From 6df05f2e24f4b357aee8614f238638b97b74e540 Mon Sep 17 00:00:00 2001
From: lingyunfly <121302812+lingyunfly@users.noreply.github.com>
Date: 星期四, 13 四月 2023 14:23:03 +0800
Subject: [PATCH] vad bug fix
---
funasr/runtime/python/onnxruntime/funasr_onnx/vad_bin.py | 43 ++++++++++++++++++++++++++++++-------------
1 files changed, 30 insertions(+), 13 deletions(-)
diff --git a/funasr/runtime/python/onnxruntime/funasr_onnx/vad_bin.py b/funasr/runtime/python/onnxruntime/funasr_onnx/vad_bin.py
index 0b7ecff..5ad4266 100644
--- a/funasr/runtime/python/onnxruntime/funasr_onnx/vad_bin.py
+++ b/funasr/runtime/python/onnxruntime/funasr_onnx/vad_bin.py
@@ -53,39 +53,56 @@
proj_dim = self.encoder_conf["proj_dim"]
lorder = self.encoder_conf["lorder"]
for i in range(fsmn_layers):
- cache = np.zeros(1, proj_dim, lorder-1, 1).astype(np.float32)
+ cache = np.zeros((1, proj_dim, lorder-1, 1)).astype(np.float32)
in_cache.append(cache)
return in_cache
- def __call__(self, wav_content: Union[str, np.ndarray, List[str]], **kwargs) -> List:
- waveform_list = self.load_data(wav_content, self.frontend.opts.frame_opts.samp_freq)
+ def __call__(self, audio_in: Union[str, np.ndarray, List[str]], **kwargs) -> List:
+ waveform_list = self.load_data(audio_in, self.frontend.opts.frame_opts.samp_freq)
waveform_nums = len(waveform_list)
is_final = kwargs.get('kwargs', False)
- asr_res = []
+ segments = [[]] * self.batch_size
for beg_idx in range(0, waveform_nums, self.batch_size):
end_idx = min(waveform_nums, beg_idx + self.batch_size)
waveform = waveform_list[beg_idx:end_idx]
feats, feats_len = self.extract_feat(waveform)
+ waveform = np.array(waveform)
param_dict = kwargs.get('param_dict', dict())
- in_cache = param_dict.get('cache', list())
+ in_cache = param_dict.get('in_cache', list())
in_cache = self.prepare_cache(in_cache)
try:
- inputs = [feats]
- inputs.extend(in_cache)
- scores, out_caches = self.infer(inputs)
- param_dict['cache'] = out_caches
- segments = self.vad_scorer(scores, waveform[0][None, :], is_final=is_final, max_end_sil=self.max_end_sil)
+ t_offset = 0
+ step = int(min(feats_len.max(), 6000))
+ for t_offset in range(0, int(feats_len), min(step, feats_len - t_offset)):
+ if t_offset + step >= feats_len - 1:
+ step = feats_len - t_offset
+ is_final = True
+ else:
+ is_final = False
+ feats_package = feats[:, t_offset:int(t_offset + step), :]
+ waveform_package = waveform[:, t_offset * 160:min(waveform.shape[-1], (int(t_offset + step) - 1) * 160 + 400)]
+
+ inputs = [feats_package]
+ # inputs = [feats]
+ inputs.extend(in_cache)
+ scores, out_caches = self.infer(inputs)
+ in_cache = out_caches
+ segments_part = self.vad_scorer(scores, waveform_package, is_final=is_final, max_end_sil=self.max_end_sil, online=False)
+ # segments = self.vad_scorer(scores, waveform[0][None, :], is_final=is_final, max_end_sil=self.max_end_sil)
+
+ if segments_part:
+ for batch_num in range(0, self.batch_size):
+ segments[batch_num] += segments_part[batch_num]
except ONNXRuntimeError:
# logging.warning(traceback.format_exc())
logging.warning("input wav is silence or noise")
segments = ''
- asr_res.append(segments)
- return asr_res
+ return segments
def load_data(self,
wav_content: Union[str, np.ndarray, List[str]], fs: int = None) -> List:
@@ -134,4 +151,4 @@
outputs = self.ort_infer(feats)
scores, out_caches = outputs[0], outputs[1:]
return scores, out_caches
-
\ No newline at end of file
+
--
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