---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: speecht5_finetuned_kaab_tts_ur_5000
  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_kaab_tts_ur_5000

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: 0.4719

## 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 OptimizerNames.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: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.6313        | 0.3772  | 100  | 0.5546          |
| 0.5936        | 0.7544  | 200  | 0.5346          |
| 0.5665        | 1.1282  | 300  | 0.5138          |
| 0.5564        | 1.5054  | 400  | 0.5165          |
| 0.5446        | 1.8826  | 500  | 0.5033          |
| 0.5416        | 2.2565  | 600  | 0.4978          |
| 0.5365        | 2.6337  | 700  | 0.4919          |
| 0.5023        | 3.0075  | 800  | 0.4927          |
| 0.5223        | 3.3847  | 900  | 0.4952          |
| 0.5238        | 3.7619  | 1000 | 0.4921          |
| 0.5217        | 4.1358  | 1100 | 0.4857          |
| 0.5052        | 4.5130  | 1200 | 0.4969          |
| 0.5107        | 4.8901  | 1300 | 0.4815          |
| 0.5057        | 5.2640  | 1400 | 0.4825          |
| 0.5018        | 5.6412  | 1500 | 0.4766          |
| 0.4912        | 6.0151  | 1600 | 0.4766          |
| 0.5018        | 6.3923  | 1700 | 0.4800          |
| 0.4987        | 6.7694  | 1800 | 0.4771          |
| 0.4954        | 7.1433  | 1900 | 0.4739          |
| 0.4926        | 7.5205  | 2000 | 0.4789          |
| 0.4913        | 7.8977  | 2100 | 0.4729          |
| 0.491         | 8.2716  | 2200 | 0.4724          |
| 0.4879        | 8.6488  | 2300 | 0.4704          |
| 0.4688        | 9.0226  | 2400 | 0.4755          |
| 0.4817        | 9.3998  | 2500 | 0.4689          |
| 0.4824        | 9.7770  | 2600 | 0.4739          |
| 0.4805        | 10.1509 | 2700 | 0.4735          |
| 0.4782        | 10.5281 | 2800 | 0.4724          |
| 0.4797        | 10.9052 | 2900 | 0.4728          |
| 0.4704        | 11.2791 | 3000 | 0.4721          |
| 0.4802        | 11.6563 | 3100 | 0.4685          |
| 0.4539        | 12.0302 | 3200 | 0.4687          |
| 0.4696        | 12.4074 | 3300 | 0.4713          |
| 0.4656        | 12.7845 | 3400 | 0.4705          |
| 0.4672        | 13.1584 | 3500 | 0.4677          |
| 0.4643        | 13.5356 | 3600 | 0.4722          |
| 0.4659        | 13.9128 | 3700 | 0.4654          |
| 0.4694        | 14.2867 | 3800 | 0.4681          |
| 0.4638        | 14.6638 | 3900 | 0.4702          |
| 0.4447        | 15.0377 | 4000 | 0.4675          |
| 0.4628        | 15.4149 | 4100 | 0.4696          |
| 0.46          | 15.7921 | 4200 | 0.4678          |
| 0.4592        | 16.1660 | 4300 | 0.4705          |
| 0.4616        | 16.5431 | 4400 | 0.4680          |
| 0.4578        | 16.9203 | 4500 | 0.4696          |
| 0.4576        | 17.2942 | 4600 | 0.4678          |
| 0.4555        | 17.6714 | 4700 | 0.4706          |
| 0.4394        | 18.0453 | 4800 | 0.4710          |
| 0.4485        | 18.4224 | 4900 | 0.4702          |
| 0.4563        | 18.7996 | 5000 | 0.4719          |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.1
- Tokenizers 0.21.0