Metadata-Version: 2.1
Name: mmanalyser
Version: 0.6.0
Summary: a tool for multimedia
Home-page: UNKNOWN
Author: xujc
Author-email: 
License: UNKNOWN
Description: # mmanalysis
        
        `mmanalysis` 是多媒体特征分析工具的库
        
        ### 安装
        ```
        $ pip install mmanalyser
        ```
        
        ### mmanalyser examples
        
        #### AudioAnalyser
        
        音频格式现在只支持16000HZ，单声道
        
        ```python
        from mmanalyser.audio_analyser import AudioAnalyser
        aa = AudioAnalyser()
        
        # 转化音频为16000HZ，单身道
        # source_path 可以是视频，URL，音频
        # desc_path 是生成文件的路径
        aa.convert_to_wav(source_path='f0.mp4', desc_path='f0.wav'):
        
        # 判断音频是否有效
        # 返回label(True为有效) 和 0到1之间的数值（越靠近1，即越有效）。
        aa.is_valid_audio('f0.wav')
        
        # 判断长段音频是否为人声（大于5s）
        # 返回0到1之间的值，越靠近1，即越有可能是人声
        aa.is_human_voice('f0.wav')
        
        # 判断音频片段是否为人声（3s-5s)
        # 同上
        aa.is_human_voice_snippet('f0.wav')
        
        # 去掉静音片段，获取去掉静音片段的音频，同时也可以获得有声片段的时间区间和对应的音频，有声片段的时间总长
        # 返回 文件列表  [去掉静音片段的音频地址，其余为有声片段的时间区间对应的音频]
        #      有声片段时间区间
        #      有声音频长度
        #      音频长度
        aa.delete_silence_snippet('f0.wav')
        ```
        
        ### API
        
        #### ExpressionAnalyser
        
        ```python
        class ExpressionAnalyser:
            '''
            表达能力分析器
        
            asr数据格式：{"pause_detection": [[3280, 3922], [7781, 8688]], "text_corrected":  [], "text":  [[[0, 1170], "领导你好，"],[[8940, 12250], "我在嘉兴，"], [[12250, 13490], "有两家造纸厂，"]]}
            '''
        
            def get_expression_score(self, asr, audio_path=None):
                '''
                获取表达能力分数
        
                Args:
                    asr: asr结果
                    audio_path:  音频地址
        
                Returns:
                     dict: {
                    'talk_speed': float,        // 语速
                    'audio_clear': bool,        //声音是否清晰
                    'mandarin_score': float,    //普通话标准程度
                    'expression_score': float,  //表达能力分数
                    }
                '''
        
        
            def get_talk_speed(self, asr):
                '''
                获取语速指标
        
                Args:
                    asr: asr 结果
                Returns:
                   float: 语速
                '''
        
        
            def get_mandarin_score(self, asr):
                '''
                获取普通话标准程度
        
                Args:
                    asr: asr结果
        
                Returns:
                    float: 普通话标准程度
                '''
        
            def get_audio_clear(self, asr, audio_path):
                '''
                获取声音是否清晰
        
                Args:
                    asr: asr结果
                    audio_path: 音频地址
        
                Returns:
                    bool: 清晰返回true 不清晰返回false
                '''
        ```
        
        #### EmotionAnalyser
        
        ```python
        class EmotionAnalyser:
            '''
            情绪分析器
            '''
        
            def get_emotion_global_distribution(self, emotion):
                """
        
                获取情绪的分布
        
                Args:
                    emotion: [{"anger": 0.1, "neutral": 0.1, "disgust": 0.1, "fear": 0.005670338961718315, "happiness": 0.2 "sadness": 0.1, "surprise": 0.2}, ...]
        
                Returns:
                    dict: distribution -> {'anger': 0.04678370220399322, 'neutral': 0.04147910314122111, 'disgust': 0.05076599764308418, 'fear': 0.012459961661656426, 'happiness': 0.3805726751609995, 'sadness': 0.001341534333740512, 'surprise': 0.46659702585530505}
                """
        
        
            def get_fluctuation_from_emotion(self, emotion):
                """
        
                获取情绪的波动
        
                Args:
                    emotion: [{"anger": 0.1, "neutral": 0.1, "disgust": 0.1, "fear": 0.005670338961718315, "happiness": 0.2 "sadness": 0.1, "surprise": 0.2}, ...]
        
                Returns:
                    list: fluction -> [0.2625822317841726, 0.286318327656083, 0.19226654361134174, 0.26339393328225225, 0.3021995866171042, 0.2843571171657221, ...]
                """
        
        
            def parse(self, emotion):
                '''
                情绪整体分析，返回情绪数据，情绪总体分布，情绪波动
        
                Args:
                    emotion: [{"anger": 0.1, "neutral": 0.1, "disgust": 0.1, "fear": 0.005670338961718315, "happiness": 0.2 "sadness": 0.1, "surprise": 0.2}, ...]
        
                Returns:
                    list: data ->[[2.639, 1.415, 45.941, 3.918, 34.431, 0.159, 11.497], [0.685, 18.661, ... ] ... ]  顺序为['anger', 'neutral', 'disgust', 'fear', 'happiness', 'sadness', 'surprise']
                    dict: distribution
                    list: fluction
                '''
        ```
        
        #### HeartRateAnalyser
        
        ```python
        class HeartRateAnalyser:
            '''
            心率分析器
            '''
            def get_bpm_from_heart_rate(self, heart_rate):
                '''
        
                Args:
                    heart_rate: [[70, 1000.0], [75, 2000.0], ...]
        
                Returns:
                    list: [70, 71, ...] 平滑过的心率波动
                '''
        ```
        
        #### FluctuationDetector
        
        ```python
        class FluctuationDetector:
            '''
            波动检测器
            '''
            def get_abnormal_fluctuation_range_from_heart_rate(self, heart_rate_infos):
                '''
                从心率信息中抽取异常波动时间范围
        
                Args:
                    heart_rate_infos: [[70, 1000.0], [75, 2000.0], ...]
        
                Returns:
                    list: [start_time, end_time]
                '''
        
            def get_abnormal_fluctuation_range_from_emotion_fluction(self, emotion_fluctuation):
                '''
                从情绪波动信息中抽取异常波动时间范围
        
                Args:
                    emotion_fluctuation: [0.2625822317841726, 0.286318327656083, 0.19226654361134174, 0.26339393328225225, 0.3021995866171042, 0.2843571171657221, ...]
        
                Returns:
                    list: [start_time, end_time]
        
                '''
        
            def get_text_from_emotion_abnormal_fluction(self, text_with_timestamp, emotion_fluctuation):
                '''
                结合带时间戳的文本抽取当情绪波动异常时的文本
        
                Args:
                    text_with_timestamp: [[[[0, 1170], "领导你好，"], [[1170, 2370], "我叫，"]], ...]
                    emotion_fluctuation: [0.2625822317841726, 0.286318327656083, 0.19226654361134174, 0.26339393328225225, 0.3021995866171042, 0.2843571171657221, ...]
        
                Returns
                    str: "abnormal_text"
                '''
        
            def get_text_from_heart_rate_abnormal_fluction(self, text_with_timestamp, heart_rate):
                '''
                结合带时间戳的文本抽取心率波动异常时的文本
        
                Args:
                    text_with_timestamp: [[[[0, 1170], "领导你好，"], [[1170, 2370], "我叫，"]], ...]
                    heart_rate: [[70, 1000.0], [75, 2000.0], ...]
        
                Returns:
                    str: "abnormal_text"
                '''
        ```
        
        # release
        
        ```
        python3 setup.py sdist bdist_wheel
        twine upload dist/*
        ```
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.5
Description-Content-Type: text/markdown
