Python vggish_params.INPUT_TENSOR_NAME Examples

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Example #1
Source File: audio_transfer_learning.py    From sklearn-audio-transfer-learning with ISC License 5 votes vote down vote up 
def extract_vggish_features(paths, path2gt, model): 
    """Extracts VGGish features and their corresponding ground_truth and identifiers (the path).

       VGGish features are extracted from non-overlapping audio patches of 0.96 seconds, 
       where each audio patch covers 64 mel bands and 96 frames of 10 ms each.

       We repeat ground_truth and identifiers to fit the number of extracted VGGish features.
    """
    # 1) Extract log-mel spectrograms
    first_audio = True
    for p in paths:
        if first_audio:
            input_data = vggish_input.wavfile_to_examples(config['audio_folder'] + p)
            ground_truth = np.repeat(path2gt[p], input_data.shape[0], axis=0)
            identifiers = np.repeat(p, input_data.shape[0], axis=0)
            first_audio = False
        else:
            tmp_in = vggish_input.wavfile_to_examples(config['audio_folder'] + p)
            input_data = np.concatenate((input_data, tmp_in), axis=0)
            tmp_gt = np.repeat(path2gt[p], tmp_in.shape[0], axis=0)
            ground_truth = np.concatenate((ground_truth, tmp_gt), axis=0)
            tmp_id = np.repeat(p, tmp_in.shape[0], axis=0)
            identifiers = np.concatenate((identifiers, tmp_id), axis=0)

    # 2) Load Tensorflow model to extract VGGish features
    with tf.Graph().as_default(), tf.Session() as sess:
        vggish_slim.define_vggish_slim(training=False)
        vggish_slim.load_vggish_slim_checkpoint(sess, 'vggish_model.ckpt')
        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)
        extracted_feat = sess.run([embedding_tensor], feed_dict={features_tensor: input_data})
        feature = np.squeeze(np.asarray(extracted_feat))

    return [feature, ground_truth, identifiers]
Example #2
Source File: extract_audioset_embedding.py    From audioset_classification with MIT License 4 votes vote down vote up 
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))