by ahmedjaved812
Open source · 1k downloads · 0 likes
This model is a fine-tuned version of SpeechT5, specifically adapted for Urdu speech synthesis. It converts text into natural and fluent speech in Urdu, with appropriate intonation and prosody. Designed for applications requiring realistic Urdu voices, it is particularly suited for voice assistants, audiobooks, or accessibility tools for Urdu speakers. What sets it apart is its ability to generate clear and expressive speech while remaining faithful to the linguistic nuances of Urdu. Its training on specific data ensures better performance than generic models for this language.
This model is a fine-tuned version of microsoft/speecht5_tts on an unknown dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 4.6416 | 1.5198 | 500 | 1.0342 |
| 4.2408 | 3.0395 | 1000 | 0.9665 |
| 4.0776 | 4.5593 | 1500 | 0.9377 |
| 4.0257 | 6.0790 | 2000 | 0.9284 |
| 3.9388 | 7.5988 | 2500 | 0.9060 |
| 3.8638 | 9.1185 | 3000 | 0.9002 |
| 3.8240 | 10.6383 | 3500 | 0.8884 |
| 3.7701 | 12.1581 | 4000 | 0.8894 |
| 3.7587 | 13.6778 | 4500 | 0.8772 |
| 3.7120 | 15.1976 | 5000 | 0.8787 |
| 3.6871 | 16.7173 | 5500 | 0.8724 |
| 3.6936 | 18.2371 | 6000 | 0.8732 |
| 3.6681 | 19.7568 | 6500 | 0.8782 |
| 3.6397 | 21.2766 | 7000 | 0.8798 |
| 3.6289 | 22.7964 | 7500 | 0.8654 |
| 3.6120 | 24.3161 | 8000 | 0.8669 |
| 3.6059 | 25.8359 | 8500 | 0.8608 |
| 3.5933 | 27.3556 | 9000 | 0.8610 |
| 3.5507 | 28.8754 | 9500 | 0.8674 |
| 3.5522 | 30.3951 | 10000 | 0.8633 |
| 3.5674 | 31.9149 | 10500 | 0.8654 |
| 3.5469 | 33.4347 | 11000 | 0.8605 |
| 3.5538 | 34.9544 | 11500 | 0.8577 |
| 3.5262 | 36.4742 | 12000 | 0.8677 |
| 3.5307 | 37.9939 | 12500 | 0.8621 |
| 3.5248 | 39.5137 | 13000 | 0.8601 |
| 3.5209 | 41.0334 | 13500 | 0.8564 |
| 3.5113 | 42.5532 | 14000 | 0.8597 |
| 3.5083 | 44.0729 | 14500 | 0.8650 |
| 3.5342 | 45.5927 | 15000 | 0.8595 |
| 3.4962 | 47.1125 | 15500 | 0.8660 |
| 3.4923 | 48.6322 | 16000 | 0.8640 |
| 3.4882 | 50.1520 | 16500 | 0.8669 |
| 3.4894 | 51.6717 | 17000 | 0.8677 |
| 3.4748 | 53.1915 | 17500 | 0.8645 |
| 3.4710 | 54.7112 | 18000 | 0.8662 |
| 3.4755 | 56.2310 | 18500 | 0.8673 |
| 3.4795 | 57.7508 | 19000 | 0.8628 |
| 3.4528 | 59.2705 | 19500 | 0.8697 |
| 3.4802 | 60.7903 | 20000 | 0.8746 |
| 3.4582 | 62.3100 | 20500 | 0.8695 |
| 3.4559 | 63.8298 | 21000 | 0.8697 |
| 3.4333 | 65.3495 | 21500 | 0.8690 |
| 3.4699 | 66.8693 | 22000 | 0.8696 |
| 3.4595 | 68.3891 | 22500 | 0.8700 |
| 3.4625 | 69.9088 | 23000 | 0.8700 |
