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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils import weight_norm
from torchaudio.transforms import Resample
from .nvSTFT import STFT
from .pcmer import PCmer
def l2_regularization(model, l2_alpha):
l2_loss = []
for module in model.modules():
if type(module) is nn.Conv2d:
l2_loss.append((module.weight ** 2).sum() / 2.0)
return l2_alpha * sum(l2_loss)
class FCPE(nn.Module):
def __init__(
self,
input_channel=128,
out_dims=360,
n_layers=12,
n_chans=512,
use_siren=False,
use_full=False,
loss_mse_scale=10,
loss_l2_regularization=False,
loss_l2_regularization_scale=1,
loss_grad1_mse=False,
loss_grad1_mse_scale=1,
f0_max=1975.5,
f0_min=32.70,
confidence=False,
threshold=0.05,
use_input_conv=True
):
super().__init__()
if use_siren is True:
raise ValueError("Siren is not supported yet.")
if use_full is True:
raise ValueError("Full model is not supported yet.")
self.loss_mse_scale = loss_mse_scale if (loss_mse_scale is not None) else 10
self.loss_l2_regularization = loss_l2_regularization if (loss_l2_regularization is not None) else False
self.loss_l2_regularization_scale = loss_l2_regularization_scale if (loss_l2_regularization_scale
is not None) else 1
self.loss_grad1_mse = loss_grad1_mse if (loss_grad1_mse is not None) else False
self.loss_grad1_mse_scale = loss_grad1_mse_scale if (loss_grad1_mse_scale is not None) else 1
self.f0_max = f0_max if (f0_max is not None) else 1975.5
self.f0_min = f0_min if (f0_min is not None) else 32.70
self.confidence = confidence if (confidence is not None) else False
self.threshold = threshold if (threshold is not None) else 0.05
self.use_input_conv = use_input_conv if (use_input_conv is not None) else True
self.cent_table_b = torch.Tensor(
np.linspace(self.f0_to_cent(torch.Tensor([f0_min]))[0], self.f0_to_cent(torch.Tensor([f0_max]))[0],
out_dims))
self.register_buffer("cent_table", self.cent_table_b)
# conv in stack
_leaky = nn.LeakyReLU()
self.stack = nn.Sequential(
nn.Conv1d(input_channel, n_chans, 3, 1, 1),
nn.GroupNorm(4, n_chans),
_leaky,
nn.Conv1d(n_chans, n_chans, 3, 1, 1))
# transformer
self.decoder = PCmer(
num_layers=n_layers,
num_heads=8,
dim_model=n_chans,
dim_keys=n_chans,
dim_values=n_chans,
residual_dropout=0.1,
attention_dropout=0.1)
self.norm = nn.LayerNorm(n_chans)
# out
self.n_out = out_dims
self.dense_out = weight_norm(
nn.Linear(n_chans, self.n_out))
def forward(self, mel, infer=True, gt_f0=None, return_hz_f0=False, cdecoder = "local_argmax"):
"""
input:
B x n_frames x n_unit
return:
dict of B x n_frames x feat
"""
if cdecoder == "argmax":
self.cdecoder = self.cents_decoder
elif cdecoder == "local_argmax":
self.cdecoder = self.cents_local_decoder
if self.use_input_conv:
x = self.stack(mel.transpose(1, 2)).transpose(1, 2)
else:
x = mel
x = self.decoder(x)
x = self.norm(x)
x = self.dense_out(x) # [B,N,D]
x = torch.sigmoid(x)
if not infer:
gt_cent_f0 = self.f0_to_cent(gt_f0) # mel f0 #[B,N,1]
gt_cent_f0 = self.gaussian_blurred_cent(gt_cent_f0) # #[B,N,out_dim]
loss_all = self.loss_mse_scale * F.binary_cross_entropy(x, gt_cent_f0) # bce loss
# l2 regularization
if self.loss_l2_regularization:
loss_all = loss_all + l2_regularization(model=self, l2_alpha=self.loss_l2_regularization_scale)
x = loss_all
if infer:
x = self.cdecoder(x)
x = self.cent_to_f0(x)
if not return_hz_f0:
x = (1 + x / 700).log()
return x
def cents_decoder(self, y, mask=True):
B, N, _ = y.size()
ci = self.cent_table[None, None, :].expand(B, N, -1)
rtn = torch.sum(ci * y, dim=-1, keepdim=True) / torch.sum(y, dim=-1, keepdim=True) # cents: [B,N,1]
if mask:
confident = torch.max(y, dim=-1, keepdim=True)[0]
confident_mask = torch.ones_like(confident)
confident_mask[confident <= self.threshold] = float("-INF")
rtn = rtn * confident_mask
if self.confidence:
return rtn, confident
else:
return rtn
def cents_local_decoder(self, y, mask=True):
B, N, _ = y.size()
ci = self.cent_table[None, None, :].expand(B, N, -1)
confident, max_index = torch.max(y, dim=-1, keepdim=True)
local_argmax_index = torch.arange(0,9).to(max_index.device) + (max_index - 4)
local_argmax_index[local_argmax_index<0] = 0
local_argmax_index[local_argmax_index>=self.n_out] = self.n_out - 1
ci_l = torch.gather(ci,-1,local_argmax_index)
y_l = torch.gather(y,-1,local_argmax_index)
rtn = torch.sum(ci_l * y_l, dim=-1, keepdim=True) / torch.sum(y_l, dim=-1, keepdim=True) # cents: [B,N,1]
if mask:
confident_mask = torch.ones_like(confident)
confident_mask[confident <= self.threshold] = float("-INF")
rtn = rtn * confident_mask
if self.confidence:
return rtn, confident
else:
return rtn
def cent_to_f0(self, cent):
return 10. * 2 ** (cent / 1200.)
def f0_to_cent(self, f0):
return 1200. * torch.log2(f0 / 10.)
def gaussian_blurred_cent(self, cents): # cents: [B,N,1]
mask = (cents > 0.1) & (cents < (1200. * np.log2(self.f0_max / 10.)))
B, N, _ = cents.size()
ci = self.cent_table[None, None, :].expand(B, N, -1)
return torch.exp(-torch.square(ci - cents) / 1250) * mask.float()
class FCPEInfer:
def __init__(self, model_path, device=None, dtype=torch.float32):
if device is None:
device = 'cuda' if torch.cuda.is_available() else 'cpu'
self.device = device
ckpt = torch.load(model_path, map_location=torch.device(self.device))
self.args = DotDict(ckpt["config"])
self.dtype = dtype
model = FCPE(
input_channel=self.args.model.input_channel,
out_dims=self.args.model.out_dims,
n_layers=self.args.model.n_layers,
n_chans=self.args.model.n_chans,
use_siren=self.args.model.use_siren,
use_full=self.args.model.use_full,
loss_mse_scale=self.args.loss.loss_mse_scale,
loss_l2_regularization=self.args.loss.loss_l2_regularization,
loss_l2_regularization_scale=self.args.loss.loss_l2_regularization_scale,
loss_grad1_mse=self.args.loss.loss_grad1_mse,
loss_grad1_mse_scale=self.args.loss.loss_grad1_mse_scale,
f0_max=self.args.model.f0_max,
f0_min=self.args.model.f0_min,
confidence=self.args.model.confidence,
)
model.to(self.device).to(self.dtype)
model.load_state_dict(ckpt['model'])
model.eval()
self.model = model
self.wav2mel = Wav2Mel(self.args, dtype=self.dtype, device=self.device)
@torch.no_grad()
def __call__(self, audio, sr, threshold=0.05):
self.model.threshold = threshold
audio = audio[None,:]
mel = self.wav2mel(audio=audio, sample_rate=sr).to(self.dtype)
f0 = self.model(mel=mel, infer=True, return_hz_f0=True)
return f0
class Wav2Mel:
def __init__(self, args, device=None, dtype=torch.float32):
# self.args = args
self.sampling_rate = args.mel.sampling_rate
self.hop_size = args.mel.hop_size
if device is None:
device = 'cuda' if torch.cuda.is_available() else 'cpu'
self.device = device
self.dtype = dtype
self.stft = STFT(
args.mel.sampling_rate,
args.mel.num_mels,
args.mel.n_fft,
args.mel.win_size,
args.mel.hop_size,
args.mel.fmin,
args.mel.fmax
)
self.resample_kernel = {}
def extract_nvstft(self, audio, keyshift=0, train=False):
mel = self.stft.get_mel(audio, keyshift=keyshift, train=train).transpose(1, 2) # B, n_frames, bins
return mel
def extract_mel(self, audio, sample_rate, keyshift=0, train=False):
audio = audio.to(self.dtype).to(self.device)
# resample
if sample_rate == self.sampling_rate:
audio_res = audio
else:
key_str = str(sample_rate)
if key_str not in self.resample_kernel:
self.resample_kernel[key_str] = Resample(sample_rate, self.sampling_rate, lowpass_filter_width=128)
self.resample_kernel[key_str] = self.resample_kernel[key_str].to(self.dtype).to(self.device)
audio_res = self.resample_kernel[key_str](audio)
# extract
mel = self.extract_nvstft(audio_res, keyshift=keyshift, train=train) # B, n_frames, bins
n_frames = int(audio.shape[1] // self.hop_size) + 1
if n_frames > int(mel.shape[1]):
mel = torch.cat((mel, mel[:, -1:, :]), 1)
if n_frames < int(mel.shape[1]):
mel = mel[:, :n_frames, :]
return mel
def __call__(self, audio, sample_rate, keyshift=0, train=False):
return self.extract_mel(audio, sample_rate, keyshift=keyshift, train=train)
class DotDict(dict):
def __getattr__(*args):
val = dict.get(*args)
return DotDict(val) if type(val) is dict else val
__setattr__ = dict.__setitem__
__delattr__ = dict.__delitem__
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