feat: add OmniVoice Mix Voices node for blended speaker cloning

Concatenates 2-3 reference audio clips (with per-voice duration weights)
to create a blended speaker embedding. Merges transcripts for ref_text.
Handles mismatched sample rates and mono conversion automatically.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

nodes/mix_voices.py

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+import torch
+
+
+def _to_mono(waveform):
+    """(1, channels, samples) → (1, 1, samples)"""
+    if waveform.shape[1] > 1:
+        return waveform.mean(dim=1, keepdim=True)
+    return waveform
+
+
+def _resample(waveform, src_sr, dst_sr):
+    """Resample waveform tensor (1, 1, samples) to dst_sr."""
+    if src_sr == dst_sr:
+        return waveform
+    try:
+        import torchaudio
+        resampler = torchaudio.transforms.Resample(orig_freq=src_sr, new_freq=dst_sr)
+        return resampler(waveform)
+    except Exception:
+        # fallback: nearest-neighbour via interpolate
+        ratio = dst_sr / src_sr
+        new_len = int(waveform.shape[-1] * ratio)
+        return torch.nn.functional.interpolate(
+            waveform.float(), size=new_len, mode='linear', align_corners=False
+        )
+
+
+class OmniVoiceMixVoices:
+    """Concatenate two (or three) reference voices to create a blended speaker embedding."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "audio_1": ("AUDIO", {
+                    "tooltip": "First reference voice.",
+                }),
+                "audio_2": ("AUDIO", {
+                    "tooltip": "Second reference voice.",
+                }),
+                "weight_1": ("FLOAT", {
+                    "default": 1.0, "min": 0.0, "max": 1.0, "step": 0.05,
+                    "tooltip": "Relative duration weight for audio_1. Higher = more of this voice in the mix.",
+                }),
+                "weight_2": ("FLOAT", {
+                    "default": 1.0, "min": 0.0, "max": 1.0, "step": 0.05,
+                    "tooltip": "Relative duration weight for audio_2.",
+                }),
+            },
+            "optional": {
+                "audio_3": ("AUDIO", {
+                    "tooltip": "Optional third reference voice.",
+                }),
+                "weight_3": ("FLOAT", {
+                    "default": 1.0, "min": 0.0, "max": 1.0, "step": 0.05,
+                    "tooltip": "Relative duration weight for audio_3.",
+                }),
+                "text_1": ("STRING", {
+                    "default": "",
+                    "tooltip": "Transcript for audio_1.",
+                }),
+                "text_2": ("STRING", {
+                    "default": "",
+                    "tooltip": "Transcript for audio_2.",
+                }),
+                "text_3": ("STRING", {
+                    "default": "",
+                    "tooltip": "Transcript for audio_3 (optional).",
+                }),
+            },
+        }
+
+    RETURN_TYPES = ("AUDIO", "STRING")
+    RETURN_NAMES = ("ref_audio", "ref_text")
+    FUNCTION = "mix"
+    CATEGORY = "OmniVoice"
+
+    def mix(self, audio_1, audio_2, weight_1=1.0, weight_2=1.0,
+            audio_3=None, weight_3=1.0,
+            text_1="", text_2="", text_3=""):
+
+        audios = [audio_1, audio_2]
+        weights = [weight_1, weight_2]
+        texts = [text_1, text_2]
+        if audio_3 is not None:
+            audios.append(audio_3)
+            weights.append(weight_3)
+            texts.append(text_3)
+
+        # Use the highest sample rate among inputs as target
+        target_sr = max(a["sample_rate"] for a in audios)
+
+        clips = []
+        for audio, weight in zip(audios, weights):
+            w = _to_mono(audio["waveform"])           # (1, 1, samples)
+            w = _resample(w, audio["sample_rate"], target_sr)
+
+            # trim/repeat to match requested weight in seconds (normalise later)
+            clips.append((w, weight))
+
+        # Determine target samples per unit weight
+        # Scale each clip so that weight=1.0 keeps its full length,
+        # and trim/tile accordingly relative to the largest weighted clip.
+        max_samples = max(c.shape[-1] * wt for c, wt in clips)
+        target_per_unit = max_samples  # samples for weight=1.0
+
+        trimmed = []
+        for clip, weight in clips:
+            n_samples = int(target_per_unit * weight)
+            if n_samples <= 0:
+                continue
+            src_len = clip.shape[-1]
+            if src_len >= n_samples:
+                trimmed.append(clip[..., :n_samples])
+            else:
+                # tile then trim
+                reps = (n_samples // src_len) + 1
+                tiled = clip.repeat(1, 1, reps)
+                trimmed.append(tiled[..., :n_samples])
+
+        mixed = torch.cat(trimmed, dim=-1)  # (1, 1, total_samples)
+
+        merged_text = " ".join(t.strip() for t in texts if t.strip())
+
+        return ({"waveform": mixed, "sample_rate": target_sr}, merged_text)