---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: speecht5_finetuned_Mar
  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_finetuned_Mar

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: 0.4785

## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- 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: 50
- training_steps: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.6818        | 0.7976  | 100  | 0.6565          |
| 0.6107        | 1.5952  | 200  | 0.5797          |
| 0.6033        | 2.3928  | 300  | 0.5716          |
| 0.5893        | 3.1904  | 400  | 0.5686          |
| 0.5919        | 3.9880  | 500  | 0.5624          |
| 0.5732        | 4.7856  | 600  | 0.5932          |
| 0.5677        | 5.5833  | 700  | 0.5485          |
| 0.5521        | 6.3809  | 800  | 0.5407          |
| 0.5513        | 7.1785  | 900  | 0.5348          |
| 0.5488        | 7.9761  | 1000 | 0.5366          |
| 0.5344        | 8.7737  | 1100 | 0.5267          |
| 0.5329        | 9.5713  | 1200 | 0.5163          |
| 0.5118        | 10.3689 | 1300 | 0.5172          |
| 0.5126        | 11.1665 | 1400 | 0.5089          |
| 0.5256        | 11.9641 | 1500 | 0.5104          |
| 0.5126        | 12.7617 | 1600 | 0.5054          |
| 0.5034        | 13.5593 | 1700 | 0.5023          |
| 0.4986        | 14.3569 | 1800 | 0.4974          |
| 0.4926        | 15.1545 | 1900 | 0.5081          |
| 0.4961        | 15.9521 | 2000 | 0.4929          |
| 0.4886        | 16.7498 | 2100 | 0.4863          |
| 0.491         | 17.5474 | 2200 | 0.4925          |
| 0.4845        | 18.3450 | 2300 | 0.4859          |
| 0.4726        | 19.1426 | 2400 | 0.4835          |
| 0.4786        | 19.9402 | 2500 | 0.4829          |
| 0.4732        | 20.7378 | 2600 | 0.4800          |
| 0.4713        | 21.5354 | 2700 | 0.4737          |
| 0.4571        | 22.3330 | 2800 | 0.4789          |
| 0.4559        | 23.1306 | 2900 | 0.4771          |
| 0.4552        | 23.9282 | 3000 | 0.4785          |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3