From 33d3d2084403fd34b79c835d2f2fe04f6cd8f738 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 13 九月 2023 09:33:54 +0800
Subject: [PATCH] Merge branch 'main' of github.com:alibaba-damo-academy/FunASR add

---
 funasr/models/frontend/wav_frontend.py |   14 ++++++--------
 1 files changed, 6 insertions(+), 8 deletions(-)

diff --git a/funasr/models/frontend/wav_frontend.py b/funasr/models/frontend/wav_frontend.py
index 35fab57..ca5aed6 100644
--- a/funasr/models/frontend/wav_frontend.py
+++ b/funasr/models/frontend/wav_frontend.py
@@ -6,7 +6,6 @@
 import torch
 import torchaudio.compliance.kaldi as kaldi
 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
@@ -31,8 +30,8 @@
                 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)
+    means = np.array(means_list).astype(np.float32)
+    vars = np.array(vars_list).astype(np.float32)
     cmvn = np.array([means, vars])
     cmvn = torch.as_tensor(cmvn, dtype=torch.float32)
     return cmvn
@@ -95,7 +94,6 @@
             snip_edges: bool = True,
             upsacle_samples: bool = True,
     ):
-        assert check_argument_types()
         super().__init__()
         self.fs = fs
         self.window = window
@@ -227,7 +225,6 @@
             snip_edges: bool = True,
             upsacle_samples: bool = True,
     ):
-        assert check_argument_types()
         super().__init__()
         self.fs = fs
         self.window = window
@@ -395,8 +392,10 @@
         return feats_pad, feats_lens, lfr_splice_frame_idxs
 
     def forward(
-            self, input: torch.Tensor, input_lengths: torch.Tensor, is_final: bool = False
+        self, input: torch.Tensor, input_lengths: torch.Tensor, is_final: bool = False, reset: bool = False
     ) -> Tuple[torch.Tensor, torch.Tensor]:
+        if reset:
+            self.cache_reset()
         batch_size = input.shape[0]
         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
@@ -464,7 +463,6 @@
             lfr_m: int = 1,
             lfr_n: int = 1,
     ):
-        assert check_argument_types()
         super().__init__()
         self.fs = fs
         self.frame_length = frame_length
@@ -500,4 +498,4 @@
         feats_pad = pad_sequence(feats,
                                  batch_first=True,
                                  padding_value=0.0)
-        return feats_pad, feats_lens
\ No newline at end of file
+        return feats_pad, feats_lens

--
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