---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: speecht5_finetuned_k_voice_v3
  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_k_voice_v3

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: 0.4215

## 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: 5e-06
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 200
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.6081        | 0.2579 | 500  | 0.5853          |
| 0.5601        | 0.5159 | 1000 | 0.4924          |
| 0.5331        | 0.7738 | 1500 | 0.4709          |
| 0.5263        | 1.0317 | 2000 | 0.4698          |
| 0.5151        | 1.2897 | 2500 | 0.4615          |
| 0.5172        | 1.5476 | 3000 | 0.4618          |
| 0.5109        | 1.8055 | 3500 | 0.4486          |
| 0.5025        | 2.0635 | 4000 | 0.4461          |
| 0.4822        | 2.3214 | 4500 | 0.4356          |
| 0.4911        | 2.5793 | 5000 | 0.4439          |
| 0.4931        | 2.8372 | 5500 | 0.4331          |
| 0.4904        | 3.0952 | 6000 | 0.4304          |
| 0.474         | 3.3531 | 6500 | 0.4330          |
| 0.4716        | 3.6110 | 7000 | 0.4307          |
| 0.4673        | 3.8690 | 7500 | 0.4274          |
| 0.4654        | 4.1269 | 8000 | 0.4250          |
| 0.4609        | 4.3848 | 8500 | 0.4215          |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.6.0
- Datasets 3.5.0
- Tokenizers 0.19.1