def generate_speech_and_text_corpora(data_dir,
wav16_dir,
debug,
sequitur_model_path,
lexicon_file_name,
audio_corpora,
prompt_words):
logging.info("loading lexicon...")
lex = Lexicon(file_name=lexicon_file_name)
logging.info("loading lexicon...done.")
logging.info("loading transcripts...")
if sequitur_model_path:
add_all = True
else:
add_all = False
ts_all = {}
ts_train = {}
ts_test = {}
transcript_objs = []
for audio_corpus in audio_corpora:
transcripts = Transcripts(corpus_name=audio_corpus)
ts_all_, ts_train_, ts_test_ = transcripts.split(limit=debug, add_all=add_all)
logging.info("loading transcripts from %s (%d train, %d test) ..." % (audio_corpus, len(ts_train_), len(ts_test_)))
ts_all.update(ts_all_)
ts_train.update(ts_train_)
ts_test.update(ts_test_)
transcript_objs.append(transcripts)
logging.info("loading transcripts (%d train, %d test) ...done." % (
len(ts_train), len(ts_test)))
export_kaldi_data(wav16_dir, audio_corpora, '%s/train/' % data_dir, ts_train)
export_kaldi_data(wav16_dir, audio_corpora, '%s/test/' % data_dir, ts_test)
if sequitur_model_path:
for transcript_obj in transcript_objs:
lex = add_missing_words(transcript_obj, lex, sequitur_model_path)
ps, utt_dict = export_dictionary(ts_all,
lex,
'%s/local/dict/lexicon.txt' % data_dir,
prompt_words)
write_nonsilence_phones(
ps, '%s/local/dict/nonsilence_phones.txt' % data_dir)
write_silence_phones('%s/local/dict/silence_phones.txt' % data_dir)
write_optional_silence('%s/local/dict/optional_silence.txt' % data_dir)
write_extra_questions(ps, '%s/local/dict/extra_questions.txt' % data_dir)
create_training_data_for_language_model(transcript_objs, utt_dict, data_dir)
def proc_transcripts(corpus_name):
global use_prompts, lang
transcripts = Transcripts(corpus_name=corpus_name)
if use_prompts:
transcripts_set = set(
(u' '.join(tokenize(transcripts[key]["prompt"], lang)))
for key in transcripts)
else:
transcripts_set = set((transcripts[key]["ts"] for key in transcripts))
for ts in transcripts_set:
yield ts
def main(verbose=False, *speech_corpora):
"""Scan directory for audio files and convert them to wav files
For each speech corpus `speech_corpus`
1. the resulting wav files are written to the directory
`.speechrc.wav16`/<speech_corpus>/
2. the transcripts in data/src/speech/<speech_corpus>/transcripts_*.csv are
updated.
"""
misc.init_app('speech_audio_scan')
config = misc.load_config('.speechrc')
speech_corpora_dir = Path(config.get("speech", "speech_corpora"))
wav16 = Path(config.get("speech", "wav16"))
if len(speech_corpora) < 1:
logging.error("At least one speech corpus must be provided.")
sys.exit(1)
if verbose:
logging.basicConfig(level=logging.DEBUG)
else:
logging.basicConfig(level=logging.INFO)
exit_if_corpus_is_missing(speech_corpora_dir, speech_corpora)
for speech_corpus in speech_corpora:
transcripts = Transcripts(corpus_name=speech_corpus, create_db=True)
out_wav16_subdir = wav16 / speech_corpus
out_wav16_subdir.mkdir(parents=True, exist_ok=True)
in_root_corpus_dir = speech_corpora_dir / speech_corpus
scan_audiodir(str(in_root_corpus_dir), transcripts,
str(out_wav16_subdir))
transcripts.save()
print speech_corpus, "new transcripts saved."
print
with codecs.open(lexfn, 'r', 'utf8') as lexf:
for line in lexf:
parts = line.strip().split(' ')
lex[parts[0]] = ' '.join(parts[1:])
logging.info('reading lexicon %s ... done. %d entries.' % (lexfn, len(lex)))
# print repr(lex)
#
# export audio, prompts (for lm)
#
logging.info("exporting transcripts from %s ..." % audio_corpus)
transcripts = Transcripts(corpus_name=audio_corpus)
utt_num = 0
destdirfn = '%s/test' % data_dir
prompts = set()
for utt_id in transcripts:
ts = transcripts[utt_id]
prompts.add(u' '.join(tokenize(transcripts[utt_id]["prompt"],
options.lang)))
if ts['quality'] != 0:
continue
else:
logging.basicConfig(level=logging.INFO)
if len(args) != 1:
parser.print_usage()
sys.exit(1)
corpus_in = args[0]
corpus_out = corpus_in + '_phone'
#
# load transcripts
#
logging.info("loading transcripts...")
transcripts = Transcripts(corpus_name=corpus_in)
logging.info("loading transcripts...done.")
#
# config
#
config = misc.load_config('.speechrc')
corpora = config.get("speech", "speech_corpora")
wav16_dir = config.get("speech", "wav16")
out_dir = '%s/%s' % (corpora, corpus_out)
tmpfn_base = '/tmp/tmp16_%08x' % os.getpid()
if os.path.exists(out_dir):
data_dir = "%s/data" % work_dir
mfcc_dir = "%s/mfcc" % work_dir
wav16_dir = config.get("speech", "wav16_dir_de")
#
# load lexicon, transcripts
#
print "loading lexicon..."
lex = Lexicon()
print "loading lexicon...done."
print "loading transcripts..."
transcripts = Transcripts()
ts_all, ts_train, ts_test = transcripts.split(limit=DEBUG_LIMIT,
add_all=add_all)
print "loading transcripts (%d train, %d test) ...done." % (len(ts_train),
len(ts_test))
#
# create work_dir structure
#
utils.mkdirs('%s/lexicon' % data_dir)
utils.mkdirs('%s/local/dict' % data_dir)
utils.mkdirs(wav16_dir)
utils.mkdirs(mfcc_dir)
utils.symlink('%s/egs/wsj/s5/steps' % kaldi_root, '%s/steps' % work_dir)
utils.symlink('%s/egs/wsj/s5/utils' % kaldi_root, '%s/utils' % work_dir)
if len(args) < 1:
parser.print_help()
sys.exit(1)
if options.verbose:
logging.basicConfig(level=logging.DEBUG)
else:
logging.basicConfig(level=logging.INFO)
#
# load lexicon, transcripts
#
logging.info("loading transcripts...")
transcripts = Transcripts(lang=options.lang)
logging.info("loading transcripts...done.")
#
# main
#
cnt = 0
for csvfn in args:
logging.info("applying results from %s ..." % csvfn)
with open(csvfn, 'r') as csvf:
for line in csvf:
scan_dirs.append(config.get("speech", "librivoxdir"))
wav16_dir = config.get("speech", "wav16_dir_en")
else:
print "***ERROR: language %s not supported yet." % lang
print
sys.exit(1)
#
# load transcripts
#
print "loading transcripts..."
transcripts = Transcripts(lang=lang)
print "loading transcripts...done."
def audio_convert(cfn, subdir, fn, audiodir):
# global mfcc_dir
global wav16_dir
# convert audio if not done yet
w16filename = "%s/%s.wav" % (wav16_dir, cfn)
if not os.path.isfile(w16filename):
wavfilename = "%s/%s/wav/%s.wav" % (audiodir, subdir, fn)
logging.basicConfig(level=logging.INFO)
#
# config
#
work_dir = WORKDIR %options.lang
wav16_dir = config.get("speech", "wav16_dir_%s" % options.lang)
#
# load transcripts
#
logging.info ( "loading transcripts...")
transcripts = Transcripts(corpus_name=options.lang)
logging.info ( "loading transcripts...done. %d transcripts." % len(transcripts))
logging.info ("splitting transcripts...")
ts_all, ts_train, ts_test = transcripts.split()
logging.info ("splitting transcripts done, %d train, %d test." % (len(ts_train), len(ts_test)))
#
# create work_dir
#
misc.mkdirs('%s' % work_dir)
# export csv files
csv_train_fn = '%s/train.csv' % work_dir
csv_dev_fn = '%s/dev.csv' % work_dir
def proc_transcripts(corpus_name):
transcripts = Transcripts(corpus_name=corpus_name)
transcripts_set = set((transcripts[key]["ts"] for key in transcripts))
for ts in transcripts_set:
yield ts
help="enable debug output")
(options, args) = parser.parse_args()
if options.verbose:
logging.basicConfig(level=logging.DEBUG)
logging.getLogger("requests").setLevel(logging.WARNING)
else:
logging.basicConfig(level=logging.INFO)
#
# load transcripts
#
logging.info("loading transcripts...")
transcripts = Transcripts(lang=LANG)
logging.info("loading transcripts...done.")
#
# config
#
config = misc.load_config('.speechrc')
wav16_dir = config.get("speech", "wav16_dir_%s" % LANG)
noise_dir = config.get("speech", "noise_dir")
bg_dir = '%s/bg' % noise_dir
fg_dir = '%s/fg/16kHz' % noise_dir
out_dir = OUT_DIR
dest="verbose",
help="verbose output")
(options, args) = parser.parse_args()
if options.verbose:
logging.basicConfig(level=logging.DEBUG)
else:
logging.basicConfig(level=logging.INFO)
#
# load transcripts
#
logging.info('loading transcripts from %s ...' % AUDIO_CORPUS)
transcripts = Transcripts(corpus_name=AUDIO_CORPUS)
logging.info('loading transcripts from %s ... done.' % AUDIO_CORPUS)
#
# build set of accepted submissions
#
accept = set()
mics = set(['Kinect-RAW', 'Realtek', 'Samson', 'Yamaha', 'Kinect-Beam'])
for utt_id in transcripts:
data = transcripts[utt_id]
if data['quality'] < 2:
continue
parts = utt_id.split('-')
action="store_true",
dest="verbose",
help="verbose output")
(options, speech_corpora) = parser.parse_args()
if options.verbose:
logging.basicConfig(level=logging.DEBUG)
else:
logging.basicConfig(level=logging.INFO)
if len(speech_corpora) < 1:
logging.error("At least one speech corpus must be provided.")
sys.exit(1)
exit_if_corpus_is_missing(speech_corpora_dir, speech_corpora)
for speech_corpus in speech_corpora:
transcripts = Transcripts(corpus_name=speech_corpus, create_db=True)
out_wav16_subdir = '%s/%s' % (wav16, speech_corpus)
misc.mkdirs(out_wav16_subdir)
in_root_corpus_dir = '%s/%s' % (speech_corpora_dir, speech_corpus)
scan_audiodir(str(in_root_corpus_dir), transcripts,
str(out_wav16_subdir))
transcripts.save()
print(speech_corpus, "new transcripts saved.")
print("\n")