I am currently trying to implemenent a RNN network for regression purposes, such that I can map a known input to a known output. The problem in my case is that the input does not have static length, as it consist of samples of audio files, and the audio files have different length. But the output length is always consistent, and has length 14.

It was due to this inconsistency of the input vector, I decided to use RNN in the first place.

I am using tensorflow at the moment, and it seems like that there is no pretty way of handling this issue. I tried a hack solution based on this post somehow ended with the same issue as in the begining.

Here is my implementation:

import tensorflow as tf
from tensorflow.models.rnn import rnn_cell
from tensorflow.models.rnn import rnn
import numpy as np
import librosa
import glob
import matplotlib.pyplot as plt
from os import listdir
from os.path import isfile, join
import os
from os import walk
from os.path import splitext
from os.path import join
import time
rng = np.random
import functools

start_time = time.time()

print "Preprocessing"

def lazy_property(function):
    attribute = '_' + function.__name__

    def wrapper(self):
        if not hasattr(self, attribute):
            setattr(self, attribute, function(self))
        return getattr(self, attribute)
    return wrapper
## Class definition ##
class VariableSequenceLabelling:

    def __init__(self, data, target, num_hidden=200, num_layers=3):
        self.data = data
        self.target = target
        self._num_hidden = num_hidden
        self._num_layers = num_layers

    def length(self):
        used = tf.sign(tf.reduce_max(tf.abs(self.data), reduction_indices=2))
        length = tf.reduce_sum(used, reduction_indices=1)
        length = tf.cast(length, tf.int32)
        return length

    def prediction(self):
        # Recurrent network.
        output, _ = tf.nn.dynamic_rnn(
        # Softmax layer.
        max_length = int(self.target.get_shape()[1])
        num_classes = int(self.target.get_shape()[2])
        weight, bias = self._weight_and_bias(self._num_hidden, num_classes)
        # Flatten to apply same weights to all time steps.
        output = tf.reshape(output, [-1, self._num_hidden])
        prediction = tf.nn.softmax(tf.matmul(output, weight) + bias)
        prediction = tf.reshape(prediction, [-1, max_length, num_classes])
        return prediction

    def cost(self):
        # Compute cross entropy for each frame.
        cross_entropy = self.target * tf.log(self.prediction)
        cross_entropy = -tf.reduce_sum(cross_entropy, reduction_indices=2)
        mask = tf.sign(tf.reduce_max(tf.abs(self.target), reduction_indices=2))
        cross_entropy *= mask
        # Average over actual sequence lengths.
        cross_entropy = tf.reduce_sum(cross_entropy, reduction_indices=1)
        cross_entropy /= tf.cast(self.length, tf.float32)
        return tf.reduce_mean(cross_entropy)

    def optimize(self):
        learning_rate = 0.0003
        optimizer = tf.train.AdamOptimizer(learning_rate)
        return optimizer.minimize(self.cost)

    def error(self):
        mistakes = tf.not_equal(
            tf.argmax(self.target, 2), tf.argmax(self.prediction, 2))
        mistakes = tf.cast(mistakes, tf.float32)
        mask = tf.sign(tf.reduce_max(tf.abs(self.target), reduction_indices=2))
        mistakes *= mask
        # Average over actual sequence lengths.
        mistakes = tf.reduce_sum(mistakes, reduction_indices=1)
        mistakes /= tf.cast(self.length, tf.float32)
        return tf.reduce_mean(mistakes)

    def _weight_and_bias(in_size, out_size):
        weight = tf.truncated_normal([in_size, out_size], stddev=0.01)
        bias = tf.constant(0.1, shape=[out_size])
        return tf.Variable(weight), tf.Variable(bias)

#Converting file to .wav from .sph file format... God dammit!!!

#with open(train_filelist, 'r') as train_filelist, open(test_filelist, 'r') as test_filelist:
    #train_mylist = train_filelist.read().splitlines()
    #test_mylist = test_filelist.read().splitlines()
    #for line in train_mylist:
        #new_line = ' '.join(reversed(line))
        #index_start = new_line.find('h')
        #index_end = new_line.find('/')
        #edited_line = ''.join(reversed(new_line[index_start+5:index_end])).strip().replace(" ","")
        #new_file = edited_line + 'wav'
        #os.system(line + ' >> ' + dnn_train + new_file)
    #for line in test_mylist:
        #new_line = ' '.join(reversed(line))
        #index_start = new_line.find('h')
        #index_end = new_line.find('/')
        #edited_line = ''.join(reversed(new_line[index_start+5:index_end])).strip().replace(" ","")
        #new_file = edited_line + 'wav'
        #os.system(line + ' >> ' + dnn_test + new_file)

path_train =  "/home/JoeS/kaldi-trunk/egs/start/s5/data/train"
path_test =  "/home/JoeS/kaldi-trunk/egs/start/s5/data/test"
dnn_train = "/home/JoeS/kaldi-trunk/dnn/train/"
dnn_test = "/home/JoeS/kaldi-trunk/dnn/test/"
dnn = "/home/JoeS/kaldi-trunk/dnn/"
path  = "/home/JoeS/kaldi-trunk/egs/start/s5/data/"
MFCC_dir = "/home/JoeS/kaldi-trunk/egs/start/s5/mfcc/raw_mfcc_train.txt"

train_filelist = path_train+"/wav_train.txt"
test_filelist = path_test+"/wav_test.txt"


def binify(number):
    divider = (36471330-10533580)/6
    if number >= divider*0 and number < divider*1:
        return 1
    if number >= divider*1 and number < divider*2:
        return 2
    if number >= divider*2 and number < divider*3:
        return 3
    if number >= divider*3 and number < divider*4:
        return 4
    if number >= divider*5 and number < divider*6:
        return 5
    if number >= divider*6:
        return 6

def find_all(a_str, sub):
    start = 0
    while True:
        start = a_str.find(sub, start)
        if start == -1: return
        yield start
        start += len(sub) # use start += 1 to find overlapping matches

def load_sound_files(file_paths ,  names_input, data_input):
    raw_sounds = []
    names_output = []
    data_output = []
    class_output = []
    for fp in file_paths:
        X,sr = librosa.load(fp)
        index = list(find_all(fp,'-'))
        input_index = names_input.index(fp[index[1]+1:index[2]])
    return raw_sounds, names_output, data_output, class_output

def generate_list_of_names_data(file_path):
    # Proprocess
    # extract name and data
    name = []
    data = []
    with open(MFCC_dir) as mfcc_feature_list:
        content = [x.strip('\n') for x in mfcc_feature_list.readlines()] # remove endlines
        start_index_data = 0
        end_index_data = 2
        for number in range(0,42):
            start = list(find_all(content[start_index_data],'['))[0]
            end = list(find_all(content[end_index_data],']'))[0]
            end_name = list(find_all(content[start_index_data],' '))[0]
            substring_data = content[start_index_data][start+1 :]+content[end_index_data][: end]
            substring_name = content[start_index_data][:end_name]
            arr = np.array(substring_data.split(), dtype = float)
            start_index_data = start_index_data + +3
            end_index_data = end_index_data +3
    return name, data

files_train_path = [dnn_train+f for f in listdir(dnn_train) if isfile(join(dnn_train, f))]
files_test_path = [dnn_test+f for f in listdir(dnn_test) if isfile(join(dnn_test, f))]

files_train_name = [f for f in listdir(dnn_train) if isfile(join(dnn_train, f))]
files_test_name = [f for f in listdir(dnn_test) if isfile(join(dnn_test, f))]


train_name,train_data = generate_list_of_names_data(files_train_path)
train_data, train_names, train_output_data, train_class_output = load_sound_files(files_train_path,train_name,train_data)

max_length = 0 ## Used for variable sequence input

for element in train_data:
    if element.size > max_length:
        max_length = element.size

NUM_EXAMPLES = len(train_data)/2

test_data = train_data[NUM_EXAMPLES:]
test_output = train_output_data[NUM_EXAMPLES:]

train_data = train_data[:NUM_EXAMPLES]
train_output = train_output_data[:NUM_EXAMPLES]
print("--- %s seconds ---" % (time.time() - start_time))

if __name__ == '__main__':
    data = tf.placeholder(tf.float32, [None, max_length, 1])
    target = tf.placeholder(tf.float32, [None, 14, 1])
    model = VariableSequenceLabelling(data, target)
    sess = tf.Session()
    for epoch in range(10):
        for sample_set in range(100):
            batch_train = train_data[sample_set]
            batch_target = train_output[sample_set]
            sess.run(model.optimize, {data: batch_train, target: batch_target})
        test_set = test_data[epoch]
        test_set_output = test_output[epoch]
        error = sess.run(model.error, {data: test_set, target: test_set_output})
        print('Epoch {:2d} error {:3.1f}%'.format(epoch + 1, 100 * error))

The error message I am receiving is that

Traceback (most recent call last):
  File "tensorflow_datapreprocess_mfcc_extraction_rnn.py", line 239, in <module>
    sess.run(model.optimize, {data: batch_train, target: batch_target})
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 340, in run
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 553, in _run
    % (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))
ValueError: Cannot feed value of shape (63945,) for Tensor u'Placeholder:0', which has shape '(?, 138915, 1)'

I guess debugging is more a stackoverflow question than a data science question, so instead of asking for help solving my issue regarding the code, would I like to know whether there is other framework which do natively support this, such I don't need to hack my solution. I added my code such you know the structure of my input and output data. It would be very appreciated if I could keep the structure.


Since tensors can be only of a fixed size, you have to zero-pad your sequences (usually from the left) to the maximum occurring length with something like:

max_len = max([len(x) for x in sequences])
sequences = [np.pad(x, (max_len - len(x), 0), 'constant', constant_values = (0.,0.)) for x in sequences]

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