facebook/voxpopuli
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How to use vsisik/speecht5_tts_SK_v3 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-to-audio", model="vsisik/speecht5_tts_SK_v3") # Load model directly
from transformers import AutoProcessor, AutoModelForTextToSpectrogram
processor = AutoProcessor.from_pretrained("vsisik/speecht5_tts_SK_v3")
model = AutoModelForTextToSpectrogram.from_pretrained("vsisik/speecht5_tts_SK_v3")This model is a fine-tuned version of microsoft/speecht5_tts on the 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 |
|---|---|---|---|
| 0.5259 | 3.2258 | 500 | 0.4625 |
| 0.4823 | 6.4516 | 1000 | 0.4345 |
| 0.4702 | 9.6774 | 1500 | 0.4258 |
| 0.4502 | 12.9032 | 2000 | 0.4189 |
| 0.4579 | 16.1290 | 2500 | 0.4173 |
| 0.4418 | 19.3548 | 3000 | 0.4134 |
| 0.448 | 22.5806 | 3500 | 0.4117 |
| 0.4467 | 25.8065 | 4000 | 0.4094 |
| 0.4388 | 29.0323 | 4500 | 0.4084 |
| 0.4327 | 32.2581 | 5000 | 0.4071 |
| 0.4398 | 35.4839 | 5500 | 0.4069 |
| 0.4381 | 38.7097 | 6000 | 0.4065 |
| 0.4357 | 41.9355 | 6500 | 0.4053 |
| 0.4352 | 45.1613 | 7000 | 0.4059 |
| 0.4298 | 48.3871 | 7500 | 0.4050 |
| 0.4293 | 51.6129 | 8000 | 0.4043 |
| 0.4342 | 54.8387 | 8500 | 0.4050 |
| 0.4309 | 58.0645 | 9000 | 0.4045 |
| 0.4277 | 61.2903 | 9500 | 0.4047 |
| 0.4319 | 64.5161 | 10000 | 0.4046 |
Base model
microsoft/speecht5_tts