app.py

import torch
import spaces
import gradio as gr
import os
from pyannote.audio import Pipeline
from pydub import AudioSegment

# 获取 Hugging Face 认证令牌
HF_TOKEN = os.environ.get("HUGGINGFACE_READ_TOKEN")
pipeline = 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

# 时间戳转换为秒
def timestamp_to_seconds(timestamp):
    h, m, s = map(float, timestamp.split(':'))
    return 3600 * h + 60 * m + s

# 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)
        print("说话人分离结果:")
        for turn, _, speaker in diarization.itertracks(yield_label=True):
            print(f"[{turn.start:.3f} --> {turn.end:.3f}] {speaker}")
        return diarization
    except Exception as e:
        return f"处理音频时出错: {e}"

# 获取目标说话人的时间段（排除目标音频时间段）
def get_speaker_segments(diarization, target_start_time, target_end_time, final_audio_length):
    speaker_segments = {}
    
    # 遍历所有说话人时间段
    for turn, _, speaker in diarization.itertracks(yield_label=True):
        start = turn.start
        end = turn.end
        
        # 如果是目标说话人
        if speaker == 'SPEAKER_00':
            # 如果时间段与目标音频有重叠，需要截断
            if start < target_end_time and end > target_start_time:
                # 记录被截断的时间段
                if start < target_start_time:
                    # 目标音频开始前的时间段
                    speaker_segments.setdefault(speaker, []).append((start, min(target_start_time, end)))
                
                if end > target_end_time:
                    # 目标音频结束后的时间段
                    speaker_segments.setdefault(speaker, []).append((max(target_end_time, start), min(end, final_audio_length)))
            else:
                # 完全不与目标音频重叠的时间段
                if end <= target_start_time or start >= target_end_time:
                    speaker_segments.setdefault(speaker, []).append((start, end))
    
    return speaker_segments

# 处理音频文件并返回输出
def process_audio(target_audio, mixed_audio):
    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)
    
    # 如果音频拼接出错，返回错误信息
    if isinstance(time_dict, str):
        return time_dict
    
    # 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  # 出错时返回错误信息
    else:
        # 获取拼接后的音频长度
        final_audio_length = len(AudioSegment.from_wav("final_output.wav")) / 1000  # 秒为单位
        
        # 获取目标说话人的时间段（排除目标音频时间段）
        speaker_segments = get_speaker_segments(
            diarization_result, 
            time_dict['start_time'], 
            time_dict['end_time'], 
            final_audio_length
        )
        
        if speaker_segments and 'SPEAKER_00' in speaker_segments:
            # 返回目标说话人的时间段（已排除和截断目标音频时间段）
            return {
                'segments': speaker_segments['SPEAKER_00'],
                'total_duration': sum(end - start for start, end in speaker_segments['SPEAKER_00'])
            }
        else:
            return "没有找到SPEAKER_00的时间段。"

# 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。
    结果包括目标说话人（SPEAKER_00）的时间段，已排除和截断目标录音时间段。
    """)
    
    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="说话人时间段")

    # 点击按钮时触发处理音频
    process_button.click(
        fn=process_audio,
        inputs=[target_audio_input, mixed_audio_input],
        outputs=[diarization_output]
    )

demo.launch(share=True)