mozilla-foundation/common_voice_13_0
Updated • 2.05k • 4
How to use Stopwolf/speecht5_pt_full with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-to-speech", model="Stopwolf/speecht5_pt_full") # Load model directly
from transformers import AutoProcessor, AutoModelForTextToSpectrogram
processor = AutoProcessor.from_pretrained("Stopwolf/speecht5_pt_full")
model = AutoModelForTextToSpectrogram.from_pretrained("Stopwolf/speecht5_pt_full")This model is a fine-tuned version of microsoft/speecht5_tts on the mozilla-foundation/common_voice_13_0 dataset. It achieves the following results on the evaluation set:
DISCLAIMER: This model is trained for the sole purpose of finishing the HuggingFace Audio course. It doesn't have any usability and outputs pure noise. If you have an idea of how to improve the model, feel free to create a post in the Community tab of this model. Thank you!
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.8882 | 0.18 | 100 | 0.7494 |
| 0.7726 | 0.36 | 200 | 0.6657 |
| 0.642 | 0.54 | 300 | 0.5767 |
| 0.6042 | 0.71 | 400 | 0.5545 |
| 0.5972 | 0.89 | 500 | 0.5342 |
| 0.5832 | 1.07 | 600 | 0.5337 |
| 0.5851 | 1.25 | 700 | 0.5291 |
| 0.5744 | 1.43 | 800 | 0.5245 |
| 0.5638 | 1.61 | 900 | 0.5186 |
| 0.5562 | 1.78 | 1000 | 0.5174 |
| 0.56 | 1.96 | 1100 | 0.5133 |
| 0.5446 | 2.14 | 1200 | 0.5113 |
| 0.5556 | 2.32 | 1300 | 0.5099 |
| 0.5457 | 2.5 | 1400 | 0.5071 |
| 0.5504 | 2.68 | 1500 | 0.5087 |
| 0.5497 | 2.85 | 1600 | 0.5039 |
| 0.545 | 3.03 | 1700 | 0.5034 |
| 0.5503 | 3.21 | 1800 | 0.5051 |
| 0.5621 | 3.39 | 1900 | 0.5040 |
| 0.5347 | 3.57 | 2000 | 0.5021 |
Base model
microsoft/speecht5_tts