---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: voice_clone
  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. -->

# voice_clone

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: 0.4976

## 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: 8
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- 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: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.9069        | 0.8734  | 50   | 0.8148          |
| 0.8979        | 1.7336  | 100  | 0.7481          |
| 0.8288        | 2.5939  | 150  | 0.7062          |
| 0.8054        | 3.4541  | 200  | 0.6817          |
| 0.7422        | 4.3144  | 250  | 0.6609          |
| 0.7022        | 5.1747  | 300  | 0.6083          |
| 0.6318        | 6.0349  | 350  | 0.5642          |
| 0.6123        | 6.9083  | 400  | 0.5512          |
| 0.5917        | 7.7686  | 450  | 0.5467          |
| 0.5866        | 8.6288  | 500  | 0.5385          |
| 0.581         | 9.4891  | 550  | 0.5322          |
| 0.5605        | 10.3493 | 600  | 0.5318          |
| 0.5562        | 11.2096 | 650  | 0.5258          |
| 0.5565        | 12.0699 | 700  | 0.5196          |
| 0.566         | 12.9432 | 750  | 0.5230          |
| 0.561         | 13.8035 | 800  | 0.5204          |
| 0.5486        | 14.6638 | 850  | 0.5171          |
| 0.554         | 15.5240 | 900  | 0.5192          |
| 0.5367        | 16.3843 | 950  | 0.5153          |
| 0.5347        | 17.2445 | 1000 | 0.5144          |
| 0.5373        | 18.1048 | 1050 | 0.5152          |
| 0.5386        | 18.9782 | 1100 | 0.5127          |
| 0.5421        | 19.8384 | 1150 | 0.5094          |
| 0.5347        | 20.6987 | 1200 | 0.5101          |
| 0.5415        | 21.5590 | 1250 | 0.5116          |
| 0.5225        | 22.4192 | 1300 | 0.5087          |
| 0.5222        | 23.2795 | 1350 | 0.5087          |
| 0.5219        | 24.1397 | 1400 | 0.5080          |
| 0.5134        | 25.0    | 1450 | 0.5050          |
| 0.5342        | 25.8734 | 1500 | 0.5068          |
| 0.5265        | 26.7336 | 1550 | 0.5064          |
| 0.5279        | 27.5939 | 1600 | 0.5074          |
| 0.5304        | 28.4541 | 1650 | 0.5061          |
| 0.5132        | 29.3144 | 1700 | 0.5042          |
| 0.5091        | 30.1747 | 1750 | 0.5048          |
| 0.5152        | 31.0349 | 1800 | 0.5067          |
| 0.5192        | 31.9083 | 1850 | 0.5030          |
| 0.5232        | 32.7686 | 1900 | 0.5031          |
| 0.5247        | 33.6288 | 1950 | 0.5033          |
| 0.5261        | 34.4891 | 2000 | 0.5044          |
| 0.5116        | 35.3493 | 2050 | 0.5049          |
| 0.5049        | 36.2096 | 2100 | 0.5015          |
| 0.5044        | 37.0699 | 2150 | 0.5012          |
| 0.5195        | 37.9432 | 2200 | 0.5016          |
| 0.5186        | 38.8035 | 2250 | 0.5014          |
| 0.5245        | 39.6638 | 2300 | 0.5014          |
| 0.5248        | 40.5240 | 2350 | 0.5028          |
| 0.4969        | 41.3843 | 2400 | 0.5034          |
| 0.5009        | 42.2445 | 2450 | 0.5055          |
| 0.5019        | 43.1048 | 2500 | 0.5002          |
| 0.522         | 43.9782 | 2550 | 0.4999          |
| 0.5187        | 44.8384 | 2600 | 0.5020          |
| 0.5129        | 45.6987 | 2650 | 0.4994          |
| 0.5182        | 46.5590 | 2700 | 0.5009          |
| 0.4975        | 47.4192 | 2750 | 0.5014          |
| 0.5024        | 48.2795 | 2800 | 0.4971          |
| 0.5012        | 49.1397 | 2850 | 0.5004          |
| 0.5032        | 50.0    | 2900 | 0.5030          |
| 0.517         | 50.8734 | 2950 | 0.5008          |
| 0.5139        | 51.7336 | 3000 | 0.4994          |
| 0.5107        | 52.5939 | 3050 | 0.5007          |
| 0.5127        | 53.4541 | 3100 | 0.4998          |
| 0.499         | 54.3144 | 3150 | 0.4975          |
| 0.4954        | 55.1747 | 3200 | 0.4994          |
| 0.4994        | 56.0349 | 3250 | 0.5000          |
| 0.5109        | 56.9083 | 3300 | 0.4986          |
| 0.5145        | 57.7686 | 3350 | 0.4994          |
| 0.5155        | 58.6288 | 3400 | 0.4990          |
| 0.51          | 59.4891 | 3450 | 0.5001          |
| 0.501         | 60.3493 | 3500 | 0.5003          |
| 0.484         | 61.2096 | 3550 | 0.4989          |
| 0.4955        | 62.0699 | 3600 | 0.5006          |
| 0.5147        | 62.9432 | 3650 | 0.4992          |
| 0.5189        | 63.8035 | 3700 | 0.5009          |
| 0.5014        | 64.6638 | 3750 | 0.4994          |
| 0.5159        | 65.5240 | 3800 | 0.5020          |
| 0.4942        | 66.3843 | 3850 | 0.4989          |
| 0.5001        | 67.2445 | 3900 | 0.5002          |
| 0.4902        | 68.1048 | 3950 | 0.4981          |
| 0.5126        | 68.9782 | 4000 | 0.4976          |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0