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---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: speecht5_feniks
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_feniks
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5319
## 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: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- 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 |
|:-------------:|:-------:|:----:|:---------------:|
| 0.4478 | 3.0418 | 100 | 0.4543 |
| 0.4534 | 6.0837 | 200 | 0.4621 |
| 0.4373 | 9.1255 | 300 | 0.4543 |
| 0.4224 | 12.1673 | 400 | 0.4494 |
| 0.4127 | 15.2091 | 500 | 0.4657 |
| 0.4134 | 18.2510 | 600 | 0.4529 |
| 0.4047 | 21.2928 | 700 | 0.4724 |
| 0.3932 | 24.3346 | 800 | 0.4777 |
| 0.3907 | 27.3764 | 900 | 0.4942 |
| 0.3855 | 30.4183 | 1000 | 0.4870 |
| 0.3783 | 33.4601 | 1100 | 0.4860 |
| 0.3794 | 36.5019 | 1200 | 0.4867 |
| 0.3704 | 39.5437 | 1300 | 0.4965 |
| 0.3687 | 42.5856 | 1400 | 0.5151 |
| 0.3674 | 45.6274 | 1500 | 0.5165 |
| 0.3618 | 48.6692 | 1600 | 0.5377 |
| 0.3536 | 51.7110 | 1700 | 0.5206 |
| 0.3621 | 54.7529 | 1800 | 0.5419 |
| 0.3533 | 57.7947 | 1900 | 0.5337 |
| 0.3513 | 60.8365 | 2000 | 0.5319 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
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