Instructions to use ahmedjaved812/urdu-tts-phonemes-finetuned-extended with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ahmedjaved812/urdu-tts-phonemes-finetuned-extended with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="ahmedjaved812/urdu-tts-phonemes-finetuned-extended")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("ahmedjaved812/urdu-tts-phonemes-finetuned-extended") model = AutoModelForTextToSpectrogram.from_pretrained("ahmedjaved812/urdu-tts-phonemes-finetuned-extended") - Notebooks
- Google Colab
- Kaggle
urdu-tts-phonemes-finetuned-extended
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8014
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: 6
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 48
- total_eval_batch_size: 4
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 4.6038 | 0.9149 | 500 | 0.9382 |
| 3.9723 | 1.8289 | 1000 | 0.8982 |
| 3.8796 | 2.7429 | 1500 | 0.8837 |
| 3.7655 | 3.6569 | 2000 | 0.8691 |
| 3.7279 | 4.5709 | 2500 | 0.8611 |
| 3.6874 | 5.4849 | 3000 | 0.8507 |
| 3.6253 | 6.3989 | 3500 | 0.8450 |
| 3.5747 | 7.3129 | 4000 | 0.8403 |
| 3.5732 | 8.2269 | 4500 | 0.8419 |
| 3.5595 | 9.1409 | 5000 | 0.8367 |
| 3.5520 | 10.0549 | 5500 | 0.8339 |
| 3.5297 | 10.9698 | 6000 | 0.8357 |
| 3.5303 | 11.8838 | 6500 | 0.8333 |
| 3.5060 | 12.7978 | 7000 | 0.8260 |
| 3.4967 | 13.7118 | 7500 | 0.8256 |
| 3.5028 | 14.6258 | 8000 | 0.8239 |
| 3.4786 | 15.5398 | 8500 | 0.8211 |
| 3.4918 | 16.4538 | 9000 | 0.8209 |
| 3.4901 | 17.3678 | 9500 | 0.8193 |
| 3.4815 | 18.2818 | 10000 | 0.8204 |
| 3.4688 | 19.1958 | 10500 | 0.8163 |
| 3.4426 | 20.1098 | 11000 | 0.8160 |
| 3.4519 | 21.0238 | 11500 | 0.8157 |
| 3.4514 | 21.9387 | 12000 | 0.8138 |
| 3.4341 | 22.8527 | 12500 | 0.8148 |
| 3.4341 | 23.7667 | 13000 | 0.8114 |
| 3.4261 | 24.6807 | 13500 | 0.8129 |
| 3.4276 | 25.5947 | 14000 | 0.8111 |
| 3.4234 | 26.5087 | 14500 | 0.8086 |
| 3.4379 | 27.4227 | 15000 | 0.8083 |
| 3.4171 | 28.3367 | 15500 | 0.8055 |
| 3.4083 | 29.2507 | 16000 | 0.8067 |
| 3.4227 | 30.1647 | 16500 | 0.8090 |
| 3.4050 | 31.0787 | 17000 | 0.8063 |
| 3.4144 | 31.9936 | 17500 | 0.8072 |
| 3.3968 | 32.9076 | 18000 | 0.8028 |
| 3.4001 | 33.8216 | 18500 | 0.8056 |
| 3.3873 | 34.7356 | 19000 | 0.8032 |
| 3.4032 | 35.6496 | 19500 | 0.8057 |
| 3.3936 | 36.5636 | 20000 | 0.8040 |
| 3.3829 | 37.4776 | 20500 | 0.8036 |
| 3.3927 | 38.3916 | 21000 | 0.8034 |
| 3.3895 | 39.3056 | 21500 | 0.8037 |
| 3.3789 | 40.2196 | 22000 | 0.8027 |
| 3.3938 | 41.1336 | 22500 | 0.8020 |
| 3.3907 | 42.0476 | 23000 | 0.8018 |
| 3.3752 | 42.9625 | 23500 | 0.8004 |
| 3.3759 | 43.8765 | 24000 | 0.8009 |
| 3.3807 | 44.7905 | 24500 | 0.8015 |
| 3.3808 | 45.7045 | 25000 | 0.8030 |
| 3.3720 | 46.6185 | 25500 | 0.8016 |
| 3.3851 | 47.5325 | 26000 | 0.8018 |
| 3.3733 | 48.4465 | 26500 | 0.8007 |
| 3.3703 | 49.3605 | 27000 | 0.8014 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.3
- Tokenizers 0.22.2
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