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modules/tts/provider_piper.py

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+"""Провайдер TTS на Piper — полностью офлайн-фолбэк.
+
+Используется, если edge-tts недоступен (сервис Microsoft ломался в прошлом,
+а интернет у отца может пропасть). Piper работает локально на CPU.
+
+Установка и голос (однократно):
+    pip install piper-tts
+    python -m piper.download_voices ru_RU-dmitri-medium --download-dir models/piper
+
+Пайпер медленнее edge-tts и голоса проще, но работает без сети.
+API: PiperVoice.load(path); voice.synthesize(text) -> чанки AudioChunk
+(audio_int16_bytes, sample_rate, sample_width, sample_channels).
+"""
+from __future__ import annotations
+
+import time
+from pathlib import Path
+from typing import List, Optional
+
+import numpy as np
+
+from .base import SynthResult, TtsProvider
+from .text_split import split_sentences
+
+
+class PiperTtsProvider(TtsProvider):
+    name = "piper"
+
+    def __init__(
+        self,
+        model_path: Union[str, Path],
+        length_scale: float = 1.15,          # 1.0 = обычная скорость; 1.15 ≈ −15%
+        noise_scale: float = 0.667,
+        noise_w_scale: float = 0.8,
+        sentence_pause_sec: float = 0.35,
+    ) -> None:
+        from piper import PiperVoice, SynthesisConfig  # ленивый импорт
+
+        path = Path(model_path)
+        if not path.is_file():
+            raise FileNotFoundError(
+                f"Модель Piper не найдена: {path}\n"
+                f"Скачай голос: python -m piper.download_voices ru_RU-dmitri-medium "
+                f"--download-dir {path.parent}"
+            )
+        self._voice = PiperVoice.load(str(path))
+        self._config = SynthesisConfig(
+            length_scale=length_scale,
+            noise_scale=noise_scale,
+            noise_w_scale=noise_w_scale,
+        )
+        self._pause = float(sentence_pause_sec)
+
+    def synthesize(self, text: str) -> SynthResult:
+        t0 = time.perf_counter()
+        sentences = split_sentences(text) or [text]
+
+        pieces: List[np.ndarray] = []
+        samplerate: Optional[int] = None
+
+        for i, sentence in enumerate(sentences):
+            chunks = list(self._voice.synthesize(sentence, syn_config=self._config))
+            for ch in chunks:
+                if samplerate is None:
+                    samplerate = ch.sample_rate
+                pcm = np.frombuffer(ch.audio_int16_bytes, dtype=np.int16)
+                pieces.append(pcm.astype(np.float32) / 32768.0)
+            if i < len(sentences) - 1 and self._pause > 0 and samplerate:
+                pieces.append(np.zeros(int(self._pause * samplerate), dtype=np.float32))
+
+        audio = np.concatenate(pieces) if pieces else np.zeros(0, dtype=np.float32)
+        return SynthResult(
+            text=text,
+            audio=audio,
+            samplerate=samplerate or 22050,
+            generation_sec=time.perf_counter() - t0,
+            provider=self.name,
+            sentence_count=len(sentences),
+        )