Ethanfel
76118f57c3
Catching bare Exception was silently swallowing real resampling errors. Only ImportError should trigger the interpolate fallback. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
121 lines
4.3 KiB
Python
121 lines
4.3 KiB
Python
import torch
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def _to_mono(waveform):
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"""(1, channels, samples) → (1, 1, samples)"""
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if waveform.shape[1] > 1:
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return waveform.mean(dim=1, keepdim=True)
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return waveform
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def _resample(waveform, src_sr, dst_sr):
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"""Resample waveform tensor (1, 1, samples) to dst_sr."""
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if src_sr == dst_sr:
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return waveform
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try:
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import torchaudio
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# Resample expects (channels, samples), not (batch, channels, samples)
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resampler = torchaudio.transforms.Resample(orig_freq=src_sr, new_freq=dst_sr)
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return resampler(waveform.squeeze(0)).unsqueeze(0)
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except ImportError:
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ratio = dst_sr / src_sr
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new_len = int(waveform.shape[-1] * ratio)
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return torch.nn.functional.interpolate(
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waveform.float(), size=new_len, mode='linear', align_corners=False
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)
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class OmniVoiceMixVoices:
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"""Concatenate two (or three) reference voices to create a blended speaker embedding."""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"audio_1": ("AUDIO", {
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"tooltip": "First reference voice.",
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}),
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"audio_2": ("AUDIO", {
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"tooltip": "Second reference voice.",
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}),
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"weight_1": ("FLOAT", {
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"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.05,
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"tooltip": "Relative duration weight for audio_1. Higher = more of this voice in the mix.",
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}),
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"weight_2": ("FLOAT", {
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"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.05,
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"tooltip": "Relative duration weight for audio_2.",
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}),
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},
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"optional": {
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"audio_3": ("AUDIO", {
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"tooltip": "Optional third reference voice.",
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}),
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"weight_3": ("FLOAT", {
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"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.05,
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"tooltip": "Relative duration weight for audio_3.",
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}),
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"text_1": ("STRING", {
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"default": "",
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"tooltip": "Transcript for audio_1.",
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}),
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"text_2": ("STRING", {
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"default": "",
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"tooltip": "Transcript for audio_2.",
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}),
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"text_3": ("STRING", {
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"default": "",
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"tooltip": "Transcript for audio_3 (optional).",
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}),
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},
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}
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RETURN_TYPES = ("AUDIO", "STRING")
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RETURN_NAMES = ("ref_audio", "ref_text")
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FUNCTION = "mix"
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CATEGORY = "OmniVoice"
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def mix(self, audio_1, audio_2, weight_1=1.0, weight_2=1.0,
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audio_3=None, weight_3=1.0,
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text_1="", text_2="", text_3=""):
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audios = [audio_1, audio_2]
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weights = [weight_1, weight_2]
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texts = [text_1, text_2]
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if audio_3 is not None:
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audios.append(audio_3)
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weights.append(weight_3)
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texts.append(text_3)
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# Use the highest sample rate among inputs as target
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target_sr = max(a["sample_rate"] for a in audios)
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clips = []
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for audio, weight in zip(audios, weights):
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w = _to_mono(audio["waveform"]) # (1, 1, samples)
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w = _resample(w, audio["sample_rate"], target_sr)
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clips.append((w, weight))
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# Each clip contributes (natural_length * weight) samples.
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trimmed = []
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for clip, weight in clips:
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n_samples = int(clip.shape[-1] * weight)
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if n_samples <= 0:
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continue
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src_len = clip.shape[-1]
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if src_len >= n_samples:
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trimmed.append(clip[..., :n_samples])
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else:
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reps = (n_samples // src_len) + 1
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tiled = clip.repeat(1, 1, reps)
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trimmed.append(tiled[..., :n_samples])
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if not trimmed:
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raise ValueError("OmniVoice Mix Voices: all weights are 0 — nothing to mix.")
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mixed = torch.cat(trimmed, dim=-1) # (1, 1, total_samples)
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merged_text = " ".join(t.strip() for t in texts if t.strip())
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return ({"waveform": mixed, "sample_rate": target_sr}, merged_text)
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