---
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: 0.4845

## 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: 1500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.7023        | 0.7976  | 100  | 0.6419          |
| 0.6105        | 1.5952  | 200  | 0.5699          |
| 0.5895        | 2.3928  | 300  | 0.5631          |
| 0.5796        | 3.1904  | 400  | 0.5530          |
| 0.5818        | 3.9880  | 500  | 0.5388          |
| 0.5448        | 4.7856  | 600  | 0.5399          |
| 0.562         | 5.5833  | 700  | 0.5166          |
| 0.542         | 6.3809  | 800  | 0.5207          |
| 0.5374        | 7.1785  | 900  | 0.5118          |
| 0.5263        | 7.9761  | 1000 | 0.5009          |
| 0.5079        | 8.7737  | 1100 | 0.4965          |
| 0.5108        | 9.5713  | 1200 | 0.4884          |
| 0.497         | 10.3689 | 1300 | 0.4883          |
| 0.4915        | 11.1665 | 1400 | 0.4896          |
| 0.5061        | 11.9641 | 1500 | 0.4845          |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3