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customize pipline

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This commit is contained in:
wataru 2023-04-08 05:34:26 +09:00
parent e780af7fc2
commit 0d06fcd16b
3 changed files with 7 additions and 29 deletions
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4
client/demo/public/assets/gui_settings/RVC.json
View File

@ -105,10 +105,6 @@
"name": "indexRatio",
"options": {}
},
{
"name": "noiseScale",
"options": {}
},
{
"name": "silentThreshold",
"options": {}

9
server/voice_changer/RVC/RVC.py
View File

@ -180,14 +180,12 @@ class RVC:
convertSize = convertSize + (128 - (convertSize % 128))
self.audio_buffer = self.audio_buffer[-1 * convertSize:] # 変換対象の部分だけ抽出
print("convert size", convertSize, self.audio_buffer.shape)
crop = self.audio_buffer[-1 * (inputSize + crossfadeSize):-1 * (crossfadeSize)]
rms = np.sqrt(np.square(crop).mean(axis=0))
vol = max(rms, self.prevVol * 0.0)
self.prevVol = vol
print("audio len 01,", len(self.audio_buffer))
return (self.audio_buffer, convertSize, vol)
def _onnx_inference(self, data):
@ -212,7 +210,9 @@ class RVC:
return np.zeros(convertSize).astype(np.int16)
with torch.no_grad():
vc = VC(self.settings.modelSamplingRate, dev, self.is_half)
repeat = 3 if self.is_half else 1
repeat *= self.settings.rvcQuality # 0 or 3
vc = VC(self.settings.modelSamplingRate, dev, self.is_half, repeat)
sid = 0
times = [0, 0, 0]
f0_up_key = self.settings.tran
@ -245,9 +245,7 @@ class RVC:
audio = data[0]
convertSize = data[1]
vol = data[2]
print("audio len 02,", len(audio))
audio = resampy.resample(audio, self.settings.modelSamplingRate, 16000)
print("audio len 03,", len(audio))
if vol < self.settings.silentThreshold:
return np.zeros(convertSize).astype(np.int16)
@ -266,7 +264,6 @@ class RVC:
if_f0 = 1
f0_file = None
print("audio len 0,", len(audio))
audio_out = vc.pipeline(self.hubert_model, self.net_g, sid, audio, times, f0_up_key, f0_method,
file_index, file_big_npy, index_rate, if_f0, f0_file=f0_file)
result = audio_out * np.sqrt(vol)

23
server/voice_changer/RVC/custom_vc_infer_pipeline.py
View File

@ -76,7 +76,6 @@ class VC(object):
assert feats.dim() == 1, feats.dim()
feats = feats.view(1, -1)
padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
print("padding_mask", padding_mask)
inputs = {
"source": feats.to(self.device),
@ -98,9 +97,8 @@ class VC(object):
npy = npy.astype("float16")
feats = torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate + (1 - index_rate) * feats
print("feats shape1", feats.shape)
feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
print("feats shape2", feats.shape)
t1 = ttime()
p_len = audio0.shape[0] // self.window
if (feats.shape[1] < p_len):
@ -109,23 +107,18 @@ class VC(object):
pitch = pitch[:, :p_len]
pitchf = pitchf[:, :p_len]
p_len = torch.tensor([p_len], device=self.device).long()
with torch.no_grad():
print("vc audio len feat 1,", feats.shape)
if (pitch != None and pitchf != None):
print("vc audio len feat use pitch!!!!!!!,", feats.shape)
audio1 = (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] * 32768).data.cpu().float().numpy().astype(np.int16)
else:
audio1 = (net_g.infer(feats, p_len, sid)[0][0, 0] * 32768).data.cpu().float().numpy().astype(np.int16)
audio1 = (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] * 32768).data.cpu().float().numpy().astype(np.int16)
del feats, p_len, padding_mask
torch.cuda.empty_cache()
t2 = ttime()
times[0] += (t1 - t0)
times[2] += (t2 - t1)
print("vc audio return", len(audio1), audio1)
return audio1
def pipeline(self, model, net_g, sid, audio, times, f0_up_key, f0_method, file_index, file_big_npy, index_rate, if_f0, f0_file=None):
print("audio len 1,", len(audio))
if (file_big_npy != "" and file_index != "" and os.path.exists(file_big_npy) == True and os.path.exists(file_index) == True and index_rate != 0):
try:
index = faiss.read_index(file_index)
@ -135,13 +128,7 @@ class VC(object):
index = big_npy = None
else:
index = big_npy = None
audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode='reflect')
print("audio_pad len 1,", len(audio_pad))
opt_ts = []
print("audio_pad len 2,", len(audio_pad), opt_ts)
s = 0
audio_opt = []
t = None
t1 = ttime()
@ -153,7 +140,6 @@ class VC(object):
pitch, pitchf = None, None
if (if_f0 == 1):
pitch, pitchf = self.get_f0(audio_pad, p_len, f0_up_key, f0_method, inp_f0)
print("pitch!", pitch)
pitch = pitch[:p_len]
pitchf = pitchf[:p_len]
pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
@ -170,5 +156,4 @@ class VC(object):
audio_opt = np.concatenate(audio_opt)
del pitch, pitchf, sid
torch.cuda.empty_cache()
print("result", audio_opt)
return audio_opt