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wataru 2023-03-24 09:38:23 +09:00
parent 9f7533c037
commit ae3b7e8f76
1 changed files with 15 additions and 42 deletions
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server/voice_changer/DDSP_SVC/DDSP_SVC.py
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@ -155,8 +155,9 @@ class DDSP_SVC:
# return c, f0
def generate_input(self, newData: any, inputSize: int, crossfadeSize: int):
# newData = newData.astype(np.float32) / 32768.0
newData = newData.astype(np.float32) / 32768.0
# newData = newData.astype(np.float32) / self.hps.data.max_wav_value
hop_size = int(self.args.data.block_size * 44100 / self.args.data.sampling_rate)
if hasattr(self, "audio_buffer"):
self.audio_buffer = np.concatenate([self.audio_buffer, newData], 0) # 過去のデータに連結
@ -164,7 +165,6 @@ class DDSP_SVC:
self.audio_buffer = newData
convertSize = inputSize + crossfadeSize + self.settings.extraConvertSize
hop_size = int(self.args.data.block_size * 44100 / self.args.data.sampling_rate)
print("hopsize", hop_size)
if convertSize % hop_size != 0: # モデルの出力のホップサイズで切り捨てが発生するので補う。
convertSize = convertSize + (hop_size - (convertSize % hop_size))
@ -172,6 +172,9 @@ class DDSP_SVC:
print("convsize", convertSize)
self.audio_buffer = self.audio_buffer[-1 * convertSize:] # 変換対象の部分だけ抽出
audio = torch.from_numpy(self.audio_buffer).float().unsqueeze(0)
seg_units = self.encoder.encode(audio, 44100, hop_size)
print("audio1", audio)
# crop = self.audio_buffer[-1 * (inputSize + crossfadeSize):-1 * (crossfadeSize)]
# rms = np.sqrt(np.square(crop).mean(axis=0))
@ -180,17 +183,18 @@ class DDSP_SVC:
# c, f0 = self.get_unit_f0(self.audio_buffer, self.settings.tran)
# return (c, f0, convertSize, vol)
wavfile.write("tmp2.wav", 44100, self.audio_buffer.astype(np.int16))
wavfile.write("tmp2.wav", 44100, (self.audio_buffer * 32768.0).astype(np.int16))
return (seg_units, )
def _onnx_inference(self, data):
if hasattr(self, "onnx_session") == False or self.onnx_session == None:
print("[Voice Changer] No onnx session.")
return np.zeros(1).astype(np.int16)
c = data[0]
f0 = data[1]
convertSize = data[2]
vol = data[3]
seg_units = data[0]
# f0 = data[1]
# convertSize = data[2]
# vol = data[3]
if vol < self.settings.silentThreshold:
return np.zeros(convertSize).astype(np.int16)
@ -249,6 +253,10 @@ class DDSP_SVC:
# print("SEG:", seg_output)
audio, sample_rate = librosa.load("tmp2.wav", sr=None)
print("SR:", sample_rate)
seg_units = data[0]
if len(audio.shape) > 1:
audio = librosa.to_mono(audio)
hop_size = self.args.data.block_size * sample_rate / self.args.data.sampling_rate
@ -273,8 +281,6 @@ class DDSP_SVC:
with torch.no_grad():
start_frame = 0
seg_input = torch.from_numpy(audio).float().unsqueeze(0)
seg_units = self.encoder.encode(seg_input, sample_rate, hop_size)
seg_f0 = f0[:, start_frame: start_frame + seg_units.size(1), :]
seg_volume = volume[:, start_frame: start_frame + seg_units.size(1), :]
@ -296,41 +302,8 @@ class DDSP_SVC:
# sf.write("out.wav", result, output_sample_rate)
wavfile.write("out.wav", 44100, result)
# from tqdm import tqdm
# with torch.no_grad():
# for segment in tqdm(segments):
# # start_frame = segment[0]
# start_frame = 0
# # seg_input = torch.from_numpy(segment[1]).float().unsqueeze(0)
# seg_input = torch.from_numpy(audio).float().unsqueeze(0)
# seg_units = self.encoder.encode(seg_input, sample_rate, hop_size)
# seg_f0 = f0[:, start_frame: start_frame + seg_units.size(1), :]
# seg_volume = volume[:, start_frame: start_frame + seg_units.size(1), :]
# seg_output, _, (s_h, s_n) = self.model(seg_units, seg_f0, seg_volume, spk_id=spk_id, spk_mix_dict=None)
# seg_output *= mask[:, start_frame * self.args.data.block_size: (start_frame + seg_units.size(1)) * self.args.data.block_size]
# output_sample_rate = self.args.data.sampling_rate
# seg_output = seg_output.squeeze().cpu().numpy()
# silent_length = round(start_frame * self.args.data.block_size * output_sample_rate / self.args.data.sampling_rate) - current_length
# if silent_length >= 0:
# result = np.append(result, np.zeros(silent_length))
# result = np.append(result, seg_output)
# else:
# result = cross_fade(result, seg_output, current_length + silent_length)
# current_length = current_length + silent_length + len(seg_output)
# # sf.write("out.wav", result, output_sample_rate)
# wavfile.write("out.wav", 44100, result)
print("result:::", result)
return np.array(result * 32768.0).astype(np.int16)
return np.array(result).astype(np.int16)
# return np.zeros(1).astype(np.int16)
# return seg_output
def inference(self, data):
if self.settings.framework == "ONNX":