From 2a66366be4c2715870e4859fd5a5db6e8a9dc00a Mon Sep 17 00:00:00 2001
From: chenmengzheAAA <123789350+chenmengzheAAA@users.noreply.github.com>
Date: 星期四, 14 九月 2023 19:00:17 +0800
Subject: [PATCH] Merge pull request #956 from alibaba-damo-academy/chenmengzheAAA-patch-4
---
funasr/models/frontend/default.py | 219 +++++++++++++++++++++++++++++++++++++++++++++++++++++-
1 files changed, 212 insertions(+), 7 deletions(-)
diff --git a/funasr/models/frontend/default.py b/funasr/models/frontend/default.py
index fad6b70..b41af80 100644
--- a/funasr/models/frontend/default.py
+++ b/funasr/models/frontend/default.py
@@ -2,23 +2,22 @@
from typing import Optional
from typing import Tuple
from typing import Union
-
+import logging
import humanfriendly
import numpy as np
import torch
from torch_complex.tensor import ComplexTensor
-from typeguard import check_argument_types
from funasr.layers.log_mel import LogMel
from funasr.layers.stft import Stft
from funasr.models.frontend.abs_frontend import AbsFrontend
from funasr.modules.frontends.frontend import Frontend
from funasr.utils.get_default_kwargs import get_default_kwargs
+from funasr.modules.nets_utils import make_pad_mask
class DefaultFrontend(AbsFrontend):
"""Conventional frontend structure for ASR.
-
Stft -> WPE -> MVDR-Beamformer -> Power-spec -> Mel-Fbank -> CMVN
"""
@@ -38,8 +37,8 @@
htk: bool = False,
frontend_conf: Optional[dict] = get_default_kwargs(Frontend),
apply_stft: bool = True,
+ use_channel: int = None,
):
- assert check_argument_types()
super().__init__()
if isinstance(fs, str):
fs = humanfriendly.parse_size(fs)
@@ -76,6 +75,7 @@
htk=htk,
)
self.n_mels = n_mels
+ self.use_channel = use_channel
self.frontend_type = "default"
def output_size(self) -> int:
@@ -100,9 +100,12 @@
if input_stft.dim() == 4:
# h: (B, T, C, F) -> h: (B, T, F)
if self.training:
- # Select 1ch randomly
- ch = np.random.randint(input_stft.size(2))
- input_stft = input_stft[:, :, ch, :]
+ if self.use_channel is not None:
+ input_stft = input_stft[:, :, self.use_channel, :]
+ else:
+ # Select 1ch randomly
+ ch = np.random.randint(input_stft.size(2))
+ input_stft = input_stft[:, :, ch, :]
else:
# Use the first channel
input_stft = input_stft[:, :, 0, :]
@@ -131,3 +134,205 @@
# input_stft: (..., F, 2) -> (..., F)
input_stft = ComplexTensor(input_stft[..., 0], input_stft[..., 1])
return input_stft, feats_lens
+
+
+class MultiChannelFrontend(AbsFrontend):
+ """Conventional frontend structure for ASR.
+ Stft -> WPE -> MVDR-Beamformer -> Power-spec -> Mel-Fbank -> CMVN
+ """
+
+ def __init__(
+ self,
+ fs: Union[int, str] = 16000,
+ n_fft: int = 512,
+ win_length: int = None,
+ hop_length: int = None,
+ frame_length: int = None,
+ frame_shift: int = None,
+ window: Optional[str] = "hann",
+ center: bool = True,
+ normalized: bool = False,
+ onesided: bool = True,
+ n_mels: int = 80,
+ fmin: int = None,
+ fmax: int = None,
+ htk: bool = False,
+ frontend_conf: Optional[dict] = get_default_kwargs(Frontend),
+ apply_stft: bool = True,
+ use_channel: int = None,
+ lfr_m: int = 1,
+ lfr_n: int = 1,
+ cmvn_file: str = None,
+ mc: bool = True
+ ):
+ super().__init__()
+ if isinstance(fs, str):
+ fs = humanfriendly.parse_size(fs)
+
+ # Deepcopy (In general, dict shouldn't be used as default arg)
+ frontend_conf = copy.deepcopy(frontend_conf)
+ if win_length is None and hop_length is None:
+ self.win_length = frame_length * 16
+ self.hop_length = frame_shift * 16
+ elif frame_length is None and frame_shift is None:
+ self.win_length = self.win_length
+ self.hop_length = self.hop_length
+ else:
+ logging.error(
+ "Only one of (win_length, hop_length) and (frame_length, frame_shift)"
+ "can be set."
+ )
+ exit(1)
+
+ if apply_stft:
+ self.stft = Stft(
+ n_fft=n_fft,
+ win_length=self.win_length,
+ hop_length=self.hop_length,
+ center=center,
+ window=window,
+ normalized=normalized,
+ onesided=onesided,
+ )
+ else:
+ self.stft = None
+ self.apply_stft = apply_stft
+
+ if frontend_conf is not None:
+ self.frontend = Frontend(idim=n_fft // 2 + 1, **frontend_conf)
+ else:
+ self.frontend = None
+
+ self.logmel = LogMel(
+ fs=fs,
+ n_fft=n_fft,
+ n_mels=n_mels,
+ fmin=fmin,
+ fmax=fmax,
+ htk=htk,
+ )
+ self.n_mels = n_mels
+ self.use_channel = use_channel
+ self.mc = mc
+ if not self.mc:
+ if self.use_channel is not None:
+ logging.info("use the channel %d" % (self.use_channel))
+ else:
+ logging.info("random select channel")
+ self.cmvn_file = cmvn_file
+ if self.cmvn_file is not None:
+ mean, std = self._load_cmvn(self.cmvn_file)
+ self.register_buffer("mean", torch.from_numpy(mean))
+ self.register_buffer("std", torch.from_numpy(std))
+ self.frontend_type = "multichannelfrontend"
+
+ def output_size(self) -> int:
+ return self.n_mels
+
+ def forward(
+ self, input: torch.Tensor, input_lengths: torch.Tensor
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ # 1. Domain-conversion: e.g. Stft: time -> time-freq
+ #import pdb;pdb.set_trace()
+ if self.stft is not None:
+ input_stft, feats_lens = self._compute_stft(input, input_lengths)
+ else:
+ input_stft = ComplexTensor(input[..., 0], input[..., 1])
+ feats_lens = input_lengths
+ # 2. [Option] Speech enhancement
+ if self.frontend is not None:
+ assert isinstance(input_stft, ComplexTensor), type(input_stft)
+ # input_stft: (Batch, Length, [Channel], Freq)
+ input_stft, _, mask = self.frontend(input_stft, feats_lens)
+
+ # 3. [Multi channel case]: Select a channel(sa_asr)
+ if input_stft.dim() == 4 and not self.mc:
+ # h: (B, T, C, F) -> h: (B, T, F)
+ if self.training:
+ if self.use_channel is not None:
+ input_stft = input_stft[:, :, self.use_channel, :]
+
+ else:
+ # Select 1ch randomly
+ ch = np.random.randint(input_stft.size(2))
+ input_stft = input_stft[:, :, ch, :]
+ else:
+ # Use the first channel
+ input_stft = input_stft[:, :, 0, :]
+
+ # 4. STFT -> Power spectrum
+ # h: ComplexTensor(B, T, F) -> torch.Tensor(B, T, F)
+ input_power = input_stft.real ** 2 + input_stft.imag ** 2
+
+ # 5. Feature transform e.g. Stft -> Log-Mel-Fbank
+ # input_power: (Batch, [Channel,] Length, Freq)
+ # -> input_feats: (Batch, Length, Dim)
+ input_feats, _ = self.logmel(input_power, feats_lens)
+ if self.mc:
+ # MFCCA
+ if input_feats.dim() ==4:
+ bt = input_feats.size(0)
+ channel_size = input_feats.size(2)
+ input_feats = input_feats.transpose(1,2).reshape(bt*channel_size,-1,80).contiguous()
+ feats_lens = feats_lens.repeat(1,channel_size).squeeze()
+ else:
+ channel_size = 1
+ return input_feats, feats_lens, channel_size
+ else:
+ # 6. Apply CMVN
+ if self.cmvn_file is not None:
+ if feats_lens is None:
+ feats_lens = input_feats.new_full([input_feats.size(0)], input_feats.size(1))
+ self.mean = self.mean.to(input_feats.device, input_feats.dtype)
+ self.std = self.std.to(input_feats.device, input_feats.dtype)
+ mask = make_pad_mask(feats_lens, input_feats, 1)
+
+ if input_feats.requires_grad:
+ input_feats = input_feats + self.mean
+ else:
+ input_feats += self.mean
+ if input_feats.requires_grad:
+ input_feats = input_feats.masked_fill(mask, 0.0)
+ else:
+ input_feats.masked_fill_(mask, 0.0)
+
+ input_feats *= self.std
+
+ return input_feats, feats_lens
+
+ def _compute_stft(
+ self, input: torch.Tensor, input_lengths: torch.Tensor
+ ) -> torch.Tensor:
+ input_stft, feats_lens = self.stft(input, input_lengths)
+
+ assert input_stft.dim() >= 4, input_stft.shape
+ # "2" refers to the real/imag parts of Complex
+ assert input_stft.shape[-1] == 2, input_stft.shape
+
+ # Change torch.Tensor to ComplexTensor
+ # input_stft: (..., F, 2) -> (..., F)
+ input_stft = ComplexTensor(input_stft[..., 0], input_stft[..., 1])
+ return input_stft, feats_lens
+
+ def _load_cmvn(self, cmvn_file):
+ with open(cmvn_file, 'r', encoding='utf-8') as f:
+ lines = f.readlines()
+ means_list = []
+ vars_list = []
+ for i in range(len(lines)):
+ line_item = lines[i].split()
+ if line_item[0] == '<AddShift>':
+ line_item = lines[i + 1].split()
+ if line_item[0] == '<LearnRateCoef>':
+ add_shift_line = line_item[3:(len(line_item) - 1)]
+ means_list = list(add_shift_line)
+ continue
+ elif line_item[0] == '<Rescale>':
+ line_item = lines[i + 1].split()
+ if line_item[0] == '<LearnRateCoef>':
+ rescale_line = line_item[3:(len(line_item) - 1)]
+ vars_list = list(rescale_line)
+ continue
+ means = np.array(means_list).astype(np.float)
+ vars = np.array(vars_list).astype(np.float)
+ return means, vars
--
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