From d63b89d71843b9726ac3554bafb5274dc2ae7331 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 16 二月 2023 11:24:15 +0800
Subject: [PATCH] readme
---
funasr/runtime/python/onnxruntime/paraformer/rapid_paraformer/paraformer_onnx.py | 19 ++++++++++---------
1 files changed, 10 insertions(+), 9 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..d51c6bf 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 = []
@@ -54,8 +54,9 @@
try:
am_scores, valid_token_lens = self.infer(feats, feats_len)
except ONNXRuntimeError:
- logging.error(traceback.format_exc())
- preds = []
+ #logging.warning(traceback.format_exc())
+ logging.warning("input wav is silence or noise")
+ preds = ['']
else:
preds = self.decode(am_scores, valid_token_lens)
@@ -63,9 +64,9 @@
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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