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increase vad realtime punc

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This commit is contained in:
mengzhe.cmz 2023-05-17 17:12:14 +08:00
parent 33693c4182
commit ce7914034d
3 changed files with 5 additions and 4 deletions
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2
egs_modelscope/punctuation/punc_ct-transformer_zh-cn-common-vadrealtime-vocab272727/demo.py
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@ -9,7 +9,7 @@ logger.setLevel(logging.CRITICAL)
inference_pipeline = pipeline( inference_pipeline = pipeline(
task=Tasks.punctuation, task=Tasks.punctuation,
model='damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727', model='damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727',
output_dir="./tmp/" model_revision = 'v1.0.2'
) )
##################text二进制数据##################### ##################text二进制数据#####################

2
funasr/export/test/test_onnx_punc_vadrealtime.py
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@ -12,7 +12,7 @@ if __name__ == '__main__':
return {'inputs': np.ones((1, text_length), dtype=np.int64), return {'inputs': np.ones((1, text_length), dtype=np.int64),
'text_lengths': np.array([text_length,], dtype=np.int32), 'text_lengths': np.array([text_length,], dtype=np.int32),
'vad_masks': np.ones((1, 1, text_length, text_length), dtype=np.float32), 'vad_masks': np.ones((1, 1, text_length, text_length), dtype=np.float32),
'sub_masks': np.tril(np.ones((text_length, text_length), dtype=np.float32))[None, None, :, :].astype(np.float32) 'sub_masks': np.ones((1, 1, text_length, text_length), dtype=np.float32),
} }
def _run(feed_dict): def _run(feed_dict):

5
funasr/runtime/python/onnxruntime/funasr_onnx/punc_bin.py
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@ -186,11 +186,12 @@ class CT_Transformer_VadRealtime(CT_Transformer):
mini_sentence = cache_sent + mini_sentence mini_sentence = cache_sent + mini_sentence
mini_sentence_id = np.concatenate((cache_sent_id, mini_sentence_id), axis=0,dtype='int32') mini_sentence_id = np.concatenate((cache_sent_id, mini_sentence_id), axis=0,dtype='int32')
text_length = len(mini_sentence_id) text_length = len(mini_sentence_id)
vad_mask = self.vad_mask(text_length, len(cache))[None, None, :, :].astype(np.float32)
data = { data = {
"input": mini_sentence_id[None,:], "input": mini_sentence_id[None,:],
"text_lengths": np.array([text_length], dtype='int32'), "text_lengths": np.array([text_length], dtype='int32'),
"vad_mask": self.vad_mask(text_length, len(cache))[None, None, :, :].astype(np.float32), "vad_mask": vad_mask
"sub_masks": np.tril(np.ones((text_length, text_length), dtype=np.float32))[None, None, :, :].astype(np.float32) "sub_masks": vad_mask
} }
try: try:
outputs = self.infer(data['input'], data['text_lengths'], data['vad_mask'], data["sub_masks"]) outputs = self.infer(data['input'], data['text_lengths'], data['vad_mask'], data["sub_masks"])