facebook/voxpopuli
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How to use pppde/speecht5_finetuned_voxpopuli_de with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-to-audio", model="pppde/speecht5_finetuned_voxpopuli_de") # Load model directly
from transformers import AutoProcessor, AutoModelForTextToSpectrogram
processor = AutoProcessor.from_pretrained("pppde/speecht5_finetuned_voxpopuli_de")
model = AutoModelForTextToSpectrogram.from_pretrained("pppde/speecht5_finetuned_voxpopuli_de")This model is a fine-tuned version of microsoft/speecht5_tts on the facebook/voxpopuli 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 |
|---|---|---|---|
| 5.1518 | 0.5319 | 250 | 0.5899 |
| 4.4208 | 1.0638 | 500 | 0.5002 |
| 4.2216 | 1.5957 | 750 | 0.4824 |
| 4.1452 | 2.1277 | 1000 | 0.4751 |
| 4.1011 | 2.6596 | 1250 | 0.4713 |
| 4.0454 | 3.1915 | 1500 | 0.4669 |
| 4.0224 | 3.7234 | 1750 | 0.4646 |
| 4.0026 | 4.2553 | 2000 | 0.4626 |
| 4.028 | 4.7872 | 2250 | 0.4604 |
| 3.9629 | 5.3191 | 2500 | 0.4595 |
| 3.9708 | 5.8511 | 2750 | 0.4593 |
| 3.9927 | 6.3830 | 3000 | 0.4577 |
| 3.9592 | 6.9149 | 3250 | 0.4573 |
| 3.952 | 7.4468 | 3500 | 0.4579 |
| 3.9651 | 7.9787 | 3750 | 0.4567 |
| 3.9483 | 8.5106 | 4000 | 0.4565 |
Base model
microsoft/speecht5_tts