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---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: na_voice_clon
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. -->
# na_voice_clon
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: 0.5806
## 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: 2000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.9913 | 1.1905 | 50 | 0.8766 |
| 0.9763 | 2.3810 | 100 | 0.8198 |
| 0.9234 | 3.5714 | 150 | 0.7816 |
| 0.8756 | 4.7619 | 200 | 0.7637 |
| 0.8449 | 5.9524 | 250 | 0.7322 |
| 0.7719 | 7.1429 | 300 | 0.6737 |
| 0.7254 | 8.3333 | 350 | 0.6366 |
| 0.6845 | 9.5238 | 400 | 0.6195 |
| 0.6737 | 10.7143 | 450 | 0.6131 |
| 0.6689 | 11.9048 | 500 | 0.6178 |
| 0.6595 | 13.0952 | 550 | 0.6039 |
| 0.655 | 14.2857 | 600 | 0.6046 |
| 0.6514 | 15.4762 | 650 | 0.5944 |
| 0.6478 | 16.6667 | 700 | 0.5940 |
| 0.6327 | 17.8571 | 750 | 0.5939 |
| 0.6418 | 19.0476 | 800 | 0.5938 |
| 0.6329 | 20.2381 | 850 | 0.5887 |
| 0.6364 | 21.4286 | 900 | 0.5906 |
| 0.6284 | 22.6190 | 950 | 0.5887 |
| 0.6238 | 23.8095 | 1000 | 0.5865 |
| 0.624 | 25.0 | 1050 | 0.5859 |
| 0.6163 | 26.1905 | 1100 | 0.5845 |
| 0.6254 | 27.3810 | 1150 | 0.5840 |
| 0.6168 | 28.5714 | 1200 | 0.5831 |
| 0.6141 | 29.7619 | 1250 | 0.5791 |
| 0.614 | 30.9524 | 1300 | 0.5835 |
| 0.6121 | 32.1429 | 1350 | 0.5788 |
| 0.6227 | 33.3333 | 1400 | 0.5785 |
| 0.6198 | 34.5238 | 1450 | 0.5775 |
| 0.6142 | 35.7143 | 1500 | 0.5803 |
| 0.6183 | 36.9048 | 1550 | 0.5765 |
| 0.6161 | 38.0952 | 1600 | 0.5781 |
| 0.6061 | 39.2857 | 1650 | 0.5768 |
| 0.6167 | 40.4762 | 1700 | 0.5773 |
| 0.6063 | 41.6667 | 1750 | 0.5775 |
| 0.6107 | 42.8571 | 1800 | 0.5777 |
| 0.6084 | 44.0476 | 1850 | 0.5772 |
| 0.6074 | 45.2381 | 1900 | 0.5757 |
| 0.6023 | 46.4286 | 1950 | 0.5766 |
| 0.6077 | 47.6190 | 2000 | 0.5806 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
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