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---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- voxpopuli
model-index:
- name: speecht5_tts_voxpopuli_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_voxpopuli_it_v2
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: 0.4484
## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- 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: 200
- training_steps: 2000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.5707 | 0.2358 | 100 | 0.5183 |
| 0.5452 | 0.4717 | 200 | 0.5096 |
| 0.5313 | 0.7075 | 300 | 0.4890 |
| 0.5229 | 0.9434 | 400 | 0.4807 |
| 0.5119 | 1.1792 | 500 | 0.4802 |
| 0.5121 | 1.4151 | 600 | 0.4681 |
| 0.5037 | 1.6509 | 700 | 0.4719 |
| 0.4996 | 1.8868 | 800 | 0.4691 |
| 0.4931 | 2.1226 | 900 | 0.4621 |
| 0.4903 | 2.3585 | 1000 | 0.4620 |
| 0.4949 | 2.5943 | 1100 | 0.4573 |
| 0.4853 | 2.8302 | 1200 | 0.4579 |
| 0.4826 | 3.0660 | 1300 | 0.4547 |
| 0.4827 | 3.3019 | 1400 | 0.4535 |
| 0.4835 | 3.5377 | 1500 | 0.4523 |
| 0.4802 | 3.7736 | 1600 | 0.4514 |
| 0.4777 | 4.0094 | 1700 | 0.4503 |
| 0.4792 | 4.2453 | 1800 | 0.4499 |
| 0.4779 | 4.4811 | 1900 | 0.4491 |
| 0.4755 | 4.7170 | 2000 | 0.4484 |
### Framework versions
- Transformers 4.43.1
- Pytorch 2.2.0
- Datasets 3.0.1
- Tokenizers 0.19.1
|