---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: uz_2301_3.1_tts
  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. -->

# uz_2301_3.1_tts

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: 0.4577

## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_bnb_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 7000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5566        | 10.0  | 500  | 0.4779          |
| 0.441         | 20.0  | 1000 | 0.4353          |
| 0.4194        | 30.0  | 1500 | 0.4364          |
| 0.4018        | 40.0  | 2000 | 0.4254          |
| 0.3771        | 50.0  | 2500 | 0.4184          |
| 0.3807        | 60.0  | 3000 | 0.4363          |
| 0.3615        | 70.0  | 3500 | 0.4384          |
| 0.3492        | 80.0  | 4000 | 0.4462          |
| 0.3323        | 90.0  | 4500 | 0.4423          |
| 0.3281        | 100.0 | 5000 | 0.4445          |
| 0.3299        | 110.0 | 5500 | 0.4454          |
| 0.3145        | 120.0 | 6000 | 0.4464          |
| 0.3192        | 130.0 | 6500 | 0.4540          |
| 0.3108        | 140.0 | 7000 | 0.4577          |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0