Inference Methods

infer()

audio = tts.infer(
    text: str,
    ref_audio: str = None,
    ref_codes: Tensor = None,
    ref_text: str = None,
    voice: dict = None,
    max_chars: int = 256,
    silence_p: float = 0.15,
    crossfade_p: float = 0.0,
    temperature: float = 1.0,
    top_k: int = 50,
    skip_normalize: bool = False,
)

Parameters

Parameter	Type	Description
text	str	Text to synthesize
ref_audio	str	Path to reference audio for voice cloning
ref_codes	Tensor	Pre-encoded reference codes
ref_text	str	Transcript of reference audio
voice	dict	Preset voice dict from get_preset_voice()
max_chars	int	Max characters per chunk (default 256)
silence_p	float	Silence duration between chunks in seconds
crossfade_p	float	Crossfade duration between chunks
temperature	float	Sampling temperature
top_k	int	Top-k sampling
skip_normalize	bool	Skip text normalization

Returns

numpy.ndarray — Audio waveform at 24 kHz.

Voice Priority

1. voice dict (from preset)
2. ref_audio + ref_text
3. ref_codes + ref_text
4. Default preset voice

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infer_batch()

audios = tts.infer_batch(texts: List[str], ...)

Returns List[numpy.ndarray]. PyTorch mode uses true batch generation; GGUF processes sequentially.

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infer_stream()

for chunk in tts.infer_stream(text: str, ...):
    play_audio(chunk)

Yields numpy.ndarray chunks (GGUF only).

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save()

tts.save(audio: numpy.ndarray, output_path: str)

---

encode_reference()

codes = tts.encode_reference(ref_audio_path: str)
# Returns: torch.Tensor

---

close()

tts.close()
# Or use context manager:
with Vieneu() as tts:
    audio = tts.infer(text="...")