update

funasr/models/frontend/wav_frontend.py

@@ -1,15 +1,15 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 # Part of the implementation is borrowed from espnet/espnet.
-from abc import ABC
 from typing import Tuple
 
 import numpy as np
 import torch
 import torchaudio.compliance.kaldi as kaldi
-from funasr.models.frontend.abs_frontend import AbsFrontend
-import funasr.models.frontend.eend_ola_feature as eend_ola_feature
-from typeguard import check_argument_types
 from torch.nn.utils.rnn import pad_sequence
+from typeguard import check_argument_types
+
+import funasr.models.frontend.eend_ola_feature as eend_ola_feature
+from funasr.models.frontend.abs_frontend import AbsFrontend
 
 
 def load_cmvn(cmvn_file):
@@ -276,7 +276,8 @@ class WavFrontendOnline(AbsFrontend):
     # inputs tensor has catted the cache tensor
     # def apply_lfr(inputs: torch.Tensor, lfr_m: int, lfr_n: int, inputs_lfr_cache: torch.Tensor = None,
     #               is_final: bool = False) -> Tuple[torch.Tensor, torch.Tensor, int]:
-    def apply_lfr(inputs: torch.Tensor, lfr_m: int, lfr_n: int, is_final: bool = False) -> Tuple[torch.Tensor, torch.Tensor, int]:
+    def apply_lfr(inputs: torch.Tensor, lfr_m: int, lfr_n: int, is_final: bool = False) -> Tuple[
+        torch.Tensor, torch.Tensor, int]:
         """
         Apply lfr with data
         """
@@ -377,7 +378,8 @@ class WavFrontendOnline(AbsFrontend):
             if self.lfr_m != 1 or self.lfr_n != 1:
                 # update self.lfr_splice_cache in self.apply_lfr
                 # mat, self.lfr_splice_cache[i], lfr_splice_frame_idx = self.apply_lfr(mat, self.lfr_m, self.lfr_n, self.lfr_splice_cache[i],
-                mat, self.lfr_splice_cache[i], lfr_splice_frame_idx = self.apply_lfr(mat, self.lfr_m, self.lfr_n, is_final)
+                mat, self.lfr_splice_cache[i], lfr_splice_frame_idx = self.apply_lfr(mat, self.lfr_m, self.lfr_n,
+                                                                                     is_final)
             if self.cmvn_file is not None:
                 mat = self.apply_cmvn(mat, self.cmvn)
             feat_length = mat.size(0)
@@ -399,9 +401,10 @@ class WavFrontendOnline(AbsFrontend):
         assert batch_size == 1, 'we support to extract feature online only when the batch size is equal to 1 now'
         waveforms, feats, feats_lengths = self.forward_fbank(input, input_lengths)  # input shape: B T D
         if feats.shape[0]:
-            #if self.reserve_waveforms is None and self.lfr_m > 1:
+            # if self.reserve_waveforms is None and self.lfr_m > 1:
             #    self.reserve_waveforms = waveforms[:, :(self.lfr_m - 1) // 2 * self.frame_shift_sample_length]
-            self.waveforms = waveforms if self.reserve_waveforms is None else torch.cat((self.reserve_waveforms, waveforms), dim=1)
+            self.waveforms = waveforms if self.reserve_waveforms is None else torch.cat(
+                (self.reserve_waveforms, waveforms), dim=1)
             if not self.lfr_splice_cache:  # 初始化splice_cache
                 for i in range(batch_size):
                     self.lfr_splice_cache.append(feats[i][0, :].unsqueeze(dim=0).repeat((self.lfr_m - 1) // 2, 1))
@@ -410,7 +413,8 @@ class WavFrontendOnline(AbsFrontend):
                 lfr_splice_cache_tensor = torch.stack(self.lfr_splice_cache)  # B T D
                 feats = torch.cat((lfr_splice_cache_tensor, feats), dim=1)
                 feats_lengths += lfr_splice_cache_tensor[0].shape[0]
-                frame_from_waveforms = int((self.waveforms.shape[1] - self.frame_sample_length) / self.frame_shift_sample_length + 1)
+                frame_from_waveforms = int(
+                    (self.waveforms.shape[1] - self.frame_sample_length) / self.frame_shift_sample_length + 1)
                 minus_frame = (self.lfr_m - 1) // 2 if self.reserve_waveforms is None else 0
                 feats, feats_lengths, lfr_splice_frame_idxs = self.forward_lfr_cmvn(feats, feats_lengths, is_final)
                 if self.lfr_m == 1:
@@ -419,19 +423,22 @@ class WavFrontendOnline(AbsFrontend):
                     reserve_frame_idx = lfr_splice_frame_idxs[0] - minus_frame
                     # print('reserve_frame_idx:  ' + str(reserve_frame_idx))
                     # print('frame_frame:  ' + str(frame_from_waveforms))
-                    self.reserve_waveforms = self.waveforms[:, reserve_frame_idx * self.frame_shift_sample_length:frame_from_waveforms * self.frame_shift_sample_length]
-                    sample_length = (frame_from_waveforms - 1) * self.frame_shift_sample_length + self.frame_sample_length
+                    self.reserve_waveforms = self.waveforms[:,
+                                             reserve_frame_idx * self.frame_shift_sample_length:frame_from_waveforms * self.frame_shift_sample_length]
+                    sample_length = (
+                                                frame_from_waveforms - 1) * self.frame_shift_sample_length + self.frame_sample_length
                     self.waveforms = self.waveforms[:, :sample_length]
             else:
                 # update self.reserve_waveforms and self.lfr_splice_cache
-                self.reserve_waveforms = self.waveforms[:, :-(self.frame_sample_length - self.frame_shift_sample_length)]
+                self.reserve_waveforms = self.waveforms[:,
+                                         :-(self.frame_sample_length - self.frame_shift_sample_length)]
                 for i in range(batch_size):
                     self.lfr_splice_cache[i] = torch.cat((self.lfr_splice_cache[i], feats[i]), dim=0)
                 return torch.empty(0), feats_lengths
         else:
             if is_final:
                 self.waveforms = waveforms if self.reserve_waveforms is None else self.reserve_waveforms
-                feats = torch.stack(self.lfr_splice_cache) 
+                feats = torch.stack(self.lfr_splice_cache)
                 feats_lengths = torch.zeros(batch_size, dtype=torch.int) + feats.shape[1]
                 feats, feats_lengths, _ = self.forward_lfr_cmvn(feats, feats_lengths, is_final)
         if is_final:
@@ -466,9 +473,10 @@ class WavFrontendMel23(AbsFrontend):
         self.frame_shift = frame_shift
         self.lfr_m = lfr_m
         self.lfr_n = lfr_n
+        self.n_mels = 23
 
     def output_size(self) -> int:
-        return self.n_mels * self.lfr_m
+        return self.n_mels * (2 * self.lfr_m + 1)
 
     def forward(
             self,
@@ -494,4 +502,4 @@ class WavFrontendMel23(AbsFrontend):
         feats_pad = pad_sequence(feats,
                                  batch_first=True,
                                  padding_value=0.0)
-        return feats_pad, feats_lens
+        return feats_pad, feats_lens