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Reformat and rewrite _get_name_params (#57)

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* Reformat

* rewrite _get_name_params

* Add workflow for automatic formatting

* Revert "Add workflow for automatic formatting"

This reverts commit 9111c5dbc1.

* revert Retrieval_based_Voice_Conversion_WebUI.ipynb

---------

Co-authored-by: 源文雨 <41315874+fumiama@users.noreply.github.com>
This commit is contained in:
Ftps
2023-04-15 20:44:24 +09:00
committed by GitHub
parent aaa893c4b1
commit c8261b2ccc
45 changed files with 4878 additions and 2456 deletions
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254
uvr5_pack/utils.py
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@@ -1,6 +1,15 @@
import torch
import numpy as np
from tqdm import tqdm
import json
def load_data(file_name: str = "./uvr5_pack/data.json") -> dict:
with open(file_name, "r") as f:
data = json.load(f)
return data
def make_padding(width, cropsize, offset):
left = offset
@@ -10,233 +19,102 @@ def make_padding(width, cropsize, offset):
right = roi_size - (width % roi_size) + left
return left, right, roi_size
def inference(X_spec, device, model, aggressiveness,data):
'''
def inference(X_spec, device, model, aggressiveness, data):
"""
data ： dic configs
'''
def _execute(X_mag_pad, roi_size, n_window, device, model, aggressiveness,is_half=True):
"""
def _execute(
X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half=True
):
model.eval()
with torch.no_grad():
preds = []
iterations = [n_window]
total_iterations = sum(iterations)
for i in tqdm(range(n_window)):
total_iterations = sum(iterations)
for i in tqdm(range(n_window)):
start = i * roi_size
X_mag_window = X_mag_pad[None, :, :, start:start + data['window_size']]
X_mag_window = X_mag_pad[
None, :, :, start : start + data["window_size"]
]
X_mag_window = torch.from_numpy(X_mag_window)
if(is_half):X_mag_window=X_mag_window.half()
X_mag_window=X_mag_window.to(device)
if is_half:
X_mag_window = X_mag_window.half()
X_mag_window = X_mag_window.to(device)
pred = model.predict(X_mag_window, aggressiveness)
pred = pred.detach().cpu().numpy()
preds.append(pred[0])
pred = np.concatenate(preds, axis=2)
return pred
def preprocess(X_spec):
X_mag = np.abs(X_spec)
X_phase = np.angle(X_spec)
return X_mag, X_phase
X_mag, X_phase = preprocess(X_spec)
coef = X_mag.max()
X_mag_pre = X_mag / coef
n_frame = X_mag_pre.shape[2]
pad_l, pad_r, roi_size = make_padding(n_frame,
data['window_size'], model.offset)
pad_l, pad_r, roi_size = make_padding(n_frame, data["window_size"], model.offset)
n_window = int(np.ceil(n_frame / roi_size))
X_mag_pad = np.pad(
X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant")
if(list(model.state_dict().values())[0].dtype==torch.float16):is_half=True
else:is_half=False
pred = _execute(X_mag_pad, roi_size, n_window,
device, model, aggressiveness,is_half)
if list(model.state_dict().values())[0].dtype == torch.float16:
is_half = True
else:
is_half = False
pred = _execute(
X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half
)
pred = pred[:, :, :n_frame]
if data['tta']:
if data["tta"]:
pad_l += roi_size // 2
pad_r += roi_size // 2
n_window += 1
X_mag_pad = np.pad(
X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant")
pred_tta = _execute(X_mag_pad, roi_size, n_window,
device, model, aggressiveness,is_half)
pred_tta = pred_tta[:, :, roi_size // 2:]
pred_tta = _execute(
X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half
)
pred_tta = pred_tta[:, :, roi_size // 2 :]
pred_tta = pred_tta[:, :, :n_frame]
return (pred + pred_tta) * 0.5 * coef, X_mag, np.exp(1.j * X_phase)
return (pred + pred_tta) * 0.5 * coef, X_mag, np.exp(1.0j * X_phase)
else:
return pred * coef, X_mag, np.exp(1.j * X_phase)
return pred * coef, X_mag, np.exp(1.0j * X_phase)
def _get_name_params(model_path , model_hash):
def _get_name_params(model_path, model_hash):
data = load_data()
flag = False
ModelName = model_path
if model_hash == '47939caf0cfe52a0e81442b85b971dfd':
model_params_auto=str('uvr5_pack/lib_v5/modelparams/4band_44100.json')
param_name_auto=str('4band_44100')
if model_hash == '4e4ecb9764c50a8c414fee6e10395bbe':
model_params_auto=str('uvr5_pack/lib_v5/modelparams/4band_v2.json')
param_name_auto=str('4band_v2')
if model_hash == 'ca106edd563e034bde0bdec4bb7a4b36':
model_params_auto=str('uvr5_pack/lib_v5/modelparams/4band_v2.json')
param_name_auto=str('4band_v2')
if model_hash == 'e60a1e84803ce4efc0a6551206cc4b71':
model_params_auto=str('uvr5_pack/lib_v5/modelparams/4band_44100.json')
param_name_auto=str('4band_44100')
if model_hash == 'a82f14e75892e55e994376edbf0c8435':
model_params_auto=str('uvr5_pack/lib_v5/modelparams/4band_44100.json')
param_name_auto=str('4band_44100')
if model_hash == '6dd9eaa6f0420af9f1d403aaafa4cc06':
model_params_auto=str('uvr5_pack/lib_v5/modelparams/4band_v2_sn.json')
param_name_auto=str('4band_v2_sn')
if model_hash == '08611fb99bd59eaa79ad27c58d137727':
model_params_auto=str('uvr5_pack/lib_v5/modelparams/4band_v2_sn.json')
param_name_auto=str('4band_v2_sn')
if model_hash == '5c7bbca45a187e81abbbd351606164e5':
model_params_auto=str('uvr5_pack/lib_v5/modelparams/3band_44100_msb2.json')
param_name_auto=str('3band_44100_msb2')
if model_hash == 'd6b2cb685a058a091e5e7098192d3233':
model_params_auto=str('uvr5_pack/lib_v5/modelparams/3band_44100_msb2.json')
param_name_auto=str('3band_44100_msb2')
if model_hash == 'c1b9f38170a7c90e96f027992eb7c62b':
model_params_auto=str('uvr5_pack/lib_v5/modelparams/4band_44100.json')
param_name_auto=str('4band_44100')
if model_hash == 'c3448ec923fa0edf3d03a19e633faa53':
model_params_auto=str('uvr5_pack/lib_v5/modelparams/4band_44100.json')
param_name_auto=str('4band_44100')
if model_hash == '68aa2c8093d0080704b200d140f59e54':
model_params_auto=str('uvr5_pack/lib_v5/modelparams/3band_44100.json')
param_name_auto=str('3band_44100.json')
if model_hash == 'fdc83be5b798e4bd29fe00fe6600e147':
model_params_auto=str('uvr5_pack/lib_v5/modelparams/3band_44100_mid.json')
param_name_auto=str('3band_44100_mid.json')
if model_hash == '2ce34bc92fd57f55db16b7a4def3d745':
model_params_auto=str('uvr5_pack/lib_v5/modelparams/3band_44100_mid.json')
param_name_auto=str('3band_44100_mid.json')
if model_hash == '52fdca89576f06cf4340b74a4730ee5f':
model_params_auto=str('uvr5_pack/lib_v5/modelparams/4band_44100.json')
param_name_auto=str('4band_44100.json')
if model_hash == '41191165b05d38fc77f072fa9e8e8a30':
model_params_auto=str('uvr5_pack/lib_v5/modelparams/4band_44100.json')
param_name_auto=str('4band_44100.json')
if model_hash == '89e83b511ad474592689e562d5b1f80e':
model_params_auto=str('uvr5_pack/lib_v5/modelparams/2band_32000.json')
param_name_auto=str('2band_32000.json')
if model_hash == '0b954da81d453b716b114d6d7c95177f':
model_params_auto=str('uvr5_pack/lib_v5/modelparams/2band_32000.json')
param_name_auto=str('2band_32000.json')
for type in list(data):
for model in list(data[type][0]):
for i in range(len(data[type][0][model])):
if str(data[type][0][model][i]["hash_name"]) == model_hash:
flag = True
elif str(data[type][0][model][i]["hash_name"]) in ModelName:
flag = True
#v4 Models
if model_hash == '6a00461c51c2920fd68937d4609ed6c8':
model_params_auto=str('uvr5_pack/lib_v5/modelparams/1band_sr16000_hl512.json')
param_name_auto=str('1band_sr16000_hl512')
if model_hash == '0ab504864d20f1bd378fe9c81ef37140':
model_params_auto=str('uvr5_pack/lib_v5/modelparams/1band_sr32000_hl512.json')
param_name_auto=str('1band_sr32000_hl512')
if model_hash == '7dd21065bf91c10f7fccb57d7d83b07f':
model_params_auto=str('uvr5_pack/lib_v5/modelparams/1band_sr32000_hl512.json')
param_name_auto=str('1band_sr32000_hl512')
if model_hash == '80ab74d65e515caa3622728d2de07d23':
model_params_auto=str('uvr5_pack/lib_v5/modelparams/1band_sr32000_hl512.json')
param_name_auto=str('1band_sr32000_hl512')
if model_hash == 'edc115e7fc523245062200c00caa847f':
model_params_auto=str('uvr5_pack/lib_v5/modelparams/1band_sr33075_hl384.json')
param_name_auto=str('1band_sr33075_hl384')
if model_hash == '28063e9f6ab5b341c5f6d3c67f2045b7':
model_params_auto=str('uvr5_pack/lib_v5/modelparams/1band_sr33075_hl384.json')
param_name_auto=str('1band_sr33075_hl384')
if model_hash == 'b58090534c52cbc3e9b5104bad666ef2':
model_params_auto=str('uvr5_pack/lib_v5/modelparams/1band_sr44100_hl512.json')
param_name_auto=str('1band_sr44100_hl512')
if model_hash == '0cdab9947f1b0928705f518f3c78ea8f':
model_params_auto=str('uvr5_pack/lib_v5/modelparams/1band_sr44100_hl512.json')
param_name_auto=str('1band_sr44100_hl512')
if model_hash == 'ae702fed0238afb5346db8356fe25f13':
model_params_auto=str('uvr5_pack/lib_v5/modelparams/1band_sr44100_hl1024.json')
param_name_auto=str('1band_sr44100_hl1024')
#User Models
#1 Band
if '1band_sr16000_hl512' in ModelName:
model_params_auto=str('uvr5_pack/lib_v5/modelparams/1band_sr16000_hl512.json')
param_name_auto=str('1band_sr16000_hl512')
if '1band_sr32000_hl512' in ModelName:
model_params_auto=str('uvr5_pack/lib_v5/modelparams/1band_sr32000_hl512.json')
param_name_auto=str('1band_sr32000_hl512')
if '1band_sr33075_hl384' in ModelName:
model_params_auto=str('uvr5_pack/lib_v5/modelparams/1band_sr33075_hl384.json')
param_name_auto=str('1band_sr33075_hl384')
if '1band_sr44100_hl256' in ModelName:
model_params_auto=str('uvr5_pack/lib_v5/modelparams/1band_sr44100_hl256.json')
param_name_auto=str('1band_sr44100_hl256')
if '1band_sr44100_hl512' in ModelName:
model_params_auto=str('uvr5_pack/lib_v5/modelparams/1band_sr44100_hl512.json')
param_name_auto=str('1band_sr44100_hl512')
if '1band_sr44100_hl1024' in ModelName:
model_params_auto=str('uvr5_pack/lib_v5/modelparams/1band_sr44100_hl1024.json')
param_name_auto=str('1band_sr44100_hl1024')
#2 Band
if '2band_44100_lofi' in ModelName:
model_params_auto=str('uvr5_pack/lib_v5/modelparams/2band_44100_lofi.json')
param_name_auto=str('2band_44100_lofi')
if '2band_32000' in ModelName:
model_params_auto=str('uvr5_pack/lib_v5/modelparams/2band_32000.json')
param_name_auto=str('2band_32000')
if '2band_48000' in ModelName:
model_params_auto=str('uvr5_pack/lib_v5/modelparams/2band_48000.json')
param_name_auto=str('2band_48000')
#3 Band
if '3band_44100' in ModelName:
model_params_auto=str('uvr5_pack/lib_v5/modelparams/3band_44100.json')
param_name_auto=str('3band_44100')
if '3band_44100_mid' in ModelName:
model_params_auto=str('uvr5_pack/lib_v5/modelparams/3band_44100_mid.json')
param_name_auto=str('3band_44100_mid')
if '3band_44100_msb2' in ModelName:
model_params_auto=str('uvr5_pack/lib_v5/modelparams/3band_44100_msb2.json')
param_name_auto=str('3band_44100_msb2')
#4 Band
if '4band_44100' in ModelName:
model_params_auto=str('uvr5_pack/lib_v5/modelparams/4band_44100.json')
param_name_auto=str('4band_44100')
if '4band_44100_mid' in ModelName:
model_params_auto=str('uvr5_pack/lib_v5/modelparams/4band_44100_mid.json')
param_name_auto=str('4band_44100_mid')
if '4band_44100_msb' in ModelName:
model_params_auto=str('uvr5_pack/lib_v5/modelparams/4band_44100_msb.json')
param_name_auto=str('4band_44100_msb')
if '4band_44100_msb2' in ModelName:
model_params_auto=str('uvr5_pack/lib_v5/modelparams/4band_44100_msb2.json')
param_name_auto=str('4band_44100_msb2')
if '4band_44100_reverse' in ModelName:
model_params_auto=str('uvr5_pack/lib_v5/modelparams/4band_44100_reverse.json')
param_name_auto=str('4band_44100_reverse')
if '4band_44100_sw' in ModelName:
model_params_auto=str('uvr5_pack/lib_v5/modelparams/4band_44100_sw.json')
param_name_auto=str('4band_44100_sw')
if '4band_v2' in ModelName:
model_params_auto=str('uvr5_pack/lib_v5/modelparams/4band_v2.json')
param_name_auto=str('4band_v2')
if '4band_v2_sn' in ModelName:
model_params_auto=str('uvr5_pack/lib_v5/modelparams/4band_v2_sn.json')
param_name_auto=str('4band_v2_sn')
if 'tmodelparam' in ModelName:
model_params_auto=str('uvr5_pack/lib_v5/modelparams/tmodelparam.json')
param_name_auto=str('User Model Param Set')
return param_name_auto , model_params_auto
if flag:
model_params_auto = data[type][0][model][i]["model_params"]
param_name_auto = data[type][0][model][i]["param_name"]
if type == "equivalent":
return param_name_auto, model_params_auto
else:
flag = False
return param_name_auto, model_params_auto