chore: vendor selva_core from jnwnlee/selva@d7d40a9

Pure PyTorch SelVA source for SelvaModelLoader/FeatureExtractor/Sampler nodes. Imports rewritten from selva.* to selva_core.*. mel_converter.py: replaced librosa.filters.mel with pure-numpy implementation to avoid librosa→numba→NumPy version incompatibility in some ComfyUI environments. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-04 15:18:09 +02:00
parent 762b19fd3a
commit 6bc3fd6443
106 changed files with 11323 additions and 0 deletions
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+# Reference: # https://github.com/bytedance/Make-An-Audio-2
+from typing import Literal
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+
+def librosa_mel_fn(*, sr, n_fft, n_mels=128, fmin=0.0, fmax=None):
+    """Pure-numpy mel filterbank equivalent to librosa.filters.mel (HTK scale, no norm).
+
+    Replaces the librosa import to avoid the librosa → numba → NumPy-version
+    incompatibility that exists in some ComfyUI environments.
+    """
+    if fmax is None:
+        fmax = sr / 2.0
+
+    def hz_to_mel(f):
+        return 2595.0 * np.log10(1.0 + np.asarray(f) / 700.0)
+
+    def mel_to_hz(m):
+        return 700.0 * (10.0 ** (np.asarray(m) / 2595.0) - 1.0)
+
+    n_freqs = n_fft // 2 + 1
+    fft_freqs = np.linspace(0.0, sr / 2.0, n_freqs)
+
+    mel_min = hz_to_mel(fmin)
+    mel_max = hz_to_mel(fmax)
+    mel_points = np.linspace(mel_min, mel_max, n_mels + 2)
+    hz_points = mel_to_hz(mel_points)
+
+    weights = np.zeros((n_mels, n_freqs), dtype=np.float32)
+    for m in range(1, n_mels + 1):
+        f_lo, f_mid, f_hi = hz_points[m - 1], hz_points[m], hz_points[m + 1]
+        up   = (fft_freqs - f_lo) / (f_mid - f_lo + 1e-12)
+        down = (f_hi - fft_freqs) / (f_hi - f_mid + 1e-12)
+        weights[m - 1] = np.maximum(0.0, np.minimum(up, down))
+
+    return weights
+
+
+def dynamic_range_compression_torch(x, C=1, clip_val=1e-5, *, norm_fn):
+    return norm_fn(torch.clamp(x, min=clip_val) * C)
+
+
+def spectral_normalize_torch(magnitudes, norm_fn):
+    output = dynamic_range_compression_torch(magnitudes, norm_fn=norm_fn)
+    return output
+
+
+class MelConverter(nn.Module):
+
+    def __init__(
+        self,
+        *,
+        sampling_rate: float,
+        n_fft: int,
+        num_mels: int,
+        hop_size: int,
+        win_size: int,
+        fmin: float,
+        fmax: float,
+        norm_fn,
+    ):
+        super().__init__()
+        self.sampling_rate = sampling_rate
+        self.n_fft = n_fft
+        self.num_mels = num_mels
+        self.hop_size = hop_size
+        self.win_size = win_size
+        self.fmin = fmin
+        self.fmax = fmax
+        self.norm_fn = norm_fn
+
+        mel = librosa_mel_fn(sr=self.sampling_rate,
+                             n_fft=self.n_fft,
+                             n_mels=self.num_mels,
+                             fmin=self.fmin,
+                             fmax=self.fmax)
+        mel_basis = torch.from_numpy(mel).float()
+        hann_window = torch.hann_window(self.win_size)
+
+        self.register_buffer('mel_basis', mel_basis)
+        self.register_buffer('hann_window', hann_window)
+
+    @property
+    def device(self):
+        return self.mel_basis.device
+
+    def forward(self, waveform: torch.Tensor, center: bool = False) -> torch.Tensor:
+        waveform = waveform.clamp(min=-1., max=1.).to(self.device)
+
+        waveform = torch.nn.functional.pad(
+            waveform.unsqueeze(1),
+            [int((self.n_fft - self.hop_size) / 2),
+             int((self.n_fft - self.hop_size) / 2)],
+            mode='reflect')
+        waveform = waveform.squeeze(1)
+
+        spec = torch.stft(waveform,
+                          self.n_fft,
+                          hop_length=self.hop_size,
+                          win_length=self.win_size,
+                          window=self.hann_window,
+                          center=center,
+                          pad_mode='reflect',
+                          normalized=False,
+                          onesided=True,
+                          return_complex=True)
+
+        spec = torch.view_as_real(spec)
+        spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))
+        spec = torch.matmul(self.mel_basis, spec)
+        spec = spectral_normalize_torch(spec, self.norm_fn)
+
+        return spec
+
+
+def get_mel_converter(mode: Literal['16k', '44k']) -> MelConverter:
+    if mode == '16k':
+        return MelConverter(sampling_rate=16_000,
+                            n_fft=1024,
+                            num_mels=80,
+                            hop_size=256,
+                            win_size=1024,
+                            fmin=0,
+                            fmax=8_000,
+                            norm_fn=torch.log10)
+    elif mode == '44k':
+        return MelConverter(sampling_rate=44_100,
+                            n_fft=2048,
+                            num_mels=128,
+                            hop_size=512,
+                            win_size=2048,
+                            fmin=0,
+                            fmax=44100 / 2,
+                            norm_fn=torch.log)
+    else:
+        raise ValueError(f'Unknown mode: {mode}')