Ethanfel
6bc3fd6443
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>
140 lines
4.5 KiB
Python
140 lines
4.5 KiB
Python
# Reference: # https://github.com/bytedance/Make-An-Audio-2
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from typing import Literal
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import numpy as np
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import torch
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import torch.nn as nn
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def librosa_mel_fn(*, sr, n_fft, n_mels=128, fmin=0.0, fmax=None):
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"""Pure-numpy mel filterbank equivalent to librosa.filters.mel (HTK scale, no norm).
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Replaces the librosa import to avoid the librosa → numba → NumPy-version
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incompatibility that exists in some ComfyUI environments.
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"""
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if fmax is None:
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fmax = sr / 2.0
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def hz_to_mel(f):
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return 2595.0 * np.log10(1.0 + np.asarray(f) / 700.0)
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def mel_to_hz(m):
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return 700.0 * (10.0 ** (np.asarray(m) / 2595.0) - 1.0)
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n_freqs = n_fft // 2 + 1
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fft_freqs = np.linspace(0.0, sr / 2.0, n_freqs)
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mel_min = hz_to_mel(fmin)
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mel_max = hz_to_mel(fmax)
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mel_points = np.linspace(mel_min, mel_max, n_mels + 2)
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hz_points = mel_to_hz(mel_points)
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weights = np.zeros((n_mels, n_freqs), dtype=np.float32)
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for m in range(1, n_mels + 1):
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f_lo, f_mid, f_hi = hz_points[m - 1], hz_points[m], hz_points[m + 1]
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up = (fft_freqs - f_lo) / (f_mid - f_lo + 1e-12)
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down = (f_hi - fft_freqs) / (f_hi - f_mid + 1e-12)
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weights[m - 1] = np.maximum(0.0, np.minimum(up, down))
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return weights
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def dynamic_range_compression_torch(x, C=1, clip_val=1e-5, *, norm_fn):
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return norm_fn(torch.clamp(x, min=clip_val) * C)
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def spectral_normalize_torch(magnitudes, norm_fn):
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output = dynamic_range_compression_torch(magnitudes, norm_fn=norm_fn)
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return output
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class MelConverter(nn.Module):
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def __init__(
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self,
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*,
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sampling_rate: float,
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n_fft: int,
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num_mels: int,
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hop_size: int,
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win_size: int,
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fmin: float,
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fmax: float,
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norm_fn,
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):
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super().__init__()
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self.sampling_rate = sampling_rate
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self.n_fft = n_fft
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self.num_mels = num_mels
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self.hop_size = hop_size
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self.win_size = win_size
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self.fmin = fmin
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self.fmax = fmax
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self.norm_fn = norm_fn
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mel = librosa_mel_fn(sr=self.sampling_rate,
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n_fft=self.n_fft,
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n_mels=self.num_mels,
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fmin=self.fmin,
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fmax=self.fmax)
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mel_basis = torch.from_numpy(mel).float()
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hann_window = torch.hann_window(self.win_size)
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self.register_buffer('mel_basis', mel_basis)
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self.register_buffer('hann_window', hann_window)
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@property
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def device(self):
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return self.mel_basis.device
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def forward(self, waveform: torch.Tensor, center: bool = False) -> torch.Tensor:
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waveform = waveform.clamp(min=-1., max=1.).to(self.device)
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waveform = torch.nn.functional.pad(
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waveform.unsqueeze(1),
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[int((self.n_fft - self.hop_size) / 2),
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int((self.n_fft - self.hop_size) / 2)],
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mode='reflect')
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waveform = waveform.squeeze(1)
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spec = torch.stft(waveform,
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self.n_fft,
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hop_length=self.hop_size,
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win_length=self.win_size,
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window=self.hann_window,
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center=center,
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pad_mode='reflect',
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normalized=False,
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onesided=True,
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return_complex=True)
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spec = torch.view_as_real(spec)
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spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))
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spec = torch.matmul(self.mel_basis, spec)
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spec = spectral_normalize_torch(spec, self.norm_fn)
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return spec
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def get_mel_converter(mode: Literal['16k', '44k']) -> MelConverter:
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if mode == '16k':
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return MelConverter(sampling_rate=16_000,
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n_fft=1024,
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num_mels=80,
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hop_size=256,
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win_size=1024,
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fmin=0,
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fmax=8_000,
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norm_fn=torch.log10)
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elif mode == '44k':
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return MelConverter(sampling_rate=44_100,
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n_fft=2048,
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num_mels=128,
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hop_size=512,
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win_size=2048,
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fmin=0,
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fmax=44100 / 2,
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norm_fn=torch.log)
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else:
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raise ValueError(f'Unknown mode: {mode}')
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