Update app.py

app.py

@@ -3,183 +3,151 @@ import spaces
 import gradio as gr
 import os
 from pyannote.audio import Pipeline
-from pyannote.core import Annotation, Segment
 from pydub import AudioSegment
 
 # 获取 Hugging Face 认证令牌
 HF_TOKEN = os.environ.get("HUGGINGFACE_READ_TOKEN")
+pipeline = None
 
-class AudioProcessor:
-    def __init__(self):
-        self.pipeline = None
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-        # 尝试加载 pyannote 模型
-        try:
-            self.pipeline = Pipeline.from_pretrained(
-                "pyannote/speaker-diarization-3.1", use_auth_token=HF_TOKEN
-            )
-            self.pipeline.to(self.device)
-            print("pyannote model loaded successfully.")
-        except Exception as e:
-            print(f"Error initializing pipeline: {e}")
-            self.pipeline = None
-
-    # 音频拼接函数：拼接目标音频和混合音频，返回目标音频的起始时间和结束时间作为字典
-    def combine_audio_with_time(self, target_audio, mixed_audio):
-        if self.pipeline is None:
-            return "错误: 模型未初始化"
-
-        # 加载目标说话人的样本音频
-        try:
-            target_audio_segment = AudioSegment.from_wav(target_audio)
-        except Exception as e:
-            return f"加载目标音频时出错: {e}"
-
-        # 加载混合音频
-        try:
-            mixed_audio_segment = AudioSegment.from_wav(mixed_audio)
-        except Exception as e:
-            return f"加载混合音频时出错: {e}"
-
-        # 记录目标说话人音频的时间点（精确到0.01秒）
-        target_start_time = len(mixed_audio_segment) / 1000  # 秒为单位，精确到 0.01 秒
-
-        # 目标音频的结束时间（拼接后的音频长度）
-        target_end_time = target_start_time + len(target_audio_segment) / 1000  # 秒为单位
-
-        # 将目标说话人的音频片段添加到混合音频的最后
-        final_audio = mixed_audio_segment + target_audio_segment
-        final_audio.export("final_output.wav", format="wav")
-
-        # 返回目标音频的起始时间和结束时间
-        return {"start_time": target_start_time, "end_time": target_end_time}
-
-    # 使用 pyannote/speaker-diarization 对拼接后的音频进行说话人分离
-    @spaces.GPU(duration=60 * 2)  # 使用 GPU 加速，限制执行时间为 120 秒
-    def diarize_audio(self, temp_file):
-        if self.pipeline is None:
-            return "错误: 模型未初始化"
-
-        try:
-            diarization = self.pipeline(temp_file)  # 返回 Annotation 对象
-        except Exception as e:
-            return f"处理音频时出错: {e}"
-        print("3123", type(diarization))
-        return diarization  # 直接返回 Annotation 对象
-
-    # 将时间戳转换为秒
-    def timestamp_to_seconds(self, timestamp):
-        try:
-            h, m, s = map(float, timestamp.split(':'))
-            return 3600 * h + 60 * m + s
-        except ValueError as e:
-            print(f"转换时间戳时出错: '{timestamp}'. 错误: {e}")
-            return None
-
-    # 计算时间段的重叠部分（单位：秒）
-    def calculate_overlap(self, start1, end1, start2, end2):
-        overlap_start = max(start1, start2)
-        overlap_end = min(end1, end2)
-        overlap_duration = max(0, overlap_end - overlap_start)
-        return overlap_duration
-
-    # 获取该说话人除了目标语音时间段外的所有时间段
-    def get_speaker_time_segments(self, diarization_output, target_time, speaker_label):
-        remaining_segments = []
-
-        # 遍历 diarization 输出，查找该说话人的所有时间段
-        for segment, label in diarization_output.itertracks(yield_label=True):
-            if label == speaker_label:
-                start_seconds = segment.start
-                end_seconds = segment.end
-
-                # 计算与目标音频的重叠部分
-                overlap_start = max(start_seconds, target_time['start_time'])
-                overlap_end = min(end_seconds, target_time['end_time'])
-
-                # 如果有重叠部分，排除重叠部分
-                if overlap_start < overlap_end:
-                    if start_seconds < overlap_start:
-                        remaining_segments.append((start_seconds, overlap_start))
-                    if overlap_end < end_seconds:
-                        remaining_segments.append((overlap_end, end_seconds))
-                else:
-                    remaining_segments.append((start_seconds, end_seconds))
-
-        return remaining_segments
-
-    # 获取最佳匹配的说话人，并返回该说话人非重叠的时间段
-    def get_best_match(self, target_time, diarization_output):
-        target_start_time = target_time['start_time']
-        target_end_time = target_time['end_time']
-
-        # 用于存储每个说话人时间段的重叠比例
-        speaker_segments = []
-        for segment, label in diarization_output.itertracks(yield_label=True):
-            try:
-                start_seconds = segment.start
-                end_seconds = segment.end
-
-                # 计算目标音频时间段和说话人时间段的重叠时间
-                overlap = self.calculate_overlap(target_start_time, target_end_time, start_seconds, end_seconds)
-                overlap_ratio = overlap / (end_seconds - start_seconds)
-
-                # 记录说话人标签和重叠比例
-                speaker_segments.append((label, overlap_ratio, start_seconds, end_seconds))
-
-            except Exception as e:
-                print(f"处理行时出错: '{segment}'. 错误: {e}")
-
-        # 按照重叠比例排序，返回重叠比例最大的一段
-        best_match = max(speaker_segments, key=lambda x: x[1], default=None)
+# 尝试加载 pyannote 模型
+try:
+    pipeline = Pipeline.from_pretrained(
+        "pyannote/speaker-diarization-3.1", use_auth_token=HF_TOKEN
+    )
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    pipeline.to(device)
+except Exception as e:
+    print(f"Error initializing pipeline: {e}")
+    pipeline = None
+
+# 音频拼接函数：拼接目标音频和混合音频，返回目标音频的起始时间和结束时间作为字典
+def combine_audio_with_time(target_audio, mixed_audio):
+    if pipeline is None:
+        return "错误: 模型未初始化"
+    
+    # 打印文件路径，确保文件正确传递
+    print(f"目标音频文件路径: {target_audio}")
+    print(f"混合音频文件路径: {mixed_audio}")
+
+    # 加载目标说话人的样本音频
+    try:
+        target_audio_segment = AudioSegment.from_wav(target_audio)
+    except Exception as e:
+        return f"加载目标音频时出错: {e}"
+    
+    # 加载混合音频
+    try:
+        mixed_audio_segment = AudioSegment.from_wav(mixed_audio)
+    except Exception as e:
+        return f"加载混合音频时出错: {e}"
+
+    # 记录目标说话人音频的时间点（精确到0.01秒）
+    target_start_time = len(mixed_audio_segment) / 1000  # 秒为单位，精确到 0.01 秒
+
+    # 目标音频的结束时间（拼接后的音频长度）
+    target_end_time = target_start_time + len(target_audio_segment) / 1000  # 秒为单位
+
+    # 将目标说话人的音频片段添加到混合音频的最后
+    final_audio = mixed_audio_segment + target_audio_segment
+    final_audio.export("final_output.wav", format="wav")
+    
+    # 返回目标音频的起始时间和结束时间
+    return {"start_time": target_start_time, "end_time": target_end_time}
+
+# 使用 pyannote/speaker-diarization 对拼接后的音频进行说话人分离
+@spaces.GPU(duration=60 * 2)  # 使用 GPU 加速，限制执行时间为 120 秒
+def diarize_audio(temp_file):
+    if pipeline is None:
+        return "错误: 模型未初始化"
+    
+    try:
+        diarization = pipeline(temp_file)
+    except Exception as e:
+        return f"处理音频时出错: {e}"
+    
+    # 返回 diarization 类对象
+    return diarization
+
+# 将时间戳转换为秒
+def timestamp_to_seconds(timestamp):
+    try:
+        h, m, s = map(float, timestamp.split(':'))
+        return 3600 * h + 60 * m + s
+    except ValueError as e:
+        print(f"转换时间戳时出错: '{timestamp}'. 错误: {e}")
+        return None
+
+# 计算时间段的重叠部分（单位：秒）
+def calculate_overlap(start1, end1, start2, end2):
+    overlap_start = max(start1, start2)
+    overlap_end = min(end1, end2)
+    overlap_duration = max(0, overlap_end - overlap_start)
+    return overlap_duration
+
+# 获取目标时间段和说话人时间段的重叠比例
+def get_best_match(target_time, diarization_output):
+    target_start_time = target_time['start_time']
+    target_end_time = target_time['end_time']
+    
+    # 通过 diarization_output 获取说话人信息
+    speaker_segments = []
+    for speech_turn in diarization_output.itertracks(yield_label=True):  # 使用 itertracks 获取每个说话人的信息
+        start_seconds = speech_turn[0].start
+        end_seconds = speech_turn[0].end
+        label = speech_turn[1]
+        
+        # 计算目标音频时间段和说话人时间段的重叠时间
+        overlap = calculate_overlap(target_start_time, target_end_time, start_seconds, end_seconds)
+        overlap_ratio = overlap / (end_seconds - start_seconds)
+        
+        # 记录说话人标签和重叠比例
+        speaker_segments.append((label, overlap_ratio, start_seconds, end_seconds))
+
+    # 按照重叠比例排序，返回重叠比例最大的一段
+    best_match = max(speaker_segments, key=lambda x: x[1], default=None)
+    
+    return best_match
+
+# 处理音频文件并返回输出
+def process_audio(target_audio, mixed_audio):
+    # 打印文件路径，确保传入的文件有效
+    print(f"处理音频：目标音频: {target_audio}, 混合音频: {mixed_audio}")
+    
+    # 进行音频拼接并返回目标音频的起始和结束时间（作为字典）
+    time_dict = combine_audio_with_time(target_audio, mixed_audio)
+    
+    # 执行说话人分离
+    diarization_result = diarize_audio("final_output.wav")
+    
+    if isinstance(diarization_result, str) and diarization_result.startswith("错误"):
+        return diarization_result, None  # 出错时返回错误信息
+    else:
+        # 获取最佳匹配的说话人标签和时间段
+        best_match = get_best_match(time_dict, diarization_result)
         
         if best_match:
-            speaker_label = best_match[0]  # 取出最佳匹配说话人的标签
-            remaining_segments = self.get_speaker_time_segments(diarization_output, target_time, speaker_label)
-            return speaker_label, remaining_segments
-        else:
-            return None, None
-
-    # 处理音频文件并返回输出
-    def process_audio(self, target_audio, mixed_audio):
-        # 进行音频拼接并返回目标音频的起始和结束时间（作为字典）
-        time_dict = self.combine_audio_with_time(target_audio, mixed_audio)
-
-        # 执行说话人分离
-        diarization_result = self.diarize_audio("final_output.wav")
-
-        print("123",diarization_result)
-
-        if isinstance(diarization_result, str) and diarization_result.startswith("错误"):
-            return diarization_result, None  # 出错时返回错误信息
-        else:
-            # 获取最佳匹配的说话人标签和时间段
-            speaker_label, remaining_segments = self.get_best_match(time_dict, diarization_result)
-
-            if speaker_label and remaining_segments:
-                # 返回最佳匹配说话人的标签和时间段
-                return speaker_label, remaining_segments
+            # 返回最佳匹配说话人的标签和时间段
+            return best_match[0], best_match[2], best_match[3]
 
 # Gradio 接口
 with gr.Blocks() as demo:
     gr.Markdown("""
     # 🗣️ 音频拼接与说话人分类 🗣️
-    上传目标音频和混合音频，拼接并进行说话人分类。结果包括最佳匹配说话人的时间段（排除目标音频时间段）。
+    上传目标音频和混合音频，拼接并进行说话人分类。结果包括最佳匹配说话人的时间段。
     """)
-
+    
     mixed_audio_input = gr.Audio(type="filepath", label="上传混合音频")
     target_audio_input = gr.Audio(type="filepath", label="上传目标说话人音频")
-
+    
     process_button = gr.Button("处理音频")
-
+    
     # 输出结果
     diarization_output = gr.Textbox(label="最佳匹配说话人")
     time_range_output = gr.Textbox(label="最佳匹配时间段")
 
     # 点击按钮时触发处理音频
     process_button.click(
-        fn=AudioProcessor().process_audio,
+        fn=process_audio,
         inputs=[target_audio_input, mixed_audio_input],
         outputs=[diarization_output, time_range_output]
     )