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from gyraudio.audio_separation.architecture.flat_conv import FlatConvolutional
from gyraudio.audio_separation.architecture.unet import ResUNet
from gyraudio.audio_separation.architecture.wave_unet import WaveUNet
from gyraudio.audio_separation.architecture.neutral import NeutralModel
from gyraudio.audio_separation.architecture.transformer import TransformerModel
from gyraudio.audio_separation.properties import (
NAME, ANNOTATIONS, MAX_STEPS_PER_EPOCH, EPOCHS, BATCH_SIZE,
OPTIMIZER, LEARNING_RATE,
DATALOADER,
WEIGHT_DECAY,
LOSS, LOSS_L1,
AUGMENTATION, AUG_TRIM, AUG_AWGN, AUG_RESCALE,
LENGTHS, LENGTH_DIVIDER, TRIM_PROB,
SCHEDULER, SCHEDULER_CONFIGURATION
)
from gyraudio.audio_separation.experiment_tracking.experiments_decorator import (
registered_experiment, REGISTERED_EXPERIMENTS_LIST
)
@registered_experiment(major=9999)
def neutral(config, model: bool = None, minor=None):
config[BATCH_SIZE] = [4, 4, 4]
config[EPOCHS] = 1
config[NAME] = "Neutral"
config[ANNOTATIONS] = "Neutral"
if model is None:
model = NeutralModel()
config[NAME] = "Neutral"
return config, model
@registered_experiment(major=0)
def exp_unit_test(config, model: bool = None, minor=None):
config[MAX_STEPS_PER_EPOCH] = 2
config[BATCH_SIZE] = [4, 4, 4]
config[EPOCHS] = 2
config[NAME] = "Unit Test - Flat Convolutional"
config[ANNOTATIONS] = "Baseline"
config[SCHEDULER] = "ReduceLROnPlateau"
config[SCHEDULER_CONFIGURATION] = dict(patience=5, factor=0.8)
if model is None:
model = FlatConvolutional()
return config, model
# ---------------- Low Baseline -----------------
def exp_low_baseline(
config: dict,
batch_size: int = 16,
h_dim: int = 16,
k_size: int = 9,
dilation: int = 0,
model: bool = None,
minor=None
):
config[BATCH_SIZE] = [batch_size, batch_size, batch_size]
config[NAME] = "Flat Convolutional"
config[ANNOTATIONS] = f"Baseline H={h_dim}_K={k_size}"
if dilation > 1:
config[ANNOTATIONS] += f"_dil={dilation}"
config["Architecture"] = {
"name": "Flat-Conv",
"h_dim": h_dim,
"scales": 1,
"k_size": k_size,
"dilation": dilation
}
if model is None:
model = FlatConvolutional(k_size=k_size, h_dim=h_dim)
return config, model
@registered_experiment(major=1)
def exp_1(config, model: bool = None, minor=None):
config, model = exp_low_baseline(config, batch_size=32, k_size=5)
return config, model
@registered_experiment(major=2)
def exp_2(config, model: bool = None, minor=None):
config, model = exp_low_baseline(config, batch_size=32, k_size=9)
return config, model
@registered_experiment(major=3)
def exp_3(config, model: bool = None, minor=None):
config, model = exp_low_baseline(config, batch_size=32, k_size=9, dilation=2)
return config, model
@registered_experiment(major=4)
def exp_4(config, model: bool = None, minor=None):
config, model = exp_low_baseline(config, batch_size=16, k_size=9)
return config, model
# ------------------ Res U-Net ------------------
def exp_resunet(config, h_dim=16, k_size=5, model=None):
config[NAME] = "Res-UNet"
scales = 4
config[ANNOTATIONS] = f"Res-UNet-{scales}scales_h={h_dim}_k={k_size}"
config["Architecture"] = {
"name": "Res-UNet",
"h_dim": h_dim,
"scales": scales,
"k_size": k_size,
}
if model is None:
model = ResUNet(h_dim=h_dim, k_size=k_size)
return config, model
@registered_experiment(major=2000)
def exp_2000_waveunet(config, model: bool = None, minor=None):
config[EPOCHS] = 60
config, model = exp_resunet(config)
return config, model
@registered_experiment(major=2001)
def exp_2001_waveunet(config, model: bool = None, minor=None):
config[EPOCHS] = 60
config, model = exp_resunet(config, h_dim=32, k_size=5)
return config, model
# ------------------ Wave U-Net ------------------
def exp_wave_unet(config: dict,
channels_extension: int = 24,
k_conv_ds: int = 15,
k_conv_us: int = 5,
num_layers: int = 4,
dropout: float = 0.0,
bias: bool = True,
model=None):
config[NAME] = "Wave-UNet"
config[ANNOTATIONS] = f"Wave-UNet-{num_layers}scales_h_ext={channels_extension}_k={k_conv_ds}ds-{k_conv_us}us"
if dropout > 0:
config[ANNOTATIONS] += f"-dr{dropout:.1e}"
if not bias:
config[ANNOTATIONS] += "-BiasFree"
config["Architecture"] = {
"k_conv_us": k_conv_us,
"k_conv_ds": k_conv_ds,
"num_layers": num_layers,
"channels_extension": channels_extension,
"dropout": dropout,
"bias": bias
}
if model is None:
model = WaveUNet(
**config["Architecture"]
)
config["Architecture"][NAME] = "Wave-UNet"
return config, model
@registered_experiment(major=1000)
def exp_1000_waveunet(config, model: bool = None, minor=None):
config[EPOCHS] = 60
config, model = exp_wave_unet(config, model=model, num_layers=4, channels_extension=24)
# 4 layers, ext +24 - Nvidia T500 4Gb RAM - 16 batch size
return config, model
@registered_experiment(major=1001)
def exp_1001_waveunet(config, model: bool = None, minor=None):
# OVERFIT 1M param ?
config[EPOCHS] = 60
config, model = exp_wave_unet(config, model=model, num_layers=7, channels_extension=16)
# 7 layers, ext +16 - Nvidia T500 4Gb RAM - 16 batch size
return config, model
@registered_experiment(major=1002)
def exp_1002_waveunet(config, model: bool = None, minor=None):
# OVERFIT 1M param ?
config[EPOCHS] = 60
config, model = exp_wave_unet(config, model=model, num_layers=7, channels_extension=16)
config[DATALOADER][AUGMENTATION] = {
AUG_TRIM: {LENGTHS: [8192, 80000], LENGTH_DIVIDER: 1024, TRIM_PROB: 0.8},
AUG_RESCALE: True
}
# 7 layers, ext +16 - Nvidia T500 4Gb RAM - 16 batch size
return config, model
@registered_experiment(major=1003)
def exp_1003_waveunet(config, model: bool = None, minor=None):
# OVERFIT 2.3M params
config[EPOCHS] = 60
config, model = exp_wave_unet(config, model=model, num_layers=7, channels_extension=24)
# 7 layers, ext +24 - Nvidia RTX3060 6Gb RAM - 16 batch size
return config, model
@registered_experiment(major=1004)
def exp_1004_waveunet(config, model: bool = None, minor=None):
config[EPOCHS] = 120
config, model = exp_wave_unet(config, model=model, num_layers=7, channels_extension=28)
# 7 layers, ext +28 - Nvidia RTX3060 6Gb RAM - 16 batch size
return config, model
@registered_experiment(major=1014)
def exp_1014_waveunet(config, model: bool = None, minor=None):
# trained with min and max mixing snr hard coded between -2 and -1
config[EPOCHS] = 50
config, model = exp_wave_unet(config, model=model, num_layers=7, channels_extension=28)
# 7 layers, ext +28 - Nvidia RTX3060 6Gb RAM - 16 batch size
return config, model
@registered_experiment(major=1005)
def exp_1005_waveunet(config, model: bool = None, minor=None):
config[EPOCHS] = 150
config, model = exp_wave_unet(config, model=model, num_layers=7, channels_extension=16)
config[DATALOADER][AUGMENTATION] = {
AUG_TRIM: {LENGTHS: [8192, 80000], LENGTH_DIVIDER: 1024, TRIM_PROB: 0.8},
AUG_RESCALE: True
}
# 7 layers, ext +16 - Nvidia T500 4Gb RAM - 16 batch size
return config, model
@registered_experiment(major=1006)
def exp_1006_waveunet(config, model: bool = None, minor=None):
config[EPOCHS] = 150
config, model = exp_wave_unet(config, model=model, num_layers=11, channels_extension=16)
config[DATALOADER][AUGMENTATION] = {
AUG_TRIM: {LENGTHS: [8192, 80000], LENGTH_DIVIDER: 4096, TRIM_PROB: 0.8},
AUG_RESCALE: True
}
# 11 layers, ext +16 - Nvidia T500 4Gb RAM - 16 batch size
return config, model
@registered_experiment(major=1007)
def exp_1007_waveunet(config, model: bool = None, minor=None):
config[EPOCHS] = 150
config, model = exp_wave_unet(config, model=model, num_layers=9, channels_extension=16)
config[DATALOADER][AUGMENTATION] = {
AUG_TRIM: {LENGTHS: [8192, 80000], LENGTH_DIVIDER: 4096, TRIM_PROB: 0.8},
AUG_RESCALE: True
}
# 11 layers, ext +16 - Nvidia T500 4Gb RAM - 16 batch size
return config, model
@registered_experiment(major=1008)
def exp_1008_waveunet(config, model: bool = None, minor=None):
# CHEAP BASELINE
config[EPOCHS] = 150
config, model = exp_wave_unet(config, model=model, num_layers=4, channels_extension=16)
config[DATALOADER][AUGMENTATION] = {
AUG_TRIM: {LENGTHS: [8192, 80000], LENGTH_DIVIDER: 1024, TRIM_PROB: 0.8},
AUG_RESCALE: True
}
# 4 layers, ext +16 - Nvidia T500 4Gb RAM - 16 batch size
return config, model
@registered_experiment(major=3000)
def exp_3000_waveunet(config, model: bool = None, minor=None):
config[EPOCHS] = 120
config, model = exp_wave_unet(config, model=model, num_layers=7, channels_extension=28, bias=False)
# 7 layers, ext +28 - Nvidia RTX3060 6Gb RAM - 16 batch size
return config, model
@registered_experiment(major=3001)
def exp_3001_waveunet(config, model: bool = None, minor=None):
config[EPOCHS] = 200
config, model = exp_wave_unet(config, model=model, num_layers=7, channels_extension=28, bias=False)
# 7 layers, ext +28 - Nvidia RTX3060 6Gb RAM - 16 batch size
config[SCHEDULER] = "ReduceLROnPlateau"
config[SCHEDULER_CONFIGURATION] = dict(patience=5, factor=0.8)
config[OPTIMIZER][LEARNING_RATE] = 0.002
return config, model
@registered_experiment(major=3002)
def exp_3002_waveunet(config, model: bool = None, minor=None):
# TRAINED WITH SNR -12db +12db (code changed manually!)
# See f910c6da3123e3d35cc0ce588bb5a72ce4a8c422
config[EPOCHS] = 200
config, model = exp_wave_unet(config, model=model, num_layers=7, channels_extension=28, bias=False)
# 7 layers, ext +28 - Nvidia RTX3060 6Gb RAM - 16 batch size
config[SCHEDULER] = "ReduceLROnPlateau"
config[SCHEDULER_CONFIGURATION] = dict(patience=5, factor=0.8)
config[OPTIMIZER][LEARNING_RATE] = 0.002
return config, model
@registered_experiment(major=4000)
def exp_4000_bias_free_waveunet_l1(config, model: bool = None, minor=None):
# config[MAX_STEPS_PER_EPOCH] = 2
# config[BATCH_SIZE] = [2, 2, 2]
config[EPOCHS] = 200
config[LOSS] = LOSS_L1
config, model = exp_wave_unet(config, model=model, num_layers=7, channels_extension=28, bias=False)
# 7 layers, ext +28 - Nvidia RTX3060 6Gb RAM - 16 batch size
config[SCHEDULER] = "ReduceLROnPlateau"
config[SCHEDULER_CONFIGURATION] = dict(patience=5, factor=0.8)
config[OPTIMIZER][LEARNING_RATE] = 0.002
return config, model
def get_experiment_generator(exp_major: int):
assert exp_major in REGISTERED_EXPERIMENTS_LIST, f"Experiment {exp_major} not registered"
exp_generator = REGISTERED_EXPERIMENTS_LIST[exp_major]
return exp_generator
if __name__ == "__main__":
print(f"Available experiments: {list(REGISTERED_EXPERIMENTS_LIST.keys())}")
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