mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2026-08-29 01:59:23 +02:00
446 lines
14 KiB
Python
446 lines
14 KiB
Python
import math
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import librosa
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import numpy as np
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import torch
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from infer.audio import resample_audio, resample_audio_tensor
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_STFT_WINDOWS = {}
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def _stft_window(n_fft, device):
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key = (n_fft, str(device))
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window = _STFT_WINDOWS.get(key)
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if window is None:
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window = torch.hann_window(
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n_fft,
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periodic=True,
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device=device,
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dtype=torch.float32,
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)
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_STFT_WINDOWS[key] = window
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return window
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def _wave_to_spectrogram_torch(
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wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False
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):
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wave = wave.to(dtype=torch.float32)
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if reverse:
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transformed = torch.flip(wave[:2], dims=(-1,))
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elif mid_side:
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transformed = torch.stack(
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((wave[0] + wave[1]) / 2, wave[0] - wave[1])
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)
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elif mid_side_b2:
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transformed = torch.stack(
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(wave[1] + wave[0] * 0.5, wave[0] - wave[1] * 0.5)
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)
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else:
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transformed = wave[:2]
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return torch.stft(
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transformed,
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n_fft=n_fft,
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hop_length=hop_length,
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window=_stft_window(n_fft, transformed.device),
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center=True,
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pad_mode="constant",
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normalized=False,
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onesided=True,
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return_complex=True,
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)
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def crop_center(h1, h2):
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h1_shape = h1.size()
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h2_shape = h2.size()
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if h1_shape[3] == h2_shape[3]:
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return h1
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elif h1_shape[3] < h2_shape[3]:
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raise ValueError("h1_shape[3] must be greater than h2_shape[3]")
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# s_freq = (h2_shape[2] - h1_shape[2]) // 2
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# e_freq = s_freq + h1_shape[2]
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s_time = (h1_shape[3] - h2_shape[3]) // 2
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e_time = s_time + h2_shape[3]
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h1 = h1[:, :, :, s_time:e_time]
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return h1
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def wave_to_spectrogram_mt(wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False):
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if torch.is_tensor(wave):
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return _wave_to_spectrogram_torch(
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wave, hop_length, n_fft, mid_side, mid_side_b2, reverse
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)
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import threading
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if reverse:
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wave_left = np.flip(np.asfortranarray(wave[0]))
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wave_right = np.flip(np.asfortranarray(wave[1]))
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elif mid_side:
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wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
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wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
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elif mid_side_b2:
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wave_left = np.asfortranarray(np.add(wave[1], wave[0] * 0.5))
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wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * 0.5))
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else:
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wave_left = np.asfortranarray(wave[0])
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wave_right = np.asfortranarray(wave[1])
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def run_thread(**kwargs):
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global spec_left
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spec_left = librosa.stft(**kwargs)
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thread = threading.Thread(
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target=run_thread,
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kwargs={"y": wave_left, "n_fft": n_fft, "hop_length": hop_length},
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)
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thread.start()
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spec_right = librosa.stft(wave_right, n_fft=n_fft, hop_length=hop_length)
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thread.join()
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spec = np.asfortranarray([spec_left, spec_right])
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return spec
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def combine_spectrograms(specs, mp):
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l = min([specs[i].shape[2] for i in specs])
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first = specs[next(iter(specs))]
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if torch.is_tensor(first):
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spec_c = torch.zeros(
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(2, mp.param["bins"] + 1, l),
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dtype=torch.complex64,
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device=first.device,
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)
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else:
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spec_c = np.zeros(shape=(2, mp.param["bins"] + 1, l), dtype=np.complex64)
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offset = 0
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bands_n = len(mp.param["band"])
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for d in range(1, bands_n + 1):
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h = mp.param["band"][d]["crop_stop"] - mp.param["band"][d]["crop_start"]
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spec_c[:, offset : offset + h, :l] = specs[d][
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:, mp.param["band"][d]["crop_start"] : mp.param["band"][d]["crop_stop"], :l
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]
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offset += h
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if offset > mp.param["bins"]:
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raise ValueError("Too much bins")
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# lowpass fiter
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if mp.param["pre_filter_start"] > 0: # and mp.param['band'][bands_n]['res_type'] in ['scipy', 'polyphase']:
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if bands_n == 1:
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spec_c = fft_lp_filter(spec_c, mp.param["pre_filter_start"], mp.param["pre_filter_stop"])
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else:
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gp = 1
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for b in range(mp.param["pre_filter_start"] + 1, mp.param["pre_filter_stop"]):
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g = math.pow(10, -(b - mp.param["pre_filter_start"]) * (3.5 - gp) / 20.0)
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gp = g
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spec_c[:, b, :] *= g
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if torch.is_tensor(spec_c):
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return spec_c.contiguous()
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return np.asfortranarray(spec_c)
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def mask_silence(mag, ref, thres=0.2, min_range=64, fade_size=32):
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if min_range < fade_size * 2:
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raise ValueError("min_range must be >= fade_area * 2")
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if torch.is_tensor(mag):
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mag = mag.clone()
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idx = torch.where(ref.mean(dim=(0, 1)) < thres)[0]
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if idx.numel() == 0:
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return mag
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breaks = torch.where(torch.diff(idx) != 1)[0]
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starts = torch.cat((idx[:1], idx[breaks + 1]))
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ends = torch.cat((idx[breaks], idx[-1:]))
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informative = torch.where(ends - starts > min_range)[0]
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old_e = None
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for position in informative.tolist():
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s = int(starts[position].item())
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e = int(ends[position].item())
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if old_e is not None and s - old_e < fade_size:
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s = old_e - fade_size * 2
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if s != 0:
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weight = torch.linspace(
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0,
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1,
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fade_size,
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device=mag.device,
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dtype=mag.dtype,
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)
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mag[:, :, s : s + fade_size] += (
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weight * ref[:, :, s : s + fade_size]
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)
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else:
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s -= fade_size
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if e != mag.shape[2]:
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weight = torch.linspace(
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1,
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0,
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fade_size,
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device=mag.device,
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dtype=mag.dtype,
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)
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mag[:, :, e - fade_size : e] += (
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weight * ref[:, :, e - fade_size : e]
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)
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else:
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e += fade_size
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mag[:, :, s + fade_size : e - fade_size] += ref[
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:, :, s + fade_size : e - fade_size
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]
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old_e = e
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return mag
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mag = mag.copy()
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idx = np.where(ref.mean(axis=(0, 1)) < thres)[0]
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starts = np.insert(idx[np.where(np.diff(idx) != 1)[0] + 1], 0, idx[0])
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ends = np.append(idx[np.where(np.diff(idx) != 1)[0]], idx[-1])
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uninformative = np.where(ends - starts > min_range)[0]
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if len(uninformative) > 0:
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starts = starts[uninformative]
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ends = ends[uninformative]
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old_e = None
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for s, e in zip(starts, ends):
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if old_e is not None and s - old_e < fade_size:
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s = old_e - fade_size * 2
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if s != 0:
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weight = np.linspace(0, 1, fade_size)
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mag[:, :, s : s + fade_size] += weight * ref[:, :, s : s + fade_size]
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else:
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s -= fade_size
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if e != mag.shape[2]:
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weight = np.linspace(1, 0, fade_size)
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mag[:, :, e - fade_size : e] += weight * ref[:, :, e - fade_size : e]
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else:
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e += fade_size
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mag[:, :, s + fade_size : e - fade_size] += ref[:, :, s + fade_size : e - fade_size]
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old_e = e
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return mag
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def spectrogram_to_wave(spec, hop_length, mid_side, mid_side_b2, reverse):
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if torch.is_tensor(spec):
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n_fft = (spec.shape[1] - 1) * 2
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wave = torch.istft(
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spec.to(dtype=torch.complex64),
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n_fft=n_fft,
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hop_length=hop_length,
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window=_stft_window(n_fft, spec.device),
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center=True,
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normalized=False,
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onesided=True,
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return_complex=False,
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)
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wave_left, wave_right = wave[0], wave[1]
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if reverse:
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return torch.stack(
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(torch.flip(wave_left, dims=(-1,)), torch.flip(wave_right, dims=(-1,)))
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)
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if mid_side:
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return torch.stack(
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(wave_left + wave_right / 2, wave_left - wave_right / 2)
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)
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if mid_side_b2:
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return torch.stack(
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(wave_right / 1.25 + 0.4 * wave_left, wave_left / 1.25 - 0.4 * wave_right)
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)
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return wave
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spec_left = np.asfortranarray(spec[0])
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spec_right = np.asfortranarray(spec[1])
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wave_left = librosa.istft(spec_left, hop_length=hop_length)
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wave_right = librosa.istft(spec_right, hop_length=hop_length)
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if reverse:
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return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
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elif mid_side:
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return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)])
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elif mid_side_b2:
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return np.asfortranarray(
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[
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np.add(wave_right / 1.25, 0.4 * wave_left),
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np.subtract(wave_left / 1.25, 0.4 * wave_right),
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]
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)
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else:
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return np.asfortranarray([wave_left, wave_right])
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def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None):
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wave_band = {}
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bands_n = len(mp.param["band"])
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offset = 0
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for d in range(1, bands_n + 1):
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bp = mp.param["band"][d]
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shape = (2, bp["n_fft"] // 2 + 1, spec_m.shape[2])
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if torch.is_tensor(spec_m):
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spec_s = torch.zeros(shape, dtype=spec_m.dtype, device=spec_m.device)
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else:
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spec_s = np.ndarray(shape=shape, dtype=complex)
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h = bp["crop_stop"] - bp["crop_start"]
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spec_s[:, bp["crop_start"] : bp["crop_stop"], :] = spec_m[:, offset : offset + h, :]
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offset += h
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if d == bands_n: # higher
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if extra_bins_h: # if --high_end_process bypass
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max_bin = bp["n_fft"] // 2
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spec_s[:, max_bin - extra_bins_h : max_bin, :] = extra_bins[:, :extra_bins_h, :]
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if bp["hpf_start"] > 0:
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spec_s = fft_hp_filter(spec_s, bp["hpf_start"], bp["hpf_stop"] - 1)
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if bands_n == 1:
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wave = spectrogram_to_wave(
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spec_s,
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bp["hl"],
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mp.param["mid_side"],
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mp.param["mid_side_b2"],
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mp.param["reverse"],
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)
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else:
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wave = wave + spectrogram_to_wave(
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spec_s,
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bp["hl"],
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mp.param["mid_side"],
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mp.param["mid_side_b2"],
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mp.param["reverse"],
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)
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else:
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sr = mp.param["band"][d + 1]["sr"]
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if d == 1: # lower
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spec_s = fft_lp_filter(spec_s, bp["lpf_start"], bp["lpf_stop"])
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band_wave = spectrogram_to_wave(
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spec_s,
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bp["hl"],
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mp.param["mid_side"],
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mp.param["mid_side_b2"],
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mp.param["reverse"],
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)
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if torch.is_tensor(band_wave):
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wave = resample_audio_tensor(
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band_wave, bp["sr"], sr, force_mono=False
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)
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else:
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wave = resample_audio(
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band_wave,
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bp["sr"],
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sr,
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force_mono=False,
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res_type="sinc_fastest",
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)
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else: # mid
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spec_s = fft_hp_filter(spec_s, bp["hpf_start"], bp["hpf_stop"] - 1)
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spec_s = fft_lp_filter(spec_s, bp["lpf_start"], bp["lpf_stop"])
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wave2 = wave + spectrogram_to_wave(
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spec_s,
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bp["hl"],
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mp.param["mid_side"],
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mp.param["mid_side_b2"],
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mp.param["reverse"],
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)
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if torch.is_tensor(wave2):
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wave = resample_audio_tensor(
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wave2, bp["sr"], sr, force_mono=False
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)
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else:
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wave = resample_audio(
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wave2,
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bp["sr"],
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sr,
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force_mono=False,
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res_type="scipy",
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)
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return wave.transpose(0, 1) if torch.is_tensor(wave) else wave.T
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def fft_lp_filter(spec, bin_start, bin_stop):
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g = 1.0
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for b in range(bin_start, bin_stop):
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g -= 1 / (bin_stop - bin_start)
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spec[:, b, :] = g * spec[:, b, :]
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spec[:, bin_stop:, :] *= 0
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return spec
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def fft_hp_filter(spec, bin_start, bin_stop):
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g = 1.0
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for b in range(bin_start, bin_stop, -1):
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g -= 1 / (bin_start - bin_stop)
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spec[:, b, :] = g * spec[:, b, :]
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spec[:, 0 : bin_stop + 1, :] *= 0
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return spec
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def mirroring(a, spec_m, input_high_end, mp):
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if torch.is_tensor(spec_m):
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source = spec_m[
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:,
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mp.param["pre_filter_start"]
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- 10
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- input_high_end.shape[1] : mp.param["pre_filter_start"]
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- 10,
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:,
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]
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mirror = torch.flip(torch.abs(source), dims=(1,))
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if "mirroring" == a:
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mirror = torch.polar(mirror, torch.angle(input_high_end))
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return torch.where(
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torch.abs(input_high_end) <= torch.abs(mirror),
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input_high_end,
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mirror,
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)
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if "mirroring2" == a:
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mirror = mirror * input_high_end * 1.7
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return torch.where(
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torch.abs(input_high_end) <= torch.abs(mirror),
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input_high_end,
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mirror,
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)
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if "mirroring" == a:
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mirror = np.flip(
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np.abs(
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spec_m[
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:,
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mp.param["pre_filter_start"] - 10 - input_high_end.shape[1] : mp.param["pre_filter_start"] - 10,
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:,
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]
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),
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1,
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)
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mirror = mirror * np.exp(1.0j * np.angle(input_high_end))
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return np.where(np.abs(input_high_end) <= np.abs(mirror), input_high_end, mirror)
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if "mirroring2" == a:
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mirror = np.flip(
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np.abs(
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spec_m[
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:,
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mp.param["pre_filter_start"] - 10 - input_high_end.shape[1] : mp.param["pre_filter_start"] - 10,
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:,
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]
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),
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1,
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)
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mi = np.multiply(mirror, input_high_end * 1.7)
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return np.where(np.abs(input_high_end) <= np.abs(mi), input_high_end, mi)
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