Russian narration for the Enshrouded notes: mod 0.4.0, installer, generation pipeline

ensvoice/tts/local.py

@@ -0,0 +1,33 @@
+"""qwen-tts (Qwen3-TTS 1.7B, fine-tune siriusfreak/qwen3-tts-12hz-1.7b-ru-stress-cf) on a local GPU.
+
+ICL voice clone: the reference clip and its exact text are encoded once; texts are spoken in batches
+of tts.batch with torch.manual_seed(seed + batch start), so a seed is repeatable for the same segments
+in the same order. fp16 on Volta/Turing, bfloat16 on Ampere and newer (tts.dtype).
+"""
+import soundfile as sf
+import torch
+
+
+class LocalTTS:
+    def __init__(self, cfg, ref_wav, ref_text):
+        from qwen_tts import Qwen3TTSModel
+        t = cfg["tts"]
+        self.model = Qwen3TTSModel.from_pretrained(t.get("model", "siriusfreak/qwen3-tts-12hz-1.7b-ru-stress-cf"),
+                                                   device_map=t.get("device", "cuda:0"),
+                                                   dtype=getattr(torch, t.get("dtype", "bfloat16")))
+        self.batch = int(t.get("batch", 8))
+        self.set_voice(ref_wav, ref_text)
+
+    def set_voice(self, ref_wav, ref_text):
+        self.prompt = self.model.create_voice_clone_prompt(ref_audio=str(ref_wav), ref_text=ref_text)
+
+    def speak(self, items, seed, log=print):
+        items = sorted(items, key=lambda x: len(x[0]))  # similar lengths in one batch: less padding
+        for i in range(0, len(items), self.batch):
+            b = items[i:i + self.batch]
+            torch.manual_seed(seed + i)
+            wavs, sr = self.model.generate_voice_clone(text=[t for t, _ in b], language=["Russian"] * len(b),
+                                                       voice_clone_prompt=self.prompt * len(b))
+            for (_, out), w in zip(b, wavs):
+                sf.write(out, w, sr)
+            log(f"seed {seed}: {min(i + self.batch, len(items))}/{len(items)}")