Text-to-Speech
Transformers
ONNX
teratts_onnx
feature-extraction
onnxruntime
russian
english
custom-code
custom_code
Instructions to use Disya/TeraTTSv2-Duplicate-for-testing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Disya/TeraTTSv2-Duplicate-for-testing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="Disya/TeraTTSv2-Duplicate-for-testing", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Disya/TeraTTSv2-Duplicate-for-testing", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download models/text_encoder.onnx from Disya/TeraTTSv2-Duplicate-for-testing: direct link, hf CLI and curl.
- Browser
- Download file 27.9 MB
-
https://huggingface.co/Disya/TeraTTSv2-Duplicate-for-testing/resolve/main/models/text_encoder.onnx
- Command line
-
hf download hf://Disya/TeraTTSv2-Duplicate-for-testing/models/text_encoder.onnx
-
curl -L -o text_encoder.onnx https://huggingface.co/Disya/TeraTTSv2-Duplicate-for-testing/resolve/main/models/text_encoder.onnx
27.9 MB
- Xet hash:
- dc5b54bdf0f65de6a76f2cdb1528a4f41b466005b0bf06519fad992567a98f8b
- Size of remote file:
- 27.9 MB
- SHA256:
- d59b7911fe9e465988f26bedbf7c34ef9b2c5588d9b0da5f86d41340a3324a12
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