From b15db52e4e67da8a133a67e8ffa415386de48b40 Mon Sep 17 00:00:00 2001
From: zhuyunfeng <10596244@qq.com>
Date: 星期二, 09 五月 2023 23:03:15 +0800
Subject: [PATCH] Add contributor

---
 funasr/runtime/python/onnxruntime/funasr_onnx/punc_bin.py |   18 +++++++++++++++---
 1 files changed, 15 insertions(+), 3 deletions(-)

diff --git a/funasr/runtime/python/onnxruntime/funasr_onnx/punc_bin.py b/funasr/runtime/python/onnxruntime/funasr_onnx/punc_bin.py
index 0eb764f..6fd01e4 100644
--- a/funasr/runtime/python/onnxruntime/funasr_onnx/punc_bin.py
+++ b/funasr/runtime/python/onnxruntime/funasr_onnx/punc_bin.py
@@ -1,4 +1,6 @@
 # -*- encoding: utf-8 -*-
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+#  MIT License  (https://opensource.org/licenses/MIT)
 
 import os.path
 from pathlib import Path
@@ -13,6 +15,11 @@
 
 
 class CT_Transformer():
+    """
+    Author: Speech Lab of DAMO Academy, Alibaba Group
+    CT-Transformer: Controllable time-delay transformer for real-time punctuation prediction and disfluency detection
+    https://arxiv.org/pdf/2003.01309.pdf
+    """
     def __init__(self, model_dir: Union[str, Path] = None,
                  batch_size: int = 1,
                  device_id: Union[str, int] = "-1",
@@ -57,7 +64,7 @@
             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.array(cache_sent_id + mini_sentence_id, dtype='int64')
+            mini_sentence_id = np.array(cache_sent_id + mini_sentence_id, dtype='int32')
             data = {
                 "text": mini_sentence_id[None,:],
                 "text_lengths": np.array([len(mini_sentence_id)], dtype='int32'),
@@ -119,6 +126,11 @@
 
 
 class CT_Transformer_VadRealtime(CT_Transformer):
+    """
+    Author: Speech Lab of DAMO Academy, Alibaba Group
+    CT-Transformer: Controllable time-delay transformer for real-time punctuation prediction and disfluency detection
+    https://arxiv.org/pdf/2003.01309.pdf
+    """
     def __init__(self, model_dir: Union[str, Path] = None,
                  batch_size: int = 1,
                  device_id: Union[str, int] = "-1",
@@ -136,7 +148,7 @@
         else:
             precache = ""
             cache = []
-        full_text = precache + text
+        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)
@@ -154,7 +166,7 @@
             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)
+            mini_sentence_id = np.concatenate((cache_sent_id, mini_sentence_id), axis=0,dtype='int32')
             text_length = len(mini_sentence_id)
             data = {
                 "input": mini_sentence_id[None,:],

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