444 lines
16 KiB
Python
444 lines
16 KiB
Python
from io import BytesIO
|
||
import os
|
||
import pickle
|
||
import sys
|
||
import traceback
|
||
from infer.lib import jit
|
||
from infer.lib.jit.get_synthesizer import get_synthesizer
|
||
from time import time as ttime
|
||
import fairseq
|
||
import faiss
|
||
import numpy as np
|
||
import parselmouth
|
||
import pyworld
|
||
import scipy.signal as signal
|
||
import torch
|
||
import torch.nn as nn
|
||
import torch.nn.functional as F
|
||
import torchcrepe
|
||
|
||
from infer.lib.infer_pack.models import (
|
||
SynthesizerTrnMs256NSFsid,
|
||
SynthesizerTrnMs256NSFsid_nono,
|
||
SynthesizerTrnMs768NSFsid,
|
||
SynthesizerTrnMs768NSFsid_nono,
|
||
)
|
||
|
||
now_dir = os.getcwd()
|
||
sys.path.append(now_dir)
|
||
from multiprocessing import Manager as M
|
||
|
||
from configs.config import Config
|
||
|
||
# config = Config()
|
||
|
||
mm = M()
|
||
|
||
|
||
def printt(strr, *args):
|
||
if len(args) == 0:
|
||
print(strr)
|
||
else:
|
||
print(strr % args)
|
||
|
||
|
||
# config.device=torch.device("cpu")########强制cpu测试
|
||
# config.is_half=False########强制cpu测试
|
||
class RVC:
|
||
def __init__(
|
||
self,
|
||
key,
|
||
pth_path,
|
||
index_path,
|
||
index_rate,
|
||
n_cpu,
|
||
inp_q,
|
||
opt_q,
|
||
config: Config,
|
||
last_rvc=None,
|
||
) -> None:
|
||
"""
|
||
初始化
|
||
"""
|
||
try:
|
||
if config.dml == True:
|
||
|
||
def forward_dml(ctx, x, scale):
|
||
ctx.scale = scale
|
||
res = x.clone().detach()
|
||
return res
|
||
|
||
fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml
|
||
# global config
|
||
self.config = config
|
||
self.inp_q = inp_q
|
||
self.opt_q = opt_q
|
||
# device="cpu"########强制cpu测试
|
||
self.device = config.device
|
||
self.f0_up_key = key
|
||
self.f0_min = 50
|
||
self.f0_max = 1100
|
||
self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
|
||
self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
|
||
self.n_cpu = n_cpu
|
||
self.use_jit = self.config.use_jit
|
||
self.is_half = config.is_half
|
||
|
||
if index_rate != 0:
|
||
self.index = faiss.read_index(index_path)
|
||
self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
|
||
printt("Index search enabled")
|
||
self.pth_path: str = pth_path
|
||
self.index_path = index_path
|
||
self.index_rate = index_rate
|
||
self.cache_pitch: torch.Tensor = torch.zeros(
|
||
1024, device=self.device, dtype=torch.long
|
||
)
|
||
self.cache_pitchf = torch.zeros(
|
||
1024, device=self.device, dtype=torch.float32
|
||
)
|
||
|
||
if last_rvc is None:
|
||
models, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
|
||
["assets/hubert/hubert_base.pt"],
|
||
suffix="",
|
||
)
|
||
hubert_model = models[0]
|
||
hubert_model = hubert_model.to(self.device)
|
||
if self.is_half:
|
||
hubert_model = hubert_model.half()
|
||
else:
|
||
hubert_model = hubert_model.float()
|
||
hubert_model.eval()
|
||
self.model = hubert_model
|
||
else:
|
||
self.model = last_rvc.model
|
||
|
||
self.net_g: nn.Module = None
|
||
|
||
def set_default_model():
|
||
self.net_g, cpt = get_synthesizer(self.pth_path, self.device)
|
||
self.tgt_sr = cpt["config"][-1]
|
||
cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
|
||
self.if_f0 = cpt.get("f0", 1)
|
||
self.version = cpt.get("version", "v1")
|
||
if self.is_half:
|
||
self.net_g = self.net_g.half()
|
||
else:
|
||
self.net_g = self.net_g.float()
|
||
|
||
def set_jit_model():
|
||
jit_pth_path = self.pth_path.rstrip(".pth")
|
||
jit_pth_path += ".half.jit" if self.is_half else ".jit"
|
||
reload = False
|
||
if str(self.device) == "cuda":
|
||
self.device = torch.device("cuda:0")
|
||
if os.path.exists(jit_pth_path):
|
||
cpt = jit.load(jit_pth_path)
|
||
model_device = cpt["device"]
|
||
if model_device != str(self.device):
|
||
reload = True
|
||
else:
|
||
reload = True
|
||
|
||
if reload:
|
||
cpt = jit.synthesizer_jit_export(
|
||
self.pth_path,
|
||
"script",
|
||
None,
|
||
device=self.device,
|
||
is_half=self.is_half,
|
||
)
|
||
|
||
self.tgt_sr = cpt["config"][-1]
|
||
self.if_f0 = cpt.get("f0", 1)
|
||
self.version = cpt.get("version", "v1")
|
||
self.net_g = torch.jit.load(
|
||
BytesIO(cpt["model"]), map_location=self.device
|
||
)
|
||
self.net_g.infer = self.net_g.forward
|
||
self.net_g.eval().to(self.device)
|
||
|
||
def set_synthesizer():
|
||
if self.use_jit and not config.dml:
|
||
if self.is_half and "cpu" in str(self.device):
|
||
printt(
|
||
"Use default Synthesizer model. \
|
||
Jit is not supported on the CPU for half floating point"
|
||
)
|
||
set_default_model()
|
||
else:
|
||
set_jit_model()
|
||
else:
|
||
set_default_model()
|
||
|
||
if last_rvc is None or last_rvc.pth_path != self.pth_path:
|
||
set_synthesizer()
|
||
else:
|
||
self.tgt_sr = last_rvc.tgt_sr
|
||
self.if_f0 = last_rvc.if_f0
|
||
self.version = last_rvc.version
|
||
self.is_half = last_rvc.is_half
|
||
if last_rvc.use_jit != self.use_jit:
|
||
set_synthesizer()
|
||
else:
|
||
self.net_g = last_rvc.net_g
|
||
|
||
if last_rvc is not None and hasattr(last_rvc, "model_rmvpe"):
|
||
self.model_rmvpe = last_rvc.model_rmvpe
|
||
if last_rvc is not None and hasattr(last_rvc, "model_fcpe"):
|
||
self.device_fcpe = last_rvc.device_fcpe
|
||
self.model_fcpe = last_rvc.model_fcpe
|
||
except:
|
||
printt(traceback.format_exc())
|
||
|
||
def change_key(self, new_key):
|
||
self.f0_up_key = new_key
|
||
|
||
def change_index_rate(self, new_index_rate):
|
||
if new_index_rate != 0 and self.index_rate == 0:
|
||
self.index = faiss.read_index(self.index_path)
|
||
self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
|
||
printt("Index search enabled")
|
||
self.index_rate = new_index_rate
|
||
|
||
def get_f0_post(self, f0):
|
||
if not torch.is_tensor(f0):
|
||
f0 = torch.from_numpy(f0)
|
||
f0 = f0.float().to(self.device).squeeze()
|
||
f0_mel = 1127 * torch.log(1 + f0 / 700)
|
||
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - self.f0_mel_min) * 254 / (
|
||
self.f0_mel_max - self.f0_mel_min
|
||
) + 1
|
||
f0_mel[f0_mel <= 1] = 1
|
||
f0_mel[f0_mel > 255] = 255
|
||
f0_coarse = torch.round(f0_mel).long()
|
||
return f0_coarse, f0
|
||
|
||
def get_f0(self, x, f0_up_key, n_cpu, method="harvest"):
|
||
n_cpu = int(n_cpu)
|
||
if method == "crepe":
|
||
return self.get_f0_crepe(x, f0_up_key)
|
||
if method == "rmvpe":
|
||
return self.get_f0_rmvpe(x, f0_up_key)
|
||
if method == "fcpe":
|
||
return self.get_f0_fcpe(x, f0_up_key)
|
||
x = x.cpu().numpy()
|
||
if method == "pm":
|
||
p_len = x.shape[0] // 160 + 1
|
||
f0_min = 65
|
||
l_pad = int(np.ceil(1.5 / f0_min * 16000))
|
||
r_pad = l_pad + 1
|
||
s = parselmouth.Sound(np.pad(x, (l_pad, r_pad)), 16000).to_pitch_ac(
|
||
time_step=0.01,
|
||
voicing_threshold=0.6,
|
||
pitch_floor=f0_min,
|
||
pitch_ceiling=1100,
|
||
)
|
||
assert np.abs(s.t1 - 1.5 / f0_min) < 0.001
|
||
f0 = s.selected_array["frequency"]
|
||
if len(f0) < p_len:
|
||
f0 = np.pad(f0, (0, p_len - len(f0)))
|
||
f0 = f0[:p_len]
|
||
f0 *= pow(2, f0_up_key / 12)
|
||
return self.get_f0_post(f0)
|
||
if n_cpu == 1:
|
||
f0, t = pyworld.harvest(
|
||
x.astype(np.double),
|
||
fs=16000,
|
||
f0_ceil=1100,
|
||
f0_floor=50,
|
||
frame_period=10,
|
||
)
|
||
f0 = signal.medfilt(f0, 3)
|
||
f0 *= pow(2, f0_up_key / 12)
|
||
return self.get_f0_post(f0)
|
||
f0bak = np.zeros(x.shape[0] // 160 + 1, dtype=np.float64)
|
||
length = len(x)
|
||
part_length = 160 * ((length // 160 - 1) // n_cpu + 1)
|
||
n_cpu = (length // 160 - 1) // (part_length // 160) + 1
|
||
ts = ttime()
|
||
res_f0 = mm.dict()
|
||
for idx in range(n_cpu):
|
||
tail = part_length * (idx + 1) + 320
|
||
if idx == 0:
|
||
self.inp_q.put((idx, x[:tail], res_f0, n_cpu, ts))
|
||
else:
|
||
self.inp_q.put(
|
||
(idx, x[part_length * idx - 320 : tail], res_f0, n_cpu, ts)
|
||
)
|
||
while 1:
|
||
res_ts = self.opt_q.get()
|
||
if res_ts == ts:
|
||
break
|
||
f0s = [i[1] for i in sorted(res_f0.items(), key=lambda x: x[0])]
|
||
for idx, f0 in enumerate(f0s):
|
||
if idx == 0:
|
||
f0 = f0[:-3]
|
||
elif idx != n_cpu - 1:
|
||
f0 = f0[2:-3]
|
||
else:
|
||
f0 = f0[2:]
|
||
f0bak[
|
||
part_length * idx // 160 : part_length * idx // 160 + f0.shape[0]
|
||
] = f0
|
||
f0bak = signal.medfilt(f0bak, 3)
|
||
f0bak *= pow(2, f0_up_key / 12)
|
||
return self.get_f0_post(f0bak)
|
||
|
||
def get_f0_crepe(self, x, f0_up_key):
|
||
if "privateuseone" in str(self.device): ###不支持dml，cpu又太慢用不成，拿fcpe顶替
|
||
return self.get_f0(x, f0_up_key, 1, "fcpe")
|
||
# printt("using crepe,device:%s"%self.device)
|
||
f0, pd = torchcrepe.predict(
|
||
x.unsqueeze(0).float(),
|
||
16000,
|
||
160,
|
||
self.f0_min,
|
||
self.f0_max,
|
||
"full",
|
||
batch_size=512,
|
||
# device=self.device if self.device.type!="privateuseone" else "cpu",###crepe不用半精度全部是全精度所以不愁###cpu延迟高到没法用
|
||
device=self.device,
|
||
return_periodicity=True,
|
||
)
|
||
pd = torchcrepe.filter.median(pd, 3)
|
||
f0 = torchcrepe.filter.mean(f0, 3)
|
||
f0[pd < 0.1] = 0
|
||
f0 *= pow(2, f0_up_key / 12)
|
||
return self.get_f0_post(f0)
|
||
|
||
def get_f0_rmvpe(self, x, f0_up_key):
|
||
if hasattr(self, "model_rmvpe") == False:
|
||
from infer.lib.rmvpe import RMVPE
|
||
|
||
printt("Loading rmvpe model")
|
||
self.model_rmvpe = RMVPE(
|
||
"assets/rmvpe/rmvpe.pt",
|
||
is_half=self.is_half,
|
||
device=self.device,
|
||
use_jit=self.config.use_jit,
|
||
)
|
||
f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
|
||
f0 *= pow(2, f0_up_key / 12)
|
||
return self.get_f0_post(f0)
|
||
|
||
def get_f0_fcpe(self, x, f0_up_key):
|
||
if hasattr(self, "model_fcpe") == False:
|
||
from torchfcpe import spawn_bundled_infer_model
|
||
|
||
printt("Loading fcpe model")
|
||
if "privateuseone" in str(self.device):
|
||
self.device_fcpe = "cpu"
|
||
else:
|
||
self.device_fcpe = self.device
|
||
self.model_fcpe = spawn_bundled_infer_model(self.device_fcpe)
|
||
f0 = self.model_fcpe.infer(
|
||
x.to(self.device_fcpe).unsqueeze(0).float(),
|
||
sr=16000,
|
||
decoder_mode="local_argmax",
|
||
threshold=0.006,
|
||
)
|
||
f0 *= pow(2, f0_up_key / 12)
|
||
return self.get_f0_post(f0)
|
||
|
||
def infer(
|
||
self,
|
||
input_wav: torch.Tensor,
|
||
block_frame_16k,
|
||
skip_head,
|
||
return_length,
|
||
f0method,
|
||
) -> np.ndarray:
|
||
t1 = ttime()
|
||
with torch.no_grad():
|
||
if self.config.is_half:
|
||
feats = input_wav.half().view(1, -1)
|
||
else:
|
||
feats = input_wav.float().view(1, -1)
|
||
padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
|
||
inputs = {
|
||
"source": feats,
|
||
"padding_mask": padding_mask,
|
||
"output_layer": 9 if self.version == "v1" else 12,
|
||
}
|
||
logits = self.model.extract_features(**inputs)
|
||
feats = (
|
||
self.model.final_proj(logits[0]) if self.version == "v1" else logits[0]
|
||
)
|
||
feats = torch.cat((feats, feats[:, -1:, :]), 1)
|
||
t2 = ttime()
|
||
try:
|
||
if hasattr(self, "index") and self.index_rate != 0:
|
||
npy = feats[0][skip_head // 2 :].cpu().numpy().astype("float32")
|
||
score, ix = self.index.search(npy, k=8)
|
||
if (ix >= 0).all():
|
||
weight = np.square(1 / score)
|
||
weight /= weight.sum(axis=1, keepdims=True)
|
||
npy = np.sum(
|
||
self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1
|
||
)
|
||
if self.config.is_half:
|
||
npy = npy.astype("float16")
|
||
feats[0][skip_head // 2 :] = (
|
||
torch.from_numpy(npy).unsqueeze(0).to(self.device)
|
||
* self.index_rate
|
||
+ (1 - self.index_rate) * feats[0][skip_head // 2 :]
|
||
)
|
||
else:
|
||
printt(
|
||
"Invalid index. You MUST use added_xxxx.index but not trained_xxxx.index!"
|
||
)
|
||
else:
|
||
printt("Index search FAILED or disabled")
|
||
except:
|
||
traceback.print_exc()
|
||
printt("Index search FAILED")
|
||
t3 = ttime()
|
||
p_len = input_wav.shape[0] // 160
|
||
if self.if_f0 == 1:
|
||
f0_extractor_frame = block_frame_16k + 800
|
||
if f0method == "rmvpe":
|
||
f0_extractor_frame = 5120 * ((f0_extractor_frame - 1) // 5120 + 1) - 160
|
||
pitch, pitchf = self.get_f0(
|
||
input_wav[-f0_extractor_frame:], self.f0_up_key, self.n_cpu, f0method
|
||
)
|
||
shift = block_frame_16k // 160
|
||
self.cache_pitch[:-shift] = self.cache_pitch[shift:].clone()
|
||
self.cache_pitchf[:-shift] = self.cache_pitchf[shift:].clone()
|
||
self.cache_pitch[4 - pitch.shape[0] :] = pitch[3:-1]
|
||
self.cache_pitchf[4 - pitch.shape[0] :] = pitchf[3:-1]
|
||
cache_pitch = self.cache_pitch[None, -p_len:]
|
||
cache_pitchf = self.cache_pitchf[None, -p_len:]
|
||
t4 = ttime()
|
||
feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
|
||
feats = feats[:, :p_len, :]
|
||
p_len = torch.LongTensor([p_len]).to(self.device)
|
||
sid = torch.LongTensor([0]).to(self.device)
|
||
skip_head = torch.LongTensor([skip_head])
|
||
return_length = torch.LongTensor([return_length])
|
||
with torch.no_grad():
|
||
if self.if_f0 == 1:
|
||
infered_audio, _, _ = self.net_g.infer(
|
||
feats,
|
||
p_len,
|
||
cache_pitch,
|
||
cache_pitchf,
|
||
sid,
|
||
skip_head,
|
||
return_length,
|
||
)
|
||
else:
|
||
infered_audio, _, _ = self.net_g.infer(
|
||
feats, p_len, sid, skip_head, return_length
|
||
)
|
||
t5 = ttime()
|
||
printt(
|
||
"Spent time: fea = %.3fs, index = %.3fs, f0 = %.3fs, model = %.3fs",
|
||
t2 - t1,
|
||
t3 - t2,
|
||
t4 - t3,
|
||
t5 - t4,
|
||
)
|
||
return infered_audio.squeeze().float()
|