From cec84de3b676b5fdcd6c2f5dc30fe4b3571ed574 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 15 二月 2023 20:09:21 +0800
Subject: [PATCH] Merge branch 'main' of github.com:alibaba-damo-academy/FunASR add

---
 funasr/runtime/python/onnxruntime/paraformer/rapid_paraformer/paraformer_onnx.py |   14 +++++++-------
 1 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/funasr/runtime/python/onnxruntime/paraformer/rapid_paraformer/paraformer_onnx.py b/funasr/runtime/python/onnxruntime/paraformer/rapid_paraformer/paraformer_onnx.py
index b6ff0cd..621b903 100644
--- a/funasr/runtime/python/onnxruntime/paraformer/rapid_paraformer/paraformer_onnx.py
+++ b/funasr/runtime/python/onnxruntime/paraformer/rapid_paraformer/paraformer_onnx.py
@@ -9,11 +9,11 @@
 import librosa
 import numpy as np
 
-from .utils.utils import (CharTokenizer, Hypothesis, ONNXRuntimeError,
+from utils.utils import (CharTokenizer, Hypothesis, ONNXRuntimeError,
                     OrtInferSession, TokenIDConverter, get_logger,
                     read_yaml)
-from .utils.postprocess_utils import sentence_postprocess
-from .utils.frontend import WavFrontend
+from utils.postprocess_utils import sentence_postprocess
+from utils.frontend import WavFrontend
 
 logging = get_logger()
 
@@ -41,8 +41,8 @@
         self.ort_infer = OrtInferSession(model_file, device_id)
         self.batch_size = batch_size
 
-    def __call__(self, wav_content: Union[str, np.ndarray, List[str]]) -> List:
-        waveform_list = self.load_data(wav_content)
+    def __call__(self, wav_content: Union[str, np.ndarray, List[str]], fs: int = None) -> List:
+        waveform_list = self.load_data(wav_content, fs)
         waveform_nums = len(waveform_list)
 
         asr_res = []
@@ -63,9 +63,9 @@
         return asr_res
 
     def load_data(self,
-                  wav_content: Union[str, np.ndarray, List[str]]) -> List:
+                  wav_content: Union[str, np.ndarray, List[str]], fs: int = None) -> List:
         def load_wav(path: str) -> np.ndarray:
-            waveform, _ = librosa.load(path, sr=None)
+            waveform, _ = librosa.load(path, sr=fs)
             return waveform
 
         if isinstance(wav_content, np.ndarray):

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