Add OmniVoice Voice Preset node with two female voice samples

Two built-in presets, auto-downloaded and cached to ComfyUI/models/omnivoice/presets/:
- "Nature – female, warm" (F5-TTS basic_ref_en.wav, transcript included)
- "Shadowheart – female, expressive" (Chatterbox demo, connect Whisper for transcript)

Outputs ref_audio (AUDIO) and ref_text (STRING) — wire directly into
OmniVoice Generate. Updated default workflow to use this node.

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

nodes/__init__.py

@@ -1,5 +1,6 @@
 from .loader import OmniVoiceModelLoader
 from .generator import OmniVoiceGenerate
 from .epub_loader import OmniVoiceEpubLoader
+from .voice_presets import OmniVoiceVoicePreset
 
-__all__ = ["OmniVoiceModelLoader", "OmniVoiceGenerate", "OmniVoiceEpubLoader"]
+__all__ = ["OmniVoiceModelLoader", "OmniVoiceGenerate", "OmniVoiceEpubLoader", "OmniVoiceVoicePreset"]

nodes/voice_presets.py

@@ -0,0 +1,69 @@
+import os
+import urllib.request
+import numpy as np
+import torch
+import soundfile as sf
+
+try:
+    import folder_paths
+    _CACHE_DIR = os.path.join(folder_paths.models_dir, "omnivoice", "presets")
+except ImportError:
+    _CACHE_DIR = os.path.join(os.path.expanduser("~"), ".cache", "omnivoice", "presets")
+
+# Each entry: (display_name, url, transcript)
+# transcript="" means unknown — connect a Whisper node to ref_text to fill it.
+PRESETS = {
+    "Nature – female, warm (F5-TTS ref)": (
+        "https://raw.githubusercontent.com/SWivid/F5-TTS/main/src/f5_tts/infer/examples/basic/basic_ref_en.wav",
+        "Some call me nature, others call me mother nature.",
+    ),
+    "Shadowheart – female, expressive (Chatterbox ref)": (
+        "https://storage.googleapis.com/chatterbox-demo-samples/prompts/female_shadowheart4.flac",
+        "",  # transcript unknown — connect Whisper node to ref_text
+    ),
+}
+
+
+def _load_audio(url):
+    """Download (once) and return (waveform_tensor, sample_rate)."""
+    os.makedirs(_CACHE_DIR, exist_ok=True)
+    filename = os.path.basename(url.split("?")[0])
+    cache_path = os.path.join(_CACHE_DIR, filename)
+    if not os.path.exists(cache_path):
+        urllib.request.urlretrieve(url, cache_path)
+    audio_np, sr = sf.read(cache_path, dtype="float32")
+    if audio_np.ndim == 1:
+        audio_np = audio_np[np.newaxis, :]        # (1, samples)
+    else:
+        audio_np = audio_np.T                     # (channels, samples)
+    waveform = torch.from_numpy(audio_np).unsqueeze(0)  # (1, channels, samples)
+    return waveform, sr
+
+
+class OmniVoiceVoicePreset:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "preset": (
+                    list(PRESETS.keys()),
+                    {
+                        "tooltip": (
+                            "Pre-fetched reference voice for OmniVoice Generate.\n"
+                            "Connect ref_audio → ref_audio and ref_text → ref_text.\n"
+                            "If ref_text is blank, connect a Whisper node to supply the transcript."
+                        ),
+                    },
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("AUDIO", "STRING")
+    RETURN_NAMES = ("ref_audio", "ref_text")
+    FUNCTION = "load_preset"
+    CATEGORY = "OmniVoice"
+
+    def load_preset(self, preset):
+        url, transcript = PRESETS[preset]
+        waveform, sr = _load_audio(url)
+        return ({"waveform": waveform, "sample_rate": sr}, transcript)