m-aliabbas/common_voice_urdu1
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How to use pocketmonkey/speecht5_tts_urdu with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-to-audio", model="pocketmonkey/speecht5_tts_urdu") # Load model directly
from transformers import AutoProcessor, AutoModelForTextToSpectrogram
processor = AutoProcessor.from_pretrained("pocketmonkey/speecht5_tts_urdu")
model = AutoModelForTextToSpectrogram.from_pretrained("pocketmonkey/speecht5_tts_urdu")This model is a fine-tuned version of microsoft/speecht5_tts on the common_voice_urdu1 dataset. It achieves the following results on the evaluation set:
trianed using roman urdu, using a transliteration function normal urdu was mapped to roman urdu.
More information needed
More information needed
The following hyperparameters were used during training:
| 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 |
Base model
microsoft/speecht5_tts