---
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: 0.5556

## 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: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9351        | 0.1894 | 100  | 0.8355          |
| 0.8426        | 0.3788 | 200  | 0.7500          |
| 0.8314        | 0.5682 | 300  | 0.7244          |
| 0.7912        | 0.7576 | 400  | 0.7078          |
| 0.778         | 0.9470 | 500  | 0.6908          |
| 0.7205        | 1.1364 | 600  | 0.6744          |
| 0.7272        | 1.3258 | 700  | 0.6469          |
| 0.7394        | 1.5152 | 800  | 0.6176          |
| 0.6816        | 1.7045 | 900  | 0.5874          |
| 0.6653        | 1.8939 | 1000 | 0.5748          |
| 0.658         | 2.0833 | 1100 | 0.5683          |
| 0.628         | 2.2727 | 1200 | 0.5662          |
| 0.6376        | 2.4621 | 1300 | 0.5632          |
| 0.6232        | 2.6515 | 1400 | 0.5612          |
| 0.625         | 2.8409 | 1500 | 0.5583          |
| 0.63          | 3.0303 | 1600 | 0.5588          |
| 0.6299        | 3.2197 | 1700 | 0.5567          |
| 0.6332        | 3.4091 | 1800 | 0.5558          |
| 0.6083        | 3.5985 | 1900 | 0.5551          |
| 0.6161        | 3.7879 | 2000 | 0.5556          |


### Framework versions

- Transformers 4.43.1
- Pytorch 2.2.0
- Datasets 3.0.1
- Tokenizers 0.19.1