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wataru 2023-03-24 10:52:36 +09:00
parent f3374ba21a
commit 3c1c785dfa
1 changed files with 12 additions and 3 deletions
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15
server/voice_changer/DDSP_SVC/DDSP_SVC.py
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@ -19,6 +19,7 @@ import onnxruntime
import pyworld as pw import pyworld as pw
import ddsp.vocoder as vo import ddsp.vocoder as vo
from ddsp.core import upsample from ddsp.core import upsample
from enhancer import Enhancer
from slicer import Slicer from slicer import Slicer
import librosa import librosa
providers = ['OpenVINOExecutionProvider', "CUDAExecutionProvider", "DmlExecutionProvider", "CPUExecutionProvider"] providers = ['OpenVINOExecutionProvider', "CUDAExecutionProvider", "DmlExecutionProvider", "CPUExecutionProvider"]
@ -95,7 +96,7 @@ class DDSP_SVC:
float(1100)) float(1100))
self.volume_extractor = vo.Volume_Extractor(self.hop_size) self.volume_extractor = vo.Volume_Extractor(self.hop_size)
self.enhancer = Enhancer(self.args.enhancer.type, "./model_DDSP-SVC/enhancer/model", "cpu")
return self.get_info() return self.get_info()
def update_setteings(self, key: str, val: any): def update_setteings(self, key: str, val: any):
@ -155,7 +156,7 @@ class DDSP_SVC:
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 # f0
f0 = self.f0_detector.extract(self.audio_buffer, uv_interp=True) f0 = self.f0_detector.extract(self.audio_buffer * 32768.0, uv_interp=True)
f0 = torch.from_numpy(f0).float().unsqueeze(-1).unsqueeze(0) f0 = torch.from_numpy(f0).float().unsqueeze(-1).unsqueeze(0)
f0 = f0 * 2 ** (float(self.settings.tran) / 12) f0 = f0 * 2 ** (float(self.settings.tran) / 12)
@ -233,10 +234,18 @@ class DDSP_SVC:
return np.zeros(convertSize).astype(np.int16) return np.zeros(convertSize).astype(np.int16)
with torch.no_grad(): with torch.no_grad():
spk_id = torch.LongTensor(np.array([[int(1)]])) spk_id = torch.LongTensor(np.array([[int(2)]]))
seg_output, _, (s_h, s_n) = self.model(c, f0, volume, spk_id=spk_id, spk_mix_dict=None) seg_output, _, (s_h, s_n) = self.model(c, f0, volume, spk_id=spk_id, spk_mix_dict=None)
seg_output *= mask seg_output *= mask
seg_output, output_sample_rate = self.enhancer.enhance(
seg_output,
self.args.data.sampling_rate,
f0,
self.args.data.block_size,
adaptive_key=float(0))
result = seg_output.squeeze().cpu().numpy() * 32768.0 result = seg_output.squeeze().cpu().numpy() * 32768.0
return np.array(result).astype(np.int16) return np.array(result).astype(np.int16)
def inference(self, data): def inference(self, data):