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)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TeraSpace/TeraTTSv2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add TeraTTS remote-code interface
Browse files- .gitattributes +0 -3
- README.md +11 -4
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README.md
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# TeraTTSv2 ONNX
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TeraTTSv2 is a self-contained ONNX Runtime text-to-speech release with
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selectable diffusion samplers,
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streamed audio output.
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This release uses the clean English/Russian 25-second teacher and its matching
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> **Important — cross-language prompts.** When an English reference voice is
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> speaking Russian, experiment with `duration_scale` below `1` (for example
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> `0.8`). It is usually a better starting point than the default `1`.
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## Installation
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| Control | Values | Effect |
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| `voice` | `ru_f1`, `
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| `duration_scale` | Positive float, default `1` | Higher values produce slower, longer speech. |
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| `diffusion_model` | `distilled` (default), `teacher` | Distilled is faster; teacher supports adjustable CFG. |
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| `ruaccent_mode` | `full` (default), `dictionary` | Full uses RUAccent neural ONNX graphs plus dictionaries; dictionary mode loads dictionaries only. |
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## Language tags, numbers, and Russian stress
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```python
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waveform = tts.generate_speech(
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# TeraTTSv2 ONNX
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TeraTTSv2 is a self-contained ONNX Runtime text-to-speech release with
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selectable diffusion samplers, ten voice styles, Russian stress marking, and
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streamed audio output.
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This release uses the clean English/Russian 25-second teacher and its matching
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> **Important — cross-language prompts.** When an English reference voice is
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> speaking Russian, experiment with `duration_scale` below `1` (for example
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> `0.8`). It is usually a better starting point than the default `1`.
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>
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> **Recommended voices:** `ru_f1` and `ru_m5` are the preferred Russian voice
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> prompts.
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## Installation
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| Control | Values | Effect |
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| --- | --- | --- |
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| `voice` | `ru_f1` ★, `ru_m5` ★, `ru_f2`, `ru_m1`, `eng_f3`, `eng_f4_whisper`, `eng_f5`, `eng_m2_whisper`, `eng_m3`, `eng_m4` | Selects a bundled precomputed voice style named after its reference audio. ★ marks the recommended Russian prompts. |
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| `duration_scale` | Positive float, default `1` | Higher values produce slower, longer speech. |
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| `diffusion_model` | `distilled` (default), `teacher` | Distilled is faster; teacher supports adjustable CFG. |
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| `ruaccent_mode` | `full` (default), `dictionary` | Full uses RUAccent neural ONNX graphs plus dictionaries; dictionary mode loads dictionaries only. |
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## Language tags, numbers, and Russian stress
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Language tags are required: wrap text in `<en>…</en>` or `<ru>…</ru>`. The
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runtime rejects untagged or unbalanced input with a tag-specific error. Before
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number expansion and stress marking, it inserts spaces after punctuation and
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between a number and a following word. Characters outside the model vocabulary
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are skipped with a runtime warning. Numbers inside language tags are expanded to
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words in the matching language before synthesis:
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```python
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waveform = tts.generate_speech(
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