From 52fee96d71ba96fd09ad453dbae1926a1d601a56 Mon Sep 17 00:00:00 2001
From: 语帆 <yf352572@alibaba-inc.com>
Date: 星期三, 28 二月 2024 15:31:14 +0800
Subject: [PATCH] test

---
 funasr/models/lcbnet/model.py |    9 ++-------
 1 files changed, 2 insertions(+), 7 deletions(-)

diff --git a/funasr/models/lcbnet/model.py b/funasr/models/lcbnet/model.py
index f45e71d..f8bbf7a 100644
--- a/funasr/models/lcbnet/model.py
+++ b/funasr/models/lcbnet/model.py
@@ -274,15 +274,12 @@
                 ind: int
         """
         with autocast(False):
-            pdb.set_trace()
             # Data augmentation
             if self.specaug is not None and self.training:
                 speech, speech_lengths = self.specaug(speech, speech_lengths)
-            pdb.set_trace()
             # Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
             if self.normalize is not None:
                 speech, speech_lengths = self.normalize(speech, speech_lengths)
-        pdb.set_trace()
         # Forward encoder
         # feats: (Batch, Length, Dim)
         # -> encoder_out: (Batch, Length2, Dim2)
@@ -426,7 +423,6 @@
         else:
             # extract fbank feats
             time1 = time.perf_counter()
-            pdb.set_trace()
             sample_list = load_audio_text_image_video(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000),
                                                             data_type=kwargs.get("data_type", "sound"),
                                                             tokenizer=tokenizer)
@@ -434,13 +430,12 @@
             meta_data["load_data"] = f"{time2 - time1:0.3f}"
             audio_sample_list = sample_list[0]
             ocr_sample_list = sample_list[1]
-            pdb.set_trace()
             speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
                                                    frontend=frontend)
-            pdb.set_trace()
             time3 = time.perf_counter()
             meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
-            meta_data["batch_data_time"] = speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
+            frame_shift = 10 
+            meta_data["batch_data_time"] = speech_lengths.sum().item() * frame_shift / 1000
 
         speech = speech.to(device=kwargs["device"])
         speech_lengths = speech_lengths.to(device=kwargs["device"])

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