调整目录结构

Ref_Audio_Selector/ref_audio_selector_webui.py

@@ -3,6 +3,8 @@ import os.path
 import gradio as gr
 import Ref_Audio_Selector.tool.audio_similarity as audio_similarity
 import Ref_Audio_Selector.tool.audio_inference as audio_inference
+import Ref_Audio_Selector.tool.audio_asr as audio_asr
+import Ref_Audio_Selector.tool.audio_config as audio_config
 import Ref_Audio_Selector.common.common as common
 from tools.i18n.i18n import I18nAuto
 
@@ -49,13 +51,14 @@ def sample(text_work_space_dir, text_character, text_sample_dir, text_base_voice
         if text_sample_num is None or text_sample_num == '':
             raise Exception(i18n("每段随机抽样个数不能为空"))
 
-        similarity_list = audio_similarity.start_similarity_analysis(text_work_space_dir, text_sample_dir, text_base_voice_path, checkbox_similarity_output)
-        
+        similarity_list = audio_similarity.start_similarity_analysis(text_work_space_dir, text_sample_dir,
+                                                                     text_base_voice_path, checkbox_similarity_output)
+
         if similarity_list is None:
             raise Exception(i18n("相似度分析失败"))
-        
+
         audio_similarity.sample(ref_audio_dir, similarity_list, text_subsection_num, text_sample_num)
-        
+
     except Exception as e:
         text_sample_info = f"发生异常：{e}"
         ref_audio_dir = ''
@@ -94,7 +97,8 @@ def model_inference(text_work_space_dir, text_character, text_model_inference_vo
         ref_audio_manager = common.RefAudioListManager(text_model_inference_voice_dir)
         if len(ref_audio_manager.get_audio_list()) == 0:
             raise Exception(i18n("待推理的参考音频不能为空"))
-        audio_inference.generate_audio_files(url_composer, text_list, ref_audio_manager.get_ref_audio_list(), inference_dir)
+        audio_inference.generate_audio_files(url_composer, text_list, ref_audio_manager.get_ref_audio_list(),
+                                             inference_dir)
     except Exception as e:
         text_model_inference_info = f"发生异常：{e}"
         text_asr_audio_dir = ''
@@ -104,9 +108,9 @@ def model_inference(text_work_space_dir, text_character, text_model_inference_vo
 # 对推理生成音频执行asr
 def asr(text_work_space_dir, text_character, text_asr_audio_dir, dropdown_asr_model,
         dropdown_asr_size, dropdown_asr_lang):
-    asr_file = os.path.join(text_work_space_dir, 'asr.list')
-    text_text_similarity_analysis_path = asr_file
-    text_asr_info = f"asr成功：生成文件asr.list"
+    asr_file = None
+    text_text_similarity_analysis_path = None
+    text_asr_info = None
     try:
         check_base_info(text_work_space_dir, text_character)
         if text_asr_audio_dir is None or text_asr_audio_dir == '':
@@ -117,7 +121,10 @@ def asr(text_work_space_dir, text_character, text_asr_audio_dir, dropdown_asr_mo
             raise Exception(i18n("asr模型大小不能为空"))
         if dropdown_asr_lang is None or dropdown_asr_lang == '':
             raise Exception(i18n("asr语言不能为空"))
-        pass
+        asr_file = audio_asr.open_asr(text_asr_audio_dir, text_work_space_dir, dropdown_asr_model, dropdown_asr_size,
+                                      dropdown_asr_lang)
+        text_text_similarity_analysis_path = asr_file
+        text_asr_info = f"asr成功：生成文件{asr_file}"
     except Exception as e:
         text_asr_info = f"发生异常：{e}"
         text_text_similarity_analysis_path = ''
@@ -149,7 +156,14 @@ def similarity_audio_output(text_work_space_dir, text_character, text_base_audio
             raise Exception(i18n("基准音频路径不能为空"))
         if text_compare_audio_dir is None or text_compare_audio_dir == '':
             raise Exception(i18n("待分析的音频所在目录不能为空"))
-        pass
+        similarity_list, similarity_file, similarity_file_dir = audio_similarity.start_similarity_analysis(
+            text_work_space_dir, text_compare_audio_dir, text_base_audio_path, True)
+
+        if similarity_list is None:
+            raise Exception(i18n("相似度分析失败"))
+
+        text_similarity_audio_output_info = f'相似度分析成功：生成目录{similarity_file_dir}，文件{similarity_file}'
+
     except Exception as e:
         text_similarity_audio_output_info = f"发生异常：{e}"
     return text_similarity_audio_output_info
@@ -181,7 +195,8 @@ def create_config(text_work_space_dir, text_character, text_template, text_sync_
             raise Exception(i18n("参考音频抽样目录不能为空"))
         if text_sync_ref_audio_dir2 is None or text_sync_ref_audio_dir2 == '':
             raise Exception(i18n("参考音频目录不能为空"))
-        pass
+        ref_audio_manager = common.RefAudioListManager(text_sync_ref_audio_dir2)
+        audio_config.generate_audio_config(text_template, ref_audio_manager.get_ref_audio_list(), config_file)
     except Exception as e:
         text_create_config_info = f"发生异常：{e}"
     return text_create_config_info
@@ -191,9 +206,9 @@ def create_config(text_work_space_dir, text_character, text_template, text_sync_
 def whole_url(text_url, text_text, text_ref_path, text_ref_text, text_emotion):
     url_composer = audio_inference.URLComposer(text_url, text_emotion, text_text, text_ref_path, text_ref_text)
     if url_composer.is_emotion():
-        text_whole_url = url_composer.build_url_with_emotion('测试内容','情绪类型')
+        text_whole_url = url_composer.build_url_with_emotion('测试内容', '情绪类型')
     else:
-        text_whole_url = url_composer.build_url_with_ref('测试内容','参考路径','参考文本')
+        text_whole_url = url_composer.build_url_with_ref('测试内容', '参考路径', '参考文本')
     return text_whole_url
 
 

Ref_Audio_Selector/tool/asr/funasr_asr_multi_level_dir.py

@@ -0,0 +1,111 @@
+# -*- coding:utf-8 -*-
+
+import argparse
+import os
+import traceback
+from tqdm import tqdm
+
+from funasr import AutoModel
+
+path_asr  = 'tools/asr/models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'
+path_vad  = 'tools/asr/models/speech_fsmn_vad_zh-cn-16k-common-pytorch'
+path_punc = 'tools/asr/models/punc_ct-transformer_zh-cn-common-vocab272727-pytorch'
+path_asr  = path_asr  if os.path.exists(path_asr)  else "iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
+path_vad  = path_vad  if os.path.exists(path_vad)  else "iic/speech_fsmn_vad_zh-cn-16k-common-pytorch"
+path_punc = path_punc if os.path.exists(path_punc) else "iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
+
+model = AutoModel(
+    model               = path_asr,
+    model_revision      = "v2.0.4",
+    vad_model           = path_vad,
+    vad_model_revision  = "v2.0.4",
+    punc_model          = path_punc,
+    punc_model_revision = "v2.0.4",
+)
+
+def only_asr(input_file):
+    try:
+        text = model.generate(input=input_file)[0]["text"]
+    except:
+        text = ''
+        print(traceback.format_exc())
+    return text
+
+def execute_asr(input_folder, output_folder, model_size, language):
+    input_file_names = os.listdir(input_folder)
+    input_file_names.sort()
+
+    output = []
+    output_file_name = os.path.basename(input_folder)
+
+    for name in tqdm(input_file_names):
+        try:
+            text = model.generate(input="%s/%s"%(input_folder, name))[0]["text"]
+            output.append(f"{input_folder}/{name}|{output_file_name}|{language.upper()}|{text}")
+        except:
+            print(traceback.format_exc())
+
+    output_folder = output_folder or "output/asr_opt"
+    os.makedirs(output_folder, exist_ok=True)
+    output_file_path = os.path.abspath(f'{output_folder}/{output_file_name}.list')
+
+    with open(output_file_path, "w", encoding="utf-8") as f:
+        f.write("\n".join(output))
+        print(f"ASR 任务完成->标注文件路径: {output_file_path}\n")
+    return output_file_path
+
+
+def execute_asr_multi_level_dir(input_folder, output_folder, model_size, language):
+    output = []
+    output_file_name = os.path.basename(input_folder)
+    # 递归遍历输入目录及所有子目录
+    for root, dirs, files in os.walk(input_folder):
+        for name in sorted(files):
+            # 只处理wav文件（假设是wav文件）
+            if name.endswith(".wav"):
+                try:
+                    # 构造完整的输入音频文件路径
+                    input_file_path = os.path.join(root, name)
+                    input_file_path = os.path.normpath(input_file_path)  # 先标准化可能存在混合斜杠的情况
+                    text = model.generate(input=input_file_path)[0]["text"]
+
+                    output.append(f"{input_file_path}|{output_file_name}|{language.upper()}|{text}")
+
+                except:
+                    print(traceback.format_exc())
+
+    # 创建或打开指定的输出目录
+    output_folder = output_folder or "output/asr_opt"
+    output_dir_abs = os.path.abspath(output_folder)
+    os.makedirs(output_dir_abs, exist_ok=True)
+
+    # 构造输出文件路径
+    output_file_path = os.path.join(output_dir_abs, f'{output_file_name}.list')
+
+    # 将输出写入文件
+    with open(output_file_path, "w", encoding="utf-8") as f:
+        f.write("\n".join(output))
+        print(f"ASR 任务完成->标注文件路径: {output_file_path}\n")
+
+    return output_file_path
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-i", "--input_folder", type=str, required=True,
+                        help="Path to the folder containing WAV files.")
+    parser.add_argument("-o", "--output_folder", type=str, required=True,
+                        help="Output folder to store transcriptions.")
+    parser.add_argument("-s", "--model_size", type=str, default='large',
+                        help="Model Size of FunASR is Large")
+    parser.add_argument("-l", "--language", type=str, default='zh', choices=['zh'],
+                        help="Language of the audio files.")
+    parser.add_argument("-p", "--precision", type=str, default='float16', choices=['float16','float32'],
+                        help="fp16 or fp32")#还没接入
+
+    cmd = parser.parse_args()
+    execute_asr_multi_level_dir(
+        input_folder  = cmd.input_folder,
+        output_folder = cmd.output_folder,
+        model_size    = cmd.model_size,
+        language      = cmd.language,
+    )

Ref_Audio_Selector/tool/audio_asr.py

@@ -0,0 +1,34 @@
+import os
+from config import python_exec,is_half
+from tools import my_utils
+from tools.asr.config import asr_dict
+from subprocess import Popen
+def open_asr(asr_inp_dir, asr_opt_dir, asr_model, asr_model_size, asr_lang):
+    global p_asr
+    if(p_asr==None):
+        asr_inp_dir=my_utils.clean_path(asr_inp_dir)
+        asr_py_path = asr_dict[asr_model]["path"]
+        if asr_py_path == 'funasr_asr.py':
+            asr_py_path = 'funasr_asr_multi_level_dir.py'
+        if asr_py_path == 'fasterwhisper.py':
+            asr_py_path = 'fasterwhisper_asr_multi_level_dir.py'
+        cmd = f'"{python_exec}" tools/asr/{asr_py_path}'
+        cmd += f' -i "{asr_inp_dir}"'
+        cmd += f' -o "{asr_opt_dir}"'
+        cmd += f' -s {asr_model_size}'
+        cmd += f' -l {asr_lang}'
+        cmd += " -p %s"%("float16"if is_half==True else "float32")
+
+        print(cmd)
+        p_asr = Popen(cmd, shell=True)
+        p_asr.wait()
+        p_asr=None
+
+        output_dir_abs = os.path.abspath(asr_opt_dir)
+        output_file_name = os.path.basename(asr_inp_dir)
+        # 构造输出文件路径
+        output_file_path = os.path.join(output_dir_abs, f'{output_file_name}.list')
+        return output_file_path
+        
+    else:
+        return None

Ref_Audio_Selector/tool/audio_config.py

@@ -0,0 +1,26 @@
+import os
+
+
+def generate_audio_config(template_str, audio_list, output_file_path):
+    # 定义一个空字符串来存储最终要写入文件的内容
+    file_content = ""
+
+    # 遍历参考音频列表
+    for audio_info in audio_list:
+        emotion = audio_info['emotion']
+        ref_path = audio_info['ref_path']
+        ref_text = audio_info['ref_text']
+
+        # 使用字符串模板替换变量
+        formatted_line = template_str.replace('${emotion}', emotion).replace('${ref_path}', ref_path).replace(
+            '${ref_text}', ref_text)
+
+        # 将格式化后的行添加到内容中，使用逗号和换行符分隔
+        file_content += formatted_line + ",\n"
+
+    # 删除最后一个逗号和换行符，确保格式整洁
+    file_content = file_content[:-2]
+
+    # 将内容写入输出文件
+    with open(output_file_path, 'w', encoding='utf-8') as output_file:
+        output_file.write(file_content)

Ref_Audio_Selector/tool/audio_similarity.py

@@ -95,7 +95,7 @@ def start_similarity_analysis(work_space_dir, sample_dir, base_voice_path, need_
 
     global p_similarity
     if(p_similarity==None):
-        cmd = f'"{python_exec}" tools/speaker_verification/audio_similarity.py '
+        cmd = f'"{python_exec}" tools/speaker_verification/voice_similarity.py '
         cmd += f' -r "{base_voice_path}"'
         cmd += f' -c "{sample_dir}"'
         cmd += f' -o {similarity_file}'
@@ -110,9 +110,9 @@ def start_similarity_analysis(work_space_dir, sample_dir, base_voice_path, need_
             ref_audio_opt.copy_and_move(similarity_file_dir, similarity_list)
 
         p_similarity=None
-        return similarity_list
+        return similarity_list, similarity_file, similarity_file_dir
     else:
-        return similarity_list
+        return similarity_list, None, None
 
 
 def parse_similarity_file(file_path):

Ref_Audio_Selector/tool/text_comparison/text_comparison.py

@@ -0,0 +1,53 @@
+import torch
+from transformers import AutoTokenizer, AutoModel
+from scipy.spatial.distance import cosine
+import math
+
+bert_path = os.environ.get(
+    "bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
+)
+
+
+tokenizer = AutoTokenizer.from_pretrained(bert_path)
+model = AutoModel.from_pretrained(bert_path)
+
+
+def calculate_similarity(text1, text2, max_length=512):
+    # 预处理文本，设置最大长度
+    inputs1 = tokenizer(text1, padding=True, truncation=True, max_length=max_length, return_tensors='pt')
+    inputs2 = tokenizer(text2, padding=True, truncation=True, max_length=max_length, return_tensors='pt')
+
+    # 获取句子向量（这里是取CLS token的向量并展平为一维）
+    with torch.no_grad():
+        encoded_text1 = model(**inputs1)[0][:, 0, :].flatten()
+        encoded_text2 = model(**inputs2)[0][:, 0, :].flatten()
+
+    # 确保转换为numpy数组并且是一维的
+    similarity = 1 - cosine(encoded_text1.cpu().numpy().flatten(), encoded_text2.cpu().numpy().flatten())
+
+    return similarity
+
+# 对0.8-1区间的值进行放大
+def adjusted_similarity(similarity_score2, boundary=0.8):
+
+    if similarity_score2 < boundary:
+        return 0
+
+    # 倍数
+    multiple = 1/(1 - boundary)
+
+    adjusted_score = (similarity_score2 - boundary)*multiple
+
+    return adjusted_score
+
+
+def calculate_result(t1, t2):
+    # 计算并打印相似度
+    similarity_score2 = calculate_similarity(t1, t2)
+
+    # 调整相似度
+    adjusted_similarity_score2 = adjusted_similarity(similarity_score2)
+
+    return similarity_score2, adjusted_similarity_score2
+
+