From 79007d36f1636eb51e0cead6bc0e6b18ff1f8253 Mon Sep 17 00:00:00 2001
From: 九耳 <mengzhe.cmz@alibaba-inc.com>
Date: 星期五, 07 四月 2023 14:43:32 +0800
Subject: [PATCH] vad_realtime onnx runnable
---
funasr/runtime/python/onnxruntime/funasr_onnx/punc_bin.py | 130 +++++++++++++++++++++++++++++++++++++++++++
1 files changed, 130 insertions(+), 0 deletions(-)
diff --git a/funasr/runtime/python/onnxruntime/funasr_onnx/punc_bin.py b/funasr/runtime/python/onnxruntime/funasr_onnx/punc_bin.py
index 949172e..0dc728a 100644
--- a/funasr/runtime/python/onnxruntime/funasr_onnx/punc_bin.py
+++ b/funasr/runtime/python/onnxruntime/funasr_onnx/punc_bin.py
@@ -117,3 +117,133 @@
outputs = self.ort_infer([feats, feats_len])
return outputs
+
+class CT_Transformer_VadRealtime(CT_Transformer):
+ def __init__(self, model_dir: Union[str, Path] = None,
+ batch_size: int = 1,
+ device_id: Union[str, int] = "-1",
+ quantize: bool = False,
+ intra_op_num_threads: int = 4
+ ):
+ super(CT_Transformer_VadRealtime, self).__init__(model_dir, batch_size, device_id, quantize, intra_op_num_threads)
+
+ def __call__(self, text: str, param_dict: map, split_size=20):
+ cache_key = "cache"
+ assert cache_key in param_dict
+ cache = param_dict[cache_key]
+ if cache is not None and len(cache) > 0:
+ precache = "".join(cache)
+ else:
+ precache = ""
+ cache = []
+ full_text = precache + text
+ split_text = code_mix_split_words(full_text)
+ split_text_id = self.converter.tokens2ids(split_text)
+ mini_sentences = split_to_mini_sentence(split_text, split_size)
+ mini_sentences_id = split_to_mini_sentence(split_text_id, split_size)
+ new_mini_sentence_punc = []
+ assert len(mini_sentences) == len(mini_sentences_id)
+
+ cache_sent = []
+ cache_sent_id = np.array([], dtype='int32')
+ sentence_punc_list = []
+ sentence_words_list = []
+ cache_pop_trigger_limit = 200
+ skip_num = 0
+ for mini_sentence_i in range(len(mini_sentences)):
+ mini_sentence = mini_sentences[mini_sentence_i]
+ mini_sentence_id = mini_sentences_id[mini_sentence_i]
+ mini_sentence = cache_sent + mini_sentence
+ mini_sentence_id = np.concatenate((cache_sent_id, mini_sentence_id), axis=0)
+ text_length = len(mini_sentence_id)
+ data = {
+ "input": mini_sentence_id[None,:],
+ "text_lengths": np.array([text_length], dtype='int32'),
+ "vad_mask": self.vad_mask(text_length, len(cache) - 1)[None, None, :, :].astype(np.float32),
+ "sub_masks": np.tril(np.ones((text_length, text_length), dtype=np.float32))[None, None, :, :].astype(np.float32)
+ }
+ try:
+ outputs = self.infer(data['input'], data['text_lengths'], data['vad_mask'], data["sub_masks"])
+ y = outputs[0]
+ punctuations = np.argmax(y,axis=-1)[0]
+ assert punctuations.size == len(mini_sentence)
+ except ONNXRuntimeError:
+ logging.warning("error")
+
+ # Search for the last Period/QuestionMark as cache
+ if mini_sentence_i < len(mini_sentences) - 1:
+ sentenceEnd = -1
+ last_comma_index = -1
+ for i in range(len(punctuations) - 2, 1, -1):
+ if self.punc_list[punctuations[i]] == "銆�" or self.punc_list[punctuations[i]] == "锛�":
+ sentenceEnd = i
+ break
+ if last_comma_index < 0 and self.punc_list[punctuations[i]] == "锛�":
+ last_comma_index = i
+
+ if sentenceEnd < 0 and len(mini_sentence) > cache_pop_trigger_limit and last_comma_index >= 0:
+ # The sentence it too long, cut off at a comma.
+ sentenceEnd = last_comma_index
+ punctuations[sentenceEnd] = self.period
+ cache_sent = mini_sentence[sentenceEnd + 1:]
+ cache_sent_id = mini_sentence_id[sentenceEnd + 1:]
+ mini_sentence = mini_sentence[0:sentenceEnd + 1]
+ punctuations = punctuations[0:sentenceEnd + 1]
+
+ punctuations_np = [int(x) for x in punctuations]
+ new_mini_sentence_punc += punctuations_np
+ sentence_punc_list += [self.punc_list[int(x)] for x in punctuations_np]
+ sentence_words_list += mini_sentence
+
+ assert len(sentence_punc_list) == len(sentence_words_list)
+ words_with_punc = []
+ sentence_punc_list_out = []
+ for i in range(0, len(sentence_words_list)):
+ if i > 0:
+ if len(sentence_words_list[i][0].encode()) == 1 and len(sentence_words_list[i - 1][-1].encode()) == 1:
+ sentence_words_list[i] = " " + sentence_words_list[i]
+ if skip_num < len(cache):
+ skip_num += 1
+ else:
+ words_with_punc.append(sentence_words_list[i])
+ if skip_num >= len(cache):
+ sentence_punc_list_out.append(sentence_punc_list[i])
+ if sentence_punc_list[i] != "_":
+ words_with_punc.append(sentence_punc_list[i])
+ sentence_out = "".join(words_with_punc)
+
+ sentenceEnd = -1
+ for i in range(len(sentence_punc_list) - 2, 1, -1):
+ if sentence_punc_list[i] == "銆�" or sentence_punc_list[i] == "锛�":
+ sentenceEnd = i
+ break
+ cache_out = sentence_words_list[sentenceEnd + 1:]
+ if sentence_out[-1] in self.punc_list:
+ sentence_out = sentence_out[:-1]
+ sentence_punc_list_out[-1] = "_"
+ param_dict[cache_key] = cache_out
+ return sentence_out, sentence_punc_list_out, cache_out
+
+ def vad_mask(self, size, vad_pos, dtype=np.bool):
+ """Create mask for decoder self-attention.
+
+ :param int size: size of mask
+ :param int vad_pos: index of vad index
+ :param torch.dtype dtype: result dtype
+ :rtype: torch.Tensor (B, Lmax, Lmax)
+ """
+ ret = np.ones((size, size), dtype=dtype)
+ if vad_pos <= 0 or vad_pos >= size:
+ return ret
+ sub_corner = np.zeros(
+ (vad_pos - 1, size - vad_pos), dtype=dtype)
+ ret[0:vad_pos - 1, vad_pos:] = sub_corner
+ return ret
+
+ def infer(self, feats: np.ndarray,
+ feats_len: np.ndarray,
+ vad_mask: np.ndarray,
+ sub_masks: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+ outputs = self.ort_infer([feats, feats_len, vad_mask, sub_masks])
+ return outputs
+
--
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