import torch
import torch_complex
from packaging.version import parse as V
from torch_complex.tensor import ComplexTensor
from espnet2.enh.decoder.abs_decoder import AbsDecoder
from espnet2.enh.layers.complex_utils import is_torch_complex_tensor
from espnet2.layers.stft import Stft
is_torch_1_9_plus = V(torch.__version__) >= V("1.9.0")
[docs]class STFTDecoder(AbsDecoder):
"""STFT decoder for speech enhancement and separation"""
def __init__(
self,
n_fft: int = 512,
win_length: int = None,
hop_length: int = 128,
window="hann",
center: bool = True,
normalized: bool = False,
onesided: bool = True,
default_fs: int = 16000,
spec_transform_type: str = None,
spec_factor: float = 0.15,
spec_abs_exponent: float = 0.5,
):
super().__init__()
self.stft = Stft(
n_fft=n_fft,
win_length=win_length,
hop_length=hop_length,
window=window,
center=center,
normalized=normalized,
onesided=onesided,
)
self.win_length = win_length if win_length else n_fft
self.n_fft = n_fft
self.hop_length = hop_length
self.window = window
self.center = center
self.default_fs = default_fs
# spec transform related. See equation (1) in paper
# 'Speech Enhancement and Dereverberation With Diffusion-Based Generative
# Models'. The default value of 0.15, 0.5 also come from the paper.
# spec_transform_type: "exponent", "log", or "none"
self.spec_transform_type = spec_transform_type
# the output specturm will be scaled with: spec * self.spec_factor
self.spec_factor = spec_factor
# the exponent factor used in the "exponent" transform
self.spec_abs_exponent = spec_abs_exponent
[docs] @torch.cuda.amp.autocast(enabled=False)
def forward(self, input: ComplexTensor, ilens: torch.Tensor, fs: int = None):
"""Forward.
Args:
input (ComplexTensor): spectrum [Batch, T, (C,) F]
ilens (torch.Tensor): input lengths [Batch]
fs (int): sampling rate in Hz
If not None, reconfigure iSTFT window and hop lengths for a new
sampling rate while keeping their duration fixed.
"""
if not isinstance(input, ComplexTensor) and (
is_torch_1_9_plus and not torch.is_complex(input)
):
raise TypeError("Only support complex tensors for stft decoder")
if fs is not None:
self._reconfig_for_fs(fs)
input = self.spec_back(input)
bs = input.size(0)
if input.dim() == 4:
multi_channel = True
# input: (Batch, T, C, F) -> (Batch * C, T, F)
input = input.transpose(1, 2).reshape(-1, input.size(1), input.size(3))
else:
multi_channel = False
# for supporting half-precision training
if input.dtype in (torch.float16, torch.bfloat16):
wav, wav_lens = self.stft.inverse(input.float(), ilens)
wav = wav.to(dtype=input.dtype)
elif (
is_torch_complex_tensor(input)
and hasattr(torch, "complex32")
and input.dtype == torch.complex32
):
wav, wav_lens = self.stft.inverse(input.cfloat(), ilens)
wav = wav.to(dtype=input.dtype)
else:
wav, wav_lens = self.stft.inverse(input, ilens)
if multi_channel:
# wav: (Batch * C, Nsamples) -> (Batch, Nsamples, C)
wav = wav.reshape(bs, -1, wav.size(1)).transpose(1, 2)
self._reset_config()
return wav, wav_lens
def _reset_config(self):
"""Reset the configuration of iSTFT window and hop lengths."""
self._reconfig_for_fs(self.default_fs)
def _reconfig_for_fs(self, fs):
"""Reconfigure iSTFT window and hop lengths for a new sampling rate
while keeping their duration fixed.
Args:
fs (int): new sampling rate
"""
assert fs % self.default_fs == 0 or self.default_fs % fs == 0
self.stft.n_fft = self.n_fft * fs // self.default_fs
self.stft.win_length = self.win_length * fs // self.default_fs
self.stft.hop_length = self.hop_length * fs // self.default_fs
def _get_window_func(self):
window_func = getattr(torch, f"{self.window}_window")
window = window_func(self.win_length)
n_pad_left = (self.n_fft - window.shape[0]) // 2
n_pad_right = self.n_fft - window.shape[0] - n_pad_left # noqa
return window
[docs] def spec_back(self, spec):
if self.spec_transform_type == "exponent":
spec = spec / self.spec_factor
if self.spec_abs_exponent != 1:
e = self.spec_abs_exponent
spec = spec.abs() ** (1 / e) * torch.exp(1j * spec.angle())
elif self.spec_transform_type == "log":
spec = spec / self.spec_factor
spec = (torch.exp(spec.abs()) - 1) * torch.exp(1j * spec.angle())
elif self.spec_transform_type == "none":
spec = spec
return spec
[docs] def forward_streaming(self, input_frame: torch.Tensor):
"""Forward.
Args:
input (ComplexTensor): spectrum [Batch, 1, F]
output: wavs [Batch, 1, self.win_length]
"""
input_frame = self.spec_back(input_frame)
input_frame = input_frame.real + 1j * input_frame.imag
output_wav = (
torch.fft.irfft(input_frame)
if self.stft.onesided
else torch.fft.ifft(input_frame).real
)
output_wav = output_wav.squeeze(1)
n_pad_left = (self.n_fft - self.win_length) // 2
output_wav = output_wav[..., n_pad_left : n_pad_left + self.win_length]
return output_wav * self._get_window_func()
[docs] def streaming_merge(self, chunks, ilens=None):
"""streaming_merge. It merges the frame-level processed audio chunks
in the streaming *simulation*. It is noted that, in real applications,
the processed audio should be sent to the output channel frame by frame.
You may refer to this function to manage your streaming output buffer.
Args:
chunks: List [(B, frame_size),]
ilens: [B]
Returns:
merge_audio: [B, T]
""" # noqa: H405
frame_size = self.win_length
hop_size = self.hop_length
num_chunks = len(chunks)
batch_size = chunks[0].shape[0]
audio_len = int(hop_size * num_chunks + frame_size - hop_size)
output = torch.zeros((batch_size, audio_len), dtype=chunks[0].dtype).to(
chunks[0].device
)
for i, chunk in enumerate(chunks):
output[:, i * hop_size : i * hop_size + frame_size] += chunk
window_sq = self._get_window_func().pow(2)
window_envelop = torch.zeros((batch_size, audio_len), dtype=chunks[0].dtype).to(
chunks[0].device
)
for i in range(len(chunks)):
window_envelop[:, i * hop_size : i * hop_size + frame_size] += window_sq
output = output / window_envelop
# We need to trim the front padding away if center.
start = (frame_size // 2) if self.center else 0
end = -(frame_size // 2) if ilens.max() is None else start + ilens.max()
return output[..., start:end]
if __name__ == "__main__":
from espnet2.enh.encoder.stft_encoder import STFTEncoder
input_audio = torch.randn((1, 100))
ilens = torch.LongTensor([100])
nfft = 32
win_length = 28
hop = 10
encoder = STFTEncoder(
n_fft=nfft,
win_length=win_length,
hop_length=hop,
onesided=True,
spec_transform_type="exponent",
)
decoder = STFTDecoder(
n_fft=nfft,
win_length=win_length,
hop_length=hop,
onesided=True,
spec_transform_type="exponent",
)
frames, flens = encoder(input_audio, ilens)
wav, ilens = decoder(frames, ilens)
splited = encoder.streaming_frame(input_audio)
sframes = [encoder.forward_streaming(s) for s in splited]
swavs = [decoder.forward_streaming(s) for s in sframes]
merged = decoder.streaming_merge(swavs, ilens)
if not (is_torch_1_9_plus and encoder.use_builtin_complex):
sframes = torch_complex.cat(sframes, dim=1)
else:
sframes = torch.cat(sframes, dim=1)
torch.testing.assert_close(sframes.real, frames.real)
torch.testing.assert_close(sframes.imag, frames.imag)
torch.testing.assert_close(wav, input_audio)
torch.testing.assert_close(wav, merged)
print("all_check passed")