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from __future__ import unicode_literals, print_function

import json
from pathlib import Path

from snips_nlu import SnipsNLUEngine, load_resources
from snips_nlu.default_configs import CONFIG_EN

SAMPLE_DATASET_PATH = Path(__file__).parent / "sample_dataset.json"

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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",

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from scipy.io import wavfile
filename = 'your_favourite_audio.wav'
rate, data = wavfile.read('angry/a01.wav')
print len(data)

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online2-wav-nnet3-latgen-faster \
  --online=false \
  --do-endpointing=false \
  --frame-subsampling-factor=3 \
  --config=new/conf/online.conf \
  --max-active=7000 \
  --beam=15.0 \
  --lattice-beam=6.0 \
  --acoustic-scale=1.0 \
  --word-symbol-table=new/graph/words.txt \

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#!/bin/sh
# run this file from $KALDI_HOME/egs/aspire/s5/ directory. And also this script assumes
# your words.dic and lm.arpa is present in the following directory
# $KALDI_HOME/egs/aspire/s5/data/local/dict/cmudict/sphinxdict

# First create the required directories for our new model
mkdir -p new/local/dict
mkdir -p new/local/lang

# lets copy some data files

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#!/bin/sh
cat corpus.txt | tr '[:lower:]' '[:upper:]' > corpus_upper.txt
ngram-count -text corpus_upper.txt -order 3 -limit-vocab -vocab words.txt -unk -map-unk "<unk>" -kndiscount -interpolate -lm lm.arpa

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#!/bin/sh
# g2p model generates pronunciation of a words after it is trained. But for training it needs
# at least some words to pronunciation mapping. For our purpose we can use the cmudict already
# provided in Kaldi in $KALDI_HOME/egs/aspire/s5/data/local/dict/cmudict/sphinxdict directory.
# you should run this script from the above directory

g2p.py --train cmudict_SPHINX_40 --devel 5% --write-model model-1
 
g2p.py --train cmudict_SPHINX_40 --devel 5% --write-model model-1
g2p.py --model model-1 --test cmudict_SPHINX_40 > model-1-test

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http://www.math.utah.edu/~wisnia/glossary.html#s

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