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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 main.app.variables import translations
from main.library.utils import extract_features
from main.library.predictors.Generator import Generator
from main.inference.extracting.rms import RMSEnergyExtractor
from main.inference.conversion.utils import change_rms, clear_gpu_cache, get_onnx_argument
bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
class Pipeline:
def __init__(self, tgt_sr, config):
self.x_pad = config.x_pad
self.x_query = config.x_query
self.x_center = config.x_center
self.x_max = config.x_max
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.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):
pitch_guidance = pitch != None and pitchf != None
energy_use = energy != None
feats = torch.from_numpy(audio0)
feats = feats.half() if self.is_half else feats.float()
feats = feats.mean(-1) if feats.dim() == 2 else feats
assert feats.dim() == 1, feats.dim()
feats = feats.view(1, -1)
with torch.no_grad():
if self.embed_suffix == ".pt":
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]
elif self.embed_suffix == ".onnx": feats = extract_features(model, feats.to(self.device), version).to(self.device)
elif self.embed_suffix == ".safetensors":
logits = model(feats.to(self.device))["last_hidden_state"]
feats = model.final_proj(logits[0]).unsqueeze(0) if version == "v1" else logits
else: raise ValueError(translations["option_not_valid"])
feats0 = feats.clone() if protect < 0.5 and pitch_guidance else None
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)
p_len = min(audio0.shape[0] // self.window, feats.shape[1])
if pitch_guidance: pitch, pitchf = pitch[:, :p_len], pitchf[:, :p_len]
if energy_use: energy = energy[:, :p_len]
if feats0 is not None:
pitchff = pitchf.clone()
pitchff[pitchf > 0] = 1
pitchff[pitchf < 1] = protect
pitchff = pitchff.unsqueeze(-1)
feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(0, 2, 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()
) if self.suffix == ".pth" else (
net_g.run(
[net_g.get_outputs()[0].name], (
get_onnx_argument(
net_g,
feats,
p_len,
sid,
pitch,
pitchf,
energy,
pitch_guidance,
energy_use
)
)
)[0][0, 0]
)
if self.embed_suffix == ".pt": del padding_mask
del feats, feats0, p_len
clear_gpu_cache()
return audio1
def pipeline(self, logger, model, net_g, sid, audio, f0_up_key, f0_method, file_index, index_rate, pitch_guidance, filter_radius, rms_mix_rate, version, protect, hop_length, f0_autotune, f0_autotune_strength, suffix, embed_suffix, f0_file=None, f0_onnx=False, pbar=None, proposal_pitch=False, proposal_pitch_threshold=255.0, energy_use=False):
self.suffix = suffix
self.embed_suffix = embed_suffix
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:
logger.error(translations["read_faiss_index_error"].format(e=e))
index = big_npy = None
else: index = big_npy = None
if pbar: pbar.update(1)
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, inp_f0 = None, 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 hasattr(f0_file, "name"):
try:
with open(f0_file.name, "r") as f:
raw_lines = f.read()
if len(raw_lines) > 0:
inp_f0 = []
for line in raw_lines.strip("\n").split("\n"):
inp_f0.append([float(i) for i in line.split(",")])
inp_f0 = np.array(inp_f0, dtype=np.float32)
except:
logger.error(translations["error_readfile"])
inp_f0 = None
if pbar: pbar.update(1)
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, f0_onnx, f0_onnx)
pitch, pitchf = self.f0_generator.calculator(self.x_pad, f0_method, audio_pad, f0_up_key, p_len, filter_radius, f0_autotune, f0_autotune_strength, manual_f0=inp_f0, proposal_pitch=proposal_pitch, proposal_pitch_threshold=proposal_pitch_threshold)
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 pbar: pbar.update(1)
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()
if pbar: pbar.update(1)
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 pbar: pbar.update(1)
if rms_mix_rate != 1: audio_opt = change_rms(audio, self.sample_rate, audio_opt, self.sample_rate, rms_mix_rate)
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 |