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Python vggish_postprocess.Postprocessor方法代碼示例

本文整理匯總了Python中vggish_postprocess.Postprocessor方法的典型用法代碼示例。如果您正苦於以下問題:Python vggish_postprocess.Postprocessor方法的具體用法?Python vggish_postprocess.Postprocessor怎麽用?Python vggish_postprocess.Postprocessor使用的例子?那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在vggish_postprocess的用法示例。


在下文中一共展示了vggish_postprocess.Postprocessor方法的2個代碼示例,這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚,您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: __init__

# 需要導入模塊: import vggish_postprocess [as 別名]
# 或者: from vggish_postprocess import Postprocessor [as 別名]
def __init__(self, checkpoint, pca_params, input_tensor_name, output_tensor_name):
        """Create a new Graph and a new Session for every VGGishExtractor object."""
        super(VGGishExtractor, self).__init__()
        
        self.graph = tf.Graph()
        with self.graph.as_default():
            vggish_slim.define_vggish_slim(training=False)

        sess_config = tf.ConfigProto(allow_soft_placement=True)
        sess_config.gpu_options.allow_growth = True
        self.sess = tf.Session(graph=self.graph, config=sess_config)
        vggish_slim.load_defined_vggish_slim_checkpoint(self.sess, checkpoint)
        
        # use the self.sess to init others
        self.input_tensor = self.graph.get_tensor_by_name(input_tensor_name)
        self.output_tensor = self.graph.get_tensor_by_name(output_tensor_name)

        # postprocessor
        self.postprocess = vggish_postprocess.Postprocessor(pca_params) 
開發者ID:luuil,項目名稱:Tensorflow-Audio-Classification,代碼行數:21,代碼來源:audio_feature_extractor.py

示例2: extract_audioset_embedding

# 需要導入模塊: import vggish_postprocess [as 別名]
# 或者: from vggish_postprocess import Postprocessor [as 別名]
def extract_audioset_embedding():
    """Extract log mel spectrogram features. 
    """
    
    # Arguments & parameters
    mel_bins = vggish_params.NUM_BANDS
    sample_rate = vggish_params.SAMPLE_RATE
    input_len = vggish_params.NUM_FRAMES
    embedding_size = vggish_params.EMBEDDING_SIZE
    
    '''You may modify the EXAMPLE_HOP_SECONDS in vggish_params.py to change the 
    hop size. '''

    # Paths
    audio_path = 'appendixes/01.wav'
    checkpoint_path = os.path.join('vggish_model.ckpt')
    pcm_params_path = os.path.join('vggish_pca_params.npz')
    
    if not os.path.isfile(checkpoint_path):
        raise Exception('Please download vggish_model.ckpt from '
            'https://storage.googleapis.com/audioset/vggish_model.ckpt '
            'and put it in the root of this codebase. ')
        
    if not os.path.isfile(pcm_params_path):
        raise Exception('Please download pcm_params_path from '
        'https://storage.googleapis.com/audioset/vggish_pca_params.npz '
        'and put it in the root of this codebase. ')
    
    # Load model
    sess = tf.Session()
    
    vggish_slim.define_vggish_slim(training=False)
    vggish_slim.load_vggish_slim_checkpoint(sess, checkpoint_path)
    features_tensor = sess.graph.get_tensor_by_name(vggish_params.INPUT_TENSOR_NAME)
    embedding_tensor = sess.graph.get_tensor_by_name(vggish_params.OUTPUT_TENSOR_NAME)
    
    pproc = vggish_postprocess.Postprocessor(pcm_params_path)

    # Read audio
    (audio, _) = read_audio(audio_path, target_fs=sample_rate)
    
    # Extract log mel feature
    logmel = vggish_input.waveform_to_examples(audio, sample_rate)

    # Extract embedding feature
    [embedding_batch] = sess.run([embedding_tensor], feed_dict={features_tensor: logmel})
    
    # PCA
    postprocessed_batch = pproc.postprocess(embedding_batch)
    
    print('Audio length: {}'.format(len(audio)))
    print('Log mel shape: {}'.format(logmel.shape))
    print('Embedding feature shape: {}'.format(postprocessed_batch.shape)) 
開發者ID:qiuqiangkong,項目名稱:audioset_classification,代碼行數:55,代碼來源:extract_audioset_embedding.py


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