File size: 3,926 Bytes
8dae870 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | # Generation Parameters
Parameters can be passed as keyword arguments to `model.generate(...)` or via the `OmniVoiceGenerationConfig` dataclass. See below for the full list and which category each belongs to.
```python
# 1) Direct keyword arguments
audio = model.generate(text="Hello world", num_step=32, guidance_scale=2.0)
# 2) Via OmniVoiceGenerationConfig dataclass
from omnivoice import OmniVoiceGenerationConfig
config = OmniVoiceGenerationConfig(num_step=32, guidance_scale=2.0)
audio = model.generate(text="Hello world", generation_config=config)
```
## Decoding
| Parameter | Type | Default | Description |
|---|---|---|---|
| `num_step` | int | 32 | Number of iterative unmasking steps. Higher values improve quality but slow down generation. Use 16 for faster inference. |
| `denoise` | bool | True | Prepend the `<|denoise|>` token to the input, which signals the model to produce cleaner speech. |
| `guidance_scale` | float | 2.0 | Classifier-free guidance scale.|
| `t_shift` | float | 0.1 | Time-step shift for the noise schedule. Smaller values emphasise earlier steps in decoding. |
## Sampling
| Parameter | Type | Default | Description |
|---|---|---|---|
| `position_temperature` | float | 5.0 | Temperature for mask-position selection. 0 = greedy (deterministic). Higher values increase randomness. |
| `class_temperature` | float | 0.0 | Temperature for token sampling at each step. 0 = greedy (deterministic). Higher values increase randomness. |
| `layer_penalty_factor` | float | 5.0 | Penalty applied to deeper codebook layers, encouraging earlier (lower) layers to unmask first. |
## Duration & Speed
These accept a single value applied to all items, or a per-item list (useful in batch mode):
```python
# Fixed 10-second output
audio = model.generate(text="Hello, this is a test of duration control", duration=10.0)
# Faster speech (1.2x faster than estimated)
audio = model.generate(text="Hello, this is a test of duration control", speed=1.2)
```
| Parameter | Type | Default | Description |
|---|---|---|---|
| `duration` | float or list[float \| None] | None | Fixed output duration in seconds. Overrides `speed` when set. |
| `speed` | float or list[float \| None] | None | Speed factor. Values > 1.0 produce shorter audio (faster); values < 1.0 produce longer audio (slower). Ignored when `duration` is set. Defaults to 1.0 when both are None. |
Priority: `duration` > `speed`.
> **Note:** When using `duration`, the default post-processing step may trim trailing silence, causing the actual output to be slightly shorter than the requested duration. If you need the output duration to **exactly** match the specified value, set `postprocess_output=False` to disable silence removal.
## Pre/Post Processing
| Parameter | Type | Default | Description |
|---|---|---|---|
| `preprocess_prompt` | bool | True | Whether to apply preprocessing to the voice-clone prompt audio (remove long silences in reference audio, add punctuation in the end of reference text). |
| `postprocess_output` | bool | True | Apply post-processing to generated audio (remove long silences). |
## Long-Form Generation
To support stable long-form speech generation with low VRAM consumption, the text is automatically split into smaller segments when the estimated duration of the generated speech exceeds `audio_chunk_duration`, with each segment producing approximately `audio_chunk_duration` seconds of audio. This approach allows the model to accept arbitrarily long text and generate arbitrarily long speech with near-constant VRAM consumption.
| Parameter | Type | Default | Description |
|---|---|---|---|
| `audio_chunk_duration` | float | 15.0 | Target chunk duration (seconds) when splitting long text. |
| `audio_chunk_threshold` | float | 30.0 | Estimated audio duration (seconds) above which chunking is activated. |
|