def myprocess(in_fname, out_fname):
if os.path.isfile(out_fname):
return 'already done'
dname = os.path.dirname(out_fname)
if not os.path.isdir(dname):
os.makedirs(dname)
results = g(in_fname)
seg2csv(results, out_fname)
return 0
def _do_segmentation_(input_files, chunk_id, seg, odir):
start_time = time.time()
odircsv = os.path.join(odir, "seg_csv")
odirw = os.path.join(odir, "seg_wav")
segment_dict = {}
for i, e in enumerate(input_files):
print('processing file %d-%d/%d: %s' %
(chunk_id, i + 1, len(input_files), e))
e = e.rstrip().lstrip()
base, _ = os.path.splitext(os.path.basename(e))
seg2csv(seg(e), '%s/%s.csv' % (odircsv, base))
segs = _do_wav_split_(e, os.path.join(odircsv, base + ".csv"), odirw)
segment_dict[base] = segs
elapsed_time = time.time() - start_time
print(format(elapsed_time) + ' seconds elapsed for ' + str(chunk_id))
return segment_dict
from inaSpeechSegmenter import Segmenter, seg2csv
import time
media = './VAD/s3_1.mp3'
seg = Segmenter()
start = int(round(time.time() * 1000))
segmentation = seg(media)
delta = int(round(time.time() * 1000)) - start
print("Time %d" % delta)
seg2csv(segmentation, 'myseg.csv')
help=
"(default: 'true'). If set to 'true', segments detected as speech will be splitted into 'male' and 'female' segments. If set to 'false', segments corresponding to speech will be labelled as 'speech' (faster)"
)
args = parser.parse_args()
# Preprocess arguments and check their consistency
input_files = []
for e in args.input:
input_files += glob.glob(e)
assert len(
input_files
) > 0, 'No existing media selected for analysis! Bad values provided to -i (%s)' % args.input
odir = args.output_directory
assert os.access(odir, os.W_OK), 'Directory %s is not writable!' % odir
# Do processings
from inaSpeechSegmenter import Segmenter, seg2csv
# load neural network into memory, may last few seconds
detect_gender = bool(distutils.util.strtobool(args.detect_gender))
seg = Segmenter(vad_engine=args.vad_engine, detect_gender=detect_gender)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
for i, e in enumerate(input_files):
print('processing file %d/%d: %s' % (i + 1, len(input_files), e))
base, _ = os.path.splitext(os.path.basename(e))
seg2csv(seg(e), '%s/%s.csv' % (odir, base))
from inaSpeechSegmenter import Segmenter, seg2csv media = 'audio/original/cn.wav' seg = Segmenter() segmentation = seg(media) seg2csv(segmentation, 'csv/cn_segment.csv') media = 'audio/original/en.wav' seg = Segmenter() segmentation = seg(media) seg2csv(segmentation, 'csv/en_segment.csv')
def call_activity(filename):
al=int(call_len(filename))
print('')
print('Processing: ', 'm'+filename+'1.wav')
print('')
if os.path.isfile(data_directory+filename+'1.csv'):
df_co=pd.read_csv(data_directory+filename+'1.csv',sep='\t', header=None)
else:
segmentation = seg(data_directory+'m'+filename+'1.wav')
seg2csv(segmentation, data_directory+filename+'1.csv')
df_co=pd.DataFrame(segmentation)
xgb=df_co.groupby(0).count()
if xgb.loc[xgb.index=='Male'].shape[0]==0:
gender_co=1
elif xgb.loc[xgb.index=='Female'].shape[0]==0:
gender_co=0
else:
if np.array(xgb.loc[xgb.index=='Male'][1])[0]>np.array(xgb.loc[xgb.index=='Female'][1])[0]:
gender_co=0
else:
gender_co=1
print('')
print('Processing: ', 'm'+filename+'0.wav')
print('')
if os.path.isfile(data_directory+filename+'0.csv'):
df_cu=pd.read_csv(data_directory+filename+'0.csv',sep='\t', header=None)
else:
segmentation = seg(data_directory+'m'+filename+'0.wav')
seg2csv(segmentation, data_directory+filename+'0.csv')
df_cu=pd.DataFrame(segmentation)
xgb=df_cu.groupby(0).count()
if xgb.loc[xgb.index=='Male'].shape[0]==0:
gender_cu=1
elif xgb.loc[xgb.index=='Female'].shape[0]==0:
gender_cu=0
else:
if np.array(xgb.loc[xgb.index=='Male'][1])[0]>np.array(xgb.loc[xgb.index=='Female'][1])[0]:
gender_cu=0
else:
gender_cu=1
df_co.replace('NOACTIVITY',0, inplace=True)
df_co.replace('Male',2, inplace=True)
df_co.replace('Female',2, inplace=True)
df_co.replace('Music',1, inplace=True)
df_cu.replace('NOACTIVITY',0, inplace=True)
df_cu.replace('Male',2, inplace=True)
df_cu.replace('Female',2, inplace=True)
df_cu.replace('Music',1, inplace=True)
act_co=np.zeros(al)
act_cu=np.zeros(al)
ar_co=df_co.values
ar_cu=df_cu.values
for i in range(0,ar_co.shape[0]):
for s in range(int(ar_co[i,1]),int(ar_co[i,2])):
act_co[s]=int(ar_co[i,0])
for i in range(0,ar_cu.shape[0]):
for s in range(int(ar_cu[i,1]),int(ar_cu[i,2])):
act_cu[s]=int(ar_cu[i,0])
return df_co, df_cu, act_co, act_cu, gender_co, gender_cu
print(Audio.frame_rate,
Audio.channels,
Audio.DEFAULT_CODECS,
Audio.frame_width,
Audio.channels,
Audio.dBFS,
Audio.max_dBFS)
'''
# Returns a list of tuples with segment information eg, ('Male',0.92,3)
print('Segmenting ...')
SegmentInfoTupleList= segmenter(Media)
print('segments done')
seg2csv(SegmentInfoTupleList, OutDirectory+
FileName[:len(FileName)-4]+
'AudioSegmentation.csv')
# File to be segmented into pydub.audio_segment.AudioSegment
# Iterate over tuples in the list
for tupl in SegmentInfoTupleList[:]:
if tupl[0] not in ('Male','Female', 'NOACTIVITY'):
print('deleting')
SegmentInfoTupleList.remove(tupl)
seg2csv(SegmentInfoTupleList, OutDirectory+
FileName[:len(FileName)-4]+
'AudioSegmentation_Cleaned.csv')
'''
#Incase you want to know about your audio file
input_files += [e]
else:
input_files += glob.glob(e)
assert len(input_files) > 0, "No existing media selected for analysis! Bad values provided to -i ({})".format(args.input)
odir = args.output_directory.strip(" \t\n\r").rstrip('/')
assert os.access(odir, os.W_OK), 'Directory %s is not writable!' % odir
# Do processings
from inaSpeechSegmenter import Segmenter, seg2csv, to_parse
# load neural network into memory, may last few seconds
seg = Segmenter()
# case of a file of files
files = to_parse(input_files)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
#print('processing file %d/%d: %s' % (i+1, len(input_files), e))
base = [os.path.splitext(os.path.basename(e)) for e in files]
base = [base[i][0] for i in range(len(base))]
if len(odir) > 0:
fout = ['%s/%s.csv' % (odir, elem) for elem in base]
seg2csv(seg(files, verbose=args.no_verbose), fout)
# 区間検出実行（たったこれだけでOK）
segmentation = seg(input_file)
# ('区間ラベル', 区間開始時刻（秒）, 区間終了時刻（秒）)というタプルが
# リスト化されているのが変数 segmentation
# print(segmentation)
# inaSpeechSegmenter単体では分割されたwavを作成してくれないので、
# pydubのAudioSegmentにお世話になる (ありがたいライブラリ)
speech_segment_index = 0
for segment in segmentation:
# segmentはタプル
# タプルの第1要素が区間のラベル
segment_label = segment[0]
if (segment_label == 'speech'): # 音声区間
# 区間の開始時刻の単位を秒からミリ秒に変換
start_time = segment[1] * 1000
end_time = segment[2] * 1000
# 分割結果をwavに出力
newAudio = AudioSegment.from_wav(input_file)
newAudio = newAudio[start_time:end_time]
output_file = output_file + str(speech_segment_index) + '.wav'
newAudio.export(output_file, format="wav")
speech_segment_index += 1
del newAudio
seg2csv(segmentation, f"./segment/myseg_{}.csv")
