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: 500
- training_steps: 4000

### Training results

| Training Loss | Epoch   | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.443         | 380.95  | 1000 | 0.5600          |
| 0.4045        | 761.9   | 2000 | 0.5717          |
| 0.3877        | 1142.86 | 3000 | 0.5647          |
| 0.3845        | 1523.81 | 4000 | 0.5676          |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3