From edec2fe85eda80ff1e24aef30b36c7bbbb55ec2a Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 03 七月 2023 15:08:11 +0800
Subject: [PATCH] Update SDK_tutorial_zh.md

---
 funasr/bin/asr_inference_launch.py |   20 ++++++++++----------
 1 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/funasr/bin/asr_inference_launch.py b/funasr/bin/asr_inference_launch.py
index 656a965..81513ae 100644
--- a/funasr/bin/asr_inference_launch.py
+++ b/funasr/bin/asr_inference_launch.py
@@ -19,8 +19,8 @@
 import numpy as np
 import torch
 import torchaudio
+import soundfile
 import yaml
-from typeguard import check_argument_types
 
 from funasr.bin.asr_infer import Speech2Text
 from funasr.bin.asr_infer import Speech2TextMFCCA
@@ -79,7 +79,6 @@
         param_dict: dict = None,
         **kwargs,
 ):
-    assert check_argument_types()
     ncpu = kwargs.get("ncpu", 1)
     torch.set_num_threads(ncpu)
     if batch_size > 1:
@@ -239,7 +238,6 @@
         param_dict: dict = None,
         **kwargs,
 ):
-    assert check_argument_types()
     ncpu = kwargs.get("ncpu", 1)
     torch.set_num_threads(ncpu)
 
@@ -480,7 +478,6 @@
         param_dict: dict = None,
         **kwargs,
 ):
-    assert check_argument_types()
     ncpu = kwargs.get("ncpu", 1)
     torch.set_num_threads(ncpu)
 
@@ -748,7 +745,6 @@
         param_dict: dict = None,
         **kwargs,
 ):
-    assert check_argument_types()
 
     if word_lm_train_config is not None:
         raise NotImplementedError("Word LM is not implemented")
@@ -863,7 +859,13 @@
             raw_inputs = _load_bytes(data_path_and_name_and_type[0])
             raw_inputs = torch.tensor(raw_inputs)
         if data_path_and_name_and_type is not None and data_path_and_name_and_type[2] == "sound":
-            raw_inputs = torchaudio.load(data_path_and_name_and_type[0])[0][0]
+            try:
+                raw_inputs = torchaudio.load(data_path_and_name_and_type[0])[0][0]
+            except:
+                raw_inputs = soundfile.read(data_path_and_name_and_type[0], dtype='float32')[0]
+                if raw_inputs.ndim == 2:
+                    raw_inputs = raw_inputs[:, 0]
+                raw_inputs = torch.tensor(raw_inputs)
         if data_path_and_name_and_type is None and raw_inputs is not None:
             if isinstance(raw_inputs, np.ndarray):
                 raw_inputs = torch.tensor(raw_inputs)
@@ -950,7 +952,6 @@
         param_dict: dict = None,
         **kwargs,
 ):
-    assert check_argument_types()
     ncpu = kwargs.get("ncpu", 1)
     torch.set_num_threads(ncpu)
     if batch_size > 1:
@@ -1119,7 +1120,6 @@
         param_dict: dict = None,
         **kwargs,
 ):
-    assert check_argument_types()
     ncpu = kwargs.get("ncpu", 1)
     torch.set_num_threads(ncpu)
     if batch_size > 1:
@@ -1307,7 +1307,6 @@
         right_context: Number of frames in right context AFTER subsampling.
         display_partial_hypotheses: Whether to display partial hypotheses.
     """
-    assert check_argument_types()
 
     if batch_size > 1:
         raise NotImplementedError("batch decoding is not implemented")
@@ -1457,7 +1456,6 @@
         param_dict: dict = None,
         **kwargs,
 ):
-    assert check_argument_types()
     if batch_size > 1:
         raise NotImplementedError("batch decoding is not implemented")
     if word_lm_train_config is not None:
@@ -1606,6 +1604,8 @@
         return inference_mfcca(**kwargs)
     elif mode == "rnnt":
         return inference_transducer(**kwargs)
+    elif mode == "bat":
+        return inference_transducer(**kwargs)
     elif mode == "sa_asr":
         return inference_sa_asr(**kwargs)
     else:

--
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