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---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- m-aliabbas/common_voice_urdu1
model-index:
- name: SpeechT5 TTS urdu
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 TTS urdu
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the common_voice_urdu1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4796
## Model description
trianed using roman urdu, using a transliteration function normal urdu was mapped to roman urdu.
## Use
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: 300
- training_steps: 10500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:-----:|:---------------:|
| 0.5782 | 4.3103 | 500 | 0.5071 |
| 0.5248 | 8.6207 | 1000 | 0.4863 |
| 0.5125 | 12.9310 | 1500 | 0.4746 |
| 0.5081 | 17.2414 | 2000 | 0.4727 |
| 0.4967 | 21.5517 | 2500 | 0.4683 |
| 0.4905 | 25.8621 | 3000 | 0.4645 |
| 0.4794 | 30.1724 | 3500 | 0.4668 |
| 0.4829 | 34.4828 | 4000 | 0.4647 |
| 0.477 | 38.7931 | 4500 | 0.4645 |
| 0.4637 | 43.1034 | 5000 | 0.4710 |
| 0.4743 | 47.4138 | 5500 | 0.4683 |
| 0.4595 | 51.7241 | 6000 | 0.4695 |
| 0.4735 | 56.0345 | 6500 | 0.4684 |
| 0.4613 | 60.3448 | 7000 | 0.4724 |
| 0.4678 | 64.6552 | 7500 | 0.4732 |
| 0.4538 | 68.9655 | 8000 | 0.4723 |
| 0.4536 | 73.2759 | 8500 | 0.4747 |
| 0.4587 | 77.5862 | 9000 | 0.4740 |
| 0.4536 | 81.8966 | 9500 | 0.4762 |
| 0.4606 | 86.2069 | 10000 | 0.4768 |
| 0.4528 | 90.5172 | 10500 | 0.4796 |
### Framework versions
- Transformers 4.43.0.dev0
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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