Cool_Tools/Retrieval-based-Voice-Conversion-WebUI
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135
extract_feature_print.py
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import os, sys, traceback
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
os.environ["PYTORCH_MPS_HIGH_WATERMARK_RATIO"] = "0.0"
device = sys.argv[1]
n_part = int(sys.argv[2])
i_part = int(sys.argv[3])
if len(sys.argv) == 6:
exp_dir = sys.argv[4]
version = sys.argv[5]
else:
i_gpu = sys.argv[4]
exp_dir = sys.argv[5]
os.environ["CUDA_VISIBLE_DEVICES"] = str(i_gpu)
version = sys.argv[6]
import torch
import torch.nn.functional as F
import soundfile as sf
import numpy as np
import fairseq
if "privateuseone" not in device:
device = "cpu"
if torch.cuda.is_available():
device = "cuda"
elif torch.backends.mps.is_available():
device = "mps"
else:
import torch_directml
device = torch_directml.device(torch_directml.default_device())
def forward_dml(ctx, x, scale):
ctx.scale = scale
res = x.clone().detach()
return res
fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml
f = open("%s/extract_f0_feature.log" % exp_dir, "a+")
def printt(strr):
print(strr)
f.write("%s\n" % strr)
f.flush()
printt(sys.argv)
model_path = "hubert_base.pt"
printt(exp_dir)
wavPath = "%s/1_16k_wavs" % exp_dir
outPath = (
"%s/3_feature256" % exp_dir if version == "v1" else "%s/3_feature768" % exp_dir
)
os.makedirs(outPath, exist_ok=True)
# wave must be 16k, hop_size=320
def readwave(wav_path, normalize=False):
wav, sr = sf.read(wav_path)
assert sr == 16000
feats = torch.from_numpy(wav).float()
if feats.dim() == 2: # double channels
feats = feats.mean(-1)
assert feats.dim() == 1, feats.dim()
if normalize:
with torch.no_grad():
feats = F.layer_norm(feats, feats.shape)
feats = feats.view(1, -1)
return feats
# HuBERT model
printt("load model(s) from {}".format(model_path))
# if hubert model is exist
if os.access(model_path, os.F_OK) == False:
printt(
"Error: Extracting is shut down because %s does not exist, you may download it from https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main"
% model_path
)
exit(0)
models, saved_cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[model_path],
suffix="",
)
model = models[0]
model = model.to(device)
printt("move model to %s" % device)
if device not in ["mps", "cpu"]:
model = model.half()
model.eval()
todo = sorted(list(os.listdir(wavPath)))[i_part::n_part]
n = max(1, len(todo) // 10) # 最多打印十条
if len(todo) == 0:
printt("no-feature-todo")
else:
printt("all-feature-%s" % len(todo))
for idx, file in enumerate(todo):
try:
if file.endswith(".wav"):
wav_path = "%s/%s" % (wavPath, file)
out_path = "%s/%s" % (outPath, file.replace("wav", "npy"))
if os.path.exists(out_path):
continue
feats = readwave(wav_path, normalize=saved_cfg.task.normalize)
padding_mask = torch.BoolTensor(feats.shape).fill_(False)
inputs = {
"source": feats.half().to(device)
if device not in ["mps", "cpu"]
else feats.to(device),
"padding_mask": padding_mask.to(device),
"output_layer": 9 if version == "v1" else 12, # layer 9
}
with torch.no_grad():
logits = model.extract_features(**inputs)
feats = (
model.final_proj(logits[0]) if version == "v1" else logits[0]
)
feats = feats.squeeze(0).float().cpu().numpy()
if np.isnan(feats).sum() == 0:
np.save(out_path, feats, allow_pickle=False)
else:
printt("%s-contains nan" % file)
if idx % n == 0:
printt("now-%s,all-%s,%s,%s" % (len(todo), idx, file, feats.shape))
except:
printt(traceback.format_exc())
printt("all-feature-done")

216
infer_batch_rvc.py
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"""
v1
runtime\python.exe myinfer-v2-0528.py 0 "E:\codes\py39\RVC-beta\todo-songs" "E:\codes\py39\logs\mi-test\added_IVF677_Flat_nprobe_7.index" harvest "E:\codes\py39\RVC-beta\output" "E:\codes\py39\test-20230416b\weights\mi-test.pth" 0.66 cuda:0 True 3 0 1 0.33
v2
runtime\python.exe myinfer-v2-0528.py 0 "E:\codes\py39\RVC-beta\todo-songs" "E:\codes\py39\test-20230416b\logs\mi-test-v2\aadded_IVF677_Flat_nprobe_1_v2.index" harvest "E:\codes\py39\RVC-beta\output_v2" "E:\codes\py39\test-20230416b\weights\mi-test-v2.pth" 0.66 cuda:0 True 3 0 1 0.33
"""
import os, sys, pdb, torch
now_dir = os.getcwd()
sys.path.append(now_dir)
import sys
import torch
import tqdm as tq
from multiprocessing import cpu_count
class Config:
def __init__(self, device, is_half):
self.device = device
self.is_half = is_half
self.n_cpu = 0
self.gpu_name = None
self.gpu_mem = None
self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
def device_config(self) -> tuple:
if torch.cuda.is_available():
i_device = int(self.device.split(":")[-1])
self.gpu_name = torch.cuda.get_device_name(i_device)
if (
("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
or "P40" in self.gpu_name.upper()
or "1060" in self.gpu_name
or "1070" in self.gpu_name
or "1080" in self.gpu_name
):
print("16系/10系显卡和P40强制单精度")
self.is_half = False
for config_file in ["32k.json", "40k.json", "48k.json"]:
with open(f"configs/{config_file}", "r") as f:
strr = f.read().replace("true", "false")
with open(f"configs/{config_file}", "w") as f:
f.write(strr)
with open("trainset_preprocess_pipeline_print.py", "r") as f:
strr = f.read().replace("3.7", "3.0")
with open("trainset_preprocess_pipeline_print.py", "w") as f:
f.write(strr)
else:
self.gpu_name = None
self.gpu_mem = int(
torch.cuda.get_device_properties(i_device).total_memory
/ 1024
/ 1024
/ 1024
+ 0.4
)
if self.gpu_mem <= 4:
with open("trainset_preprocess_pipeline_print.py", "r") as f:
strr = f.read().replace("3.7", "3.0")
with open("trainset_preprocess_pipeline_print.py", "w") as f:
f.write(strr)
elif torch.backends.mps.is_available():
print("没有发现支持的N卡, 使用MPS进行推理")
self.device = "mps"
else:
print("没有发现支持的N卡, 使用CPU进行推理")
self.device = "cpu"
self.is_half = True
if self.n_cpu == 0:
self.n_cpu = cpu_count()
if self.is_half:
# 6G显存配置
x_pad = 3
x_query = 10
x_center = 60
x_max = 65
else:
# 5G显存配置
x_pad = 1
x_query = 6
x_center = 38
x_max = 41
if self.gpu_mem != None and self.gpu_mem <= 4:
x_pad = 1
x_query = 5
x_center = 30
x_max = 32
return x_pad, x_query, x_center, x_max
f0up_key = sys.argv[1]
input_path = sys.argv[2]
index_path = sys.argv[3]
f0method = sys.argv[4] # harvest or pm
opt_path = sys.argv[5]
model_path = sys.argv[6]
index_rate = float(sys.argv[7])
device = sys.argv[8]
is_half = sys.argv[9].lower() != "false"
filter_radius = int(sys.argv[10])
resample_sr = int(sys.argv[11])
rms_mix_rate = float(sys.argv[12])
protect = float(sys.argv[13])
print(sys.argv)
config = Config(device, is_half)
now_dir = os.getcwd()
sys.path.append(now_dir)
from lib.train.vc_infer_pipeline import VC
from lib.infer_pack.models import (
SynthesizerTrnMs256NSFsid,
SynthesizerTrnMs256NSFsid_nono,
SynthesizerTrnMs768NSFsid,
SynthesizerTrnMs768NSFsid_nono,
)
from lib.audio import load_audio
from fairseq import checkpoint_utils
from scipy.io import wavfile
hubert_model = None
def load_hubert(hubert_model_path="hubert_base.pt"):
global hubert_model
models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
[hubert_model_path],
suffix="",
)
hubert_model = models[0]
hubert_model = hubert_model.to(device)
if is_half:
hubert_model = hubert_model.half()
else:
hubert_model = hubert_model.float()
hubert_model.eval()
def vc_single(sid, input_audio, f0_up_key, f0_file, f0_method, file_index, index_rate):
global tgt_sr, net_g, vc, hubert_model, version
if input_audio is None:
return "You need to upload an audio", None
f0_up_key = int(f0_up_key)
audio = load_audio(input_audio, 16000)
times = [0, 0, 0]
if hubert_model == None:
load_hubert()
if_f0 = cpt.get("f0", 1)
# audio_opt=vc.pipeline(hubert_model,net_g,sid,audio,times,f0_up_key,f0_method,file_index,file_big_npy,index_rate,if_f0,f0_file=f0_file)
audio_opt = vc.pipeline(
hubert_model,
net_g,
sid,
audio,
input_audio,
times,
f0_up_key,
f0_method,
file_index,
index_rate,
if_f0,
filter_radius,
tgt_sr,
resample_sr,
rms_mix_rate,
version,
protect,
f0_file=f0_file,
)
print(times)
return audio_opt
def get_vc(model_path):
global n_spk, tgt_sr, net_g, vc, cpt, device, is_half, version
print("loading pth %s" % model_path)
cpt = torch.load(model_path, map_location="cpu")
tgt_sr = cpt["config"][-1]
cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
if_f0 = cpt.get("f0", 1)
version = cpt.get("version", "v1")
if version == "v1":
if if_f0 == 1:
net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
else:
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
elif version == "v2":
if if_f0 == 1: #
net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=is_half)
else:
net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
del net_g.enc_q
print(net_g.load_state_dict(cpt["weight"], strict=False)) # 不加这一行清不干净，真奇葩
net_g.eval().to(device)
if is_half:
net_g = net_g.half()
else:
net_g = net_g.float()
vc = VC(tgt_sr, config)
n_spk = cpt["config"][-3]
# return {"visible": True,"maximum": n_spk, "__type__": "update"}
if __name__ == "__main__":
get_vc(model_path)
audios = os.listdir(input_path)
for file in tq.tqdm(audios):
if file.endswith(".wav"):
file_path = os.path.join(input_path, file)
wav_opt = vc_single(
0, file_path, f0up_key, None, f0method, index_path, index_rate
)
out_path = os.path.join(opt_path, file)
wavfile.write(out_path, tgt_sr, wav_opt)

363
infer_uvr5.py
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import os, sys, torch, warnings, pdb
now_dir = os.getcwd()
sys.path.append(now_dir)
from json import load as ll
warnings.filterwarnings("ignore")
import librosa
import importlib
import numpy as np
import hashlib, math
from tqdm import tqdm
from lib.uvr5_pack.lib_v5 import spec_utils
from lib.uvr5_pack.utils import _get_name_params, inference
from lib.uvr5_pack.lib_v5.model_param_init import ModelParameters
import soundfile as sf
from lib.uvr5_pack.lib_v5.nets_new import CascadedNet
from lib.uvr5_pack.lib_v5 import nets_61968KB as nets
class _audio_pre_:
def __init__(self, agg, model_path, device, is_half):
self.model_path = model_path
self.device = device
self.data = {
# Processing Options
"postprocess": False,
"tta": False,
# Constants
"window_size": 512,
"agg": agg,
"high_end_process": "mirroring",
}
mp = ModelParameters("lib/uvr5_pack/lib_v5/modelparams/4band_v2.json")
model = nets.CascadedASPPNet(mp.param["bins"] * 2)
cpk = torch.load(model_path, map_location="cpu")
model.load_state_dict(cpk)
model.eval()
if is_half:
model = model.half().to(device)
else:
model = model.to(device)
self.mp = mp
self.model = model
def _path_audio_(self, music_file, ins_root=None, vocal_root=None, format="flac"):
if ins_root is None and vocal_root is None:
return "No save root."
name = os.path.basename(music_file)
if ins_root is not None:
os.makedirs(ins_root, exist_ok=True)
if vocal_root is not None:
os.makedirs(vocal_root, exist_ok=True)
X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {}
bands_n = len(self.mp.param["band"])
# print(bands_n)
for d in range(bands_n, 0, -1):
bp = self.mp.param["band"][d]
if d == bands_n: # high-end band
(
X_wave[d],
_,
) = librosa.core.load( # 理论上librosa读取可能对某些音频有bug，应该上ffmpeg读取，但是太麻烦了弃坑
music_file,
bp["sr"],
False,
dtype=np.float32,
res_type=bp["res_type"],
)
if X_wave[d].ndim == 1:
X_wave[d] = np.asfortranarray([X_wave[d], X_wave[d]])
else: # lower bands
X_wave[d] = librosa.core.resample(
X_wave[d + 1],
self.mp.param["band"][d + 1]["sr"],
bp["sr"],
res_type=bp["res_type"],
)
# Stft of wave source
X_spec_s[d] = spec_utils.wave_to_spectrogram_mt(
X_wave[d],
bp["hl"],
bp["n_fft"],
self.mp.param["mid_side"],
self.mp.param["mid_side_b2"],
self.mp.param["reverse"],
)
# pdb.set_trace()
if d == bands_n and self.data["high_end_process"] != "none":
input_high_end_h = (bp["n_fft"] // 2 - bp["crop_stop"]) + (
self.mp.param["pre_filter_stop"] - self.mp.param["pre_filter_start"]
)
input_high_end = X_spec_s[d][
:, bp["n_fft"] // 2 - input_high_end_h : bp["n_fft"] // 2, :
]
X_spec_m = spec_utils.combine_spectrograms(X_spec_s, self.mp)
aggresive_set = float(self.data["agg"] / 100)
aggressiveness = {
"value": aggresive_set,
"split_bin": self.mp.param["band"][1]["crop_stop"],
}
with torch.no_grad():
pred, X_mag, X_phase = inference(
X_spec_m, self.device, self.model, aggressiveness, self.data
)
# Postprocess
if self.data["postprocess"]:
pred_inv = np.clip(X_mag - pred, 0, np.inf)
pred = spec_utils.mask_silence(pred, pred_inv)
y_spec_m = pred * X_phase
v_spec_m = X_spec_m - y_spec_m
if ins_root is not None:
if self.data["high_end_process"].startswith("mirroring"):
input_high_end_ = spec_utils.mirroring(
self.data["high_end_process"], y_spec_m, input_high_end, self.mp
)
wav_instrument = spec_utils.cmb_spectrogram_to_wave(
y_spec_m, self.mp, input_high_end_h, input_high_end_
)
else:
wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, self.mp)
print("%s instruments done" % name)
if format in ["wav", "flac"]:
sf.write(
os.path.join(
ins_root,
"instrument_{}_{}.{}".format(name, self.data["agg"], format),
),
(np.array(wav_instrument) * 32768).astype("int16"),
self.mp.param["sr"],
) #
else:
path = os.path.join(
ins_root, "instrument_{}_{}.wav".format(name, self.data["agg"])
)
sf.write(
path,
(np.array(wav_instrument) * 32768).astype("int16"),
self.mp.param["sr"],
)
if os.path.exists(path):
os.system(
"ffmpeg -i %s -vn %s -q:a 2 -y"
% (path, path[:-4] + ".%s" % format)
)
if vocal_root is not None:
if self.data["high_end_process"].startswith("mirroring"):
input_high_end_ = spec_utils.mirroring(
self.data["high_end_process"], v_spec_m, input_high_end, self.mp
)
wav_vocals = spec_utils.cmb_spectrogram_to_wave(
v_spec_m, self.mp, input_high_end_h, input_high_end_
)
else:
wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, self.mp)
print("%s vocals done" % name)
if format in ["wav", "flac"]:
sf.write(
os.path.join(
vocal_root,
"vocal_{}_{}.{}".format(name, self.data["agg"], format),
),
(np.array(wav_vocals) * 32768).astype("int16"),
self.mp.param["sr"],
)
else:
path = os.path.join(
vocal_root, "vocal_{}_{}.wav".format(name, self.data["agg"])
)
sf.write(
path,
(np.array(wav_vocals) * 32768).astype("int16"),
self.mp.param["sr"],
)
if os.path.exists(path):
os.system(
"ffmpeg -i %s -vn %s -q:a 2 -y"
% (path, path[:-4] + ".%s" % format)
)
class _audio_pre_new:
def __init__(self, agg, model_path, device, is_half):
self.model_path = model_path
self.device = device
self.data = {
# Processing Options
"postprocess": False,
"tta": False,
# Constants
"window_size": 512,
"agg": agg,
"high_end_process": "mirroring",
}
mp = ModelParameters("lib/uvr5_pack/lib_v5/modelparams/4band_v3.json")
nout = 64 if "DeReverb" in model_path else 48
model = CascadedNet(mp.param["bins"] * 2, nout)
cpk = torch.load(model_path, map_location="cpu")
model.load_state_dict(cpk)
model.eval()
if is_half:
model = model.half().to(device)
else:
model = model.to(device)
self.mp = mp
self.model = model
def _path_audio_(
self, music_file, vocal_root=None, ins_root=None, format="flac"
): # 3个VR模型vocal和ins是反的
if ins_root is None and vocal_root is None:
return "No save root."
name = os.path.basename(music_file)
if ins_root is not None:
os.makedirs(ins_root, exist_ok=True)
if vocal_root is not None:
os.makedirs(vocal_root, exist_ok=True)
X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {}
bands_n = len(self.mp.param["band"])
# print(bands_n)
for d in range(bands_n, 0, -1):
bp = self.mp.param["band"][d]
if d == bands_n: # high-end band
(
X_wave[d],
_,
) = librosa.core.load( # 理论上librosa读取可能对某些音频有bug，应该上ffmpeg读取，但是太麻烦了弃坑
music_file,
bp["sr"],
False,
dtype=np.float32,
res_type=bp["res_type"],
)
if X_wave[d].ndim == 1:
X_wave[d] = np.asfortranarray([X_wave[d], X_wave[d]])
else: # lower bands
X_wave[d] = librosa.core.resample(
X_wave[d + 1],
self.mp.param["band"][d + 1]["sr"],
bp["sr"],
res_type=bp["res_type"],
)
# Stft of wave source
X_spec_s[d] = spec_utils.wave_to_spectrogram_mt(
X_wave[d],
bp["hl"],
bp["n_fft"],
self.mp.param["mid_side"],
self.mp.param["mid_side_b2"],
self.mp.param["reverse"],
)
# pdb.set_trace()
if d == bands_n and self.data["high_end_process"] != "none":
input_high_end_h = (bp["n_fft"] // 2 - bp["crop_stop"]) + (
self.mp.param["pre_filter_stop"] - self.mp.param["pre_filter_start"]
)
input_high_end = X_spec_s[d][
:, bp["n_fft"] // 2 - input_high_end_h : bp["n_fft"] // 2, :
]
X_spec_m = spec_utils.combine_spectrograms(X_spec_s, self.mp)
aggresive_set = float(self.data["agg"] / 100)
aggressiveness = {
"value": aggresive_set,
"split_bin": self.mp.param["band"][1]["crop_stop"],
}
with torch.no_grad():
pred, X_mag, X_phase = inference(
X_spec_m, self.device, self.model, aggressiveness, self.data
)
# Postprocess
if self.data["postprocess"]:
pred_inv = np.clip(X_mag - pred, 0, np.inf)
pred = spec_utils.mask_silence(pred, pred_inv)
y_spec_m = pred * X_phase
v_spec_m = X_spec_m - y_spec_m
if ins_root is not None:
if self.data["high_end_process"].startswith("mirroring"):
input_high_end_ = spec_utils.mirroring(
self.data["high_end_process"], y_spec_m, input_high_end, self.mp
)
wav_instrument = spec_utils.cmb_spectrogram_to_wave(
y_spec_m, self.mp, input_high_end_h, input_high_end_
)
else:
wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, self.mp)
print("%s instruments done" % name)
if format in ["wav", "flac"]:
sf.write(
os.path.join(
ins_root,
"instrument_{}_{}.{}".format(name, self.data["agg"], format),
),
(np.array(wav_instrument) * 32768).astype("int16"),
self.mp.param["sr"],
) #
else:
path = os.path.join(
ins_root, "instrument_{}_{}.wav".format(name, self.data["agg"])
)
sf.write(
path,
(np.array(wav_instrument) * 32768).astype("int16"),
self.mp.param["sr"],
)
if os.path.exists(path):
os.system(
"ffmpeg -i %s -vn %s -q:a 2 -y"
% (path, path[:-4] + ".%s" % format)
)
if vocal_root is not None:
if self.data["high_end_process"].startswith("mirroring"):
input_high_end_ = spec_utils.mirroring(
self.data["high_end_process"], v_spec_m, input_high_end, self.mp
)
wav_vocals = spec_utils.cmb_spectrogram_to_wave(
v_spec_m, self.mp, input_high_end_h, input_high_end_
)
else:
wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, self.mp)
print("%s vocals done" % name)
if format in ["wav", "flac"]:
sf.write(
os.path.join(
vocal_root,
"vocal_{}_{}.{}".format(name, self.data["agg"], format),
),
(np.array(wav_vocals) * 32768).astype("int16"),
self.mp.param["sr"],
)
else:
path = os.path.join(
vocal_root, "vocal_{}_{}.wav".format(name, self.data["agg"])
)
sf.write(
path,
(np.array(wav_vocals) * 32768).astype("int16"),
self.mp.param["sr"],
)
if os.path.exists(path):
os.system(
"ffmpeg -i %s -vn %s -q:a 2 -y"
% (path, path[:-4] + ".%s" % format)
)
if __name__ == "__main__":
device = "cuda"
is_half = True
# model_path = "uvr5_weights/2_HP-UVR.pth"
# model_path = "uvr5_weights/VR-DeEchoDeReverb.pth"
# model_path = "uvr5_weights/VR-DeEchoNormal.pth"
model_path = "uvr5_weights/DeEchoNormal.pth"
# pre_fun = _audio_pre_(model_path=model_path, device=device, is_half=True,agg=10)
pre_fun = _audio_pre_new(model_path=model_path, device=device, is_half=True, agg=10)
audio_path = "雪雪伴奏对消HP5.wav"
save_path = "opt"
pre_fun._path_audio_(audio_path, save_path, save_path)

21
lib/audio.py
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@ -1,21 +0,0 @@
import ffmpeg
import numpy as np
def load_audio(file, sr):
try:
# https://github.com/openai/whisper/blob/main/whisper/audio.py#L26
# This launches a subprocess to decode audio while down-mixing and resampling as necessary.
# Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
file = (
file.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
) # 防止小白拷路径头尾带了空格和"和回车
out, _ = (
ffmpeg.input(file, threads=0)
.output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr)
.run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
)
except Exception as e:
raise RuntimeError(f"Failed to load audio: {e}")
return np.frombuffer(out, np.float32).flatten()

692
lib/rmvpe.py
View File

@ -1,692 +0,0 @@
import torch, numpy as np, pdb
import torch.nn as nn
import torch.nn.functional as F
import torch, pdb
import numpy as np
import torch.nn.functional as F
from scipy.signal import get_window
from librosa.util import pad_center, tiny, normalize
###stft codes from https://github.com/pseeth/torch-stft/blob/master/torch_stft/util.py
def window_sumsquare(
window,
n_frames,
hop_length=200,
win_length=800,
n_fft=800,
dtype=np.float32,
norm=None,
):
"""
# from librosa 0.6
Compute the sum-square envelope of a window function at a given hop length.
This is used to estimate modulation effects induced by windowing
observations in short-time fourier transforms.
Parameters
----------
window : string, tuple, number, callable, or list-like
Window specification, as in `get_window`
n_frames : int > 0
The number of analysis frames
hop_length : int > 0
The number of samples to advance between frames
win_length : [optional]
The length of the window function. By default, this matches `n_fft`.
n_fft : int > 0
The length of each analysis frame.
dtype : np.dtype
The data type of the output
Returns
-------
wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))`
The sum-squared envelope of the window function
"""
if win_length is None:
win_length = n_fft
n = n_fft + hop_length * (n_frames - 1)
x = np.zeros(n, dtype=dtype)
# Compute the squared window at the desired length
win_sq = get_window(window, win_length, fftbins=True)
win_sq = normalize(win_sq, norm=norm) ** 2
win_sq = pad_center(win_sq, n_fft)
# Fill the envelope
for i in range(n_frames):
sample = i * hop_length
x[sample : min(n, sample + n_fft)] += win_sq[: max(0, min(n_fft, n - sample))]
return x
class STFT(torch.nn.Module):
def __init__(
self, filter_length=1024, hop_length=512, win_length=None, window="hann"
):
"""
This module implements an STFT using 1D convolution and 1D transpose convolutions.
This is a bit tricky so there are some cases that probably won't work as working
out the same sizes before and after in all overlap add setups is tough. Right now,
this code should work with hop lengths that are half the filter length (50% overlap
between frames).
Keyword Arguments:
filter_length {int} -- Length of filters used (default: {1024})
hop_length {int} -- Hop length of STFT (restrict to 50% overlap between frames) (default: {512})
win_length {[type]} -- Length of the window function applied to each frame (if not specified, it
equals the filter length). (default: {None})
window {str} -- Type of window to use (options are bartlett, hann, hamming, blackman, blackmanharris)
(default: {'hann'})
"""
super(STFT, self).__init__()
self.filter_length = filter_length
self.hop_length = hop_length
self.win_length = win_length if win_length else filter_length
self.window = window
self.forward_transform = None
self.pad_amount = int(self.filter_length / 2)
scale = self.filter_length / self.hop_length
fourier_basis = np.fft.fft(np.eye(self.filter_length))
cutoff = int((self.filter_length / 2 + 1))
fourier_basis = np.vstack(
[np.real(fourier_basis[:cutoff, :]), np.imag(fourier_basis[:cutoff, :])]
)
forward_basis = torch.FloatTensor(fourier_basis[:, None, :])
inverse_basis = torch.FloatTensor(
np.linalg.pinv(scale * fourier_basis).T[:, None, :]
)
assert filter_length >= self.win_length
# get window and zero center pad it to filter_length
fft_window = get_window(window, self.win_length, fftbins=True)
fft_window = pad_center(fft_window, size=filter_length)
fft_window = torch.from_numpy(fft_window).float()
# window the bases
forward_basis *= fft_window
inverse_basis *= fft_window
self.register_buffer("forward_basis", forward_basis.float())
self.register_buffer("inverse_basis", inverse_basis.float())
def transform(self, input_data):
"""Take input data (audio) to STFT domain.
Arguments:
input_data {tensor} -- Tensor of floats, with shape (num_batch, num_samples)
Returns:
magnitude {tensor} -- Magnitude of STFT with shape (num_batch,
num_frequencies, num_frames)
phase {tensor} -- Phase of STFT with shape (num_batch,
num_frequencies, num_frames)
"""
num_batches = input_data.shape[0]
num_samples = input_data.shape[-1]
self.num_samples = num_samples
# similar to librosa, reflect-pad the input
input_data = input_data.view(num_batches, 1, num_samples)
# print(1234,input_data.shape)
input_data = F.pad(
input_data.unsqueeze(1),
(self.pad_amount, self.pad_amount, 0, 0, 0, 0),
mode="reflect",
).squeeze(1)
# print(2333,input_data.shape,self.forward_basis.shape,self.hop_length)
# pdb.set_trace()
forward_transform = F.conv1d(
input_data, self.forward_basis, stride=self.hop_length, padding=0
)
cutoff = int((self.filter_length / 2) + 1)
real_part = forward_transform[:, :cutoff, :]
imag_part = forward_transform[:, cutoff:, :]
magnitude = torch.sqrt(real_part**2 + imag_part**2)
# phase = torch.atan2(imag_part.data, real_part.data)
return magnitude # , phase
def inverse(self, magnitude, phase):
"""Call the inverse STFT (iSTFT), given magnitude and phase tensors produced
by the ```transform``` function.
Arguments:
magnitude {tensor} -- Magnitude of STFT with shape (num_batch,
num_frequencies, num_frames)
phase {tensor} -- Phase of STFT with shape (num_batch,
num_frequencies, num_frames)
Returns:
inverse_transform {tensor} -- Reconstructed audio given magnitude and phase. Of
shape (num_batch, num_samples)
"""
recombine_magnitude_phase = torch.cat(
[magnitude * torch.cos(phase), magnitude * torch.sin(phase)], dim=1
)
inverse_transform = F.conv_transpose1d(
recombine_magnitude_phase,
self.inverse_basis,
stride=self.hop_length,
padding=0,
)
if self.window is not None:
window_sum = window_sumsquare(
self.window,
magnitude.size(-1),
hop_length=self.hop_length,
win_length=self.win_length,
n_fft=self.filter_length,
dtype=np.float32,
)
# remove modulation effects
approx_nonzero_indices = torch.from_numpy(
np.where(window_sum > tiny(window_sum))[0]
)
window_sum = torch.from_numpy(window_sum).to(inverse_transform.device)
inverse_transform[:, :, approx_nonzero_indices] /= window_sum[
approx_nonzero_indices
]
# scale by hop ratio
inverse_transform *= float(self.filter_length) / self.hop_length
inverse_transform = inverse_transform[..., self.pad_amount :]
inverse_transform = inverse_transform[..., : self.num_samples]
inverse_transform = inverse_transform.squeeze(1)
return inverse_transform
def forward(self, input_data):
"""Take input data (audio) to STFT domain and then back to audio.
Arguments:
input_data {tensor} -- Tensor of floats, with shape (num_batch, num_samples)
Returns:
reconstruction {tensor} -- Reconstructed audio given magnitude and phase. Of
shape (num_batch, num_samples)
"""
self.magnitude, self.phase = self.transform(input_data)
reconstruction = self.inverse(self.magnitude, self.phase)
return reconstruction
from time import time as ttime
class BiGRU(nn.Module):
def __init__(self, input_features, hidden_features, num_layers):
super(BiGRU, self).__init__()
self.gru = nn.GRU(
input_features,
hidden_features,
num_layers=num_layers,
batch_first=True,
bidirectional=True,
)
def forward(self, x):
return self.gru(x)[0]
class ConvBlockRes(nn.Module):
def __init__(self, in_channels, out_channels, momentum=0.01):
super(ConvBlockRes, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=(3, 3),
stride=(1, 1),
padding=(1, 1),
bias=False,
),
nn.BatchNorm2d(out_channels, momentum=momentum),
nn.ReLU(),
nn.Conv2d(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=(3, 3),
stride=(1, 1),
padding=(1, 1),
bias=False,
),
nn.BatchNorm2d(out_channels, momentum=momentum),
nn.ReLU(),
)
if in_channels != out_channels:
self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1))
self.is_shortcut = True
else:
self.is_shortcut = False
def forward(self, x):
if self.is_shortcut:
return self.conv(x) + self.shortcut(x)
else:
return self.conv(x) + x
class Encoder(nn.Module):
def __init__(
self,
in_channels,
in_size,
n_encoders,
kernel_size,
n_blocks,
out_channels=16,
momentum=0.01,
):
super(Encoder, self).__init__()
self.n_encoders = n_encoders
self.bn = nn.BatchNorm2d(in_channels, momentum=momentum)
self.layers = nn.ModuleList()
self.latent_channels = []
for i in range(self.n_encoders):
self.layers.append(
ResEncoderBlock(
in_channels, out_channels, kernel_size, n_blocks, momentum=momentum
)
)
self.latent_channels.append([out_channels, in_size])
in_channels = out_channels
out_channels *= 2
in_size //= 2
self.out_size = in_size
self.out_channel = out_channels
def forward(self, x):
concat_tensors = []
x = self.bn(x)
for i in range(self.n_encoders):
_, x = self.layers[i](x)
concat_tensors.append(_)
return x, concat_tensors
class ResEncoderBlock(nn.Module):
def __init__(
self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01
):
super(ResEncoderBlock, self).__init__()
self.n_blocks = n_blocks
self.conv = nn.ModuleList()
self.conv.append(ConvBlockRes(in_channels, out_channels, momentum))
for i in range(n_blocks - 1):
self.conv.append(ConvBlockRes(out_channels, out_channels, momentum))
self.kernel_size = kernel_size
if self.kernel_size is not None:
self.pool = nn.AvgPool2d(kernel_size=kernel_size)
def forward(self, x):
for i in range(self.n_blocks):
x = self.conv[i](x)
if self.kernel_size is not None:
return x, self.pool(x)
else:
return x
class Intermediate(nn.Module): #
def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01):
super(Intermediate, self).__init__()
self.n_inters = n_inters
self.layers = nn.ModuleList()
self.layers.append(
ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum)
)
for i in range(self.n_inters - 1):
self.layers.append(
ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum)
)
def forward(self, x):
for i in range(self.n_inters):
x = self.layers[i](x)
return x
class ResDecoderBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01):
super(ResDecoderBlock, self).__init__()
out_padding = (0, 1) if stride == (1, 2) else (1, 1)
self.n_blocks = n_blocks
self.conv1 = nn.Sequential(
nn.ConvTranspose2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=(3, 3),
stride=stride,
padding=(1, 1),
output_padding=out_padding,
bias=False,
),
nn.BatchNorm2d(out_channels, momentum=momentum),
nn.ReLU(),
)
self.conv2 = nn.ModuleList()
self.conv2.append(ConvBlockRes(out_channels * 2, out_channels, momentum))
for i in range(n_blocks - 1):
self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum))
def forward(self, x, concat_tensor):
x = self.conv1(x)
x = torch.cat((x, concat_tensor), dim=1)
for i in range(self.n_blocks):
x = self.conv2[i](x)
return x
class Decoder(nn.Module):
def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01):
super(Decoder, self).__init__()
self.layers = nn.ModuleList()
self.n_decoders = n_decoders
for i in range(self.n_decoders):
out_channels = in_channels // 2
self.layers.append(
ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum)
)
in_channels = out_channels
def forward(self, x, concat_tensors):
for i in range(self.n_decoders):
x = self.layers[i](x, concat_tensors[-1 - i])
return x
class DeepUnet(nn.Module):
def __init__(
self,
kernel_size,
n_blocks,
en_de_layers=5,
inter_layers=4,
in_channels=1,
en_out_channels=16,
):
super(DeepUnet, self).__init__()
self.encoder = Encoder(
in_channels, 128, en_de_layers, kernel_size, n_blocks, en_out_channels
)
self.intermediate = Intermediate(
self.encoder.out_channel // 2,
self.encoder.out_channel,
inter_layers,
n_blocks,
)
self.decoder = Decoder(
self.encoder.out_channel, en_de_layers, kernel_size, n_blocks
)
def forward(self, x):
x, concat_tensors = self.encoder(x)
x = self.intermediate(x)
x = self.decoder(x, concat_tensors)
return x
class E2E(nn.Module):
def __init__(
self,
n_blocks,
n_gru,
kernel_size,
en_de_layers=5,
inter_layers=4,
in_channels=1,
en_out_channels=16,
):
super(E2E, self).__init__()
self.unet = DeepUnet(
kernel_size,
n_blocks,
en_de_layers,
inter_layers,
in_channels,
en_out_channels,
)
self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1))
if n_gru:
self.fc = nn.Sequential(
BiGRU(3 * 128, 256, n_gru),
nn.Linear(512, 360),
nn.Dropout(0.25),
nn.Sigmoid(),
)
else:
self.fc = nn.Sequential(
nn.Linear(3 * nn.N_MELS, nn.N_CLASS), nn.Dropout(0.25), nn.Sigmoid()
)
def forward(self, mel):
# print(mel.shape)
mel = mel.transpose(-1, -2).unsqueeze(1)
x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2)
x = self.fc(x)
# print(x.shape)
return x
from librosa.filters import mel
class MelSpectrogram(torch.nn.Module):
def __init__(
self,
is_half,
n_mel_channels,
sampling_rate,
win_length,
hop_length,
n_fft=None,
mel_fmin=0,
mel_fmax=None,
clamp=1e-5,
):
super().__init__()
n_fft = win_length if n_fft is None else n_fft
self.hann_window = {}
mel_basis = mel(
sr=sampling_rate,
n_fft=n_fft,
n_mels=n_mel_channels,
fmin=mel_fmin,
fmax=mel_fmax,
htk=True,
)
mel_basis = torch.from_numpy(mel_basis).float()
self.register_buffer("mel_basis", mel_basis)
self.n_fft = win_length if n_fft is None else n_fft
self.hop_length = hop_length
self.win_length = win_length
self.sampling_rate = sampling_rate
self.n_mel_channels = n_mel_channels
self.clamp = clamp
self.is_half = is_half
def forward(self, audio, keyshift=0, speed=1, center=True):
factor = 2 ** (keyshift / 12)
n_fft_new = int(np.round(self.n_fft * factor))
win_length_new = int(np.round(self.win_length * factor))
hop_length_new = int(np.round(self.hop_length * speed))
keyshift_key = str(keyshift) + "_" + str(audio.device)
if keyshift_key not in self.hann_window:
self.hann_window[keyshift_key] = torch.hann_window(win_length_new).to(
# "cpu"if(audio.device.type=="privateuseone") else audio.device
audio.device
)
# fft = torch.stft(#doesn't support pytorch_dml
# # audio.cpu() if(audio.device.type=="privateuseone")else audio,
# audio,
# n_fft=n_fft_new,
# hop_length=hop_length_new,
# win_length=win_length_new,
# window=self.hann_window[keyshift_key],
# center=center,
# return_complex=True,
# )
# magnitude = torch.sqrt(fft.real.pow(2) + fft.imag.pow(2))
# print(1111111111)
# print(222222222222222,audio.device,self.is_half)
if hasattr(self, "stft") == False:
# print(n_fft_new,hop_length_new,win_length_new,audio.shape)
self.stft = STFT(
filter_length=n_fft_new,
hop_length=hop_length_new,
win_length=win_length_new,
window="hann",
).to(audio.device)
magnitude = self.stft.transform(audio) # phase
# if (audio.device.type == "privateuseone"):
# magnitude=magnitude.to(audio.device)
if keyshift != 0:
size = self.n_fft // 2 + 1
resize = magnitude.size(1)
if resize < size:
magnitude = F.pad(magnitude, (0, 0, 0, size - resize))
magnitude = magnitude[:, :size, :] * self.win_length / win_length_new
mel_output = torch.matmul(self.mel_basis, magnitude)
if self.is_half == True:
mel_output = mel_output.half()
log_mel_spec = torch.log(torch.clamp(mel_output, min=self.clamp))
# print(log_mel_spec.device.type)
return log_mel_spec
class RMVPE:
def __init__(self, model_path, is_half, device=None):
self.resample_kernel = {}
self.resample_kernel = {}
self.is_half = is_half
if device is None:
device = "cuda" if torch.cuda.is_available() else "cpu"
self.device = device
self.mel_extractor = MelSpectrogram(
is_half, 128, 16000, 1024, 160, None, 30, 8000
).to(device)
if "privateuseone" in str(device):
import onnxruntime as ort
ort_session = ort.InferenceSession(
"rmvpe.onnx", providers=["DmlExecutionProvider"]
)
self.model = ort_session
else:
model = E2E(4, 1, (2, 2))
ckpt = torch.load(model_path, map_location="cpu")
model.load_state_dict(ckpt)
model.eval()
if is_half == True:
model = model.half()
self.model = model
self.model = self.model.to(device)
cents_mapping = 20 * np.arange(360) + 1997.3794084376191
self.cents_mapping = np.pad(cents_mapping, (4, 4)) # 368
def mel2hidden(self, mel):
with torch.no_grad():
n_frames = mel.shape[-1]
mel = F.pad(
mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode="reflect"
)
if "privateuseone" in str(self.device):
onnx_input_name = self.model.get_inputs()[0].name
onnx_outputs_names = self.model.get_outputs()[0].name
hidden = self.model.run(
[onnx_outputs_names],
input_feed={onnx_input_name: mel.cpu().numpy()},
)[0]
else:
hidden = self.model(mel)
return hidden[:, :n_frames]
def decode(self, hidden, thred=0.03):
cents_pred = self.to_local_average_cents(hidden, thred=thred)
f0 = 10 * (2 ** (cents_pred / 1200))
f0[f0 == 10] = 0
# f0 = np.array([10 * (2 ** (cent_pred / 1200)) if cent_pred else 0 for cent_pred in cents_pred])
return f0
def infer_from_audio(self, audio, thred=0.03):
# torch.cuda.synchronize()
t0 = ttime()
mel = self.mel_extractor(
torch.from_numpy(audio).float().to(self.device).unsqueeze(0), center=True
)
# print(123123123,mel.device.type)
# torch.cuda.synchronize()
t1 = ttime()
hidden = self.mel2hidden(mel)
# torch.cuda.synchronize()
t2 = ttime()
# print(234234,hidden.device.type)
if "privateuseone" not in str(self.device):
hidden = hidden.squeeze(0).cpu().numpy()
else:
hidden = hidden[0]
if self.is_half == True:
hidden = hidden.astype("float32")
f0 = self.decode(hidden, thred=thred)
# torch.cuda.synchronize()
t3 = ttime()
# print("hmvpe:%s\t%s\t%s\t%s"%(t1-t0,t2-t1,t3-t2,t3-t0))
return f0
def to_local_average_cents(self, salience, thred=0.05):
# t0 = ttime()
center = np.argmax(salience, axis=1) # 帧长#index
salience = np.pad(salience, ((0, 0), (4, 4))) # 帧长,368
# t1 = ttime()
center += 4
todo_salience = []
todo_cents_mapping = []
starts = center - 4
ends = center + 5
for idx in range(salience.shape[0]):
todo_salience.append(salience[:, starts[idx] : ends[idx]][idx])
todo_cents_mapping.append(self.cents_mapping[starts[idx] : ends[idx]])
# t2 = ttime()
todo_salience = np.array(todo_salience) # 帧长，9
todo_cents_mapping = np.array(todo_cents_mapping) # 帧长，9
product_sum = np.sum(todo_salience * todo_cents_mapping, 1)
weight_sum = np.sum(todo_salience, 1) # 帧长
devided = product_sum / weight_sum # 帧长
# t3 = ttime()
maxx = np.max(salience, axis=1) # 帧长
devided[maxx <= thred] = 0
# t4 = ttime()
# print("decode:%s\t%s\t%s\t%s" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
return devided
if __name__ == "__main__":
import soundfile as sf, librosa
audio, sampling_rate = sf.read(r"C:\Users\liujing04\Desktop\Z\冬之花clip1.wav")
if len(audio.shape) > 1:
audio = librosa.to_mono(audio.transpose(1, 0))
audio_bak = audio.copy()
if sampling_rate != 16000:
audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
model_path = r"D:\BaiduNetdiskDownload\RVC-beta-v2-0727AMD_realtime\rmvpe.pt"
thred = 0.03 # 0.01
device = "cuda" if torch.cuda.is_available() else "cpu"
rmvpe = RMVPE(model_path, is_half=False, device=device)
t0 = ttime()
f0 = rmvpe.infer_from_audio(audio, thred=thred)
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
t1 = ttime()
print(f0.shape, t1 - t0)

260
lib/slicer2.py
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@ -1,260 +0,0 @@
import numpy as np
# This function is obtained from librosa.
def get_rms(
y,
frame_length=2048,
hop_length=512,
pad_mode="constant",
):
padding = (int(frame_length // 2), int(frame_length // 2))
y = np.pad(y, padding, mode=pad_mode)
axis = -1
# put our new within-frame axis at the end for now
out_strides = y.strides + tuple([y.strides[axis]])
# Reduce the shape on the framing axis
x_shape_trimmed = list(y.shape)
x_shape_trimmed[axis] -= frame_length - 1
out_shape = tuple(x_shape_trimmed) + tuple([frame_length])
xw = np.lib.stride_tricks.as_strided(y, shape=out_shape, strides=out_strides)
if axis < 0:
target_axis = axis - 1
else:
target_axis = axis + 1
xw = np.moveaxis(xw, -1, target_axis)
# Downsample along the target axis
slices = [slice(None)] * xw.ndim
slices[axis] = slice(0, None, hop_length)
x = xw[tuple(slices)]
# Calculate power
power = np.mean(np.abs(x) ** 2, axis=-2, keepdims=True)
return np.sqrt(power)
class Slicer:
def __init__(
self,
sr: int,
threshold: float = -40.0,
min_length: int = 5000,
min_interval: int = 300,
hop_size: int = 20,
max_sil_kept: int = 5000,
):
if not min_length >= min_interval >= hop_size:
raise ValueError(
"The following condition must be satisfied: min_length >= min_interval >= hop_size"
)
if not max_sil_kept >= hop_size:
raise ValueError(
"The following condition must be satisfied: max_sil_kept >= hop_size"
)
min_interval = sr * min_interval / 1000
self.threshold = 10 ** (threshold / 20.0)
self.hop_size = round(sr * hop_size / 1000)
self.win_size = min(round(min_interval), 4 * self.hop_size)
self.min_length = round(sr * min_length / 1000 / self.hop_size)
self.min_interval = round(min_interval / self.hop_size)
self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size)
def _apply_slice(self, waveform, begin, end):
if len(waveform.shape) > 1:
return waveform[
:, begin * self.hop_size : min(waveform.shape[1], end * self.hop_size)
]
else:
return waveform[
begin * self.hop_size : min(waveform.shape[0], end * self.hop_size)
]
# @timeit
def slice(self, waveform):
if len(waveform.shape) > 1:
samples = waveform.mean(axis=0)
else:
samples = waveform
if samples.shape[0] <= self.min_length:
return [waveform]
rms_list = get_rms(
y=samples, frame_length=self.win_size, hop_length=self.hop_size
).squeeze(0)
sil_tags = []
silence_start = None
clip_start = 0
for i, rms in enumerate(rms_list):
# Keep looping while frame is silent.
if rms < self.threshold:
# Record start of silent frames.
if silence_start is None:
silence_start = i
continue
# Keep looping while frame is not silent and silence start has not been recorded.
if silence_start is None:
continue
# Clear recorded silence start if interval is not enough or clip is too short
is_leading_silence = silence_start == 0 and i > self.max_sil_kept
need_slice_middle = (
i - silence_start >= self.min_interval
and i - clip_start >= self.min_length
)
if not is_leading_silence and not need_slice_middle:
silence_start = None
continue
# Need slicing. Record the range of silent frames to be removed.
if i - silence_start <= self.max_sil_kept:
pos = rms_list[silence_start : i + 1].argmin() + silence_start
if silence_start == 0:
sil_tags.append((0, pos))
else:
sil_tags.append((pos, pos))
clip_start = pos
elif i - silence_start <= self.max_sil_kept * 2:
pos = rms_list[
i - self.max_sil_kept : silence_start + self.max_sil_kept + 1
].argmin()
pos += i - self.max_sil_kept
pos_l = (
rms_list[
silence_start : silence_start + self.max_sil_kept + 1
].argmin()
+ silence_start
)
pos_r = (
rms_list[i - self.max_sil_kept : i + 1].argmin()
+ i
- self.max_sil_kept
)
if silence_start == 0:
sil_tags.append((0, pos_r))
clip_start = pos_r
else:
sil_tags.append((min(pos_l, pos), max(pos_r, pos)))
clip_start = max(pos_r, pos)
else:
pos_l = (
rms_list[
silence_start : silence_start + self.max_sil_kept + 1
].argmin()
+ silence_start
)
pos_r = (
rms_list[i - self.max_sil_kept : i + 1].argmin()
+ i
- self.max_sil_kept
)
if silence_start == 0:
sil_tags.append((0, pos_r))
else:
sil_tags.append((pos_l, pos_r))
clip_start = pos_r
silence_start = None
# Deal with trailing silence.
total_frames = rms_list.shape[0]
if (
silence_start is not None
and total_frames - silence_start >= self.min_interval
):
silence_end = min(total_frames, silence_start + self.max_sil_kept)
pos = rms_list[silence_start : silence_end + 1].argmin() + silence_start
sil_tags.append((pos, total_frames + 1))
# Apply and return slices.
if len(sil_tags) == 0:
return [waveform]
else:
chunks = []
if sil_tags[0][0] > 0:
chunks.append(self._apply_slice(waveform, 0, sil_tags[0][0]))
for i in range(len(sil_tags) - 1):
chunks.append(
self._apply_slice(waveform, sil_tags[i][1], sil_tags[i + 1][0])
)
if sil_tags[-1][1] < total_frames:
chunks.append(
self._apply_slice(waveform, sil_tags[-1][1], total_frames)
)
return chunks
def main():
import os.path
from argparse import ArgumentParser
import librosa
import soundfile
parser = ArgumentParser()
parser.add_argument("audio", type=str, help="The audio to be sliced")
parser.add_argument(
"--out", type=str, help="Output directory of the sliced audio clips"
)
parser.add_argument(
"--db_thresh",
type=float,
required=False,
default=-40,
help="The dB threshold for silence detection",
)
parser.add_argument(
"--min_length",
type=int,
required=False,
default=5000,
help="The minimum milliseconds required for each sliced audio clip",
)
parser.add_argument(
"--min_interval",
type=int,
required=False,
default=300,
help="The minimum milliseconds for a silence part to be sliced",
)
parser.add_argument(
"--hop_size",
type=int,
required=False,
default=10,
help="Frame length in milliseconds",
)
parser.add_argument(
"--max_sil_kept",
type=int,
required=False,
default=500,
help="The maximum silence length kept around the sliced clip, presented in milliseconds",
)
args = parser.parse_args()
out = args.out
if out is None:
out = os.path.dirname(os.path.abspath(args.audio))
audio, sr = librosa.load(args.audio, sr=None, mono=False)
slicer = Slicer(
sr=sr,
threshold=args.db_thresh,
min_length=args.min_length,
min_interval=args.min_interval,
hop_size=args.hop_size,
max_sil_kept=args.max_sil_kept,
)
chunks = slicer.slice(audio)
if not os.path.exists(out):
os.makedirs(out)
for i, chunk in enumerate(chunks):
if len(chunk.shape) > 1:
chunk = chunk.T
soundfile.write(
os.path.join(
out,
f"%s_%d.wav"
% (os.path.basename(args.audio).rsplit(".", maxsplit=1)[0], i),
),
chunk,
sr,
)
if __name__ == "__main__":
main()

1
lib/train/cmd.txt
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@ -1 +0,0 @@
python train_nsf_sim_cache_sid.py -c configs/mi_mix40k_nsf_co256_cs1sid_ms2048.json -m ft-mi

512
lib/train/data_utils.py
View File

@ -1,512 +0,0 @@
import os, traceback
import numpy as np
import torch
import torch.utils.data
from lib.train.mel_processing import spectrogram_torch
from lib.train.utils import load_wav_to_torch, load_filepaths_and_text
class TextAudioLoaderMultiNSFsid(torch.utils.data.Dataset):
"""
1) loads audio, text pairs
2) normalizes text and converts them to sequences of integers
3) computes spectrograms from audio files.
"""
def __init__(self, audiopaths_and_text, hparams):
self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
self.max_wav_value = hparams.max_wav_value
self.sampling_rate = hparams.sampling_rate
self.filter_length = hparams.filter_length
self.hop_length = hparams.hop_length
self.win_length = hparams.win_length
self.sampling_rate = hparams.sampling_rate
self.min_text_len = getattr(hparams, "min_text_len", 1)
self.max_text_len = getattr(hparams, "max_text_len", 5000)
self._filter()
def _filter(self):
"""
Filter text & store spec lengths
"""
# Store spectrogram lengths for Bucketing
# wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
# spec_length = wav_length // hop_length
audiopaths_and_text_new = []
lengths = []
for audiopath, text, pitch, pitchf, dv in self.audiopaths_and_text:
if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
audiopaths_and_text_new.append([audiopath, text, pitch, pitchf, dv])
lengths.append(os.path.getsize(audiopath) // (3 * self.hop_length))
self.audiopaths_and_text = audiopaths_and_text_new
self.lengths = lengths
def get_sid(self, sid):
sid = torch.LongTensor([int(sid)])
return sid
def get_audio_text_pair(self, audiopath_and_text):
# separate filename and text
file = audiopath_and_text[0]
phone = audiopath_and_text[1]
pitch = audiopath_and_text[2]
pitchf = audiopath_and_text[3]
dv = audiopath_and_text[4]
phone, pitch, pitchf = self.get_labels(phone, pitch, pitchf)
spec, wav = self.get_audio(file)
dv = self.get_sid(dv)
len_phone = phone.size()[0]
len_spec = spec.size()[-1]
# print(123,phone.shape,pitch.shape,spec.shape)
if len_phone != len_spec:
len_min = min(len_phone, len_spec)
# amor
len_wav = len_min * self.hop_length
spec = spec[:, :len_min]
wav = wav[:, :len_wav]
phone = phone[:len_min, :]
pitch = pitch[:len_min]
pitchf = pitchf[:len_min]
return (spec, wav, phone, pitch, pitchf, dv)
def get_labels(self, phone, pitch, pitchf):
phone = np.load(phone)
phone = np.repeat(phone, 2, axis=0)
pitch = np.load(pitch)
pitchf = np.load(pitchf)
n_num = min(phone.shape[0], 900) # DistributedBucketSampler
# print(234,phone.shape,pitch.shape)
phone = phone[:n_num, :]
pitch = pitch[:n_num]
pitchf = pitchf[:n_num]
phone = torch.FloatTensor(phone)
pitch = torch.LongTensor(pitch)
pitchf = torch.FloatTensor(pitchf)
return phone, pitch, pitchf
def get_audio(self, filename):
audio, sampling_rate = load_wav_to_torch(filename)
if sampling_rate != self.sampling_rate:
raise ValueError(
"{} SR doesn't match target {} SR".format(
sampling_rate, self.sampling_rate
)
)
audio_norm = audio
# audio_norm = audio / self.max_wav_value
# audio_norm = audio / np.abs(audio).max()
audio_norm = audio_norm.unsqueeze(0)
spec_filename = filename.replace(".wav", ".spec.pt")
if os.path.exists(spec_filename):
try:
spec = torch.load(spec_filename)
except:
print(spec_filename, traceback.format_exc())
spec = spectrogram_torch(
audio_norm,
self.filter_length,
self.sampling_rate,
self.hop_length,
self.win_length,
center=False,
)
spec = torch.squeeze(spec, 0)
torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
else:
spec = spectrogram_torch(
audio_norm,
self.filter_length,
self.sampling_rate,
self.hop_length,
self.win_length,
center=False,
)
spec = torch.squeeze(spec, 0)
torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
return spec, audio_norm
def __getitem__(self, index):
return self.get_audio_text_pair(self.audiopaths_and_text[index])
def __len__(self):
return len(self.audiopaths_and_text)
class TextAudioCollateMultiNSFsid:
"""Zero-pads model inputs and targets"""
def __init__(self, return_ids=False):
self.return_ids = return_ids
def __call__(self, batch):
"""Collate's training batch from normalized text and aduio
PARAMS
------
batch: [text_normalized, spec_normalized, wav_normalized]
"""
# Right zero-pad all one-hot text sequences to max input length
_, ids_sorted_decreasing = torch.sort(
torch.LongTensor([x[0].size(1) for x in batch]), dim=0, descending=True
)
max_spec_len = max([x[0].size(1) for x in batch])
max_wave_len = max([x[1].size(1) for x in batch])
spec_lengths = torch.LongTensor(len(batch))
wave_lengths = torch.LongTensor(len(batch))
spec_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max_spec_len)
wave_padded = torch.FloatTensor(len(batch), 1, max_wave_len)
spec_padded.zero_()
wave_padded.zero_()
max_phone_len = max([x[2].size(0) for x in batch])
phone_lengths = torch.LongTensor(len(batch))
phone_padded = torch.FloatTensor(
len(batch), max_phone_len, batch[0][2].shape[1]
) # (spec, wav, phone, pitch)
pitch_padded = torch.LongTensor(len(batch), max_phone_len)
pitchf_padded = torch.FloatTensor(len(batch), max_phone_len)
phone_padded.zero_()
pitch_padded.zero_()
pitchf_padded.zero_()
# dv = torch.FloatTensor(len(batch), 256)#gin=256
sid = torch.LongTensor(len(batch))
for i in range(len(ids_sorted_decreasing)):
row = batch[ids_sorted_decreasing[i]]
spec = row[0]
spec_padded[i, :, : spec.size(1)] = spec
spec_lengths[i] = spec.size(1)
wave = row[1]
wave_padded[i, :, : wave.size(1)] = wave
wave_lengths[i] = wave.size(1)
phone = row[2]
phone_padded[i, : phone.size(0), :] = phone
phone_lengths[i] = phone.size(0)
pitch = row[3]
pitch_padded[i, : pitch.size(0)] = pitch
pitchf = row[4]
pitchf_padded[i, : pitchf.size(0)] = pitchf
# dv[i] = row[5]
sid[i] = row[5]
return (
phone_padded,
phone_lengths,
pitch_padded,
pitchf_padded,
spec_padded,
spec_lengths,
wave_padded,
wave_lengths,
# dv
sid,
)
class TextAudioLoader(torch.utils.data.Dataset):
"""
1) loads audio, text pairs
2) normalizes text and converts them to sequences of integers
3) computes spectrograms from audio files.
"""
def __init__(self, audiopaths_and_text, hparams):
self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
self.max_wav_value = hparams.max_wav_value
self.sampling_rate = hparams.sampling_rate
self.filter_length = hparams.filter_length
self.hop_length = hparams.hop_length
self.win_length = hparams.win_length
self.sampling_rate = hparams.sampling_rate
self.min_text_len = getattr(hparams, "min_text_len", 1)
self.max_text_len = getattr(hparams, "max_text_len", 5000)
self._filter()
def _filter(self):
"""
Filter text & store spec lengths
"""
# Store spectrogram lengths for Bucketing
# wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
# spec_length = wav_length // hop_length
audiopaths_and_text_new = []
lengths = []
for audiopath, text, dv in self.audiopaths_and_text:
if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
audiopaths_and_text_new.append([audiopath, text, dv])
lengths.append(os.path.getsize(audiopath) // (3 * self.hop_length))
self.audiopaths_and_text = audiopaths_and_text_new
self.lengths = lengths
def get_sid(self, sid):
sid = torch.LongTensor([int(sid)])
return sid
def get_audio_text_pair(self, audiopath_and_text):
# separate filename and text
file = audiopath_and_text[0]
phone = audiopath_and_text[1]
dv = audiopath_and_text[2]
phone = self.get_labels(phone)
spec, wav = self.get_audio(file)
dv = self.get_sid(dv)
len_phone = phone.size()[0]
len_spec = spec.size()[-1]
if len_phone != len_spec:
len_min = min(len_phone, len_spec)
len_wav = len_min * self.hop_length
spec = spec[:, :len_min]
wav = wav[:, :len_wav]
phone = phone[:len_min, :]
return (spec, wav, phone, dv)
def get_labels(self, phone):
phone = np.load(phone)
phone = np.repeat(phone, 2, axis=0)
n_num = min(phone.shape[0], 900) # DistributedBucketSampler
phone = phone[:n_num, :]
phone = torch.FloatTensor(phone)
return phone
def get_audio(self, filename):
audio, sampling_rate = load_wav_to_torch(filename)
if sampling_rate != self.sampling_rate:
raise ValueError(
"{} SR doesn't match target {} SR".format(
sampling_rate, self.sampling_rate
)
)
audio_norm = audio
# audio_norm = audio / self.max_wav_value
# audio_norm = audio / np.abs(audio).max()
audio_norm = audio_norm.unsqueeze(0)
spec_filename = filename.replace(".wav", ".spec.pt")
if os.path.exists(spec_filename):
try:
spec = torch.load(spec_filename)
except:
print(spec_filename, traceback.format_exc())
spec = spectrogram_torch(
audio_norm,
self.filter_length,
self.sampling_rate,
self.hop_length,
self.win_length,
center=False,
)
spec = torch.squeeze(spec, 0)
torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
else:
spec = spectrogram_torch(
audio_norm,
self.filter_length,
self.sampling_rate,
self.hop_length,
self.win_length,
center=False,
)
spec = torch.squeeze(spec, 0)
torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
return spec, audio_norm
def __getitem__(self, index):
return self.get_audio_text_pair(self.audiopaths_and_text[index])
def __len__(self):
return len(self.audiopaths_and_text)
class TextAudioCollate:
"""Zero-pads model inputs and targets"""
def __init__(self, return_ids=False):
self.return_ids = return_ids
def __call__(self, batch):
"""Collate's training batch from normalized text and aduio
PARAMS
------
batch: [text_normalized, spec_normalized, wav_normalized]
"""
# Right zero-pad all one-hot text sequences to max input length
_, ids_sorted_decreasing = torch.sort(
torch.LongTensor([x[0].size(1) for x in batch]), dim=0, descending=True
)
max_spec_len = max([x[0].size(1) for x in batch])
max_wave_len = max([x[1].size(1) for x in batch])
spec_lengths = torch.LongTensor(len(batch))
wave_lengths = torch.LongTensor(len(batch))
spec_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max_spec_len)
wave_padded = torch.FloatTensor(len(batch), 1, max_wave_len)
spec_padded.zero_()
wave_padded.zero_()
max_phone_len = max([x[2].size(0) for x in batch])
phone_lengths = torch.LongTensor(len(batch))
phone_padded = torch.FloatTensor(
len(batch), max_phone_len, batch[0][2].shape[1]
)
phone_padded.zero_()
sid = torch.LongTensor(len(batch))
for i in range(len(ids_sorted_decreasing)):
row = batch[ids_sorted_decreasing[i]]
spec = row[0]
spec_padded[i, :, : spec.size(1)] = spec
spec_lengths[i] = spec.size(1)
wave = row[1]
wave_padded[i, :, : wave.size(1)] = wave
wave_lengths[i] = wave.size(1)
phone = row[2]
phone_padded[i, : phone.size(0), :] = phone
phone_lengths[i] = phone.size(0)
sid[i] = row[3]
return (
phone_padded,
phone_lengths,
spec_padded,
spec_lengths,
wave_padded,
wave_lengths,
sid,
)
class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
"""
Maintain similar input lengths in a batch.
Length groups are specified by boundaries.
Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
It removes samples which are not included in the boundaries.
Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
"""
def __init__(
self,
dataset,
batch_size,
boundaries,
num_replicas=None,
rank=None,
shuffle=True,
):
super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
self.lengths = dataset.lengths
self.batch_size = batch_size
self.boundaries = boundaries
self.buckets, self.num_samples_per_bucket = self._create_buckets()
self.total_size = sum(self.num_samples_per_bucket)
self.num_samples = self.total_size // self.num_replicas
def _create_buckets(self):
buckets = [[] for _ in range(len(self.boundaries) - 1)]
for i in range(len(self.lengths)):
length = self.lengths[i]
idx_bucket = self._bisect(length)
if idx_bucket != -1:
buckets[idx_bucket].append(i)
for i in range(len(buckets) - 1, -1, -1): #
if len(buckets[i]) == 0:
buckets.pop(i)
self.boundaries.pop(i + 1)
num_samples_per_bucket = []
for i in range(len(buckets)):
len_bucket = len(buckets[i])
total_batch_size = self.num_replicas * self.batch_size
rem = (
total_batch_size - (len_bucket % total_batch_size)
) % total_batch_size
num_samples_per_bucket.append(len_bucket + rem)
return buckets, num_samples_per_bucket
def __iter__(self):
# deterministically shuffle based on epoch
g = torch.Generator()
g.manual_seed(self.epoch)
indices = []
if self.shuffle:
for bucket in self.buckets:
indices.append(torch.randperm(len(bucket), generator=g).tolist())
else:
for bucket in self.buckets:
indices.append(list(range(len(bucket))))
batches = []
for i in range(len(self.buckets)):
bucket = self.buckets[i]
len_bucket = len(bucket)
ids_bucket = indices[i]
num_samples_bucket = self.num_samples_per_bucket[i]
# add extra samples to make it evenly divisible
rem = num_samples_bucket - len_bucket
ids_bucket = (
ids_bucket
+ ids_bucket * (rem // len_bucket)
+ ids_bucket[: (rem % len_bucket)]
)
# subsample
ids_bucket = ids_bucket[self.rank :: self.num_replicas]
# batching
for j in range(len(ids_bucket) // self.batch_size):
batch = [
bucket[idx]
for idx in ids_bucket[
j * self.batch_size : (j + 1) * self.batch_size
]
]
batches.append(batch)
if self.shuffle:
batch_ids = torch.randperm(len(batches), generator=g).tolist()
batches = [batches[i] for i in batch_ids]
self.batches = batches
assert len(self.batches) * self.batch_size == self.num_samples
return iter(self.batches)
def _bisect(self, x, lo=0, hi=None):
if hi is None:
hi = len(self.boundaries) - 1
if hi > lo:
mid = (hi + lo) // 2
if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:
return mid
elif x <= self.boundaries[mid]:
return self._bisect(x, lo, mid)
else:
return self._bisect(x, mid + 1, hi)
else:
return -1
def __len__(self):
return self.num_samples // self.batch_size

58
lib/train/losses.py
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import torch
def feature_loss(fmap_r, fmap_g):
loss = 0
for dr, dg in zip(fmap_r, fmap_g):
for rl, gl in zip(dr, dg):
rl = rl.float().detach()
gl = gl.float()
loss += torch.mean(torch.abs(rl - gl))
return loss * 2
def discriminator_loss(disc_real_outputs, disc_generated_outputs):
loss = 0
r_losses = []
g_losses = []
for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
dr = dr.float()
dg = dg.float()
r_loss = torch.mean((1 - dr) ** 2)
g_loss = torch.mean(dg**2)
loss += r_loss + g_loss
r_losses.append(r_loss.item())
g_losses.append(g_loss.item())
return loss, r_losses, g_losses
def generator_loss(disc_outputs):
loss = 0
gen_losses = []
for dg in disc_outputs:
dg = dg.float()
l = torch.mean((1 - dg) ** 2)
gen_losses.append(l)
loss += l
return loss, gen_losses
def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
"""
z_p, logs_q: [b, h, t_t]
m_p, logs_p: [b, h, t_t]
"""
z_p = z_p.float()
logs_q = logs_q.float()
m_p = m_p.float()
logs_p = logs_p.float()
z_mask = z_mask.float()
kl = logs_p - logs_q - 0.5
kl += 0.5 * ((z_p - m_p) ** 2) * torch.exp(-2.0 * logs_p)
kl = torch.sum(kl * z_mask)
l = kl / torch.sum(z_mask)
return l

130
lib/train/mel_processing.py
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import torch
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
MAX_WAV_VALUE = 32768.0
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
"""
PARAMS
------
C: compression factor
"""
return torch.log(torch.clamp(x, min=clip_val) * C)
def dynamic_range_decompression_torch(x, C=1):
"""
PARAMS
------
C: compression factor used to compress
"""
return torch.exp(x) / C
def spectral_normalize_torch(magnitudes):
return dynamic_range_compression_torch(magnitudes)
def spectral_de_normalize_torch(magnitudes):
return dynamic_range_decompression_torch(magnitudes)
# Reusable banks
mel_basis = {}
hann_window = {}
def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
"""Convert waveform into Linear-frequency Linear-amplitude spectrogram.
Args:
y :: (B, T) - Audio waveforms
n_fft
sampling_rate
hop_size
win_size
center
Returns:
:: (B, Freq, Frame) - Linear-frequency Linear-amplitude spectrogram
"""
# Validation
if torch.min(y) < -1.07:
print("min value is ", torch.min(y))
if torch.max(y) > 1.07:
print("max value is ", torch.max(y))
# Window - Cache if needed
global hann_window
dtype_device = str(y.dtype) + "_" + str(y.device)
wnsize_dtype_device = str(win_size) + "_" + dtype_device
if wnsize_dtype_device not in hann_window:
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(
dtype=y.dtype, device=y.device
)
# Padding
y = torch.nn.functional.pad(
y.unsqueeze(1),
(int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
mode="reflect",
)
y = y.squeeze(1)
# Complex Spectrogram :: (B, T) -> (B, Freq, Frame, RealComplex=2)
spec = torch.stft(
y,
n_fft,
hop_length=hop_size,
win_length=win_size,
window=hann_window[wnsize_dtype_device],
center=center,
pad_mode="reflect",
normalized=False,
onesided=True,
return_complex=False,
)
# Linear-frequency Linear-amplitude spectrogram :: (B, Freq, Frame, RealComplex=2) -> (B, Freq, Frame)
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
return spec
def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
# MelBasis - Cache if needed
global mel_basis
dtype_device = str(spec.dtype) + "_" + str(spec.device)
fmax_dtype_device = str(fmax) + "_" + dtype_device
if fmax_dtype_device not in mel_basis:
mel = librosa_mel_fn(
sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax
)
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(
dtype=spec.dtype, device=spec.device
)
# Mel-frequency Log-amplitude spectrogram :: (B, Freq=num_mels, Frame)
melspec = torch.matmul(mel_basis[fmax_dtype_device], spec)
melspec = spectral_normalize_torch(melspec)
return melspec
def mel_spectrogram_torch(
y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False
):
"""Convert waveform into Mel-frequency Log-amplitude spectrogram.
Args:
y :: (B, T) - Waveforms
Returns:
melspec :: (B, Freq, Frame) - Mel-frequency Log-amplitude spectrogram
"""
# Linear-frequency Linear-amplitude spectrogram :: (B, T) -> (B, Freq, Frame)
spec = spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center)
# Mel-frequency Log-amplitude spectrogram :: (B, Freq, Frame) -> (B, Freq=num_mels, Frame)
melspec = spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax)
return melspec

259
lib/train/process_ckpt.py
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@ -1,259 +0,0 @@
import torch, traceback, os, sys
now_dir = os.getcwd()
sys.path.append(now_dir)
from collections import OrderedDict
from i18n import I18nAuto
i18n = I18nAuto()
def savee(ckpt, sr, if_f0, name, epoch, version, hps):
try:
opt = OrderedDict()
opt["weight"] = {}
for key in ckpt.keys():
if "enc_q" in key:
continue
opt["weight"][key] = ckpt[key].half()
opt["config"] = [
hps.data.filter_length // 2 + 1,
32,
hps.model.inter_channels,
hps.model.hidden_channels,
hps.model.filter_channels,
hps.model.n_heads,
hps.model.n_layers,
hps.model.kernel_size,
hps.model.p_dropout,
hps.model.resblock,
hps.model.resblock_kernel_sizes,
hps.model.resblock_dilation_sizes,
hps.model.upsample_rates,
hps.model.upsample_initial_channel,
hps.model.upsample_kernel_sizes,
hps.model.spk_embed_dim,
hps.model.gin_channels,
hps.data.sampling_rate,
]
opt["info"] = "%sepoch" % epoch
opt["sr"] = sr
opt["f0"] = if_f0
opt["version"] = version
torch.save(opt, "weights/%s.pth" % name)
return "Success."
except:
return traceback.format_exc()
def show_info(path):
try:
a = torch.load(path, map_location="cpu")
return "模型信息:%s\n采样率:%s\n模型是否输入音高引导:%s\n版本:%s" % (
a.get("info", "None"),
a.get("sr", "None"),
a.get("f0", "None"),
a.get("version", "None"),
)
except:
return traceback.format_exc()
def extract_small_model(path, name, sr, if_f0, info, version):
try:
ckpt = torch.load(path, map_location="cpu")
if "model" in ckpt:
ckpt = ckpt["model"]
opt = OrderedDict()
opt["weight"] = {}
for key in ckpt.keys():
if "enc_q" in key:
continue
opt["weight"][key] = ckpt[key].half()
if sr == "40k":
opt["config"] = [
1025,
32,
192,
192,
768,
2,
6,
3,
0,
"1",
[3, 7, 11],
[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
[10, 10, 2, 2],
512,
[16, 16, 4, 4],
109,
256,
40000,
]
elif sr == "48k":
if version == "v1":
opt["config"] = [
1025,
32,
192,
192,
768,
2,
6,
3,
0,
"1",
[3, 7, 11],
[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
[10, 6, 2, 2, 2],
512,
[16, 16, 4, 4, 4],
109,
256,
48000,
]
else:
opt["config"] = [
1025,
32,
192,
192,
768,
2,
6,
3,
0,
"1",
[3, 7, 11],
[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
[12, 10, 2, 2],
512,
[24, 20, 4, 4],
109,
256,
48000,
]
elif sr == "32k":
if version == "v1":
opt["config"] = [
513,
32,
192,
192,
768,
2,
6,
3,
0,
"1",
[3, 7, 11],
[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
[10, 4, 2, 2, 2],
512,
[16, 16, 4, 4, 4],
109,
256,
32000,
]
else:
opt["config"] = [
513,
32,
192,
192,
768,
2,
6,
3,
0,
"1",
[3, 7, 11],
[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
[10, 8, 2, 2],
512,
[20, 16, 4, 4],
109,
256,
32000,
]
if info == "":
info = "Extracted model."
opt["info"] = info
opt["version"] = version
opt["sr"] = sr
opt["f0"] = int(if_f0)
torch.save(opt, "weights/%s.pth" % name)
return "Success."
except:
return traceback.format_exc()
def change_info(path, info, name):
try:
ckpt = torch.load(path, map_location="cpu")
ckpt["info"] = info
if name == "":
name = os.path.basename(path)
torch.save(ckpt, "weights/%s" % name)
return "Success."
except:
return traceback.format_exc()
def merge(path1, path2, alpha1, sr, f0, info, name, version):
try:
def extract(ckpt):
a = ckpt["model"]
opt = OrderedDict()
opt["weight"] = {}
for key in a.keys():
if "enc_q" in key:
continue
opt["weight"][key] = a[key]
return opt
ckpt1 = torch.load(path1, map_location="cpu")
ckpt2 = torch.load(path2, map_location="cpu")
cfg = ckpt1["config"]
if "model" in ckpt1:
ckpt1 = extract(ckpt1)
else:
ckpt1 = ckpt1["weight"]
if "model" in ckpt2:
ckpt2 = extract(ckpt2)
else:
ckpt2 = ckpt2["weight"]
if sorted(list(ckpt1.keys())) != sorted(list(ckpt2.keys())):
return "Fail to merge the models. The model architectures are not the same."
opt = OrderedDict()
opt["weight"] = {}
for key in ckpt1.keys():
# try:
if key == "emb_g.weight" and ckpt1[key].shape != ckpt2[key].shape:
min_shape0 = min(ckpt1[key].shape[0], ckpt2[key].shape[0])
opt["weight"][key] = (
alpha1 * (ckpt1[key][:min_shape0].float())
+ (1 - alpha1) * (ckpt2[key][:min_shape0].float())
).half()
else:
opt["weight"][key] = (
alpha1 * (ckpt1[key].float()) + (1 - alpha1) * (ckpt2[key].float())
).half()
# except:
# pdb.set_trace()
opt["config"] = cfg
"""
if(sr=="40k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 10, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 40000]
elif(sr=="48k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10,6,2,2,2], 512, [16, 16, 4, 4], 109, 256, 48000]
elif(sr=="32k"):opt["config"] = [513, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 4, 2, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 32000]
"""
opt["sr"] = sr
opt["f0"] = 1 if f0 == i18n("是") else 0
opt["version"] = version
opt["info"] = info
torch.save(opt, "weights/%s.pth" % name)
return "Success."
except:
return traceback.format_exc()

487
lib/train/utils.py
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@ -1,487 +0,0 @@
import os, traceback
import glob
import sys
import argparse
import logging
import json
import subprocess
import numpy as np
from scipy.io.wavfile import read
import torch
MATPLOTLIB_FLAG = False
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
logger = logging
def load_checkpoint_d(checkpoint_path, combd, sbd, optimizer=None, load_opt=1):
assert os.path.isfile(checkpoint_path)
checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
##################
def go(model, bkey):
saved_state_dict = checkpoint_dict[bkey]
if hasattr(model, "module"):
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
new_state_dict = {}
for k, v in state_dict.items(): # 模型需要的shape
try:
new_state_dict[k] = saved_state_dict[k]
if saved_state_dict[k].shape != state_dict[k].shape:
print(
"shape-%s-mismatch|need-%s|get-%s"
% (k, state_dict[k].shape, saved_state_dict[k].shape)
) #
raise KeyError
except:
# logger.info(traceback.format_exc())
logger.info("%s is not in the checkpoint" % k) # pretrain缺失的
new_state_dict[k] = v # 模型自带的随机值
if hasattr(model, "module"):
model.module.load_state_dict(new_state_dict, strict=False)
else:
model.load_state_dict(new_state_dict, strict=False)
return model
go(combd, "combd")
model = go(sbd, "sbd")
#############
logger.info("Loaded model weights")
iteration = checkpoint_dict["iteration"]
learning_rate = checkpoint_dict["learning_rate"]
if (
optimizer is not None and load_opt == 1
): ###加载不了，如果是空的的话，重新初始化，可能还会影响lr时间表的更新，因此在train文件最外围catch
# try:
optimizer.load_state_dict(checkpoint_dict["optimizer"])
# except:
# traceback.print_exc()
logger.info("Loaded checkpoint '{}' (epoch {})".format(checkpoint_path, iteration))
return model, optimizer, learning_rate, iteration
# def load_checkpoint(checkpoint_path, model, optimizer=None):
# assert os.path.isfile(checkpoint_path)
# checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
# iteration = checkpoint_dict['iteration']
# learning_rate = checkpoint_dict['learning_rate']
# if optimizer is not None:
# optimizer.load_state_dict(checkpoint_dict['optimizer'])
# # print(1111)
# saved_state_dict = checkpoint_dict['model']
# # print(1111)
#
# if hasattr(model, 'module'):
# state_dict = model.module.state_dict()
# else:
# state_dict = model.state_dict()
# new_state_dict= {}
# for k, v in state_dict.items():
# try:
# new_state_dict[k] = saved_state_dict[k]
# except:
# logger.info("%s is not in the checkpoint" % k)
# new_state_dict[k] = v
# if hasattr(model, 'module'):
# model.module.load_state_dict(new_state_dict)
# else:
# model.load_state_dict(new_state_dict)
# logger.info("Loaded checkpoint '{}' (epoch {})" .format(
# checkpoint_path, iteration))
# return model, optimizer, learning_rate, iteration
def load_checkpoint(checkpoint_path, model, optimizer=None, load_opt=1):
assert os.path.isfile(checkpoint_path)
checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
saved_state_dict = checkpoint_dict["model"]
if hasattr(model, "module"):
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
new_state_dict = {}
for k, v in state_dict.items(): # 模型需要的shape
try:
new_state_dict[k] = saved_state_dict[k]
if saved_state_dict[k].shape != state_dict[k].shape:
print(
"shape-%s-mismatch|need-%s|get-%s"
% (k, state_dict[k].shape, saved_state_dict[k].shape)
) #
raise KeyError
except:
# logger.info(traceback.format_exc())
logger.info("%s is not in the checkpoint" % k) # pretrain缺失的
new_state_dict[k] = v # 模型自带的随机值
if hasattr(model, "module"):
model.module.load_state_dict(new_state_dict, strict=False)
else:
model.load_state_dict(new_state_dict, strict=False)
logger.info("Loaded model weights")
iteration = checkpoint_dict["iteration"]
learning_rate = checkpoint_dict["learning_rate"]
if (
optimizer is not None and load_opt == 1
): ###加载不了，如果是空的的话，重新初始化，可能还会影响lr时间表的更新，因此在train文件最外围catch
# try:
optimizer.load_state_dict(checkpoint_dict["optimizer"])
# except:
# traceback.print_exc()
logger.info("Loaded checkpoint '{}' (epoch {})".format(checkpoint_path, iteration))
return model, optimizer, learning_rate, iteration
def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
logger.info(
"Saving model and optimizer state at epoch {} to {}".format(
iteration, checkpoint_path
)
)
if hasattr(model, "module"):
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
torch.save(
{
"model": state_dict,
"iteration": iteration,
"optimizer": optimizer.state_dict(),
"learning_rate": learning_rate,
},
checkpoint_path,
)
def save_checkpoint_d(combd, sbd, optimizer, learning_rate, iteration, checkpoint_path):
logger.info(
"Saving model and optimizer state at epoch {} to {}".format(
iteration, checkpoint_path
)
)
if hasattr(combd, "module"):
state_dict_combd = combd.module.state_dict()
else:
state_dict_combd = combd.state_dict()
if hasattr(sbd, "module"):
state_dict_sbd = sbd.module.state_dict()
else:
state_dict_sbd = sbd.state_dict()
torch.save(
{
"combd": state_dict_combd,
"sbd": state_dict_sbd,
"iteration": iteration,
"optimizer": optimizer.state_dict(),
"learning_rate": learning_rate,
},
checkpoint_path,
)
def summarize(
writer,
global_step,
scalars={},
histograms={},
images={},
audios={},
audio_sampling_rate=22050,
):
for k, v in scalars.items():
writer.add_scalar(k, v, global_step)
for k, v in histograms.items():
writer.add_histogram(k, v, global_step)
for k, v in images.items():
writer.add_image(k, v, global_step, dataformats="HWC")
for k, v in audios.items():
writer.add_audio(k, v, global_step, audio_sampling_rate)
def latest_checkpoint_path(dir_path, regex="G_*.pth"):
f_list = glob.glob(os.path.join(dir_path, regex))
f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
x = f_list[-1]
print(x)
return x
def plot_spectrogram_to_numpy(spectrogram):
global MATPLOTLIB_FLAG
if not MATPLOTLIB_FLAG:
import matplotlib
matplotlib.use("Agg")
MATPLOTLIB_FLAG = True
mpl_logger = logging.getLogger("matplotlib")
mpl_logger.setLevel(logging.WARNING)
import matplotlib.pylab as plt
import numpy as np
fig, ax = plt.subplots(figsize=(10, 2))
im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none")
plt.colorbar(im, ax=ax)
plt.xlabel("Frames")
plt.ylabel("Channels")
plt.tight_layout()
fig.canvas.draw()
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
plt.close()
return data
def plot_alignment_to_numpy(alignment, info=None):
global MATPLOTLIB_FLAG
if not MATPLOTLIB_FLAG:
import matplotlib
matplotlib.use("Agg")
MATPLOTLIB_FLAG = True
mpl_logger = logging.getLogger("matplotlib")
mpl_logger.setLevel(logging.WARNING)
import matplotlib.pylab as plt
import numpy as np
fig, ax = plt.subplots(figsize=(6, 4))
im = ax.imshow(
alignment.transpose(), aspect="auto", origin="lower", interpolation="none"
)
fig.colorbar(im, ax=ax)
xlabel = "Decoder timestep"
if info is not None:
xlabel += "\n\n" + info
plt.xlabel(xlabel)
plt.ylabel("Encoder timestep")
plt.tight_layout()
fig.canvas.draw()
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
plt.close()
return data
def load_wav_to_torch(full_path):
sampling_rate, data = read(full_path)
return torch.FloatTensor(data.astype(np.float32)), sampling_rate
def load_filepaths_and_text(filename, split="|"):
with open(filename, encoding="utf-8") as f:
filepaths_and_text = [line.strip().split(split) for line in f]
return filepaths_and_text
def get_hparams(init=True):
"""
todo:
结尾七人组：
保存频率、总epoch done
bs done
pretrainG、pretrainD done
卡号：os.en["CUDA_VISIBLE_DEVICES"] done
if_latest done
模型：if_f0 done
采样率：自动选择config done
是否缓存数据集进GPU:if_cache_data_in_gpu done
-m:
自动决定training_files路径,改掉train_nsf_load_pretrain.py里的hps.data.training_files done
-c不要了
"""
parser = argparse.ArgumentParser()
# parser.add_argument('-c', '--config', type=str, default="configs/40k.json",help='JSON file for configuration')
parser.add_argument(
"-se",
"--save_every_epoch",
type=int,
required=True,
help="checkpoint save frequency (epoch)",
)
parser.add_argument(
"-te", "--total_epoch", type=int, required=True, help="total_epoch"
)
parser.add_argument(
"-pg", "--pretrainG", type=str, default="", help="Pretrained Discriminator path"
)
parser.add_argument(
"-pd", "--pretrainD", type=str, default="", help="Pretrained Generator path"
)
parser.add_argument("-g", "--gpus", type=str, default="0", help="split by -")
parser.add_argument(
"-bs", "--batch_size", type=int, required=True, help="batch size"
)
parser.add_argument(
"-e", "--experiment_dir", type=str, required=True, help="experiment dir"
) # -m
parser.add_argument(
"-sr", "--sample_rate", type=str, required=True, help="sample rate, 32k/40k/48k"
)
parser.add_argument(
"-sw",
"--save_every_weights",
type=str,
default="0",
help="save the extracted model in weights directory when saving checkpoints",
)
parser.add_argument(
"-v", "--version", type=str, required=True, help="model version"
)
parser.add_argument(
"-f0",
"--if_f0",
type=int,
required=True,
help="use f0 as one of the inputs of the model, 1 or 0",
)
parser.add_argument(
"-l",
"--if_latest",
type=int,
required=True,
help="if only save the latest G/D pth file, 1 or 0",
)
parser.add_argument(
"-c",
"--if_cache_data_in_gpu",
type=int,
required=True,
help="if caching the dataset in GPU memory, 1 or 0",
)
args = parser.parse_args()
name = args.experiment_dir
experiment_dir = os.path.join("./logs", args.experiment_dir)
if not os.path.exists(experiment_dir):
os.makedirs(experiment_dir)
if args.version == "v1" or args.sample_rate == "40k":
config_path = "configs/%s.json" % args.sample_rate
else:
config_path = "configs/%s_v2.json" % args.sample_rate
config_save_path = os.path.join(experiment_dir, "config.json")
if init:
with open(config_path, "r") as f:
data = f.read()
with open(config_save_path, "w") as f:
f.write(data)
else:
with open(config_save_path, "r") as f:
data = f.read()
config = json.loads(data)
hparams = HParams(**config)
hparams.model_dir = hparams.experiment_dir = experiment_dir
hparams.save_every_epoch = args.save_every_epoch
hparams.name = name
hparams.total_epoch = args.total_epoch
hparams.pretrainG = args.pretrainG
hparams.pretrainD = args.pretrainD
hparams.version = args.version
hparams.gpus = args.gpus
hparams.train.batch_size = args.batch_size
hparams.sample_rate = args.sample_rate
hparams.if_f0 = args.if_f0
hparams.if_latest = args.if_latest
hparams.save_every_weights = args.save_every_weights
hparams.if_cache_data_in_gpu = args.if_cache_data_in_gpu
hparams.data.training_files = "%s/filelist.txt" % experiment_dir
return hparams
def get_hparams_from_dir(model_dir):
config_save_path = os.path.join(model_dir, "config.json")
with open(config_save_path, "r") as f:
data = f.read()
config = json.loads(data)
hparams = HParams(**config)
hparams.model_dir = model_dir
return hparams
def get_hparams_from_file(config_path):
with open(config_path, "r") as f:
data = f.read()
config = json.loads(data)
hparams = HParams(**config)
return hparams
def check_git_hash(model_dir):
source_dir = os.path.dirname(os.path.realpath(__file__))
if not os.path.exists(os.path.join(source_dir, ".git")):
logger.warn(
"{} is not a git repository, therefore hash value comparison will be ignored.".format(
source_dir
)
)
return
cur_hash = subprocess.getoutput("git rev-parse HEAD")
path = os.path.join(model_dir, "githash")
if os.path.exists(path):
saved_hash = open(path).read()
if saved_hash != cur_hash:
logger.warn(
"git hash values are different. {}(saved) != {}(current)".format(
saved_hash[:8], cur_hash[:8]
)
)
else:
open(path, "w").write(cur_hash)
def get_logger(model_dir, filename="train.log"):
global logger
logger = logging.getLogger(os.path.basename(model_dir))
logger.setLevel(logging.DEBUG)
formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
if not os.path.exists(model_dir):
os.makedirs(model_dir)
h = logging.FileHandler(os.path.join(model_dir, filename))
h.setLevel(logging.DEBUG)
h.setFormatter(formatter)
logger.addHandler(h)
return logger
class HParams:
def __init__(self, **kwargs):
for k, v in kwargs.items():
if type(v) == dict:
v = HParams(**v)
self[k] = v
def keys(self):
return self.__dict__.keys()
def items(self):
return self.__dict__.items()
def values(self):
return self.__dict__.values()
def __len__(self):
return len(self.__dict__)
def __getitem__(self, key):
return getattr(self, key)
def __setitem__(self, key, value):
return setattr(self, key, value)
def __contains__(self, key):
return key in self.__dict__
def __repr__(self):
return self.__dict__.__repr__()

449
lib/train/vc_infer_pipeline.py
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@ -1,449 +0,0 @@
import numpy as np, parselmouth, torch, pdb, sys, os
from time import time as ttime
import torch.nn.functional as F
import scipy.signal as signal
import pyworld, os, traceback, faiss, librosa, torchcrepe
from scipy import signal
from functools import lru_cache
now_dir = os.getcwd()
sys.path.append(now_dir)
bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
input_audio_path2wav = {}
@lru_cache
def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period):
audio = input_audio_path2wav[input_audio_path]
f0, t = pyworld.harvest(
audio,
fs=fs,
f0_ceil=f0max,
f0_floor=f0min,
frame_period=frame_period,
)
f0 = pyworld.stonemask(audio, f0, t, fs)
return f0
def change_rms(data1, sr1, data2, sr2, rate): # 1是输入音频，2是输出音频,rate是2的占比
# print(data1.max(),data2.max())
rms1 = librosa.feature.rms(
y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2
) # 每半秒一个点
rms2 = librosa.feature.rms(y=data2, frame_length=sr2 // 2 * 2, hop_length=sr2 // 2)
rms1 = torch.from_numpy(rms1)
rms1 = F.interpolate(
rms1.unsqueeze(0), size=data2.shape[0], mode="linear"
).squeeze()
rms2 = torch.from_numpy(rms2)
rms2 = F.interpolate(
rms2.unsqueeze(0), size=data2.shape[0], mode="linear"
).squeeze()
rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6)
data2 *= (
torch.pow(rms1, torch.tensor(1 - rate))
* torch.pow(rms2, torch.tensor(rate - 1))
).numpy()
return data2
class VC(object):
def __init__(self, tgt_sr, config):
self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = (
config.x_pad,
config.x_query,
config.x_center,
config.x_max,
config.is_half,
)
self.sr = 16000 # hubert输入采样率
self.window = 160 # 每帧点数
self.t_pad = self.sr * self.x_pad # 每条前后pad时间
self.t_pad_tgt = tgt_sr * self.x_pad
self.t_pad2 = self.t_pad * 2
self.t_query = self.sr * self.x_query # 查询切点前后查询时间
self.t_center = self.sr * self.x_center # 查询切点位置
self.t_max = self.sr * self.x_max # 免查询时长阈值
self.device = config.device
def get_f0(
self,
input_audio_path,
x,
p_len,
f0_up_key,
f0_method,
filter_radius,
inp_f0=None,
):
global input_audio_path2wav
time_step = self.window / self.sr * 1000
f0_min = 50
f0_max = 1100
f0_mel_min = 1127 * np.log(1 + f0_min / 700)
f0_mel_max = 1127 * np.log(1 + f0_max / 700)
if f0_method == "pm":
f0 = (
parselmouth.Sound(x, self.sr)
.to_pitch_ac(
time_step=time_step / 1000,
voicing_threshold=0.6,
pitch_floor=f0_min,
pitch_ceiling=f0_max,
)
.selected_array["frequency"]
)
pad_size = (p_len - len(f0) + 1) // 2
if pad_size > 0 or p_len - len(f0) - pad_size > 0:
f0 = np.pad(
f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
)
elif f0_method == "harvest":
input_audio_path2wav[input_audio_path] = x.astype(np.double)
f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
if filter_radius > 2:
f0 = signal.medfilt(f0, 3)
elif f0_method == "crepe":
model = "full"
# Pick a batch size that doesn't cause memory errors on your gpu
batch_size = 512
# Compute pitch using first gpu
audio = torch.tensor(np.copy(x))[None].float()
f0, pd = torchcrepe.predict(
audio,
self.sr,
self.window,
f0_min,
f0_max,
model,
batch_size=batch_size,
device=self.device,
return_periodicity=True,
)
pd = torchcrepe.filter.median(pd, 3)
f0 = torchcrepe.filter.mean(f0, 3)
f0[pd < 0.1] = 0
f0 = f0[0].cpu().numpy()
elif f0_method == "rmvpe":
if hasattr(self, "model_rmvpe") == False:
from lib.rmvpe import RMVPE
print("loading rmvpe model")
self.model_rmvpe = RMVPE(
"rmvpe.pt", is_half=self.is_half, device=self.device
)
f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
if "privateuseone" in str(self.device): # clean ortruntime memory
del self.model_rmvpe.model
del self.model_rmvpe
print("cleaning ortruntime memory")
f0 *= pow(2, f0_up_key / 12)
# with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
tf0 = self.sr // self.window # 每秒f0点数
if inp_f0 is not None:
delta_t = np.round(
(inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
).astype("int16")
replace_f0 = np.interp(
list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
)
shape = f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)].shape[0]
f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)] = replace_f0[
:shape
]
# with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
f0bak = f0.copy()
f0_mel = 1127 * np.log(1 + f0 / 700)
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
f0_mel_max - f0_mel_min
) + 1
f0_mel[f0_mel <= 1] = 1
f0_mel[f0_mel > 255] = 255
f0_coarse = np.rint(f0_mel).astype(np.int32)
return f0_coarse, f0bak # 1-0
def vc(
self,
model,
net_g,
sid,
audio0,
pitch,
pitchf,
times,
index,
big_npy,
index_rate,
version,
protect,
): # ,file_index,file_big_npy
feats = torch.from_numpy(audio0)
if self.is_half:
feats = feats.half()
else:
feats = feats.float()
if feats.dim() == 2: # double channels
feats = feats.mean(-1)
assert feats.dim() == 1, feats.dim()
feats = feats.view(1, -1)
padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
inputs = {
"source": feats.to(self.device),
"padding_mask": padding_mask,
"output_layer": 9 if version == "v1" else 12,
}
t0 = ttime()
with torch.no_grad():
logits = model.extract_features(**inputs)
feats = model.final_proj(logits[0]) if version == "v1" else logits[0]
if protect < 0.5 and pitch != None and pitchf != None:
feats0 = feats.clone()
if (
isinstance(index, type(None)) == False
and isinstance(big_npy, type(None)) == False
and index_rate != 0
):
npy = feats[0].cpu().numpy()
if self.is_half:
npy = npy.astype("float32")
# _, I = index.search(npy, 1)
# npy = big_npy[I.squeeze()]
score, ix = index.search(npy, k=8)
weight = np.square(1 / score)
weight /= weight.sum(axis=1, keepdims=True)
npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
if self.is_half:
npy = npy.astype("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 != None and pitchf != None:
feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(
0, 2, 1
)
t1 = ttime()
p_len = audio0.shape[0] // self.window
if feats.shape[1] < p_len:
p_len = feats.shape[1]
if pitch != None and pitchf != None:
pitch = pitch[:, :p_len]
pitchf = pitchf[:, :p_len]
if protect < 0.5 and pitch != None and pitchf != None:
pitchff = pitchf.clone()
pitchff[pitchf > 0] = 1
pitchff[pitchf < 1] = protect
pitchff = pitchff.unsqueeze(-1)
feats = feats * pitchff + feats0 * (1 - pitchff)
feats = feats.to(feats0.dtype)
p_len = torch.tensor([p_len], device=self.device).long()
with torch.no_grad():
if pitch != None and pitchf != None:
audio1 = (
(net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0])
.data.cpu()
.float()
.numpy()
)
else:
audio1 = (
(net_g.infer(feats, p_len, sid)[0][0, 0]).data.cpu().float().numpy()
)
del feats, p_len, padding_mask
if torch.cuda.is_available():
torch.cuda.empty_cache()
t2 = ttime()
times[0] += t1 - t0
times[2] += t2 - t1
return audio1
def pipeline(
self,
model,
net_g,
sid,
audio,
input_audio_path,
times,
f0_up_key,
f0_method,
file_index,
# file_big_npy,
index_rate,
if_f0,
filter_radius,
tgt_sr,
resample_sr,
rms_mix_rate,
version,
protect,
f0_file=None,
):
if (
file_index != ""
# and file_big_npy != ""
# and os.path.exists(file_big_npy) == True
and os.path.exists(file_index) == True
and index_rate != 0
):
try:
index = faiss.read_index(file_index)
# big_npy = np.load(file_big_npy)
big_npy = index.reconstruct_n(0, index.ntotal)
except:
traceback.print_exc()
index = big_npy = None
else:
index = big_npy = None
audio = signal.filtfilt(bh, ah, audio)
audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
opt_ts = []
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
audio_opt = []
t = None
t1 = ttime()
audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
p_len = audio_pad.shape[0] // self.window
inp_f0 = None
if hasattr(f0_file, "name") == True:
try:
with open(f0_file.name, "r") as f:
lines = f.read().strip("\n").split("\n")
inp_f0 = []
for line in lines:
inp_f0.append([float(i) for i in line.split(",")])
inp_f0 = np.array(inp_f0, dtype="float32")
except:
traceback.print_exc()
sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
pitch, pitchf = None, None
if if_f0 == 1:
pitch, pitchf = self.get_f0(
input_audio_path,
audio_pad,
p_len,
f0_up_key,
f0_method,
filter_radius,
inp_f0,
)
pitch = pitch[:p_len]
pitchf = pitchf[:p_len]
if self.device == "mps":
pitchf = pitchf.astype(np.float32)
pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
t2 = ttime()
times[1] += t2 - t1
for t in opt_ts:
t = t // self.window * self.window
if if_f0 == 1:
audio_opt.append(
self.vc(
model,
net_g,
sid,
audio_pad[s : t + self.t_pad2 + self.window],
pitch[:, s // self.window : (t + self.t_pad2) // self.window],
pitchf[:, s // self.window : (t + self.t_pad2) // self.window],
times,
index,
big_npy,
index_rate,
version,
protect,
)[self.t_pad_tgt : -self.t_pad_tgt]
)
else:
audio_opt.append(
self.vc(
model,
net_g,
sid,
audio_pad[s : t + self.t_pad2 + self.window],
None,
None,
times,
index,
big_npy,
index_rate,
version,
protect,
)[self.t_pad_tgt : -self.t_pad_tgt]
)
s = t
if if_f0 == 1:
audio_opt.append(
self.vc(
model,
net_g,
sid,
audio_pad[t:],
pitch[:, t // self.window :] if t is not None else pitch,
pitchf[:, t // self.window :] if t is not None else pitchf,
times,
index,
big_npy,
index_rate,
version,
protect,
)[self.t_pad_tgt : -self.t_pad_tgt]
)
else:
audio_opt.append(
self.vc(
model,
net_g,
sid,
audio_pad[t:],
None,
None,
times,
index,
big_npy,
index_rate,
version,
protect,
)[self.t_pad_tgt : -self.t_pad_tgt]
)
audio_opt = np.concatenate(audio_opt)
if rms_mix_rate != 1:
audio_opt = change_rms(audio, 16000, audio_opt, tgt_sr, rms_mix_rate)
if resample_sr >= 16000 and tgt_sr != resample_sr:
audio_opt = librosa.resample(
audio_opt, orig_sr=tgt_sr, target_sr=resample_sr
)
audio_max = np.abs(audio_opt).max() / 0.99
max_int16 = 32768
if audio_max > 1:
max_int16 /= audio_max
audio_opt = (audio_opt * max_int16).astype(np.int16)
del pitch, pitchf, sid
if torch.cuda.is_available():
torch.cuda.empty_cache()
return audio_opt