Text-to-Speech
Transformers
ONNX
teratts_onnx
feature-extraction
onnxruntime
russian
english
custom-code
custom_code
Instructions to use TeraSpace/TeraTTSv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TeraSpace/TeraTTSv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="TeraSpace/TeraTTSv2", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TeraSpace/TeraTTSv2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add ONNX Runtime implementation
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Add TeraTTS remote-code interface
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Expose normalized encoder text
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Document automatic stress and cross-language duration guidance
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Replace voice styles with clean-25s style_flacs speakers
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Replace teacher and distilled samplers with clean 25-second models
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Add dictionary stress mode and tagged number expansion
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Specify duration scale in inference examples
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Document voices and add WAV saving
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Accept AutoModel framework loading options
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Add TeraTTS ONNX release artifacts
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Add RUAccent attribution
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Bundle local Russian stress annotator
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Add ONNX Runtime implementation
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Add TeraTTS remote-code interface
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initial commit
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