---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_nl
  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_nl

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:
- Loss: 0.4591

## 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: 4
- 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: 4000

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7034        | 0.43  | 100  | 0.6427          |
| 0.6812        | 0.86  | 200  | 0.5998          |
| 0.5928        | 1.29  | 300  | 0.5292          |
| 0.5662        | 1.72  | 400  | 0.5095          |
| 0.5547        | 2.15  | 500  | 0.5024          |
| 0.5356        | 2.58  | 600  | 0.4929          |
| 0.532         | 3.01  | 700  | 0.4902          |
| 0.5312        | 3.44  | 800  | 0.4835          |
| 0.5171        | 3.87  | 900  | 0.4800          |
| 0.5178        | 4.3   | 1000 | 0.4780          |
| 0.5152        | 4.73  | 1100 | 0.4777          |
| 0.5064        | 5.16  | 1200 | 0.4737          |
| 0.5122        | 5.59  | 1300 | 0.4726          |
| 0.5042        | 6.02  | 1400 | 0.4700          |
| 0.5075        | 6.45  | 1500 | 0.4712          |
| 0.5057        | 6.88  | 1600 | 0.4688          |
| 0.5037        | 7.31  | 1700 | 0.4674          |
| 0.504         | 7.74  | 1800 | 0.4665          |
| 0.4977        | 8.17  | 1900 | 0.4652          |
| 0.4976        | 8.6   | 2000 | 0.4653          |
| 0.5014        | 9.03  | 2100 | 0.4650          |
| 0.4951        | 9.46  | 2200 | 0.4632          |
| 0.493         | 9.89  | 2300 | 0.4626          |
| 0.4983        | 10.32 | 2400 | 0.4628          |
| 0.4952        | 10.75 | 2500 | 0.4627          |
| 0.4961        | 11.18 | 2600 | 0.4616          |
| 0.4965        | 11.61 | 2700 | 0.4618          |
| 0.4895        | 12.04 | 2800 | 0.4615          |
| 0.4898        | 12.47 | 2900 | 0.4600          |
| 0.5008        | 12.9  | 3000 | 0.4614          |
| 0.4896        | 13.33 | 3100 | 0.4603          |
| 0.4957        | 13.76 | 3200 | 0.4612          |
| 0.4878        | 14.19 | 3300 | 0.4598          |
| 0.4923        | 14.62 | 3400 | 0.4594          |
| 0.4939        | 15.05 | 3500 | 0.4596          |
| 0.4828        | 15.48 | 3600 | 0.4591          |
| 0.4865        | 15.91 | 3700 | 0.4588          |
| 0.4999        | 16.34 | 3800 | 0.4600          |
| 0.4895        | 16.77 | 3900 | 0.4587          |
| 0.4929        | 17.2  | 4000 | 0.4591          |


### Framework versions

- Transformers 4.31.0.dev0
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1