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---
license: mit
tags:
- generated_from_trainer
datasets:
- voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_it
results: []
pipeline_tag: text-to-speech
---
<!-- 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_it
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: 0.4968
## 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: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6707 | 1.0 | 108 | 0.5946 |
| 0.6625 | 2.0 | 217 | 0.6029 |
| 0.708 | 3.0 | 325 | 0.6118 |
| 0.6588 | 4.0 | 434 | 0.7109 |
| 0.6614 | 5.0 | 542 | 0.5799 |
| 0.6375 | 6.0 | 651 | 0.5714 |
| 0.619 | 7.0 | 759 | 0.5699 |
| 0.5806 | 8.0 | 868 | 0.5538 |
| 0.6024 | 9.0 | 976 | 0.5856 |
| 0.5728 | 10.0 | 1085 | 0.5446 |
| 0.5624 | 11.0 | 1193 | 0.5508 |
| 0.5711 | 12.0 | 1302 | 0.5376 |
| 0.5438 | 13.0 | 1410 | 0.5300 |
| 0.5308 | 14.0 | 1519 | 0.5206 |
| 0.5536 | 15.0 | 1627 | 0.5359 |
| 0.5285 | 16.0 | 1736 | 0.5264 |
| 0.525 | 17.0 | 1844 | 0.5108 |
| 0.4961 | 18.0 | 1953 | 0.5116 |
| 0.5111 | 19.0 | 2061 | 0.5042 |
| 0.4869 | 20.0 | 2170 | 0.5050 |
| 0.4864 | 21.0 | 2278 | 0.4994 |
| 0.4794 | 22.0 | 2387 | 0.5039 |
| 0.4787 | 23.0 | 2495 | 0.4975 |
| 0.4692 | 24.0 | 2604 | 0.4961 |
| 0.4656 | 24.88 | 2700 | 0.4968 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3 |