---
library_name: transformers
pipeline_tag: text-to-speech
tags:
- onnxruntime
- text-to-speech
- russian
- english
- custom-code
---

# TeraTTSv2 ONNX

TeraTTSv2 is a self-contained ONNX Runtime text-to-speech release with
selectable diffusion samplers, ten voice styles, Russian stress marking, and
streamed audio output.

This release uses the clean English/Russian 25-second teacher and its matching
eight-step CFG-3 distilled student.

> **Important — Russian stress is automatic.** Text inside `<ru>…</ru>` receives
> stress markers automatically by default. Explicit `+` markers always win.
>
> **Important — cross-language prompts.** When an English reference voice is
> speaking Russian, experiment with `duration_scale` below `1` (for example
> `0.8`). It is usually a better starting point than the default `1`.
>
> **Recommended voices:** `ru_f1` and `ru_m5` are the preferred Russian voice
> prompts.

## Installation

```bash
pip install -r requirements.txt
```

`sounddevice` is only required for direct speaker playback. On Linux, install
the system PortAudio library if it is not already present.

## Load with Transformers

```python
from transformers import AutoModel

tts = AutoModel.from_pretrained(
    "TeraSpace/TeraTTSv2",
    trust_remote_code=True,
    provider="CPUExecutionProvider",
    threads=6
)

waveform = tts.generate_speech(
    "<ru>Привет от TeraTTS.</ru>",
    voice="ru_f1",
    duration_scale=1,
)
tts.save_wav("teratts.wav", waveform)
```

`waveform` is a mono `float32` NumPy array at 44,100 Hz. `save_wav` writes
standard signed-16-bit PCM WAV without an extra audio package.

To inspect the exact text passed to the encoder after number expansion, stress
marking, and Unicode normalization, call `tts.normalize_text(text)`.

## Controls

| Control | Values | Effect |
| --- | --- | --- |
| `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. |
| `duration_scale` | Positive float, default `1` | Higher values produce slower, longer speech. |
| `diffusion_model` | `distilled` (default), `teacher` | Distilled is faster; teacher supports adjustable CFG. |
| `ruaccent_mode` | `full` (default), `dictionary` | Full uses RUAccent neural ONNX graphs plus dictionaries; dictionary mode loads dictionaries only. |

The default `diffusion_model="distilled"` is the fast eight-step sampler. To
use the teacher sampler, choose it while loading:

```python
teacher_tts = AutoModel.from_pretrained(
    "TeraSpace/TeraTTSv2",
    trust_remote_code=True,
    provider="CPUExecutionProvider",
    threads=6,
    diffusion_model="teacher",
)
```

`guidance` can be adjusted when generating with the teacher sampler. The
distilled sampler has CFG 3 baked into its graph.

## Language tags, numbers, and Russian stress

Language tags are required: wrap text in `<en>…</en>` or `<ru>…</ru>`. The
runtime rejects untagged or unbalanced input with a tag-specific error. Before
number expansion and stress marking, it inserts spaces after punctuation and
between a number and a following word. Characters outside the model vocabulary
are skipped with a runtime warning. Numbers inside language tags are expanded to
words in the matching language before synthesis:

```python
waveform = tts.generate_speech(
    "<ru>У меня 21 яблоко.</ru> <en>I have 42 apples.</en>",
    voice="ru_f1",
    duration_scale=1,
)
```

Russian text is automatically stress-marked by the bundled RUAccent-derived
runtime. Manual `+` markers remain authoritative. For a lower-memory,
deterministic dictionary-only path, choose the mode while loading:

```python
dictionary_tts = AutoModel.from_pretrained(
    "TeraSpace/TeraTTSv2",
    trust_remote_code=True,
    provider="CPUExecutionProvider",
    threads=6,
    ruaccent_mode="dictionary",
)
```

Dictionary mode does not load RUAccent neural ONNX graphs. It marks known
words and applies deterministic `ё` replacements, while unknown words and
ambiguous homographs are left unchanged. Set `russian_stress=False` to disable
automatic Russian stress processing entirely.

When using an English voice such as `eng_f3` for Russian text, start by trying
`duration_scale=0.8` and adjust by ear:

```python
waveform = tts.generate_speech(
    "<ru>Это русский текст английским голосом.</ru>",
    voice="eng_f3",
    duration_scale=0.8,
)
```

## Stream audio

```python
for chunk in tts.generate_speech_stream(
    "<en>Streaming speech is ready.</en>",
    voice="eng_f3",
    duration_scale=1,
):
    # Send float32 mono chunks (44,100 Hz) to a player or network client.
    consume(chunk)
```

The remote code loads only the selected sampler graph plus shared ONNX graphs.
For security, pin a specific Hub commit when using `trust_remote_code=True`.

## Attribution

The local Russian stress annotator and its assets are adapted from
[RUAccent](https://github.com/Den4ikAI/ruaccent), Copyright 2026 Denis Petrov,
under the MIT License. See `RUACCENT_NOTICE.txt`.