From fae856e23d45fd27d5fd55fd036e8e3fc7b24915 Mon Sep 17 00:00:00 2001
From: 雾聪 <wucong.lyb@alibaba-inc.com>
Date: 星期五, 02 六月 2023 23:00:08 +0800
Subject: [PATCH] update funasr-onnx-offline

---
 funasr/fileio/sound_scp.py |   14 +++++++++-----
 1 files changed, 9 insertions(+), 5 deletions(-)

diff --git a/funasr/fileio/sound_scp.py b/funasr/fileio/sound_scp.py
index cec7cd9..c752fe6 100644
--- a/funasr/fileio/sound_scp.py
+++ b/funasr/fileio/sound_scp.py
@@ -1,7 +1,6 @@
 import collections.abc
 from pathlib import Path
 from typing import Union
-from typing import Optional
 
 import random
 import numpy as np
@@ -9,6 +8,7 @@
 import librosa
 from typeguard import check_argument_types
 
+import torch
 import torchaudio
 
 from funasr.fileio.read_text import read_2column_text
@@ -36,7 +36,7 @@
         always_2d: bool = False,
         normalize: bool = False,
         dest_sample_rate: int = 16000,
-        speed_perturb: Optional[list, tuple] = None,
+        speed_perturb: Union[list, tuple] = None,
     ):
         assert check_argument_types()
         self.fname = fname
@@ -52,19 +52,23 @@
         if self.normalize:
             # soundfile.read normalizes data to [-1,1] if dtype is not given
             array, rate = librosa.load(
-                wav, sr=self.dest_sample_rate, mono=not self.always_2d
+                wav, sr=self.dest_sample_rate, mono=self.always_2d
             )
         else:
             array, rate = librosa.load(
-                wav, sr=self.dest_sample_rate, mono=not self.always_2d, dtype=self.dtype
+                wav, sr=self.dest_sample_rate, mono=self.always_2d, dtype=self.dtype
             )
 
         if self.speed_perturb is not None:
             speed = random.choice(self.speed_perturb)
             if speed != 1.0:
                 array, _ = torchaudio.sox_effects.apply_effects_tensor(
-                    array, rate,
+                    torch.tensor(array).view(1, -1), rate,
                     [['speed', str(speed)], ['rate', str(rate)]])
+                array = array.view(-1).numpy()
+
+        if array.ndim==2:
+            array=array.transpose((1, 0))
 
         return rate, array
 

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