---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: speecht5_finetune_binisha
  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_finetune_binisha

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: 0.3836

## 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: 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: 1500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.6028        | 2.7586  | 100  | 0.5187          |
| 0.5195        | 5.5172  | 200  | 0.4851          |
| 0.5075        | 8.2759  | 300  | 0.4708          |
| 0.462         | 11.0345 | 400  | 0.4609          |
| 0.4429        | 13.7931 | 500  | 0.4294          |
| 0.4303        | 16.5517 | 600  | 0.4249          |
| 0.4172        | 19.3103 | 700  | 0.4184          |
| 0.402         | 22.0690 | 800  | 0.4077          |
| 0.3898        | 24.8276 | 900  | 0.3975          |
| 0.3966        | 27.5862 | 1000 | 0.4197          |
| 0.3773        | 30.3448 | 1100 | 0.3955          |
| 0.3658        | 33.1034 | 1200 | 0.3878          |
| 0.3644        | 35.8621 | 1300 | 0.3878          |
| 0.3622        | 38.6207 | 1400 | 0.3841          |
| 0.3671        | 41.3793 | 1500 | 0.3836          |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3