---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: speecht5_mehdi_new_as_try111
  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_mehdi_new_as_try111

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: 0.5467

## 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: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use adamw_torch 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: 100
- training_steps: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 1.0666        | 1.7778  | 100  | 0.8627          |
| 0.8711        | 3.5556  | 200  | 0.7175          |
| 0.8314        | 5.3333  | 300  | 0.6804          |
| 0.8011        | 7.1111  | 400  | 0.6603          |
| 0.7676        | 8.8889  | 500  | 0.6402          |
| 0.7335        | 10.6667 | 600  | 0.6158          |
| 0.7093        | 12.4444 | 700  | 0.5889          |
| 0.676         | 14.2222 | 800  | 0.5793          |
| 0.6617        | 16.0    | 900  | 0.5743          |
| 0.664         | 17.7778 | 1000 | 0.5711          |
| 0.6516        | 19.5556 | 1100 | 0.5664          |
| 0.6478        | 21.3333 | 1200 | 0.5609          |
| 0.6445        | 23.1111 | 1300 | 0.5590          |
| 0.642         | 24.8889 | 1400 | 0.5601          |
| 0.6341        | 26.6667 | 1500 | 0.5585          |
| 0.6415        | 28.4444 | 1600 | 0.5584          |
| 0.6373        | 30.2222 | 1700 | 0.5533          |
| 0.6257        | 32.0    | 1800 | 0.5508          |
| 0.6311        | 33.7778 | 1900 | 0.5516          |
| 0.6201        | 35.5556 | 2000 | 0.5487          |
| 0.6257        | 37.3333 | 2100 | 0.5496          |
| 0.6304        | 39.1111 | 2200 | 0.5494          |
| 0.6177        | 40.8889 | 2300 | 0.5473          |
| 0.6235        | 42.6667 | 2400 | 0.5463          |
| 0.6202        | 44.4444 | 2500 | 0.5475          |
| 0.6191        | 46.2222 | 2600 | 0.5464          |
| 0.6188        | 48.0    | 2700 | 0.5442          |
| 0.6034        | 49.7778 | 2800 | 0.5452          |
| 0.6132        | 51.5556 | 2900 | 0.5453          |
| 0.6205        | 53.3333 | 3000 | 0.5467          |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.20.3