chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,4 @@
|
||||
# Copyright (c) Facebook, Inc. and its affiliates.
|
||||
#
|
||||
# This source code is licensed under the MIT license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
@@ -0,0 +1,473 @@
|
||||
# Copyright (c) Facebook, Inc. and its affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
# author: adefossez
|
||||
|
||||
import math
|
||||
import time
|
||||
|
||||
import torch as th
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
from .resample import downsample2, upsample2
|
||||
from .utils import capture_init
|
||||
|
||||
|
||||
class BLSTM(nn.Module):
|
||||
def __init__(self, dim, layers=2, bi=True):
|
||||
super().__init__()
|
||||
klass = nn.LSTM
|
||||
self.lstm = klass(
|
||||
bidirectional=bi, num_layers=layers, hidden_size=dim, input_size=dim
|
||||
)
|
||||
self.linear = None
|
||||
if bi:
|
||||
self.linear = nn.Linear(2 * dim, dim)
|
||||
|
||||
def forward(self, x, hidden=None):
|
||||
x, hidden = self.lstm(x, hidden)
|
||||
if self.linear:
|
||||
x = self.linear(x)
|
||||
return x, hidden
|
||||
|
||||
|
||||
def rescale_conv(conv, reference):
|
||||
std = conv.weight.std().detach()
|
||||
scale = (std / reference)**0.5
|
||||
conv.weight.data /= scale
|
||||
if conv.bias is not None:
|
||||
conv.bias.data /= scale
|
||||
|
||||
|
||||
def rescale_module(module, reference):
|
||||
for sub in module.modules():
|
||||
if isinstance(sub, (nn.Conv1d, nn.ConvTranspose1d)):
|
||||
rescale_conv(sub, reference)
|
||||
|
||||
|
||||
class Demucs(nn.Module):
|
||||
"""
|
||||
Demucs speech enhancement model.
|
||||
Args:
|
||||
- chin (int): number of input channels.
|
||||
- chout (int): number of output channels.
|
||||
- hidden (int): number of initial hidden channels.
|
||||
- depth (int): number of layers.
|
||||
- kernel_size (int): kernel size for each layer.
|
||||
- stride (int): stride for each layer.
|
||||
- causal (bool): if false, uses BiLSTM instead of LSTM.
|
||||
- resample (int): amount of resampling to apply to the input/output.
|
||||
Can be one of 1, 2 or 4.
|
||||
- growth (float): number of channels is multiplied by this for every layer.
|
||||
- max_hidden (int): maximum number of channels. Can be useful to
|
||||
control the size/speed of the model.
|
||||
- normalize (bool): if true, normalize the input.
|
||||
- glu (bool): if true uses GLU instead of ReLU in 1x1 convolutions.
|
||||
- rescale (float): controls custom weight initialization.
|
||||
See https://arxiv.org/abs/1911.13254.
|
||||
- floor (float): stability flooring when normalizing.
|
||||
|
||||
"""
|
||||
@capture_init
|
||||
def __init__(self,
|
||||
chin=1,
|
||||
chout=1,
|
||||
hidden=48,
|
||||
depth=5,
|
||||
kernel_size=8,
|
||||
stride=4,
|
||||
causal=True,
|
||||
resample=4,
|
||||
growth=2,
|
||||
max_hidden=10_000,
|
||||
normalize=True,
|
||||
glu=True,
|
||||
rescale=0.1,
|
||||
floor=1e-3):
|
||||
|
||||
super().__init__()
|
||||
if resample not in [1, 2, 4]:
|
||||
raise ValueError("Resample should be 1, 2 or 4.")
|
||||
|
||||
self.chin = chin
|
||||
self.chout = chout
|
||||
self.hidden = hidden
|
||||
self.depth = depth
|
||||
self.kernel_size = kernel_size
|
||||
self.stride = stride
|
||||
self.causal = causal
|
||||
self.floor = floor
|
||||
self.resample = resample
|
||||
self.normalize = normalize
|
||||
|
||||
self.encoder = nn.ModuleList()
|
||||
self.decoder = nn.ModuleList()
|
||||
activation = nn.GLU(1) if glu else nn.ReLU()
|
||||
ch_scale = 2 if glu else 1
|
||||
|
||||
for index in range(depth):
|
||||
encode = []
|
||||
encode += [
|
||||
nn.Conv1d(chin, hidden, kernel_size, stride),
|
||||
nn.ReLU(),
|
||||
nn.Conv1d(hidden, hidden * ch_scale, 1), activation,
|
||||
]
|
||||
self.encoder.append(nn.Sequential(*encode))
|
||||
|
||||
decode = []
|
||||
decode += [
|
||||
nn.Conv1d(hidden, ch_scale * hidden, 1), activation,
|
||||
nn.ConvTranspose1d(hidden, chout, kernel_size, stride),
|
||||
]
|
||||
if index > 0:
|
||||
decode.append(nn.ReLU())
|
||||
self.decoder.insert(0, nn.Sequential(*decode))
|
||||
chout = hidden
|
||||
chin = hidden
|
||||
hidden = min(int(growth * hidden), max_hidden)
|
||||
|
||||
self.lstm = BLSTM(chin, bi=not causal)
|
||||
if rescale:
|
||||
rescale_module(self, reference=rescale)
|
||||
|
||||
def valid_length(self, length):
|
||||
"""
|
||||
Return the nearest valid length to use with the model so that
|
||||
there is no time steps left over in a convolutions, e.g. for all
|
||||
layers, size of the input - kernel_size % stride = 0.
|
||||
|
||||
If the mixture has a valid length, the estimated sources
|
||||
will have exactly the same length.
|
||||
"""
|
||||
length = math.ceil(length * self.resample)
|
||||
for _ in range(self.depth):
|
||||
length = math.ceil((length - self.kernel_size) / self.stride) + 1
|
||||
length = max(length, 1)
|
||||
for _ in range(self.depth):
|
||||
length = (length - 1) * self.stride + self.kernel_size
|
||||
length = int(math.ceil(length / self.resample))
|
||||
return int(length)
|
||||
|
||||
@property
|
||||
def total_stride(self):
|
||||
return self.stride ** self.depth // self.resample
|
||||
|
||||
def forward(self, mix):
|
||||
if mix.dim() == 2:
|
||||
mix = mix.unsqueeze(1)
|
||||
|
||||
if self.normalize:
|
||||
mono = mix.mean(dim=1, keepdim=True)
|
||||
std = mono.std(dim=-1, keepdim=True)
|
||||
mix = mix / (self.floor + std)
|
||||
else:
|
||||
std = 1
|
||||
length = mix.shape[-1]
|
||||
x = mix
|
||||
x = F.pad(x, (0, self.valid_length(length) - length))
|
||||
if self.resample == 2:
|
||||
x = upsample2(x)
|
||||
elif self.resample == 4:
|
||||
x = upsample2(x)
|
||||
x = upsample2(x)
|
||||
skips = []
|
||||
for encode in self.encoder:
|
||||
x = encode(x)
|
||||
skips.append(x)
|
||||
x = x.permute(2, 0, 1)
|
||||
x, _ = self.lstm(x)
|
||||
x = x.permute(1, 2, 0)
|
||||
for decode in self.decoder:
|
||||
skip = skips.pop(-1)
|
||||
x = x + skip[..., :x.shape[-1]]
|
||||
x = decode(x)
|
||||
if self.resample == 2:
|
||||
x = downsample2(x)
|
||||
elif self.resample == 4:
|
||||
x = downsample2(x)
|
||||
x = downsample2(x)
|
||||
|
||||
x = x[..., :length]
|
||||
return std * x
|
||||
|
||||
|
||||
def fast_conv(conv, x):
|
||||
"""
|
||||
Faster convolution evaluation if either kernel size is 1
|
||||
or length of sequence is 1.
|
||||
"""
|
||||
batch, chin, length = x.shape
|
||||
chout, chin, kernel = conv.weight.shape
|
||||
assert batch == 1
|
||||
if kernel == 1:
|
||||
x = x.view(chin, length)
|
||||
out = th.addmm(conv.bias.view(-1, 1),
|
||||
conv.weight.view(chout, chin), x)
|
||||
elif length == kernel:
|
||||
x = x.view(chin * kernel, 1)
|
||||
out = th.addmm(conv.bias.view(-1, 1),
|
||||
conv.weight.view(chout, chin * kernel), x)
|
||||
else:
|
||||
out = conv(x)
|
||||
return out.view(batch, chout, -1)
|
||||
|
||||
|
||||
class DemucsStreamer:
|
||||
"""
|
||||
Streaming implementation for Demucs. It supports being fed with any amount
|
||||
of audio at a time. You will get back as much audio as possible at that
|
||||
point.
|
||||
|
||||
Args:
|
||||
- demucs (Demucs): Demucs model.
|
||||
- dry (float): amount of dry (e.g. input) signal to keep. 0 is maximum
|
||||
noise removal, 1 just returns the input signal. Small values > 0
|
||||
allows to limit distortions.
|
||||
- num_frames (int): number of frames to process at once. Higher values
|
||||
will increase overall latency but improve the real time factor.
|
||||
- resample_lookahead (int): extra lookahead used for the resampling.
|
||||
- resample_buffer (int): size of the buffer of previous inputs/outputs
|
||||
kept for resampling.
|
||||
"""
|
||||
def __init__(self, demucs,
|
||||
dry=0,
|
||||
num_frames=1,
|
||||
resample_lookahead=64,
|
||||
resample_buffer=256):
|
||||
device = next(iter(demucs.parameters())).device
|
||||
self.demucs = demucs
|
||||
self.lstm_state = None
|
||||
self.conv_state = None
|
||||
self.dry = dry
|
||||
self.resample_lookahead = resample_lookahead
|
||||
resample_buffer = min(demucs.total_stride, resample_buffer)
|
||||
self.resample_buffer = resample_buffer
|
||||
self.frame_length = demucs.valid_length(1) + \
|
||||
demucs.total_stride * (num_frames - 1)
|
||||
self.total_length = self.frame_length + self.resample_lookahead
|
||||
self.stride = demucs.total_stride * num_frames
|
||||
self.resample_in = th.zeros(demucs.chin, resample_buffer, device=device)
|
||||
self.resample_out = th.zeros(
|
||||
demucs.chin, resample_buffer, device=device
|
||||
)
|
||||
|
||||
self.frames = 0
|
||||
self.total_time = 0
|
||||
self.variance = 0
|
||||
self.pending = th.zeros(demucs.chin, 0, device=device)
|
||||
|
||||
bias = demucs.decoder[0][2].bias
|
||||
weight = demucs.decoder[0][2].weight
|
||||
chin, chout, kernel = weight.shape
|
||||
self._bias = bias.view(-1, 1).repeat(1, kernel).view(-1, 1)
|
||||
self._weight = weight.permute(1, 2, 0).contiguous()
|
||||
|
||||
def reset_time_per_frame(self):
|
||||
self.total_time = 0
|
||||
self.frames = 0
|
||||
|
||||
@property
|
||||
def time_per_frame(self):
|
||||
return self.total_time / self.frames
|
||||
|
||||
def flush(self):
|
||||
"""
|
||||
Flush remaining audio by padding it with zero. Call this
|
||||
when you have no more input and want to get back the last chunk of audio.
|
||||
"""
|
||||
pending_length = self.pending.shape[1]
|
||||
padding = th.zeros(
|
||||
self.demucs.chin, self.total_length, device=self.pending.device
|
||||
)
|
||||
out = self.feed(padding)
|
||||
return out[:, :pending_length]
|
||||
|
||||
def feed(self, wav):
|
||||
"""
|
||||
Apply the model to mix using true real time evaluation.
|
||||
Normalization is done online as is the resampling.
|
||||
"""
|
||||
begin = time.time()
|
||||
demucs = self.demucs
|
||||
resample_buffer = self.resample_buffer
|
||||
stride = self.stride
|
||||
resample = demucs.resample
|
||||
|
||||
if wav.dim() != 2:
|
||||
raise ValueError("input wav should be two dimensional.")
|
||||
chin, _ = wav.shape
|
||||
if chin != demucs.chin:
|
||||
raise ValueError(f"Expected {demucs.chin} channels, got {chin}")
|
||||
|
||||
self.pending = th.cat([self.pending, wav], dim=1)
|
||||
outs = []
|
||||
while self.pending.shape[1] >= self.total_length:
|
||||
self.frames += 1
|
||||
frame = self.pending[:, :self.total_length]
|
||||
dry_signal = frame[:, :stride]
|
||||
if demucs.normalize:
|
||||
mono = frame.mean(0)
|
||||
variance = (mono**2).mean()
|
||||
self.variance = variance / self.frames + \
|
||||
(1 - 1 / self.frames) * self.variance
|
||||
frame = frame / (demucs.floor + math.sqrt(self.variance))
|
||||
frame = th.cat([self.resample_in, frame], dim=-1)
|
||||
self.resample_in[:] = frame[:, stride - resample_buffer:stride]
|
||||
|
||||
if resample == 4:
|
||||
frame = upsample2(upsample2(frame))
|
||||
elif resample == 2:
|
||||
frame = upsample2(frame)
|
||||
# remove pre sampling buffer
|
||||
frame = frame[:, resample * resample_buffer:]
|
||||
# remove extra samples after window
|
||||
frame = frame[:, :resample * self.frame_length]
|
||||
|
||||
out, extra = self._separate_frame(frame)
|
||||
padded_out = th.cat([self.resample_out, out, extra], 1)
|
||||
self.resample_out[:] = out[:, -resample_buffer:]
|
||||
if resample == 4:
|
||||
out = downsample2(downsample2(padded_out))
|
||||
elif resample == 2:
|
||||
out = downsample2(padded_out)
|
||||
else:
|
||||
out = padded_out
|
||||
|
||||
out = out[:, resample_buffer // resample:]
|
||||
out = out[:, :stride]
|
||||
|
||||
if demucs.normalize:
|
||||
out *= math.sqrt(self.variance)
|
||||
out = self.dry * dry_signal + (1 - self.dry) * out
|
||||
outs.append(out)
|
||||
self.pending = self.pending[:, stride:]
|
||||
|
||||
self.total_time += time.time() - begin
|
||||
if outs:
|
||||
out = th.cat(outs, 1)
|
||||
else:
|
||||
out = th.zeros(chin, 0, device=wav.device)
|
||||
return out
|
||||
|
||||
def _separate_frame(self, frame):
|
||||
demucs = self.demucs
|
||||
skips = []
|
||||
next_state = []
|
||||
first = self.conv_state is None
|
||||
stride = self.stride * demucs.resample
|
||||
x = frame[None]
|
||||
for idx, encode in enumerate(demucs.encoder):
|
||||
stride //= demucs.stride
|
||||
length = x.shape[2]
|
||||
if idx == demucs.depth - 1:
|
||||
# This is sligthly faster for the last conv
|
||||
x = fast_conv(encode[0], x)
|
||||
x = encode[1](x)
|
||||
x = fast_conv(encode[2], x)
|
||||
x = encode[3](x)
|
||||
else:
|
||||
if not first:
|
||||
prev = self.conv_state.pop(0)
|
||||
prev = prev[..., stride:]
|
||||
tgt = (length - demucs.kernel_size) // demucs.stride + 1
|
||||
missing = tgt - prev.shape[-1]
|
||||
offset = length - demucs.kernel_size - \
|
||||
demucs.stride * (missing - 1)
|
||||
x = x[..., offset:]
|
||||
x = encode[1](encode[0](x))
|
||||
x = fast_conv(encode[2], x)
|
||||
x = encode[3](x)
|
||||
if not first:
|
||||
x = th.cat([prev, x], -1)
|
||||
next_state.append(x)
|
||||
skips.append(x)
|
||||
|
||||
x = x.permute(2, 0, 1)
|
||||
x, self.lstm_state = demucs.lstm(x, self.lstm_state)
|
||||
x = x.permute(1, 2, 0)
|
||||
# In the following, x contains only correct samples, i.e. the one
|
||||
# for which each time position is covered by two window of the upper
|
||||
# layer. extra contains extra samples to the right, and is used only as
|
||||
# a better padding for the online resampling.
|
||||
extra = None
|
||||
for idx, decode in enumerate(demucs.decoder):
|
||||
skip = skips.pop(-1)
|
||||
x += skip[..., :x.shape[-1]]
|
||||
x = fast_conv(decode[0], x)
|
||||
x = decode[1](x)
|
||||
|
||||
if extra is not None:
|
||||
skip = skip[..., x.shape[-1]:]
|
||||
extra += skip[..., :extra.shape[-1]]
|
||||
extra = decode[2](decode[1](decode[0](extra)))
|
||||
x = decode[2](x)
|
||||
next_state.append(
|
||||
x[..., -demucs.stride:] - decode[2].bias.view(-1, 1)
|
||||
)
|
||||
if extra is None:
|
||||
extra = x[..., -demucs.stride:]
|
||||
else:
|
||||
extra[..., :demucs.stride] += next_state[-1]
|
||||
x = x[..., :-demucs.stride]
|
||||
|
||||
if not first:
|
||||
prev = self.conv_state.pop(0)
|
||||
x[..., :demucs.stride] += prev
|
||||
if idx != demucs.depth - 1:
|
||||
x = decode[3](x)
|
||||
extra = decode[3](extra)
|
||||
self.conv_state = next_state
|
||||
return x[0], extra[0]
|
||||
|
||||
|
||||
def test():
|
||||
import argparse
|
||||
parser = argparse.ArgumentParser(
|
||||
"denoiser.demucs",
|
||||
description="Benchmark the streaming Demucs implementation, as well as "
|
||||
"checking the delta with the offline implementation.")
|
||||
parser.add_argument("--depth", default=5, type=int)
|
||||
parser.add_argument("--resample", default=4, type=int)
|
||||
parser.add_argument("--hidden", default=48, type=int)
|
||||
parser.add_argument("--sample_rate", default=16000, type=float)
|
||||
parser.add_argument("--device", default="cpu")
|
||||
parser.add_argument("-t", "--num_threads", type=int)
|
||||
parser.add_argument("-f", "--num_frames", type=int, default=1)
|
||||
args = parser.parse_args()
|
||||
if args.num_threads:
|
||||
th.set_num_threads(args.num_threads)
|
||||
sr = args.sample_rate
|
||||
sr_ms = sr / 1000
|
||||
demucs = Demucs(
|
||||
depth=args.depth, hidden=args.hidden, resample=args.resample
|
||||
).to(args.device)
|
||||
x = th.randn(1, int(sr * 4)).to(args.device)
|
||||
out = demucs(x[None])[0]
|
||||
streamer = DemucsStreamer(demucs, num_frames=args.num_frames)
|
||||
out_rt = []
|
||||
frame_size = streamer.total_length
|
||||
with th.no_grad():
|
||||
while x.shape[1] > 0:
|
||||
out_rt.append(streamer.feed(x[:, :frame_size]))
|
||||
x = x[:, frame_size:]
|
||||
frame_size = streamer.demucs.total_stride
|
||||
out_rt.append(streamer.flush())
|
||||
out_rt = th.cat(out_rt, 1)
|
||||
model_size = sum(p.numel() for p in demucs.parameters()) * 4 / 2**20
|
||||
initial_lag = streamer.total_length / sr_ms
|
||||
tpf = 1000 * streamer.time_per_frame
|
||||
print(f"model size: {model_size:.1f}MB, ", end='')
|
||||
print(f"delta batch/streaming: {th.norm(out - out_rt) / th.norm(out):.2%}")
|
||||
print(f"initial lag: {initial_lag:.1f}ms, ", end='')
|
||||
print(f"stride: {streamer.stride * args.num_frames / sr_ms:.1f}ms")
|
||||
print(f"time per frame: {tpf:.1f}ms, ", end='')
|
||||
rtf = (1000 * streamer.time_per_frame) / (streamer.stride / sr_ms)
|
||||
print(f"RTF: {rtf:.2f}")
|
||||
print(f"Total lag with computation: {initial_lag + tpf:.1f}ms")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test()
|
||||
@@ -0,0 +1,81 @@
|
||||
# Copyright (c) Facebook, Inc. and its affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
# author: adefossez
|
||||
|
||||
import logging
|
||||
|
||||
import torch.hub
|
||||
|
||||
from .demucs import Demucs
|
||||
from .utils import deserialize_model
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
ROOT = "https://dl.fbaipublicfiles.com/adiyoss/denoiser/"
|
||||
DNS_48_URL = ROOT + "dns48-11decc9d8e3f0998.th"
|
||||
DNS_64_URL = ROOT + "dns64-a7761ff99a7d5bb6.th"
|
||||
MASTER_64_URL = ROOT + "master64-8a5dfb4bb92753dd.th"
|
||||
|
||||
|
||||
def _demucs(pretrained, url, **kwargs):
|
||||
model = Demucs(**kwargs)
|
||||
if pretrained:
|
||||
state_dict = torch.hub.load_state_dict_from_url(url, map_location='cpu')
|
||||
model.load_state_dict(state_dict)
|
||||
return model
|
||||
|
||||
|
||||
def dns48(pretrained=True):
|
||||
return _demucs(pretrained, DNS_48_URL, hidden=48)
|
||||
|
||||
|
||||
def dns64(pretrained=True):
|
||||
return _demucs(pretrained, DNS_64_URL, hidden=64)
|
||||
|
||||
|
||||
def master64(pretrained=True):
|
||||
return _demucs(pretrained, MASTER_64_URL, hidden=64)
|
||||
|
||||
|
||||
def add_model_flags(parser):
|
||||
group = parser.add_mutually_exclusive_group(required=False)
|
||||
group.add_argument(
|
||||
"-m", "--model_path", help="Path to local trained model."
|
||||
)
|
||||
group.add_argument(
|
||||
"--dns48", action="store_true",
|
||||
help="Use pre-trained real time H=48 model trained on DNS."
|
||||
)
|
||||
group.add_argument(
|
||||
"--dns64", action="store_true",
|
||||
help="Use pre-trained real time H=64 model trained on DNS."
|
||||
)
|
||||
group.add_argument(
|
||||
"--master64", action="store_true",
|
||||
help="Use pre-trained real time H=64 model trained on DNS and Valentini."
|
||||
)
|
||||
|
||||
|
||||
def get_model(args):
|
||||
"""
|
||||
Load local model package or torchhub pre-trained model.
|
||||
"""
|
||||
if args.model_path:
|
||||
logger.info("Loading model from %s", args.model_path)
|
||||
pkg = torch.load(args.model_path)
|
||||
model = deserialize_model(pkg)
|
||||
elif args.dns64:
|
||||
logger.info("Loading pre-trained real time H=64 model trained on DNS.")
|
||||
model = dns64()
|
||||
elif args.master64:
|
||||
logger.info(
|
||||
"Loading pre-trained real time H=64 model trained on DNS and Valentini."
|
||||
)
|
||||
model = master64()
|
||||
else:
|
||||
logger.info("Loading pre-trained real time H=48 model trained on DNS.")
|
||||
model = dns48()
|
||||
logger.debug(model)
|
||||
return model
|
||||
@@ -0,0 +1,79 @@
|
||||
# Copyright (c) Facebook, Inc. and its affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
# author: adefossez
|
||||
|
||||
import math
|
||||
|
||||
import torch as th
|
||||
from torch.nn import functional as F
|
||||
|
||||
|
||||
def sinc(t):
|
||||
"""sinc.
|
||||
|
||||
:param t: the input tensor
|
||||
"""
|
||||
return th.where(t == 0, th.tensor(1., device=t.device, dtype=t.dtype),
|
||||
th.sin(t) / t)
|
||||
|
||||
|
||||
def kernel_upsample2(zeros=56):
|
||||
"""kernel_upsample2.
|
||||
|
||||
"""
|
||||
win = th.hann_window(4 * zeros + 1, periodic=False)
|
||||
winodd = win[1::2]
|
||||
t = th.linspace(-zeros + 0.5, zeros - 0.5, 2 * zeros)
|
||||
t *= math.pi
|
||||
kernel = (sinc(t) * winodd).view(1, 1, -1)
|
||||
return kernel
|
||||
|
||||
|
||||
def upsample2(x, zeros=56):
|
||||
"""
|
||||
Upsampling the input by 2 using sinc interpolation.
|
||||
Smith, Julius, and Phil Gossett. "A flexible sampling-rate conversion method."
|
||||
ICASSP'84. IEEE International Conference on Acoustics, Speech, and Signal Processing.
|
||||
Vol. 9. IEEE, 1984.
|
||||
"""
|
||||
*other, time = x.shape
|
||||
kernel = kernel_upsample2(zeros).to(x)
|
||||
out = F.conv1d(x.view(-1, 1, time), kernel, padding=zeros)[..., 1:].view(
|
||||
*other, time
|
||||
)
|
||||
y = th.stack([x, out], dim=-1)
|
||||
return y.view(*other, -1)
|
||||
|
||||
|
||||
def kernel_downsample2(zeros=56):
|
||||
"""kernel_downsample2.
|
||||
|
||||
"""
|
||||
win = th.hann_window(4 * zeros + 1, periodic=False)
|
||||
winodd = win[1::2]
|
||||
t = th.linspace(-zeros + 0.5, zeros - 0.5, 2 * zeros)
|
||||
t.mul_(math.pi)
|
||||
kernel = (sinc(t) * winodd).view(1, 1, -1)
|
||||
return kernel
|
||||
|
||||
|
||||
def downsample2(x, zeros=56):
|
||||
"""
|
||||
Downsampling the input by 2 using sinc interpolation.
|
||||
Smith, Julius, and Phil Gossett. "A flexible sampling-rate conversion method."
|
||||
ICASSP'84. IEEE International Conference on Acoustics, Speech, and Signal Processing.
|
||||
Vol. 9. IEEE, 1984.
|
||||
"""
|
||||
if x.shape[-1] % 2 != 0:
|
||||
x = F.pad(x, (0, 1))
|
||||
xeven = x[..., ::2]
|
||||
xodd = x[..., 1::2]
|
||||
*other, time = xodd.shape
|
||||
kernel = kernel_downsample2(zeros).to(x)
|
||||
out = xeven + F.conv1d(
|
||||
xodd.view(-1, 1, time), kernel, padding=zeros
|
||||
)[..., :-1].view(*other, time)
|
||||
return out.view(*other, -1).mul(0.5)
|
||||
@@ -0,0 +1,176 @@
|
||||
# Copyright (c) Facebook, Inc. and its affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
# author: adefossez
|
||||
|
||||
import functools
|
||||
import logging
|
||||
from contextlib import contextmanager
|
||||
import inspect
|
||||
import time
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
EPS = 1e-8
|
||||
|
||||
|
||||
def capture_init(init):
|
||||
"""capture_init.
|
||||
|
||||
Decorate `__init__` with this, and you can then
|
||||
recover the *args and **kwargs passed to it in `self._init_args_kwargs`
|
||||
"""
|
||||
@functools.wraps(init)
|
||||
def __init__(self, *args, **kwargs):
|
||||
self._init_args_kwargs = (args, kwargs)
|
||||
init(self, *args, **kwargs)
|
||||
|
||||
return __init__
|
||||
|
||||
|
||||
def deserialize_model(package, strict=False):
|
||||
"""deserialize_model.
|
||||
|
||||
"""
|
||||
klass = package['class']
|
||||
if strict:
|
||||
model = klass(*package['args'], **package['kwargs'])
|
||||
else:
|
||||
sig = inspect.signature(klass)
|
||||
kw = package['kwargs']
|
||||
for key in list(kw):
|
||||
if key not in sig.parameters:
|
||||
logger.warning("Dropping inexistant parameter %s", key)
|
||||
del kw[key]
|
||||
model = klass(*package['args'], **kw)
|
||||
model.load_state_dict(package['state'])
|
||||
return model
|
||||
|
||||
|
||||
def copy_state(state):
|
||||
return {k: v.cpu().clone() for k, v in state.items()}
|
||||
|
||||
|
||||
def serialize_model(model):
|
||||
args, kwargs = model._init_args_kwargs
|
||||
state = copy_state(model.state_dict())
|
||||
return {"class": model.__class__, "args": args, "kwargs": kwargs, "state": state}
|
||||
|
||||
|
||||
@contextmanager
|
||||
def swap_state(model, state):
|
||||
"""
|
||||
Context manager that swaps the state of a model, e.g:
|
||||
|
||||
# model is in old state
|
||||
with swap_state(model, new_state):
|
||||
# model in new state
|
||||
# model back to old state
|
||||
"""
|
||||
old_state = copy_state(model.state_dict())
|
||||
model.load_state_dict(state)
|
||||
try:
|
||||
yield
|
||||
finally:
|
||||
model.load_state_dict(old_state)
|
||||
|
||||
|
||||
def pull_metric(history, name):
|
||||
out = []
|
||||
for metrics in history:
|
||||
if name in metrics:
|
||||
out.append(metrics[name])
|
||||
return out
|
||||
|
||||
|
||||
class LogProgress:
|
||||
"""
|
||||
Sort of like tqdm but using log lines and not as real time.
|
||||
Args:
|
||||
- logger: logger obtained from `logging.getLogger`,
|
||||
- iterable: iterable object to wrap
|
||||
- updates (int): number of lines that will be printed, e.g.
|
||||
if `updates=5`, log every 1/5th of the total length.
|
||||
- total (int): length of the iterable, in case it does not support
|
||||
`len`.
|
||||
- name (str): prefix to use in the log.
|
||||
- level: logging level (like `logging.INFO`).
|
||||
"""
|
||||
def __init__(self,
|
||||
logger,
|
||||
iterable,
|
||||
updates=5,
|
||||
total=None,
|
||||
name="LogProgress",
|
||||
level=logging.INFO):
|
||||
self.iterable = iterable
|
||||
self.total = total or len(iterable)
|
||||
self.updates = updates
|
||||
self.name = name
|
||||
self.logger = logger
|
||||
self.level = level
|
||||
|
||||
def update(self, **infos):
|
||||
self._infos = infos
|
||||
|
||||
def __iter__(self):
|
||||
self._iterator = iter(self.iterable)
|
||||
self._index = -1
|
||||
self._infos = {}
|
||||
self._begin = time.time()
|
||||
return self
|
||||
|
||||
def __next__(self):
|
||||
self._index += 1
|
||||
try:
|
||||
value = next(self._iterator)
|
||||
except StopIteration:
|
||||
raise
|
||||
else:
|
||||
return value
|
||||
finally:
|
||||
log_every = max(1, self.total // self.updates)
|
||||
# logging is delayed by 1 it, in order to have the metrics from update
|
||||
if self._index >= 1 and self._index % log_every == 0:
|
||||
self._log()
|
||||
|
||||
def _log(self):
|
||||
self._speed = (1 + self._index) / (time.time() - self._begin)
|
||||
infos = " | ".join(f"{k.capitalize()} {v}" for k, v in self._infos.items())
|
||||
if self._speed < 1e-4:
|
||||
speed = "oo sec/it"
|
||||
elif self._speed < 0.1:
|
||||
speed = f"{1/self._speed:.1f} sec/it"
|
||||
else:
|
||||
speed = f"{self._speed:.1f} it/sec"
|
||||
out = f"{self.name} | {self._index}/{self.total} | {speed}"
|
||||
if infos:
|
||||
out += " | " + infos
|
||||
self.logger.log(self.level, out)
|
||||
|
||||
|
||||
def colorize(text, color):
|
||||
"""
|
||||
Display text with some ANSI color in the terminal.
|
||||
"""
|
||||
code = f"\033[{color}m"
|
||||
restore = "\033[0m"
|
||||
return "".join([code, text, restore])
|
||||
|
||||
|
||||
def bold(text):
|
||||
"""
|
||||
Display text in bold in the terminal.
|
||||
"""
|
||||
return colorize(text, "1")
|
||||
|
||||
|
||||
def cal_snr(lbl, est):
|
||||
import torch
|
||||
y = 10.0 * torch.log10(
|
||||
torch.sum(lbl**2, dim=-1) / (torch.sum((est-lbl)**2, dim=-1) + EPS) +
|
||||
EPS
|
||||
)
|
||||
return y
|
||||
Reference in New Issue
Block a user