Spaces:
Running
Running
File size: 13,848 Bytes
2e66664 5eedccd 2e66664 6fc042a 0247649 2e66664 5eedccd 2e66664 6bbce1b 0247649 6bbce1b fe2ddc5 2e66664 a990e23 f5db6d5 a990e23 0ea0beb a990e23 c13752e 9e8da41 a990e23 f5db6d5 341951b f5db6d5 a990e23 f5db6d5 2e66664 6f16821 f9582e0 6bbce1b 6f16821 acc6615 6bbce1b f9582e0 6bbce1b 6f16821 488698d 5eedccd 2e66664 a8cb9ce 6bbce1b 2e66664 6fc042a 6bbce1b 6fc042a 6bbce1b 6fc042a 6bbce1b 6fc042a 6bbce1b 6fc042a c13752e 6fc042a c13752e 6fc042a 9e8da41 6fc042a 71be77a 6fc042a 2e66664 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 |
import torch
import soundfile as sf
import numpy as np
import argparse
import os
import yaml
import julius
import sys
currentdir = os.path.dirname(os.path.realpath(__file__))
sys.path.append(os.path.dirname(currentdir))
from networks import Dasp_Mastering_Style_Transfer, Effects_Encoder
from modules.loss import AudioFeatureLoss, Loss
from modules.data_normalization import Audio_Effects_Normalizer
def convert_audio(wav: torch.Tensor, from_rate: float,
to_rate: float, to_channels: int) -> torch.Tensor:
"""Convert audio to new sample rate and number of audio channels.
"""
wav = julius.resample_frac(wav, int(from_rate), int(to_rate))
wav = convert_audio_channels(wav, to_channels)
return wav
class MasteringStyleTransfer:
def __init__(self, args):
self.args = args
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Load models
self.effects_encoder = self.load_effects_encoder()
self.mastering_converter = self.load_mastering_converter()
self.fx_normalizer = Audio_Effects_Normalizer(precomputed_feature_path=args.fx_norm_feature_path, \
STEMS=['mixture'], \
EFFECTS=['eq', 'imager', 'loudness'])
def load_effects_encoder(self):
effects_encoder = Effects_Encoder(self.args.cfg_enc)
reload_weights(effects_encoder, self.args.encoder_path, self.device)
effects_encoder.to(self.device)
effects_encoder.eval()
return effects_encoder
def load_mastering_converter(self):
mastering_converter = Dasp_Mastering_Style_Transfer(num_features=2048,
sample_rate=self.args.sample_rate,
tgt_fx_names=['eq', 'distortion', 'multiband_comp', 'gain', 'imager', 'limiter'],
model_type='tcn',
config=self.args.cfg_converter,
batch_size=1)
reload_weights(mastering_converter, self.args.model_path, self.device)
mastering_converter.to(self.device)
mastering_converter.eval()
return mastering_converter
def get_reference_embedding(self, reference_tensor):
with torch.no_grad():
reference_feature = self.effects_encoder(reference_tensor)
return reference_feature
def mastering_style_transfer(self, input_tensor, reference_feature):
with torch.no_grad():
output_audio = self.mastering_converter(input_tensor, reference_feature)
predicted_params = self.mastering_converter.get_last_predicted_params()
return output_audio, predicted_params
def inference_time_optimization(self, input_tensor, reference_tensor, ito_config, initial_reference_feature):
fit_embedding = torch.nn.Parameter(initial_reference_feature)
optimizer = getattr(torch.optim, ito_config['optimizer'])([fit_embedding], lr=ito_config['learning_rate'])
af_loss = AudioFeatureLoss(
weights=ito_config['af_weights'],
sample_rate=ito_config['sample_rate'],
stem_separation=False,
use_clap=False
)
min_loss = float('inf')
min_loss_step = 0
all_results = []
for step in range(ito_config['num_steps']):
optimizer.zero_grad()
output_audio = self.mastering_converter(input_tensor, fit_embedding)
current_params = self.mastering_converter.get_last_predicted_params()
losses = af_loss(output_audio, reference_tensor)
total_loss = sum(losses.values())
if total_loss < min_loss:
min_loss = total_loss.item()
min_loss_step = step
# Log top 5 parameter differences
if step == 0:
initial_params = current_params
top_5_diff = self.get_top_n_diff_string(initial_params, current_params, top_n=5)
log_entry = f"Step {step + 1}\n Loss: {total_loss.item():.4f}\n{top_5_diff}\n"
all_results.append({
'step': step + 1,
'loss': total_loss.item(),
'audio': output_audio.detach().cpu().numpy(),
'params': current_params,
'log': log_entry
})
total_loss.backward()
optimizer.step()
return all_results, min_loss_step
def preprocess_audio(self, audio, target_sample_rate=44100, normalize=False):
sample_rate, data = audio
# Normalize audio to -1 to 1 range
if data.dtype == np.int16:
data = data.astype(np.float32) / 32768.0
elif data.dtype == np.float32:
data = np.clip(data, -1.0, 1.0)
else:
raise ValueError(f"Unsupported audio data type: {data.dtype}")
# Ensure stereo channels
if data.ndim == 1:
data = np.stack([data, data])
elif data.ndim == 2:
if data.shape[0] == 2:
pass # Already in correct shape
elif data.shape[1] == 2:
data = data.T
else:
data = np.stack([data[:, 0], data[:, 0]]) # Duplicate mono channel
else:
raise ValueError(f"Unsupported audio shape: {data.shape}")
# Resample if necessary
if sample_rate != target_sample_rate:
data = julius.resample_frac(torch.from_numpy(data), sample_rate, target_sample_rate).numpy()
# Apply fx normalization for input audio during mastering style transfer
if normalize:
data = self.fx_normalizer.normalize_audio(data.T, 'mixture').T
# Convert to torch tensor
data_tensor = torch.FloatTensor(data).unsqueeze(0)
return data_tensor.to(self.device)
def process_audio(self, input_audio, reference_audio):
input_tensor = self.preprocess_audio(input_audio, self.args.sample_rate, normalize=True)
reference_tensor = self.preprocess_audio(reference_audio, self.args.sample_rate)
reference_feature = self.get_reference_embedding(reference_tensor)
output_audio, predicted_params = self.mastering_style_transfer(input_tensor, reference_feature)
return output_audio, predicted_params, self.args.sample_rate, input_tensor
def get_param_output_string(self, params):
if params is None:
return "No parameters available"
param_mapper = {
'EQ': {
'low_shelf_gain_db': ('Low Shelf Gain', 'dB', -20, 20),
'low_shelf_cutoff_freq': ('Low Shelf Cutoff', 'Hz', 20, 2000),
'low_shelf_q_factor': ('Low Shelf Q', '', 0.1, 5.0),
'band0_gain_db': ('Low-Mid Band Gain', 'dB', -20, 20),
'band0_cutoff_freq': ('Low-Mid Band Frequency', 'Hz', 80, 2000),
'band0_q_factor': ('Low-Mid Band Q', '', 0.1, 5.0),
'band1_gain_db': ('Mid Band Gain', 'dB', -20, 20),
'band1_cutoff_freq': ('Mid Band Frequency', 'Hz', 2000, 8000),
'band1_q_factor': ('Mid Band Q', '', 0.1, 5.0),
'band2_gain_db': ('High-Mid Band Gain', 'dB', -20, 20),
'band2_cutoff_freq': ('High-Mid Band Frequency', 'Hz', 8000, 12000),
'band2_q_factor': ('High-Mid Band Q', '', 0.1, 5.0),
'band3_gain_db': ('High Band Gain', 'dB', -20, 20),
'band3_cutoff_freq': ('High Band Frequency', 'Hz', 12000, 20000), # Assuming sample_rate is 44100
'band3_q_factor': ('High Band Q', '', 0.1, 5.0),
'high_shelf_gain_db': ('High Shelf Gain', 'dB', -20, 20),
'high_shelf_cutoff_freq': ('High Shelf Cutoff', 'Hz', 4000, 20000), # Assuming sample_rate is 44100
'high_shelf_q_factor': ('High Shelf Q', '', 0.1, 5.0),
},
'DISTORTION': {
'drive_db': ('Drive', 'dB', 0, 8),
'parallel_weight_factor': ('Dry/Wet Mix', '%', 0, 100),
},
'MULTIBAND_COMP': {
'low_cutoff': ('Low/Mid Crossover', 'Hz', 20, 1000),
'high_cutoff': ('Mid/High Crossover', 'Hz', 1000, 20000),
'parallel_weight_factor': ('Dry/Wet Mix', '%', 0, 100),
'low_shelf_comp_thresh': ('Low Band Comp Threshold', 'dB', -60, 0),
'low_shelf_comp_ratio': ('Low Band Comp Ratio', ':1', 1, 20),
'low_shelf_exp_thresh': ('Low Band Exp Threshold', 'dB', -60, 0),
'low_shelf_exp_ratio': ('Low Band Exp Ratio', ':1', 1, 20),
'low_shelf_at': ('Low Band Attack Time', 'ms', 5, 100),
'low_shelf_rt': ('Low Band Release Time', 'ms', 5, 100),
'mid_band_comp_thresh': ('Mid Band Comp Threshold', 'dB', -60, 0),
'mid_band_comp_ratio': ('Mid Band Comp Ratio', ':1', 1, 20),
'mid_band_exp_thresh': ('Mid Band Exp Threshold', 'dB', -60, 0),
'mid_band_exp_ratio': ('Mid Band Exp Ratio', ':1', 1, 20),
'mid_band_at': ('Mid Band Attack Time', 'ms', 5, 100),
'mid_band_rt': ('Mid Band Release Time', 'ms', 5, 100),
'high_shelf_comp_thresh': ('High Band Comp Threshold', 'dB', -60, 0),
'high_shelf_comp_ratio': ('High Band Comp Ratio', ':1', 1, 20),
'high_shelf_exp_thresh': ('High Band Exp Threshold', 'dB', -60, 0),
'high_shelf_exp_ratio': ('High Band Exp Ratio', ':1', 1, 20),
'high_shelf_at': ('High Band Attack Time', 'ms', 5, 100),
'high_shelf_rt': ('High Band Release Time', 'ms', 5, 100),
},
'GAIN': {
'gain_db': ('Output Gain', 'dB', -24, 24),
},
'IMAGER': {
'width': ('Stereo Width', '', 0, 1),
},
'LIMITER': {
'threshold': ('Threshold', 'dB', -60, 0),
'at': ('Attack Time', 'ms', 5, 100),
'rt': ('Release Time', 'ms', 5, 100),
},
}
output = []
for fx_name, fx_params in params.items():
output.append(f"{fx_name}:")
if isinstance(fx_params, dict):
for param_name, param_value in fx_params.items():
if isinstance(param_value, torch.Tensor):
param_value = param_value.item()
if fx_name in param_mapper and param_name in param_mapper[fx_name]:
friendly_name, unit, min_val, max_val = param_mapper[fx_name][param_name]
if fx_name == 'IMAGER' and param_name == 'width':
# Convert width to a more intuitive scale
width_percentage = param_value * 200
output.append(f" {friendly_name}: {width_percentage:.2f}% (Range: 0-200%)")
else:
output.append(f" {friendly_name}: {param_value:.2f} {unit} (Range: {min_val}-{max_val})")
else:
output.append(f" {param_name}: {param_value:.2f}")
else:
if fx_name == 'IMAGER':
width_percentage = fx_params.item() * 200
output.append(f" Stereo Width: {width_percentage:.2f}% (Range: 0-200%)")
else:
output.append(f" {fx_params.item():.2f}")
return "\n".join(output)
def get_top_n_diff_string(self, initial_params, ito_params, top_n=5):
if initial_params is None or ito_params is None:
return "Cannot compare parameters"
all_diffs = []
for fx_name in initial_params.keys():
if isinstance(initial_params[fx_name], dict):
for param_name in initial_params[fx_name].keys():
initial_value = initial_params[fx_name][param_name]
ito_value = ito_params[fx_name][param_name]
param_range = self.mastering_converter.fx_processors[fx_name].param_ranges[param_name]
normalized_diff = abs((ito_value - initial_value) / (param_range[1] - param_range[0]))
all_diffs.append((fx_name, param_name, initial_value.item(), ito_value.item(), normalized_diff.item()))
else:
initial_value = initial_params[fx_name]
ito_value = ito_params[fx_name]
normalized_diff = abs(ito_value - initial_value)
all_diffs.append((fx_name, 'width', initial_value.item(), ito_value.item(), normalized_diff.item()))
top_diffs = sorted(all_diffs, key=lambda x: x[4], reverse=True)[:top_n]
output = [f" Top {top_n} parameter differences (initial / ITO / normalized diff):"]
for fx_name, param_name, initial_value, ito_value, normalized_diff in top_diffs:
output.append(f" {fx_name.upper()} - {param_name}: {initial_value:.2f} / {ito_value:.2f} / {normalized_diff:.2f}")
return "\n".join(output)
def reload_weights(model, ckpt_path, device):
checkpoint = torch.load(ckpt_path, map_location=device)
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in checkpoint["model"].items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
model.load_state_dict(new_state_dict, strict=False)
|