bug修复

Ref_Audio_Selector/ref_audio_selector_webui.py

@@ -289,7 +289,7 @@ def text_similarity_analysis(text_work_space_dir, text_role,
     return i18n(text_text_similarity_analysis_info)
 
 
-def open_text_similarity_analysis(asr_file_path, output_dir, similarity_enlarge_boundary=0.8):
+def open_text_similarity_analysis(asr_file_path, output_dir, similarity_enlarge_boundary=0.9):
     global p_text_similarity
     if p_text_similarity is None:
         cmd = f'"{python_exec}" Ref_Audio_Selector/tool/text_comparison/asr_text_process.py '

Ref_Audio_Selector/tool/audio_inference.py

@@ -85,19 +85,15 @@ def generate_audio_files(url_composer, text_list, emotion_list, output_dir_path)
     emotion_subdir = os.path.join(output_dir, params.inference_audio_emotion_aggregation_dir)
     os.makedirs(emotion_subdir, exist_ok=True)
 
+    all_count = len(text_list) * len(emotion_list)
+    has_generated_count = 0
+
     # 计算笛卡尔积
     cartesian_product = list(itertools.product(text_list, emotion_list))
 
     for text, emotion in cartesian_product:
         # Generate audio byte stream using the create_audio function
 
-        if url_composer.is_emotion():
-            real_url = url_composer.build_url_with_emotion(text, emotion['emotion'])
-        else:
-            real_url = url_composer.build_url_with_ref(text, emotion['ref_path'], emotion['ref_text'])
-
-        audio_bytes = inference_audio_from_api(real_url)
-
         emotion_name = emotion['emotion']
 
         text_subdir_text = os.path.join(text_subdir, text)
@@ -108,12 +104,28 @@ def generate_audio_files(url_composer, text_list, emotion_list, output_dir_path)
         os.makedirs(emotion_subdir_emotion, exist_ok=True)
         emotion_subdir_emotion_file_path = os.path.join(emotion_subdir_emotion, text + '.wav')
 
+        # 检查是否已经存在对应的音频文件，如果存在则跳过
+        if os.path.exists(text_subdir_text_file_path) and os.path.exists(emotion_subdir_emotion_file_path):
+            has_generated_count += 1
+            print(f"进度: {has_generated_count}/{all_count}")
+            continue
+
+        if url_composer.is_emotion():
+            real_url = url_composer.build_url_with_emotion(text, emotion['emotion'])
+        else:
+            real_url = url_composer.build_url_with_ref(text, emotion['ref_path'], emotion['ref_text'])
+
+        audio_bytes = inference_audio_from_api(real_url)
+
         # Write audio bytes to the respective files
         with open(text_subdir_text_file_path, 'wb') as f:
             f.write(audio_bytes)
         with open(emotion_subdir_emotion_file_path, 'wb') as f:
             f.write(audio_bytes)
 
+        has_generated_count += 1
+        print(f"进度: {has_generated_count}/{all_count}")
+
 
 def inference_audio_from_api(url):
     # 发起GET请求

Ref_Audio_Selector/tool/audio_similarity.py

@@ -142,7 +142,7 @@ def copy_and_move(output_audio_directory, similarity_scores):
     for item in similarity_scores:
         # 构造新的文件名
         base_name = os.path.basename(item['wav_path'])[:-4]  # 去掉.wav扩展名
-        new_name = f"{item['score']}-{base_name}.wav"
+        new_name = f"{item['score']*10000:04.0f}-{base_name}.wav"
 
         # 新文件的完整路径
         new_path = os.path.join(output_audio_directory, new_name)

Ref_Audio_Selector/tool/text_comparison/asr_text_process.py

@@ -33,10 +33,16 @@ def parse_asr_file(file_path):
     return output
 
 
+@timeit_decorator
 def calculate_similarity_and_append_to_list(input_list, boundary):
+    all_count = len(input_list)
+    has_been_processed_count = 0
     for item in input_list:
-        _, similarity_score = text_comparison.calculate_result(item['original_text'], item['asr_text'], boundary)
+        original_score, similarity_score = text_comparison.calculate_result(item['original_text'], item['asr_text'], boundary)
         item['similarity_score'] = similarity_score
+        item['original_score'] = original_score
+        has_been_processed_count += 1
+        print(f'进度：{has_been_processed_count}/{all_count}')
 
     return input_list
 
@@ -79,13 +85,27 @@ def group_and_sort_by_field(data, group_by_field):
 
 def format_list_to_text(data_list, output_filename):
     with open(output_filename, 'w', encoding='utf-8') as output_file:
+        output_file.write('放大后的相似度分值|原始分值|ASR文本|原文文本\n')
         for key, items in data_list:
             # 写入情绪标题
             output_file.write(key + '\n')
 
             # 写入每条记录
             for item in items:
-                formatted_line = f"{item['similarity_score']}|{item['original_text']}|{item['asr_text']}\n"
+                formatted_line = f"{item['similarity_score']}|{item['original_score']}|{item['asr_text']}|{item['original_text']}\n"
+                output_file.write(formatted_line)
+
+
+def format_list_to_emotion(data_list, output_filename):
+    with open(output_filename, 'w', encoding='utf-8') as output_file:
+        output_file.write('放大后的相似度分值|原始分值|ASR文本|情绪类型\n')
+        for key, items in data_list:
+            # 写入情绪标题
+            output_file.write(key + '\n')
+
+            # 写入每条记录
+            for item in items:
+                formatted_line = f"{item['similarity_score']}|{item['original_score']}|{item['asr_text']}|{item['emotion']}\n"
                 output_file.write(formatted_line)
 
 
@@ -113,7 +133,7 @@ def process(asr_file_path, output_dir, similarity_enlarge_boundary):
     original_text_detail_list = group_and_sort_by_field(records, 'original_text')
 
     original_text_detail_file = os.path.join(output_dir, f'{params.text_similarity_by_text_detail_filename}.txt')
-    format_list_to_text(original_text_detail_list, original_text_detail_file)
+    format_list_to_emotion(original_text_detail_list, original_text_detail_file)
 
     print('文本相似度分析完成。')
 

Ref_Audio_Selector/tool/text_comparison/text_comparison.py

@@ -51,3 +51,73 @@ def calculate_result(t1, t2, boundary):
     return similarity_score2, adjusted_similarity_score2
 
 
+def print_result(t1, t2, boundary):
+    print(f't2: {t2}')
+    # 计算并打印相似度
+    similarity_score2 = calculate_similarity(t1, t2)
+    print(f"两句话的相似度为: {similarity_score2:.4f}")
+
+    # 调整相似度
+    adjusted_similarity_score2 = adjusted_similarity(similarity_score2, boundary)
+    print(f"调整后的相似度为: {adjusted_similarity_score2:.4f}")
+
+
+def test(boundary):
+    # 原始文本
+    text1 = "这是第一个句子"
+    list = """
+    这是第一个句子
+    这是第二个句子。
+    那么，这是第三个表达。
+    当前呈现的是第四个句子。
+    接下来，我们有第五句话。
+    在此，展示第六条陈述。
+    继续下去，这是第七个短句。
+    不容忽视的是第八个表述。
+    顺延着序列，这是第九句。
+    此处列举的是第十个说法。
+    进入新的篇章，这是第十一个句子。
+    下一段内容即为第十二个句子。
+    显而易见，这是第十三个叙述。
+    渐进地，我们来到第十四句话。
+    向下滚动，您会看到第十五个表达。
+    此刻，呈现在眼前的是第十六个句子。
+    它们中的一个——第十七个句子在此。
+    如同链条般连接，这是第十八个断言。
+    按照顺序排列，接下来是第十九个话语。
+    逐一列举，这是第二十个陈述句。
+    结构相似，本例给出第二十一个实例句。
+    这是最初的陈述句。
+    首先表达的是这一个句子。
+    第一句内容即为此处所示。
+    这是起始的叙述段落。
+    开篇所展示的第一句话就是这个。
+    明媚的阳光洒满大地
+    窗外飘落粉色樱花瓣
+    笔尖轻触纸面思绪万千
+    深夜的月光如水般静谧
+    穿越丛林的小径蜿蜒曲折
+    浅酌清茶品味人生百态
+    破晓时分雄鸡一唱天下白
+    草原上奔驰的骏马无拘无束
+    秋叶纷飞描绘季节更替画卷
+    寒冬雪夜炉火旁围坐共话家常
+    kszdRjYXw
+    pfsMgTlVHnB
+    uQaGxIbWz
+    ZtqNhPmKcOe
+    jfyrXsStVUo
+    wDiEgLkZbn
+    yhNvAfUmqC
+    TpKjxMrWgs
+    eBzHUaFJtYd
+    oQnXcVSiPkL
+    00000
+    """
+    list2 = list.strip().split('\n')
+    for item in list2:
+        print_result(text1, item, boundary)
+
+
+if __name__ == '__main__':
+    test(0.9)