From c039cbc3bf3311c370d891c1bf67b275e95f0cd3 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 29 三月 2023 13:20:27 +0800
Subject: [PATCH] export
---
funasr/runtime/python/onnxruntime/funasr_onnx/vad_bin.py | 10 +++++-----
funasr/runtime/python/onnxruntime/demo_vad.py | 28 +++++++++++++++++++++++-----
2 files changed, 28 insertions(+), 10 deletions(-)
diff --git a/funasr/runtime/python/onnxruntime/demo_vad.py b/funasr/runtime/python/onnxruntime/demo_vad.py
index ae033cc..2e17197 100644
--- a/funasr/runtime/python/onnxruntime/demo_vad.py
+++ b/funasr/runtime/python/onnxruntime/demo_vad.py
@@ -1,12 +1,30 @@
-
+import soundfile
from funasr_onnx import Fsmn_vad
model_dir = "/Users/zhifu/Downloads/speech_fsmn_vad_zh-cn-16k-common-pytorch"
-
+wav_path = "/Users/zhifu/Downloads/speech_fsmn_vad_zh-cn-16k-common-pytorch/example/vad_example.wav"
model = Fsmn_vad(model_dir)
-wav_path = "/Users/zhifu/Downloads/speech_fsmn_vad_zh-cn-16k-common-pytorch/example/vad_example.wav"
+#offline vad
+# result = model(wav_path)
+# print(result)
-result = model(wav_path)
-print(result)
\ No newline at end of file
+#online vad
+speech, sample_rate = soundfile.read(wav_path)
+speech_length = speech.shape[0]
+
+sample_offset = 0
+step = 160 * 10
+param_dict = {'in_cache': []}
+for sample_offset in range(0, speech_length, min(step, speech_length - sample_offset)):
+ if sample_offset + step >= speech_length - 1:
+ step = speech_length - sample_offset
+ is_final = True
+ else:
+ is_final = False
+ param_dict['is_final'] = is_final
+ segments_result = model(audio_in=speech[sample_offset: sample_offset + step],
+ param_dict=param_dict)
+ print(segments_result)
+
diff --git a/funasr/runtime/python/onnxruntime/funasr_onnx/vad_bin.py b/funasr/runtime/python/onnxruntime/funasr_onnx/vad_bin.py
index 0b7ecff..cdd4578 100644
--- a/funasr/runtime/python/onnxruntime/funasr_onnx/vad_bin.py
+++ b/funasr/runtime/python/onnxruntime/funasr_onnx/vad_bin.py
@@ -53,13 +53,13 @@
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)
@@ -70,13 +70,13 @@
waveform = waveform_list[beg_idx:end_idx]
feats, feats_len = self.extract_feat(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
+ param_dict['in_cache'] = out_caches
segments = self.vad_scorer(scores, waveform[0][None, :], is_final=is_final, max_end_sil=self.max_end_sil)
except ONNXRuntimeError:
--
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