class Filter(object):
def __init__(self, is_filter=True):
## -----*----- コンストラクタ -----*----- ##
# 分離器
self.infer = Separator()
self._pa = pyaudio.PyAudio()
# 音声入力の設定
self.settings = {
'format': pyaudio.paInt16,
'channels': 1,
'rate': 8000,
'chunk': 1024
}
self.stream = self._pa.open(format=self.settings['format'],
channels=self.settings['channels'],
rate=self.settings['rate'],
input=True,
output=True,
frames_per_buffer=self.settings['chunk'])
# 音声波形を格納
self.wav = None
self.wav_separate = None
# フラグ一覧
self.is_filter = is_filter
self.is_input = True
self.is_separate = False
self.is_end_separate = False
self.strage = {
'original': np.zeros(1024 * 20),
'separated': np.zeros(1024 * 20)
}
def exe(self):
## -----*----- 処理実行 -----*----- ##
thread = threading.Thread(target=self.audio_input)
thread.start()
thread = threading.Thread(target=self.audio_seaprate)
thread.start()
self.audio_output()
def graphplot(self):
if not self.is_end_separate:
return
self.strage['original'] = np.append(
np.delete(self.strage['original'], range(1024)), self.wav)
self.strage['separated'] = np.append(
np.delete(self.strage['separated'], range(1024)),
self.wav_separate)
plt.clf()
# Original
plt.subplot(311)
plt.specgram(self.strage['original'], Fs=self.settings['rate'])
# Separated
plt.subplot(312)
plt.specgram(self.strage['separated'], Fs=self.settings['rate'])
# Pause
plt.pause(.01)
def audio_input(self):
## -----*----- 音声入力 -----*----- ##
while self.stream.is_active():
self.wav = np.fromstring(
self.stream.read(self.settings['chunk'],
exception_on_overflow=False), np.int16)
self.graphplot()
# 録音開始フラグ反転
self.is_input = False
self.is_separate = True
def audio_output(self):
## -----*----- 音声出力 -----*----- ##
while self.stream.is_active():
if not self.is_input:
# 録音開始フラグ反転
self.is_input = True
# 再生
if self.is_end_separate:
output = b''.join(self.wav_separate)
self.stream.write(output)
def audio_seaprate(self):
## -----*----- 音声分離 -----*----- ##
while self.stream.is_active():
if self.is_separate:
spec = self.infer.stft(self.wav, to_log=False).T
spec_pred = self.infer.stft(self.wav, to_log=True).T
# 分離
if self.is_filter:
for t in range(spec.shape[0]):
pred = self.infer.predict(spec_pred[t])
n = 0
for i in range(129):
# 低周波成分をカット
if n < 2:
spec[t][i] = 0
n += 1
continue
if pred[i] > 0.8:
spec[t][i] *= 1.0
elif pred[i] > 0.75:
spec[t][i] *= pred[i]
elif pred[i] > 0.6:
spec[t][i] *= 0.005
elif pred[i] > 0.5:
spec[t][i] *= 0.002
else:
spec[t][i] *= 0.001
self.wav_separate = self.infer.istft(spec.T)
self.is_separate = False
self.is_end_separate = True
class Filter(object):
def __init__(self, is_filter=True):
## -----*----- コンストラクタ -----*----- ##
# 分離器
self.infer = Separator()
self._pa = pyaudio.PyAudio()
# 音声入力の設定
self.settings = {
'format': pyaudio.paInt16,
'channels': 1,
'rate': 8000,
'chunk': 1024
}
self.stream = self._pa.open(format=self.settings['format'],
channels=self.settings['channels'],
rate=self.settings['rate'],
input=True,
output=True,
frames_per_buffer=self.settings['chunk'])
# 音声波形を格納
self.wav = None
self.wav_separate = None
# フラグ一覧
self.is_filter = is_filter
self.is_input = True
self.is_separate = False
self.is_end_separate = False
def exe(self):
## -----*----- 処理実行 -----*----- ##
thread = threading.Thread(target=self.audio_input)
thread.start()
thread = threading.Thread(target=self.audio_seaprate)
thread.start()
self.audio_output()
def audio_input(self):
## -----*----- 音声入力 -----*----- ##
while self.stream.is_active():
self.wav = np.fromstring(
self.stream.read(self.settings['chunk'],
exception_on_overflow=False), np.int16)
# 録音開始フラグ反転
self.is_input = False
self.is_separate = True
def audio_output(self):
## -----*----- 音声出力 -----*----- ##
while self.stream.is_active():
if not self.is_input:
# 録音開始フラグ反転
self.is_input = True
# 再生
if self.is_end_separate:
output = b''.join(self.wav_separate)
self.stream.write(output)
def audio_seaprate(self):
## -----*----- 音声分離 -----*----- ##
while self.stream.is_active():
if self.is_separate:
spec = self.infer.stft(self.wav, to_log=False).T
spec_pred = self.infer.stft(self.wav, to_log=True).T
# 分離
if self.is_filter:
for t in range(spec.shape[0]):
pred = self.infer.predict(spec_pred[t])
for i in range(129):
if pred[i] > 0.75:
spec[t][i] *= pred[i]
elif pred[i] > 0.5:
spec[t][i] *= 0.1
else:
spec[t][i] = 0
self.wav_separate = self.infer.istft(spec.T)
self.is_separate = False
self.is_end_separate = True
