Instructions to use Stopwolf/speecht5_pt_full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -22,7 +22,7 @@ This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingfa
|
|
| 22 |
It achieves the following results on the evaluation set:
|
| 23 |
- Loss: 0.5021
|
| 24 |
|
| 25 |
-
>
|
| 26 |
|
| 27 |
## Model description
|
| 28 |
|
|
|
|
| 22 |
It achieves the following results on the evaluation set:
|
| 23 |
- Loss: 0.5021
|
| 24 |
|
| 25 |
+
> *DISCLAIMER*: This model is trained for the sole purpose of finishing the HuggingFace [Audio course](https://huggingface.co/learn/audio-course/chapter0/introduction). 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!
|
| 26 |
|
| 27 |
## Model description
|
| 28 |
|