---
license: mit
base_model: microsoft/speecht5_tts
tags:
- text-to-speech
- generated_from_trainer
datasets:
- HeinrichWirth/example
model-index:
- name: speecht5_finetuned_nl_pet
  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_nl_pet

This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the HeinrichWirth/example dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4598

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6583        | 1.08  | 250  | 0.5768          |
| 0.5507        | 2.15  | 500  | 0.5026          |
| 0.5254        | 3.23  | 750  | 0.4884          |
| 0.5199        | 4.3   | 1000 | 0.4806          |
| 0.51          | 5.38  | 1250 | 0.4743          |
| 0.5049        | 6.46  | 1500 | 0.4718          |
| 0.5001        | 7.53  | 1750 | 0.4686          |
| 0.5013        | 8.61  | 2000 | 0.4659          |
| 0.5003        | 9.68  | 2250 | 0.4653          |
| 0.5034        | 10.76 | 2500 | 0.4634          |
| 0.4922        | 11.83 | 2750 | 0.4633          |
| 0.4938        | 12.91 | 3000 | 0.4626          |
| 0.4905        | 13.99 | 3250 | 0.4605          |
| 0.489         | 15.06 | 3500 | 0.4624          |
| 0.4909        | 16.14 | 3750 | 0.4607          |
| 0.4904        | 17.21 | 4000 | 0.4598          |


### Framework versions

- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3