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import os
import sys
import torch
import faiss
import numpy as np
import torch.nn.functional as F
from scipy import signal
sys.path.append(os.getcwd())
from modules.generator import Generator
from modules.rms import RMSEnergyExtractor
from modules.utils import change_rms, clear_gpu_cache
bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
class Pipeline:
def __init__(self, tgt_sr, config):
self.x_pad, self.x_query, self.x_center, self.x_max = config.device_config()
self.sample_rate = 16000
self.window = 160
self.t_pad = self.sample_rate * self.x_pad
self.t_pad_tgt = tgt_sr * self.x_pad
self.t_pad2 = self.t_pad * 2
self.t_query = self.sample_rate * self.x_query
self.t_center = self.sample_rate * self.x_center
self.t_max = self.sample_rate * self.x_max
self.time_step = self.window / self.sample_rate * 1000
self.f0_min = 50
self.f0_max = 1100
self.device = config.device
self.is_half = config.is_half
def voice_conversion(self, model, net_g, sid, audio0, pitch, pitchf, index, big_npy, index_rate, version, protect, energy):
feats = (torch.from_numpy(audio0).half() if self.is_half else torch.from_numpy(audio0).float())
pitch_guidance = pitch != None and pitchf != None
energy_use = energy != None
if feats.dim() == 2: feats = feats.mean(-1)
assert feats.dim() == 1, feats.dim()
feats = feats.view(1, -1)
with torch.no_grad():
padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
logits = model.extract_features(**{"source": feats.to(self.device), "padding_mask": padding_mask, "output_layer": 9 if version == "v1" else 12})
feats = model.final_proj(logits[0]) if version == "v1" else logits[0]
if protect < 0.5 and pitch_guidance: feats0 = feats.clone()
if (not isinstance(index, type(None)) and not isinstance(big_npy, type(None)) and index_rate != 0):
npy = feats[0].cpu().numpy()
if self.is_half: npy = npy.astype(np.float32)
score, ix = index.search(npy, k=8)
weight = np.square(1 / score)
npy = np.sum(big_npy[ix] * np.expand_dims(weight / weight.sum(axis=1, keepdims=True), axis=2), axis=1)
if self.is_half: npy = npy.astype(np.float16)
feats = (torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate + (1 - index_rate) * feats)
feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
if protect < 0.5 and pitch_guidance: feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
p_len = audio0.shape[0] // self.window
if feats.shape[1] < p_len:
p_len = feats.shape[1]
if pitch_guidance: pitch, pitchf = pitch[:, :p_len], pitchf[:, :p_len]
if energy_use: energy = energy[:, :p_len]
if protect < 0.5 and pitch_guidance:
pitchff = pitchf.clone()
pitchff[pitchf > 0] = 1
pitchff[pitchf < 1] = protect
pitchff = pitchff.unsqueeze(-1)
feats = (feats * pitchff + feats0 * (1 - pitchff)).to(feats0.dtype)
p_len = torch.tensor([p_len], device=self.device).long()
feats = feats.half() if self.is_half else feats.float()
if not pitch_guidance: pitch, pitchf = None, None
else: pitchf = pitchf.half() if self.is_half else pitchf.float()
if not energy_use: energy = None
else: energy = energy.half() if self.is_half else energy.float()
audio1 = (
(
net_g.infer(
feats,
p_len,
pitch,
pitchf,
sid,
energy
)[0][0, 0]
).data.cpu().float().numpy()
)
del feats, p_len, net_g, model, padding_mask
clear_gpu_cache()
return audio1
def pipeline(
self,
model,
net_g,
sid,
audio,
f0_up_key,
f0_method,
file_index,
index_rate,
pitch_guidance,
filter_radius,
volume_envelope,
version,
protect,
hop_length,
energy_use=False,
f0_autotune=False,
f0_autotune_strength=False
):
if file_index != "" and os.path.exists(file_index) and index_rate != 0:
try:
index = faiss.read_index(file_index)
big_npy = index.reconstruct_n(0, index.ntotal)
except Exception as e:
print(f"[ERROR] Error occurred while reading index file: {e}")
index = big_npy = None
else: index = big_npy = None
opt_ts, audio_opt = [], []
audio = signal.filtfilt(bh, ah, audio)
audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
if audio_pad.shape[0] > self.t_max:
audio_sum = np.zeros_like(audio)
for i in range(self.window):
audio_sum += audio_pad[i : i - self.window]
for t in range(self.t_center, audio.shape[0], self.t_center):
opt_ts.append(t - self.t_query + np.where(np.abs(audio_sum[t - self.t_query : t + self.t_query]) == np.abs(audio_sum[t - self.t_query : t + self.t_query]).min())[0][0])
s = 0
t = None
audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
p_len = audio_pad.shape[0] // self.window
if pitch_guidance:
if not hasattr(self, "f0_generator"): self.f0_generator = Generator(self.sample_rate, hop_length, self.f0_min, self.f0_max, self.is_half, self.device)
pitch, pitchf = self.f0_generator.calculator(f0_method, audio_pad, f0_up_key, p_len, filter_radius, f0_autotune, f0_autotune_strength)
if self.device == "mps": pitchf = pitchf.astype(np.float32)
pitch, pitchf = torch.tensor(pitch[:p_len], device=self.device).unsqueeze(0).long(), torch.tensor(pitchf[:p_len], device=self.device).unsqueeze(0).float()
if energy_use:
if not hasattr(self, "rms_extract"): self.rms_extract = RMSEnergyExtractor(frame_length=2048, hop_length=self.window, center=True, pad_mode = "reflect").to(self.device).eval()
energy = self.rms_extract(torch.from_numpy(audio_pad).to(self.device).unsqueeze(0)).cpu().numpy()
if self.device == "mps": energy = energy.astype(np.float32)
energy = torch.tensor(energy[:p_len], device=self.device).unsqueeze(0).float()
for t in opt_ts:
t = t // self.window * self.window
audio_opt.append(
self.voice_conversion(
model,
net_g,
sid,
audio_pad[s : t + self.t_pad2 + self.window],
pitch[:, s // self.window : (t + self.t_pad2) // self.window] if pitch_guidance else None,
pitchf[:, s // self.window : (t + self.t_pad2) // self.window] if pitch_guidance else None,
index,
big_npy,
index_rate,
version,
protect,
energy[:, s // self.window : (t + self.t_pad2) // self.window] if energy_use else None
)[self.t_pad_tgt : -self.t_pad_tgt]
)
s = t
audio_opt.append(
self.voice_conversion(
model,
net_g,
sid,
audio_pad[t:],
(pitch[:, t // self.window :] if t is not None else pitch) if pitch_guidance else None,
(pitchf[:, t // self.window :] if t is not None else pitchf) if pitch_guidance else None,
index,
big_npy,
index_rate,
version,
protect,
(energy[:, t // self.window :] if t is not None else energy) if energy_use else None
)[self.t_pad_tgt : -self.t_pad_tgt]
)
audio_opt = np.concatenate(audio_opt)
if volume_envelope != 1: audio_opt = change_rms(audio, self.sample_rate, audio_opt, self.sample_rate, volume_envelope)
audio_max = np.abs(audio_opt).max() / 0.99
if audio_max > 1: audio_opt /= audio_max
if pitch_guidance: del pitch, pitchf
del sid
clear_gpu_cache()
return audio_opt |