reuben

Keybase proof

I hereby claim:

• I am reuben on github.
• I am reuben_ (https://keybase.io/reuben_) on keybase.
• I have a public key whose fingerprint is B19B 574B E186 B1BF CD23 9A86 B21F EB8E D85D 838C

To claim this, I am signing this object:

reuben

def model(batch_x, seq_length, dropout):
    def clipped_relu(x):
        return tf.minimum(tf.nn.relu(x), relu_clip)
    
    with slim.arg_scope([slim.variable], device="/cpu:0"):
        with slim.arg_scope([slim.fully_connected], activation_fn=clipped_relu):
            with slim.arg_scope([slim.dropout], keep_prob=(1.0 - dropout)):
                fc_1 = slim.dropout(slim.fully_connected(batch_x, n_hidden_1))
                fc_2 = slim.dropout(slim.fully_connected(fc_1, n_hidden_2))
                fc_3 = slim.dropout(slim.fully_connected(fc_2, n_hidden_3))

reuben

import tensorflow as tf

q1 = tf.FIFOQueue(capacity=100, dtypes=[tf.int32], shapes=[[]])
q2 = tf.FIFOQueue(capacity=100, dtypes=[tf.int32], shapes=[[]])
q3 = tf.FIFOQueue(capacity=100, dtypes=[tf.int32], shapes=[[]])

idx = tf.placeholder(tf.int32)

def deq1():
    return tf.Print(q1.dequeue(), [1], "deq1 called")

reuben

Epoch: 0006 avg_cer= 0.353960361

################################################################################
Training WER: 0.918583
--------------------------------------------------------------------------------
 - WER:    0.333333
 - loss:   6.105622
 - source: "the day before"
 - result: "the day befr"
--------------------------------------------------------------------------------

reuben

################################################################################
Training WER: 1.155599
--------------------------------------------------------------------------------
 - WER:    0.333333
 - loss:   6.089048
 - source: "this is the"
 - result: "this is thef"
--------------------------------------------------------------------------------
 - WER:    0.500000
 - loss:   3.750518

reuben

Epoch: 0001 avg_cer= 0.778651515

################################################################################
Training WER: 0.996094
--------------------------------------------------------------------------------
 - WER:    1.000000
 - loss:   2.899460
 - source: "and"
 - result: "an"
--------------------------------------------------------------------------------

reuben

import tensorflow as tf
import numpy as np
import os

from util.audio import audiofile_to_input_vector
from util.text import *
from glob import glob
from threading import Thread
from Queue import Queue

reuben

diff --git a/util/importers/fisher.py b/util/importers/fisher.py
index 8cc4e70..53f65b3 100644
--- a/util/importers/fisher.py
+++ b/util/importers/fisher.py
@@ -150,10 +150,11 @@ def _maybe_convert_wav(data_dir, original_data, converted_data):
     for root, dirnames, filenames in os.walk(source_dir):
         for filename in fnmatch.filter(filenames, "*.sph"):
             sph_file = os.path.join(root, filename)
-            wav_filename = os.path.splitext(os.path.basename(sph_file))[0] + ".wav"
-            wav_file = os.path.join(target_dir, wav_filename)

reuben

curl -LO https://www.ldc.upenn.edu/sites/www.ldc.upenn.edu/files/ctools/sph2pipe_v2.5.tar.gz
tar xf sph2pipe_v2.5.tar.gz
pushd sph2pipe_v2.5
gcc -o sph2pipe *.c -lm
sudo cp sph2pipe /usr/bin
popd
rm -r sph2pipe_v2.5{,.tar.gz}

reuben

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import random

def rand_prime(n):
    n = n+1 if n % 2 == 0 else n
    while True:
        p = random.randrange(n, 2*n, 2)
        if all(p % n != 0 for n in range(3, int((p ** 0.5) + 1), 2)):