Instructions to use microsoft/speecht5_tts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/speecht5_tts with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="microsoft/speecht5_tts")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("microsoft/speecht5_tts") model = AutoModelForTextToSpectrogram.from_pretrained("microsoft/speecht5_tts") - Notebooks
- Google Colab
- Kaggle
Update README.md
#27
by jeevan-exa - opened
README.md
CHANGED
|
@@ -65,7 +65,7 @@ from transformers import pipeline
|
|
| 65 |
from datasets import load_dataset
|
| 66 |
import soundfile as sf
|
| 67 |
|
| 68 |
-
synthesiser = pipeline("text-to-speech", "microsoft/
|
| 69 |
|
| 70 |
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
|
| 71 |
speaker_embedding = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
|
|
|
|
| 65 |
from datasets import load_dataset
|
| 66 |
import soundfile as sf
|
| 67 |
|
| 68 |
+
synthesiser = pipeline("text-to-speech", "microsoft/speecht5_tts")
|
| 69 |
|
| 70 |
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
|
| 71 |
speaker_embedding = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
|