sotelo

from flask import Flask
from flask import request
from flask import jsonify

app = Flask(__name__)


@app.route("/")
def index():
    return "Welcome"

sotelo

import os
import glob
from shutil import copyfile
from subprocess import Popen

base_dir = '/Tmp/sotelo/data/librispeech/'
raw_dir = os.path.join(
    base_dir, 'downloads/LibriSpeech/train-clean-100')
wav_dir = os.path.join(base_dir, 'wav')

sotelo

# From https://stackoverflow.com/questions/29547218/
# remove-silence-at-the-beginning-and-at-the-end-of-wave-files-with-pydub
from pydub import AudioSegment


def detect_leading_silence(sound, silence_threshold=-50.0, chunk_size=10):
    '''
    sound is a pydub.AudioSegment
    silence_threshold in dB
    chunk_size in ms

sotelo

from run_merlin import prepare_file_path_list, read_file_list
from io_funcs.binary_io import BinaryIOCollection
import numpy
import h5py
import pickle
from fuel.datasets.hdf5 import H5PYDataset

io_fun = BinaryIOCollection()
n_outs = 63  # 187

sotelo

All the traces of the processing are in leto11.

1. Extract data with pavoque-repo

1.5 Convert wav to 16k. I use ch_wave -otype riff -F 16000 -o wav/${X} wav48/${X}

2. Copy data in /Tmp/sotelo/data/german/raw

3. cd /Tmp/sotelo/results/merlin/egs/build_your_own_voice/s1

sotelo

import os
import glob
from shutil import copyfile

base_dir = '/Tmp/sotelo/data/german/raw'
text_dir = os.path.join(base_dir, 'text')
wav_dir = os.path.join(base_dir, 'wav/')

audio_files = [

sotelo

 from blocks.main_loop import MainLoop
 from blocks.model import Model
 from blocks.utils import shared_floatx_zeros, shared_floatx
 
 from theano import tensor, config, function
 
 from play.bricks.custom import (DeepTransitionFeedback, GMMEmitter,
                                 SPF0Emitter)
 
 from blocks.extensions.monitoring import TrainingDataMonitoring

sotelo

library("igraph")

num_layer = 7
num_nodes = (num_layer+1)*num_layer/2

M <-matrix(rep(0,num_nodes*num_nodes),nrow=num_nodes,ncol=num_nodes)
layout <- matrix(rep(0, 2*num_nodes), nrow=num_nodes)

node = 0
for(layer in 1:num_layer){

sotelo

from theano import tensor
from theano.ifelse import ifelse
from blocks.utils import shared_like
from blocks.monitoring.aggregation import AggregationScheme, Aggregator

class MeanAndVariance(AggregationScheme):
    """Aggregation scheme which computes the mean.
    Parameters
    ----------
    numerator : :class:`~tensor.TensorVariable`

sotelo

import theano
import numpy

from collections import OrderedDict
from fuel.datasets import IndexableDataset
from fuel.streams import DataStream
from fuel.schemes import SequentialScheme

from theano import tensor