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---
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
|