From ae3b7e8f76c0e383bfdba7df8e4adf3c0a7ebff5 Mon Sep 17 00:00:00 2001 From: wataru Date: Fri, 24 Mar 2023 09:38:23 +0900 Subject: [PATCH] 3. move hubert --- server/voice_changer/DDSP_SVC/DDSP_SVC.py | 57 ++++++----------------- 1 file changed, 15 insertions(+), 42 deletions(-) diff --git a/server/voice_changer/DDSP_SVC/DDSP_SVC.py b/server/voice_changer/DDSP_SVC/DDSP_SVC.py index b13ede86..d3c9230d 100644 --- a/server/voice_changer/DDSP_SVC/DDSP_SVC.py +++ b/server/voice_changer/DDSP_SVC/DDSP_SVC.py @@ -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":