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Initial commit and v1.0

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Valentin Boulanger
2025-04-21 13:41:19 +02:00
commit cdcb0b9b3c
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demucs/grids/__init__.py Normal file
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demucs/grids/_explorers.py Normal file
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from dora import Explorer
import treetable as tt
class MyExplorer(Explorer):
test_metrics = ['nsdr', 'sdr_med']
def get_grid_metrics(self):
"""Return the metrics that should be displayed in the tracking table.
"""
return [
tt.group("train", [
tt.leaf("epoch"),
tt.leaf("reco", ".3f"),
], align=">"),
tt.group("valid", [
tt.leaf("penalty", ".1f"),
tt.leaf("ms", ".1f"),
tt.leaf("reco", ".2%"),
tt.leaf("breco", ".2%"),
tt.leaf("b_nsdr", ".2f"),
# tt.leaf("b_nsdr_drums", ".2f"),
# tt.leaf("b_nsdr_bass", ".2f"),
# tt.leaf("b_nsdr_other", ".2f"),
# tt.leaf("b_nsdr_vocals", ".2f"),
], align=">"),
tt.group("test", [
tt.leaf(name, ".2f")
for name in self.test_metrics
], align=">")
]
def process_history(self, history):
train = {
'epoch': len(history),
}
valid = {}
test = {}
best_v_main = float('inf')
breco = float('inf')
for metrics in history:
train.update(metrics['train'])
valid.update(metrics['valid'])
if 'main' in metrics['valid']:
best_v_main = min(best_v_main, metrics['valid']['main']['loss'])
valid['bmain'] = best_v_main
valid['breco'] = min(breco, metrics['valid']['reco'])
breco = valid['breco']
if (metrics['valid']['loss'] == metrics['valid']['best'] or
metrics['valid'].get('nsdr') == metrics['valid']['best']):
for k, v in metrics['valid'].items():
if k.startswith('reco_'):
valid['b_' + k[len('reco_'):]] = v
if k.startswith('nsdr'):
valid[f'b_{k}'] = v
if 'test' in metrics:
test.update(metrics['test'])
metrics = history[-1]
return {"train": train, "valid": valid, "test": test}

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demucs/grids/mdx.py Normal file
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Main training for the Track A MDX models.
"""
from ._explorers import MyExplorer
from ..train import main
TRACK_A = ['0d19c1c6', '7ecf8ec1', 'c511e2ab', '7d865c68']
@MyExplorer
def explorer(launcher):
launcher.slurm_(
gpus=8,
time=3 * 24 * 60,
partition='learnlab')
# Reproduce results from MDX competition Track A
# This trains the first round of models. Once this is trained,
# you will need to schedule `mdx_refine`.
for sig in TRACK_A:
xp = main.get_xp_from_sig(sig)
parent = xp.cfg.continue_from
xp = main.get_xp_from_sig(parent)
launcher(xp.argv)
launcher(xp.argv, {'quant.diffq': 1e-4})
launcher(xp.argv, {'quant.diffq': 3e-4})

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demucs/grids/mdx_extra.py Normal file
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Main training for the Track A MDX models.
"""
from ._explorers import MyExplorer
from ..train import main
TRACK_B = ['e51eebcc', 'a1d90b5c', '5d2d6c55', 'cfa93e08']
@MyExplorer
def explorer(launcher):
launcher.slurm_(
gpus=8,
time=3 * 24 * 60,
partition='learnlab')
# Reproduce results from MDX competition Track A
# This trains the first round of models. Once this is trained,
# you will need to schedule `mdx_refine`.
for sig in TRACK_B:
while sig is not None:
xp = main.get_xp_from_sig(sig)
sig = xp.cfg.continue_from
for dset in ['extra44', 'extra_test']:
sub = launcher.bind(xp.argv, dset=dset)
sub()
if dset == 'extra_test':
sub({'quant.diffq': 1e-4})
sub({'quant.diffq': 3e-4})

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demucs/grids/mdx_refine.py Normal file
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Main training for the Track A MDX models.
"""
from ._explorers import MyExplorer
from .mdx import TRACK_A
from ..train import main
@MyExplorer
def explorer(launcher):
launcher.slurm_(
gpus=8,
time=3 * 24 * 60,
partition='learnlab')
# Reproduce results from MDX competition Track A
# WARNING: all the experiments in the `mdx` grid must have completed.
for sig in TRACK_A:
xp = main.get_xp_from_sig(sig)
launcher(xp.argv)
for diffq in [1e-4, 3e-4]:
xp_src = main.get_xp_from_sig(xp.cfg.continue_from)
q_argv = [f'quant.diffq={diffq}']
actual_src = main.get_xp(xp_src.argv + q_argv)
actual_src.link.load()
assert len(actual_src.link.history) == actual_src.cfg.epochs
argv = xp.argv + q_argv + [f'continue_from="{actual_src.sig}"']
launcher(argv)

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demucs/grids/mmi.py Normal file
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from ._explorers import MyExplorer
from dora import Launcher
@MyExplorer
def explorer(launcher: Launcher):
launcher.slurm_(gpus=8, time=3 * 24 * 60, partition="devlab,learnlab,learnfair") # 3 days
sub = launcher.bind_(
{
"dset": "extra_mmi_goodclean",
"test.shifts": 0,
"model": "htdemucs",
"htdemucs.dconv_mode": 3,
"htdemucs.depth": 4,
"htdemucs.t_dropout": 0.02,
"htdemucs.t_layers": 5,
"max_batches": 800,
"ema.epoch": [0.9, 0.95],
"ema.batch": [0.9995, 0.9999],
"dset.segment": 10,
"batch_size": 32,
}
)
sub({"model": "hdemucs"})
sub({"model": "hdemucs", "dset": "extra44"})
sub({"model": "hdemucs", "dset": "musdb44"})
sparse = {
'batch_size': 3 * 8,
'augment.remix.group_size': 3,
'htdemucs.t_auto_sparsity': True,
'htdemucs.t_sparse_self_attn': True,
'htdemucs.t_sparse_cross_attn': True,
'htdemucs.t_sparsity': 0.9,
"htdemucs.t_layers": 7
}
with launcher.job_array():
for transf_layers in [5, 7]:
for bottom_channels in [0, 512]:
sub = launcher.bind({
"htdemucs.t_layers": transf_layers,
"htdemucs.bottom_channels": bottom_channels,
})
if bottom_channels == 0 and transf_layers == 5:
sub({"augment.remix.proba": 0.0})
sub({
"augment.repitch.proba": 0.0,
# when doing repitching, we trim the outut to align on the
# highest change of BPM. When removing repitching,
# we simulate it here to ensure the training context is the same.
# Another second is lost for all experiments due to the random
# shift augmentation.
"dset.segment": 10 * 0.88})
elif bottom_channels == 512 and transf_layers == 5:
sub(dset="musdb44")
sub(dset="extra44")
# Sparse kernel XP, currently not released as kernels are still experimental.
sub(sparse, {'dset.segment': 15, "htdemucs.t_layers": 7})
for duration in [5, 10, 15]:
sub({"dset.segment": duration})

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demucs/grids/mmi_ft.py Normal file
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from ._explorers import MyExplorer
from dora import Launcher
from demucs import train
def get_sub(launcher, sig):
xp = train.main.get_xp_from_sig(sig)
sub = launcher.bind(xp.argv)
sub()
sub.bind_({
'continue_from': sig,
'continue_best': True})
return sub
@MyExplorer
def explorer(launcher: Launcher):
launcher.slurm_(gpus=4, time=3 * 24 * 60, partition="devlab,learnlab,learnfair") # 3 days
ft = {
'optim.lr': 1e-4,
'augment.remix.proba': 0,
'augment.scale.proba': 0,
'augment.shift_same': True,
'htdemucs.t_weight_decay': 0.05,
'batch_size': 8,
'optim.clip_grad': 5,
'optim.optim': 'adamw',
'epochs': 50,
'dset.wav2_valid': True,
'ema.epoch': [], # let's make valid a bit faster
}
with launcher.job_array():
for sig in ['2899e11a']:
sub = get_sub(launcher, sig)
sub.bind_(ft)
for segment in [15, 18]:
for source in range(4):
w = [0] * 4
w[source] = 1
sub({'weights': w, 'dset.segment': segment})
for sig in ['955717e8']:
sub = get_sub(launcher, sig)
sub.bind_(ft)
for segment in [10, 15]:
for source in range(4):
w = [0] * 4
w[source] = 1
sub({'weights': w, 'dset.segment': segment})

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demucs/grids/repro.py Normal file
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Easier training for reproducibility
"""
from ._explorers import MyExplorer
@MyExplorer
def explorer(launcher):
launcher.slurm_(
gpus=8,
time=3 * 24 * 60,
partition='devlab,learnlab')
launcher.bind_({'ema.epoch': [0.9, 0.95]})
launcher.bind_({'ema.batch': [0.9995, 0.9999]})
launcher.bind_({'epochs': 600})
base = {'model': 'demucs', 'demucs.dconv_mode': 0, 'demucs.gelu': False,
'demucs.lstm_layers': 2}
newt = {'model': 'demucs', 'demucs.normalize': True}
hdem = {'model': 'hdemucs'}
svd = {'svd.penalty': 1e-5, 'svd': 'base2'}
with launcher.job_array():
for model in [base, newt, hdem]:
sub = launcher.bind(model)
if model is base:
# Training the v2 Demucs on MusDB HQ
sub(epochs=360)
continue
# those two will be used in the repro_mdx_a bag of models.
sub(svd)
sub(svd, seed=43)
if model == newt:
# Ablation study
sub()
abl = sub.bind(svd)
abl({'ema.epoch': [], 'ema.batch': []})
abl({'demucs.dconv_lstm': 10})
abl({'demucs.dconv_attn': 10})
abl({'demucs.dconv_attn': 10, 'demucs.dconv_lstm': 10, 'demucs.lstm_layers': 2})
abl({'demucs.dconv_mode': 0})
abl({'demucs.gelu': False})

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demucs/grids/repro_ft.py Normal file
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Fine tuning experiments
"""
from ._explorers import MyExplorer
from ..train import main
@MyExplorer
def explorer(launcher):
launcher.slurm_(
gpus=8,
time=300,
partition='devlab,learnlab')
# Mus
launcher.slurm_(constraint='volta32gb')
grid = "repro"
folder = main.dora.dir / "grids" / grid
for sig in folder.iterdir():
if not sig.is_symlink():
continue
xp = main.get_xp_from_sig(sig)
xp.link.load()
if len(xp.link.history) != xp.cfg.epochs:
continue
sub = launcher.bind(xp.argv, [f'continue_from="{xp.sig}"'])
sub.bind_({'ema.epoch': [0.9, 0.95], 'ema.batch': [0.9995, 0.9999]})
sub.bind_({'test.every': 1, 'test.sdr': True, 'epochs': 4})
sub.bind_({'dset.segment': 28, 'dset.shift': 2})
sub.bind_({'batch_size': 32})
auto = {'dset': 'auto_mus'}
auto.update({'augment.remix.proba': 0, 'augment.scale.proba': 0,
'augment.shift_same': True})
sub.bind_(auto)
sub.bind_({'batch_size': 16})
sub.bind_({'optim.lr': 1e-4})
sub.bind_({'model_segment': 44})
sub()

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demucs/grids/sdx23.py Normal file
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from ._explorers import MyExplorer
from dora import Launcher
@MyExplorer
def explorer(launcher: Launcher):
launcher.slurm_(gpus=8, time=3 * 24 * 60, partition="speechgpt,learnfair",
mem_per_gpu=None, constraint='')
launcher.bind_({"dset.use_musdb": False})
with launcher.job_array():
launcher(dset='sdx23_bleeding')
launcher(dset='sdx23_labelnoise')