1berry/wokada-voice-changer
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5. mv f0

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wataru 2023-03-24 09:42:21 +09:00
parent dcc782216f
commit 0de24ca000
1 changed files with 7 additions and 4 deletions
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server/voice_changer/DDSP_SVC/DDSP_SVC.py
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@ -172,6 +172,10 @@ class DDSP_SVC:
print("convsize", convertSize) print("convsize", convertSize)
self.audio_buffer = self.audio_buffer[-1 * convertSize:] # 変換対象の部分だけ抽出 self.audio_buffer = self.audio_buffer[-1 * convertSize:] # 変換対象の部分だけ抽出
f0 = self.f0_detector.extract(self.audio_buffer, uv_interp=True)
f0 = torch.from_numpy(f0).float().unsqueeze(-1).unsqueeze(0)
f0 = f0 * 2 ** (float(10) / 12)
audio = torch.from_numpy(self.audio_buffer).float().unsqueeze(0) audio = torch.from_numpy(self.audio_buffer).float().unsqueeze(0)
seg_units = self.encoder.encode(audio, 44100, hop_size) seg_units = self.encoder.encode(audio, 44100, hop_size)
print("audio1", audio) print("audio1", audio)
@ -184,7 +188,7 @@ class DDSP_SVC:
# c, f0 = self.get_unit_f0(self.audio_buffer, self.settings.tran) # c, f0 = self.get_unit_f0(self.audio_buffer, self.settings.tran)
# return (c, f0, convertSize, vol) # return (c, f0, convertSize, vol)
wavfile.write("tmp2.wav", 44100, (self.audio_buffer * 32768.0).astype(np.int16)) wavfile.write("tmp2.wav", 44100, (self.audio_buffer * 32768.0).astype(np.int16))
return (seg_units, ) return (seg_units, f0)
def _onnx_inference(self, data): def _onnx_inference(self, data):
if hasattr(self, "onnx_session") == False or self.onnx_session == None: if hasattr(self, "onnx_session") == False or self.onnx_session == None:
@ -256,15 +260,14 @@ class DDSP_SVC:
print("SR:", sample_rate) print("SR:", sample_rate)
seg_units = data[0] seg_units = data[0]
f0 = data[1]
if len(audio.shape) > 1: if len(audio.shape) > 1:
audio = librosa.to_mono(audio) audio = librosa.to_mono(audio)
hop_size = self.args.data.block_size * sample_rate / self.args.data.sampling_rate hop_size = self.args.data.block_size * sample_rate / self.args.data.sampling_rate
print("hop_size", hop_size) print("hop_size", hop_size)
f0 = self.f0_detector.extract(audio, uv_interp=True)
f0 = torch.from_numpy(f0).float().unsqueeze(-1).unsqueeze(0)
f0 = f0 * 2 ** (float(10) / 12)
volume_extractor = vo.Volume_Extractor(hop_size) volume_extractor = vo.Volume_Extractor(hop_size)
volume = volume_extractor.extract(audio) volume = volume_extractor.extract(audio)
mask = (volume > 10 ** (float(-60) / 20)).astype('float') mask = (volume > 10 ** (float(-60) / 20)).astype('float')