yemaozi88
3 years ago
30 changed files with 1257 additions and 31 deletions
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import os |
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os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model') |
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import sys |
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import csv |
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#import subprocess |
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#from collections import Counter |
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#import re |
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import numpy as np |
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import pandas as pd |
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#import matplotlib.pyplot as plt |
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#from sklearn.metrics import confusion_matrix |
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import acoustic_model_functions as am_func |
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import convert_xsampa2ipa |
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import defaultfiles as default |
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from forced_alignment import pyhtk |
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import novoapi |
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## ======================= convert phones ====================== |
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mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', default.ipa_xsampa_converter_dir) |
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stimmen_transcription_ = pd.ExcelFile(default.stimmen_transcription_xlsx) |
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phonelist_novo70_ = pd.ExcelFile(default.phonelist_novo70_xlsx) |
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df = pd.read_excel(phonelist_novo70_, 'list') |
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translation_key = dict() |
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for ipa, novo70 in zip(df['IPA_simple'], df['novo70_simple']): |
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if not pd.isnull(ipa): |
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print('{0}:{1}'.format(ipa, novo70)) |
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translation_key[ipa] = novo70 |
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#df = pd.read_excel(stimmen_transcription, 'check') |
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# |
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# forced alignment using novo-api. |
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# |
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# *** IMPORTANT *** |
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# This file should be treated as confidencial. |
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# This file should not be copied or uploaded to public sites. |
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# |
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# NOTES: |
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# The usage of novo api: https://bitbucket.org/novolanguage/python-novo-api |
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# I couldn't make it work as I described in the mail to Martijn Bartelds on 2018/12/03. |
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# As per the advice from him, I modified testgrammer.py and made it a function. |
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# |
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# In order to run on Python 3.6, the following points are changed in novo-api. |
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# (1) backend/__init__.py |
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# - #import session |
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# from . import session |
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# (2) backend/session.py |
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# - #except Exception, e: |
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# except Exception as e: |
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# - #print self.last_message |
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# print(self.last_message) |
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# (3) asr/segment/praat.py |
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# - def print_tier(output, title, begin, end, segs, (format, formatter)) |
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# def print_tier(output, title, begin, end, segs, format, formatter): |
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# (4) asr/spraaklab/__init.py |
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# - #import session |
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# from . import session |
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# (5) asr/spraaklab/schema.py |
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# - #print data, "validated not OK", e.message |
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# print("{0} validated not OK {1}".format(data, e.message)) |
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# - #print data, "validated OK" |
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# print("{} validated OK".format(data)) |
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# - #if isinstance(object, basestring): |
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# if isinstance(object, str) |
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# |
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# Aki Kunikoshi |
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# 428968@gmail.com |
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# |
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import argparse |
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import json |
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from novoapi.backend import session |
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# username / password cannot be passed as artuments... |
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p = argparse.ArgumentParser() |
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#p.add_argument("--user", default=None) |
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#p.add_argument("--password", default=None) |
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p.add_argument("--user", default='martijn.wieling') |
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p.add_argument("--password", default='fa0Thaic') |
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args = p.parse_args() |
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wav_file = 'c:\\OneDrive\\WSL\\test\\onetwothree.wav' |
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rec = session.Recognizer(grammar_version="1.0", lang="nl", snodeid=101, user=args.user, password=args.password, keepopen=True) # , modeldir=modeldir) |
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grammar = { |
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"type": "confusion_network", |
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"version": "1.0", |
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"data": { |
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"kind": "sequence", |
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"elements": [ |
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{ |
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"kind": "word", |
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"pronunciation": [ |
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{ |
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"phones": [ |
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"wv", |
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"a1", |
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"n" |
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], |
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"id": 0 |
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}, |
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{ |
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"phones": [ |
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"wv", |
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"uh1", |
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"n" |
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], |
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"id": 1 |
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} |
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], |
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"label": "one" |
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}, |
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{ |
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"kind": "word", |
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"pronunciation": [ |
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{ |
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"phones": [ |
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"t", |
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"uw1" |
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], |
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"id": 0 |
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} |
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], |
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"label": "two" |
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}, |
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{ |
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"kind": "word", |
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"pronunciation": [ |
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{ |
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"phones": [ |
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"t", |
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"r", |
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"iy1" |
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], |
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"id": 0 |
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}, |
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{ |
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"phones": [ |
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"s", |
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"r", |
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"iy1" |
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], |
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"id": 1 |
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} |
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], |
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"label": "three" |
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} |
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] |
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}, |
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"return_objects": [ |
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"grammar" |
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], |
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"phoneset": "novo70" |
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} |
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res = rec.setgrammar(grammar) |
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#print "Set grammar result", res |
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#res = rec.recognize_wav("test/onetwothree.wav") |
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res = rec.recognize_wav(wav_file) |
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#print "Recognition result:", json.dumps(res.export(), indent=4) |
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#!/usr/bin/env python |
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__version__ = "0.2" |
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import backend |
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#!/usr/bin/env python |
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#import segments |
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#import spraaklab |
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from . import segments |
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from . import spraaklab |
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#!/usr/bin/env python |
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from .segments import Segmentation |
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from .praat import seg2tg |
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#!/usr/bin/env python |
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# (c) 2015--2018 NovoLanguage, author: David A. van Leeuwen |
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import codecs |
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def print_header(output, begin, end, nr_tiers): |
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print >> output, 'File type = "ooTextFile"' |
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print >> output, 'Object class = "TextGrid"' |
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print >> output, '' |
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print >> output, 'xmin = %s' % begin |
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print >> output, 'xmax = %s' % end |
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print >> output, 'tiers? <exists>' |
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print >> output, 'size = %d' % nr_tiers |
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print >> output, 'item []:' |
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def print_info_tier(output, title, begin, end, label): |
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print >> output, '\titem [%d]:' % 0 |
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print >> output, '\t\tclass = "IntervalTier"' |
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print >> output, '\t\tname = "%s"' % title |
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print >> output, '\t\txmin = %s' % begin |
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print >> output, '\t\txmax = %s' % end |
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print >> output, '\t\tintervals: size = %d' % 1 |
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|
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print >> output, '\t\tintervals [1]:' |
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print >> output, '\t\t\txmin = %s' % begin |
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print >> output, '\t\t\txmax = %s' % end |
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print >> output, '\t\t\ttext = "%s"' % label |
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|
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|
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#def print_tier(output, title, begin, end, segs, (format, formatter)): |
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def print_tier(output, title, begin, end, segs, format, formatter): |
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print >> output, '\titem [%d]:' % 0 |
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print >> output, '\t\tclass = "IntervalTier"' |
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print >> output, '\t\tname = "%s"' % title |
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print >> output, '\t\txmin = %s' % begin |
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print >> output, '\t\txmax = %s' % end |
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print >> output, '\t\tintervals: size = %d' % len(segs) |
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|
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count = 1 |
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for seg in segs: |
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#print seg |
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print >> output, '\t\tintervals [%d]:' % count |
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print >> output, '\t\t\txmin = %s' % repr(int(seg['begin']) / 100.0) |
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print >> output, '\t\t\txmax = %s' % repr(int(seg['end']) / 100.0) |
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string = '\t\t\ttext = "' + format + '"' |
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print >> output, string % formatter(seg['label']) |
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count += 1 |
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|
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|
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def seg2tg(fname, segments): |
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if not segments: |
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return |
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output = codecs.open(fname, "w", encoding="utf-8") |
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confidences = [] |
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word_labels = [] |
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phones = [] |
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|
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for s in segments: |
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conf = s.llh if hasattr(s, "llh") else s.score |
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confidences.append({'begin': s.begin, 'end': s.end, 'label': conf}) |
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word_labels.append({'begin': s.begin, 'end': s.end, 'label': s.label}) |
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for p in s.phones: |
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phones.append({'begin': p.begin, 'end': p.end, 'label': p.label}) |
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|
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|
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begin = repr(int(segments[0].begin) / 100.0) |
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end = repr(int(segments[-1].end) / 100.0) |
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|
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nr_tiers = 3 |
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print_header(output, begin, end, nr_tiers) |
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print_tier(output, "confidence", begin, end, confidences, ('%.3f', lambda x: x)) |
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print_tier(output, "words", begin, end, word_labels, ('%s', lambda x: x)) |
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print_tier(output, "phones", begin, end, phones, ('%s', lambda x: x)) |
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output.close() |
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#!/usr/bin/env python |
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# (c) 2015--2018 NovoLanguage, author: David A. van Leeuwen |
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|
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## These classes can be initialized with dictionaries, as they are returned by the python spraaklab recognition system. |
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|
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class Segment(object): |
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def __init__(self, segment): |
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self.begin = segment["begin"] |
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self.end = segment["end"] |
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self.begintime = segment.get("beginTime", self.begin / 100.0) |
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self.endtime = segment.get("endTime", self.end / 100.0) |
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self.label = segment["label"] |
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self.score = segment["score"] |
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if "llh" in segment: |
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self.llh = segment["llh"] |
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if "phones" in segment: |
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self.type = "word" |
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self.phones = Segmentation(segment["phones"], ["sil"]) |
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if hasattr(self.phones[0], "llh"): |
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self.minllh = min([s.llh for s in self.phones]) ## the current word llh for error detection |
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else: |
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self.type = "phone" |
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|
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def __repr__(self): |
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res = "%8.3f -- %8.3f score %8.3f " % (self.begintime, self.endtime, self.score) |
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if hasattr(self, "llh"): |
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res += "llh %8.3f " % self.llh |
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res += self.label.encode("utf8") |
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return res |
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|
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def export(self): |
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r = {"begin": self.begin, "end": self.end, "label": self.label, "score": self.score, "type": self.type} |
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if hasattr(self, "llh"): |
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r["llh"] = self.llh |
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if hasattr(self, "phones"): |
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r["phones"] = self.phones.export() |
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return r |
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|
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class Segmentation(object): |
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def __init__(self, segments, sils=["<s>", "</s>", "!sil"]): |
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"""Create a segmentation from a spraaklab recognition structure. |
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segments: an array of words (or phones), represented by a dict with |
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"begin", "end", "label", "score", and "llh" keys. Words can also have |
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"phones" which is another array of segments.""" |
||||
self.segments = [Segment(s) for s in segments] |
||||
if self.segments: |
||||
self.type = self.segments[0].type |
||||
else: |
||||
self.type = None |
||||
self.sils = sils |
||||
self.orig = segments ## in case we want to have access to the original recognition structure |
||||
|
||||
def __getitem__(self, item): |
||||
return self.segments[item] |
||||
|
||||
def __repr__(self): |
||||
ns = len(self.segments) |
||||
res = "Segmentation with %d %s%s" % (ns, self.type, "" if ns==1 else "s") |
||||
for seg in self.segments: |
||||
res += "\n " + repr(seg) |
||||
return res |
||||
|
||||
def __len__(self): |
||||
return len(self.segments) |
||||
|
||||
def score(self, skip=None): |
||||
if not skip: |
||||
skip = self.sils |
||||
s = 0.0 |
||||
for seg in self.segments: |
||||
if seg.label not in skip: |
||||
s += seg.score |
||||
return s |
||||
|
||||
def llhs(self, skip=None): |
||||
if not skip: |
||||
skip = self.sils |
||||
return [seg.llh for seg in self.segments if hasattr(seg, "llh") and seg.label not in skip] |
||||
|
||||
def llh(self, skip=None): |
||||
return sum(self.llhs(skip)) |
||||
|
||||
def minllh(self, skip=None): |
||||
llhs = self.llhs(skip) |
||||
if llhs: |
||||
return min(llhs) |
||||
else: |
||||
return None |
||||
|
||||
def labels(self, skip=None): |
||||
if not skip: |
||||
skip = self.sils |
||||
return [seg.label for seg in self.segments if seg.label not in skip] |
||||
|
||||
def sentence(self, skip=None): |
||||
return " ".join(self.labels(skip)) |
||||
|
||||
def export(self): |
||||
return [seg.export() for seg in self.segments] |
||||
@ -0,0 +1,4 @@
@@ -0,0 +1,4 @@
|
||||
#!/usr/bin/env python |
||||
|
||||
#import schema |
||||
from . import schema |
||||
Binary file not shown.
Binary file not shown.
@ -0,0 +1,273 @@
@@ -0,0 +1,273 @@
|
||||
#!/usr/bin/env python |
||||
## (c) 2017 NovoLanguage, author: David A. van Leeuwen |
||||
|
||||
## The purpose of this to define the grammar structure in a json schema, so that it can be validated, |
||||
## (de)serialized, and perhaps even automatically converted to a Python class structure. |
||||
|
||||
import json |
||||
import jsonschema |
||||
|
||||
grammar_schema_v10 = { |
||||
"$schema": "http://json-schema.org/schema#", |
||||
"title": "NovoLanguage grammar", |
||||
"description": "A grammar specification for the NovoLanguage Automatic Speech Recognition", |
||||
"$ref": "#/definitions/group", |
||||
"definitions": { |
||||
"phones": { |
||||
"type": "array", |
||||
"items": { |
||||
"type": "string" |
||||
}, |
||||
"minItems": 1 |
||||
}, |
||||
"pronunciation": { |
||||
"type": "object", |
||||
"properties": { |
||||
"phones": { |
||||
"$ref": "#/definitions/phones" |
||||
}, |
||||
"syllables": { |
||||
"type": "array", |
||||
"items": { |
||||
"$ref": "#/definitions/syllable" |
||||
}, |
||||
"minItems": 1 |
||||
}, |
||||
"id": { |
||||
"type": "integer", |
||||
"description": "ID to distinguish this pronunciation from other variants" |
||||
}, |
||||
"meta": { |
||||
"type": "object" |
||||
} |
||||
}, |
||||
"required": ["phones"] |
||||
}, |
||||
"syllable": { |
||||
"type": "object", |
||||
"properties": { |
||||
"begin": { |
||||
"type": "integer", |
||||
"minimum": 0 |
||||
}, |
||||
"end": { |
||||
"type": "integer", |
||||
"minimum": 0 |
||||
}, |
||||
"stress": { |
||||
"type": "integer", |
||||
"minimum": 0 |
||||
}, |
||||
"tone": { |
||||
"type": "integer", |
||||
"minimum": 0 |
||||
} |
||||
}, |
||||
"required": ["begin", "end"] |
||||
}, |
||||
"word": { |
||||
"type": "object", |
||||
"properties": { |
||||
"kind": { |
||||
"type": "string", |
||||
"enum": ["word"] |
||||
}, |
||||
"label": { |
||||
"type": "string" |
||||
}, |
||||
"pronunciation": { |
||||
"anyOf": [ |
||||
{ |
||||
"$ref": "#/definitions/pronunciation" |
||||
}, |
||||
{ |
||||
"type": "array", |
||||
"items": { |
||||
"anyOf": [ |
||||
{ |
||||
"$ref": "#/definitions/pronunciation" |
||||
}, |
||||
{ |
||||
"$ref": "#/definitions/phones" |
||||
} |
||||
] |
||||
}, |
||||
"minItems": 1 |
||||
}, |
||||
{ |
||||
"$ref": "#/definitions/phones" |
||||
} |
||||
|
||||
] |
||||
}, |
||||
"syllables": { |
||||
"type": "array", |
||||
"items": { |
||||
"$ref": "#/definitions/syllable" |
||||
} |
||||
}, |
||||
"graphemes": { |
||||
"type": "array", |
||||
"items": { |
||||
"type": "string" |
||||
} |
||||
}, |
||||
"id": { |
||||
"type": "integer", |
||||
"description": "ID to distinguish this word from other words (with possibly the same label)" |
||||
}, |
||||
"meta": { |
||||
"type": "object" |
||||
} |
||||
}, |
||||
"required": ["label"] |
||||
}, |
||||
"element": { |
||||
"title": "element", |
||||
"oneOf": [ |
||||
{ |
||||
"$ref": "#/definitions/word" |
||||
}, |
||||
{ |
||||
"$ref": "#/definitions/group" |
||||
}, |
||||
{ |
||||
"type": ["string", "null"] |
||||
} |
||||
] |
||||
}, |
||||
"group": { |
||||
"title": "element group", |
||||
"type": "object", |
||||
"properties": { |
||||
"kind": { |
||||
"type": "string", |
||||
"enum": ["sequence", "alternatives", "order"] |
||||
}, |
||||
"elements": { |
||||
"type": "array", |
||||
"items": { |
||||
"$ref": "#/definitions/element" |
||||
}, |
||||
"minItems": 1, |
||||
}, |
||||
"meta": { |
||||
"type": "object" |
||||
} |
||||
}, |
||||
"required": ["kind", "elements"] |
||||
} |
||||
} |
||||
} |
||||
|
||||
grammar_schema_v01 = { |
||||
"$schema": "http://json-schema.org/schema#", |
||||
"title": "NovoLanguage grammar v0.1", |
||||
"description": "A grammar specification for the NovoLanguage Automatic Speech Recognition", |
||||
"type": "object", |
||||
"properties": { |
||||
"type": { |
||||
"type": "string", |
||||
"enum": ["multiple_choice", "word_order"] |
||||
}, |
||||
"parts": { |
||||
"type": "array", |
||||
"minItems": 1, |
||||
"maxItems": 5, |
||||
"items": { |
||||
"type": ["string", "array"], |
||||
"items": { |
||||
"type": ["string"] |
||||
} |
||||
} |
||||
} |
||||
} |
||||
} |
||||
|
||||
grammar_rpc_schema = { |
||||
"$schema": "http://json-schema.org/schema#", |
||||
"title": "NovoLanguage RPC grammar", |
||||
"type": "object", |
||||
"properties": { |
||||
"type": { |
||||
"type": "string", |
||||
"enum": ["confusion_network"] |
||||
}, |
||||
"version": { |
||||
"type": "string", |
||||
"default": "v0.1" |
||||
}, |
||||
"data": { |
||||
"type": "object" |
||||
}, |
||||
"return_dict": { |
||||
"type": "boolean" |
||||
}, |
||||
"return_objects": { |
||||
"type": "array", |
||||
"items": { |
||||
"type": "string", |
||||
"enum": ["dict", "grammar"] |
||||
} |
||||
}, |
||||
"phoneset": { |
||||
"type": "string", |
||||
"enum": ["cmu69", "novo70", "mdbg115"] |
||||
}, |
||||
"parallel_silence": { |
||||
"type": "boolean" |
||||
} |
||||
}, |
||||
"required": ["type", "data"] |
||||
} |
||||
|
||||
def validate(object, schema=grammar_schema_v10): |
||||
#if isinstance(object, basestring): |
||||
if isinstance(object, str): |
||||
object = json.loads(object) |
||||
if not isinstance(object, dict): |
||||
raise TypeError("Expected dict or json string") |
||||
try: |
||||
jsonschema.validate(object, schema) |
||||
except jsonschema.ValidationError: |
||||
return False |
||||
except Exception: |
||||
raise |
||||
else: |
||||
return True |
||||
|
||||
def validate_rpc_grammar(message): |
||||
"""validate an rpc grammar message""" |
||||
if not validate(message, grammar_rpc_schema): |
||||
raise ValueError("Not a valid RPC grammar") |
||||
version = message.get("version", "0.1") |
||||
data = message["data"] |
||||
if version == "0.1": |
||||
if not validate(data, grammar_schema_v01): |
||||
raise ValueError("Not a valid grammar v0.1") |
||||
elif version == "1.0": |
||||
if not validate(data, grammar_schema_v10): |
||||
raise ValueError("Not a valid grammar v1.0") |
||||
else: |
||||
raise ValueError("Unsupported schema version") |
||||
|
||||
|
||||
## test |
||||
def test(data=None): |
||||
if not data: |
||||
data = {"kind": "sequence", "elements": [ |
||||
{"kind": "alternatives", "elements": ["a plain string", "an alternative string"]}, |
||||
{"kind": "word", "label": "a word", "pronunciation": {"phones": ["ah", "w", "er", "d"]}}, |
||||
{"kind": "order", "elements": [{"kind": "word", "label": "another word", "visible": False}, "last word"]}]} |
||||
try: |
||||
jsonschema.validate(data, schema) |
||||
except jsonschema.ValidationError as e: |
||||
#print data, "validated not OK", e.message |
||||
print("{0} validated not OK {1}".format(data, e.message)) |
||||
else: |
||||
#print data, "validated OK" |
||||
print("{} validated OK".format(data)) |
||||
|
||||
|
||||
if __name__ == "__main__": |
||||
test() |
||||
@ -0,0 +1,4 @@
@@ -0,0 +1,4 @@
|
||||
#!/usr/bin/env python |
||||
|
||||
#import session |
||||
from . import session |
||||
Binary file not shown.
Binary file not shown.
@ -0,0 +1,254 @@
@@ -0,0 +1,254 @@
|
||||
#!/usr/bin/env python |
||||
# (c) 2015--2018 NovoLanguage, author: David A. van Leeuwen |
||||
|
||||
## Recognition interface for actual backend. Adapted from player.asr.debug. |
||||
|
||||
import json |
||||
import sys |
||||
import wave |
||||
import requests |
||||
import websocket |
||||
import logging |
||||
import collections |
||||
|
||||
import time |
||||
|
||||
from .. import asr |
||||
|
||||
logger = logging.getLogger(__name__) |
||||
|
||||
## turn off annoying warnings |
||||
requests.packages.urllib3.disable_warnings() |
||||
logging.getLogger("requests.packages.urllib3.connectionpool").setLevel(logging.WARN) |
||||
|
||||
buffer_size = 4096 |
||||
gm = "gm.novolanguage.com" ## dev |
||||
protocol = "https" |
||||
port = 443 |
||||
apiversion = 0 |
||||
|
||||
sessions = collections.Counter() |
||||
|
||||
def segmentation(result): |
||||
"""converts a raw backend recognition result to a segment of novo.asr.segments class Segmentation""" |
||||
for w in result: |
||||
w["score"] = w["confidence"]["prob"] |
||||
w["llh"] = w["confidence"]["llr"] |
||||
w["label"] = w["label"]["raw"] |
||||
w["begin"] /= 10 |
||||
w["end"] /= 10 |
||||
for p in w["phones"]: |
||||
p["score"] = p["confidence"]["prob"] |
||||
p["llh"] = p["confidence"]["llr"] |
||||
p["begin"] /= 10 |
||||
p["end"] /= 10 |
||||
return asr.segments.Segmentation(result) |
||||
|
||||
class rpcid: |
||||
id = 0 |
||||
@staticmethod |
||||
def next(): |
||||
rpcid.id += 1 |
||||
return rpcid.id |
||||
|
||||
class Recognizer(object): |
||||
def __init__(self, lang="en", gm=gm, grammar_version="0.1", user=None, password=None, snodeid=None, keepopen=False): |
||||
self.lang = lang |
||||
self.keepopen = keepopen |
||||
self.api_url = "%s://%s:%d/v%d" % (protocol, gm, port, apiversion) |
||||
self.verify = False |
||||
self.headers = {"Content-Type": "application/json"} |
||||
self.login_user(user, password) |
||||
data = {"l2": lang, "local": False, "skipupload": True} |
||||
if snodeid: |
||||
data["snodeid"] = snodeid |
||||
self.conn = None |
||||
self.init_session(data) |
||||
self.grammar_version = grammar_version |
||||
self.last_message = None |
||||
|
||||
def login_user(self, username, password): |
||||
# obtain authentication token of user |
||||
logger.info('obtain auth token at %s', self.api_url) |
||||
data = { |
||||
'username': username, |
||||
'password': password |
||||
} |
||||
try: |
||||
r = requests.post(self.api_url + '/publishers/1/login', headers=self.headers, data=json.dumps(data), verify=self.verify) |
||||
except Exception as e: |
||||
logger.error("Cannot post request to GM API for user login: %s", e.message) |
||||
sys.exit(-1) |
||||
assert r.ok, r.reason |
||||
result = r.json() |
||||
if "errors" in result["response"]: |
||||
logger.info("Error in logging in: %s", result["response"]["errors"]) |
||||
sys.exit(-1) |
||||
|
||||
user_auth_token = result['response']['user']['authentication_token'] |
||||
logger.info("User auth token is: %s", user_auth_token) |
||||
|
||||
# set auth token in header |
||||
self.headers['Authentication-Token'] = user_auth_token |
||||
|
||||
def init_session(self, data, direct=False, use_ip=False): |
||||
logger.info('Request new session: %s', data) |
||||
r = requests.post(self.api_url + '/sessions', headers=self.headers, data=json.dumps(data), verify=self.verify) |
||||
if not r.ok: |
||||
logger.error("New session request failed: %s", r.text) |
||||
return |
||||
|
||||
status_url = r.headers.get("location") |
||||
if status_url: |
||||
## we got a redirect |
||||
status = {} |
||||
while True: |
||||
logger.debug("Checking %s", status_url) |
||||
s = requests.get(status_url, verify=self.verify) |
||||
if not s.ok: |
||||
logger.error('Checking Failed: %s', s.text) |
||||
return |
||||
|
||||
status = s.json() |
||||
if status['status'] == 'PENDING': |
||||
logger.debug("Status: %s", status['status']) |
||||
time.sleep(1) |
||||
else: |
||||
break |
||||
session = status['result'][0] ## [1] is another status code... |
||||
if "error" in session: |
||||
logger.error("Error in getting a snode: %s", session["error"]) |
||||
raise Exception |
||||
else: |
||||
session = r.json() |
||||
|
||||
try: |
||||
logger.info("Session: %r", session) |
||||
if direct: |
||||
snode_ip = session["snode"]["ip"] |
||||
proxy_url = snode_ip |
||||
snode_port = session["port"] |
||||
ws_url = "%s://%s:%d/" % ("ws", snode_ip, snode_port) |
||||
else: |
||||
field = "ip" if use_ip else "hostname" |
||||
proxy_url = session['snode']['datacentre']['proxy'][field] |
||||
ws_url = 'wss://' + proxy_url + '/' + session['uuid'] |
||||
logger.info("Connecting to websocket: %s", ws_url) |
||||
conn = websocket.create_connection(ws_url, sslopt={"check_hostname": self.verify}) |
||||
logger.info("Connected.") |
||||
#except Exception, e: |
||||
except Exception as e: |
||||
logger.error("Unable to connect to websocket: %s", e.message) |
||||
raise e |
||||
|
||||
self.session_id = session['id'] |
||||
self.proxy_url = proxy_url |
||||
self.conn = conn |
||||
self.session = session |
||||
sessions[session["uuid"]] += 1 |
||||
|
||||
def setgrammar(self, grammar): ## backend grammar object: {"data": {...}, "type": "confusion_network"} |
||||
data = {"jsonrpc": "2.0", |
||||
'type': 'jsonrpc', |
||||
'method': 'set_grammar', |
||||
'params': grammar, |
||||
"id": rpcid.next()} |
||||
asr.spraaklab.schema.validate_rpc_grammar(grammar) |
||||
self.conn.send(json.dumps(data)) |
||||
result = json.loads(self.conn.recv()) |
||||
if result.get("error"): |
||||
logger.error("Exercise validation error: %s", result) |
||||
return result |
||||
|
||||
def set_alternatives_grammar(self, *args, **kwargs): |
||||
if not "version" in kwargs: |
||||
kwargs["version"] = self.grammar_version |
||||
return self.setgrammar(alternatives_grammar(*args, **kwargs)) |
||||
|
||||
def recognize_wav(self, wavf): |
||||
w = wave.open(wavf, 'r') |
||||
nchannels, sampwidth, framerate, nframes, comptype, compname = w.getparams() |
||||
if nchannels > 1: |
||||
logging.error("Please use .wav with only 1 channel, found %d channels in %s", nchannels, wavf) |
||||
return |
||||
if (sampwidth != 2): |
||||
logging.error("Please use .wav with 2-byte PCM data, found %d bytes in %s", sampwidth, wavf) |
||||
return |
||||
if (framerate != 16000.0): |
||||
logging.error("Please use .wav sampled at 16000 Hz, found %1.0f in %s", framerate, wavf) |
||||
return |
||||
if (comptype != 'NONE'): |
||||
logging.error("Please use .wav with uncompressed data, found %s in %s", compname, wavf) |
||||
return |
||||
buf = w.readframes(nframes) |
||||
w.close() |
||||
return self.recognize_data(buf) |
||||
|
||||
def recognize_data(self, buf): |
||||
nbytes_sent = 0 |
||||
start = time.time() |
||||
for j in range(0, len(buf), buffer_size): |
||||
audio_packet = str(buf[j:j + buffer_size]) |
||||
nbytes_sent += len(audio_packet) |
||||
self.conn.send_binary(audio_packet) |
||||
self.conn.send(json.dumps({"jsonrpc": "2.0", "method": "get_result", "id": rpcid.next()})) |
||||
| ||||