---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: speecht5_improved_data
  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_improved_data

This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5781

## 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-05
- train_batch_size: 8
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- 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: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.9442        | 1.5326  | 250  | 0.7612          |
| 0.7905        | 3.0651  | 500  | 0.6773          |
| 0.7439        | 4.5977  | 750  | 0.6415          |
| 0.7208        | 6.1303  | 1000 | 0.6286          |
| 0.7108        | 7.6628  | 1250 | 0.6159          |
| 0.6941        | 9.1954  | 1500 | 0.6098          |
| 0.6869        | 10.7280 | 1750 | 0.5993          |
| 0.6572        | 12.2605 | 2000 | 0.5945          |
| 0.6671        | 13.7931 | 2250 | 0.5922          |
| 0.6566        | 15.3257 | 2500 | 0.5882          |
| 0.6551        | 16.8582 | 2750 | 0.5880          |
| 0.6564        | 18.3908 | 3000 | 0.5852          |
| 0.6458        | 19.9234 | 3250 | 0.5793          |
| 0.6457        | 21.4559 | 3500 | 0.5815          |
| 0.6473        | 22.9885 | 3750 | 0.5834          |
| 0.6532        | 24.5211 | 4000 | 0.5781          |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.17.0
- Tokenizers 0.19.1