---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
model-index:
- name: speecht5_tts_commonvoice_it_v2
  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_tts_commonvoice_it_v2

This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5076

## 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-06
- train_batch_size: 32
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 0.9213        | 0.0994 | 500   | 0.7823          |
| 0.8356        | 0.1987 | 1000  | 0.7026          |
| 0.6804        | 0.2981 | 1500  | 0.6003          |
| 0.6518        | 0.3975 | 2000  | 0.5751          |
| 0.6242        | 0.4968 | 2500  | 0.5594          |
| 0.6237        | 0.5962 | 3000  | 0.5514          |
| 0.6122        | 0.6955 | 3500  | 0.5414          |
| 0.597         | 0.7949 | 4000  | 0.5335          |
| 0.5909        | 0.8943 | 4500  | 0.5322          |
| 0.6009        | 0.9936 | 5000  | 0.5283          |
| 0.6086        | 1.0930 | 5500  | 0.5258          |
| 0.5812        | 1.1924 | 6000  | 0.5209          |
| 0.5868        | 1.2917 | 6500  | 0.5191          |
| 0.5689        | 1.3911 | 7000  | 0.5177          |
| 0.5777        | 1.4905 | 7500  | 0.5182          |
| 0.577         | 1.5898 | 8000  | 0.5169          |
| 0.5594        | 1.6892 | 8500  | 0.5150          |
| 0.5728        | 1.7886 | 9000  | 0.5144          |
| 0.571         | 1.8879 | 9500  | 0.5125          |
| 0.5739        | 1.9873 | 10000 | 0.5116          |
| 0.5819        | 2.0866 | 10500 | 0.5102          |
| 0.5633        | 2.1860 | 11000 | 0.5102          |
| 0.5635        | 2.2854 | 11500 | 0.5093          |
| 0.5809        | 2.3847 | 12000 | 0.5094          |
| 0.5647        | 2.4841 | 12500 | 0.5086          |
| 0.5593        | 2.5835 | 13000 | 0.5065          |
| 0.5639        | 2.6828 | 13500 | 0.5077          |
| 0.5511        | 2.7822 | 14000 | 0.5073          |
| 0.5534        | 2.8816 | 14500 | 0.5071          |
| 0.5532        | 2.9809 | 15000 | 0.5076          |


### Framework versions

- Transformers 4.43.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1