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---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
model-index:
- name: ABG_TTS
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. -->
# ABG_TTS
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co./microsoft/speecht5_tts) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5066
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- 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 |
|:-------------:|:-------:|:----:|:---------------:|
| 0.7566 | 0.3972 | 100 | 0.6804 |
| 0.6819 | 0.7944 | 200 | 0.6558 |
| 0.6512 | 1.1917 | 300 | 0.6097 |
| 0.6315 | 1.5889 | 400 | 0.5807 |
| 0.6092 | 1.9861 | 500 | 0.5860 |
| 0.619 | 2.3833 | 600 | 0.5818 |
| 0.6099 | 2.7805 | 700 | 0.5627 |
| 0.5908 | 3.1778 | 800 | 0.5849 |
| 0.5887 | 3.5750 | 900 | 0.5505 |
| 0.585 | 3.9722 | 1000 | 0.5505 |
| 0.5773 | 4.3694 | 1100 | 0.5550 |
| 0.5708 | 4.7666 | 1200 | 0.5419 |
| 0.5664 | 5.1639 | 1300 | 0.5425 |
| 0.559 | 5.5611 | 1400 | 0.5422 |
| 0.5605 | 5.9583 | 1500 | 0.5289 |
| 0.5641 | 6.3555 | 1600 | 0.5327 |
| 0.5507 | 6.7527 | 1700 | 0.5443 |
| 0.5622 | 7.1500 | 1800 | 0.5346 |
| 0.5613 | 7.5472 | 1900 | 0.5355 |
| 0.5469 | 7.9444 | 2000 | 0.5325 |
| 0.5523 | 8.3416 | 2100 | 0.5267 |
| 0.5477 | 8.7388 | 2200 | 0.5186 |
| 0.5466 | 9.1360 | 2300 | 0.5192 |
| 0.5383 | 9.5333 | 2400 | 0.5179 |
| 0.5332 | 9.9305 | 2500 | 0.5165 |
| 0.5351 | 10.3277 | 2600 | 0.5148 |
| 0.5377 | 10.7249 | 2700 | 0.5186 |
| 0.5295 | 11.1221 | 2800 | 0.5196 |
| 0.5263 | 11.5194 | 2900 | 0.5133 |
| 0.5301 | 11.9166 | 3000 | 0.5138 |
| 0.5209 | 12.3138 | 3100 | 0.5143 |
| 0.5205 | 12.7110 | 3200 | 0.5103 |
| 0.5132 | 13.1082 | 3300 | 0.5157 |
| 0.5194 | 13.5055 | 3400 | 0.5085 |
| 0.5177 | 13.9027 | 3500 | 0.5110 |
| 0.5156 | 14.2999 | 3600 | 0.5100 |
| 0.5082 | 14.6971 | 3700 | 0.5098 |
| 0.5179 | 15.0943 | 3800 | 0.5053 |
| 0.5165 | 15.4916 | 3900 | 0.5064 |
| 0.5036 | 15.8888 | 4000 | 0.5058 |
| 0.504 | 16.2860 | 4100 | 0.5098 |
| 0.5119 | 16.6832 | 4200 | 0.5061 |
| 0.5017 | 17.0804 | 4300 | 0.5107 |
| 0.5066 | 17.4777 | 4400 | 0.5074 |
| 0.5071 | 17.8749 | 4500 | 0.5088 |
| 0.5119 | 18.2721 | 4600 | 0.5053 |
| 0.5032 | 18.6693 | 4700 | 0.5064 |
| 0.5014 | 19.0665 | 4800 | 0.5089 |
| 0.5001 | 19.4638 | 4900 | 0.5078 |
| 0.4926 | 19.8610 | 5000 | 0.5066 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.3.0+cu118
- Datasets 3.0.0
- Tokenizers 0.19.1
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