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audiocraft/grids/magnet/audio_magnet_16khz.py

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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 ..musicgen._explorers import LMExplorer
+from ...environment import AudioCraftEnvironment
+
+
+@LMExplorer
+def explorer(launcher):
+    partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
+    launcher.slurm_(gpus=32, partition=partitions)
+    launcher.bind_(solver='magnet/audio_magnet_16khz')
+    # replace this by the desired environmental sound dataset
+    launcher.bind_(dset='internal/sounds_16khz')
+
+    fsdp = {'autocast': False, 'fsdp.use': True}
+    medium = {'model/lm/model_scale': 'medium'}
+
+    # Small model (300M)
+    launcher.slurm_(gpus=32).bind_(label='32gpus')
+    with launcher.job_array():
+        sub = launcher.bind()
+        sub()
+
+    # Medium model (1.5B)
+    launcher.slurm_(gpus=64).bind_(label='64gpus')
+    with launcher.job_array():
+        sub = launcher.bind()
+        sub(medium, fsdp)

audiocraft/grids/magnet/audio_magnet_pretrained_16khz_eval.py

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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.
+
+"""
+Evaluation with objective metrics for the pretrained audio-MAGNeT models.
+This grid takes signature from the training grid and runs evaluation-only stage.
+
+When running the grid for the first time, please use:
+REGEN=1 dora grid magnet.audio_magnet_pretrained_16khz_eval
+and re-use the REGEN=1 option when the grid is changed to force regenerating it.
+
+Note that you need the proper metrics external libraries setup to use all
+the objective metrics activated in this grid. Refer to the README for more information.
+"""
+
+import os
+
+from ..musicgen._explorers import GenerationEvalExplorer
+from ...environment import AudioCraftEnvironment
+from ... import train
+
+
+def eval(launcher, batch_size: int = 32):
+    opts = {
+        'dset': 'audio/audiocaps_16khz',
+        'solver/audiogen/evaluation': 'objective_eval',
+        'execute_only': 'evaluate',
+        '+dataset.evaluate.batch_size': batch_size,
+        '+metrics.fad.tf.batch_size': 32,
+    }
+    # binary for FAD computation: replace this path with your own path
+    metrics_opts = {
+        'metrics.fad.tf.bin': '/data/home/jadecopet/local/usr/opt/google-research'
+    }
+
+    sub = launcher.bind(opts)
+    sub.bind_(metrics_opts)
+
+    # base objective metrics
+    sub()
+
+
+@GenerationEvalExplorer
+def explorer(launcher):
+    partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
+    launcher.slurm_(gpus=4, partition=partitions)
+
+    if 'REGEN' not in os.environ:
+        folder = train.main.dora.dir / 'grids' / __name__.split('.', 2)[-1]
+        with launcher.job_array():
+            for sig in folder.iterdir():
+                if not sig.is_symlink():
+                    continue
+                xp = train.main.get_xp_from_sig(sig.name)
+                launcher(xp.argv)
+        return
+
+    with launcher.job_array():
+        audio_magnet = launcher.bind(solver="magnet/audio_magnet_16khz")
+
+        fsdp = {'autocast': False, 'fsdp.use': True}
+
+        # Small audio-MAGNeT model (300M)
+        audio_magnet_small = audio_magnet.bind({'continue_from': '//pretrained/facebook/audio-magnet-small'})
+        eval(audio_magnet_small, batch_size=128)
+
+        # Medium audio-MAGNeT model (1.5B)
+        audio_magnet_medium = audio_magnet.bind({'continue_from': '//pretrained/facebook/audio-magnet-medium'})
+        audio_magnet_medium.bind_({'model/lm/model_scale': 'medium'})
+        audio_magnet_medium.bind_(fsdp)
+        eval(audio_magnet_medium, batch_size=128)

audiocraft/grids/magnet/magnet_32khz.py

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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 ..musicgen._explorers import LMExplorer
+from ...environment import AudioCraftEnvironment
+
+
+@LMExplorer
+def explorer(launcher):
+    partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
+    launcher.slurm_(gpus=32, partition=partitions)
+    launcher.bind_(solver='magnet/magnet_32khz')
+    # replace this by the desired music dataset
+    launcher.bind_(dset='internal/music_400k_32khz')
+
+    fsdp = {'autocast': False, 'fsdp.use': True}
+    medium = {'model/lm/model_scale': 'medium'}
+    adam = {'optim.optimizer': 'adamw', 'optim.lr': 1e-4}
+    segdur_10secs = {'dataset.segment_duration': 10,
+                     'dataset.batch_size': 576,
+                     'generate.lm.decoding_steps': [20, 10, 10, 10]}
+
+    # Small models (300M)
+    launcher.slurm_(gpus=32).bind_(label='32gpus')
+    with launcher.job_array():
+        # 30 seconds
+        sub = launcher.bind()
+        sub()
+
+        # 10 seconds
+        sub = launcher.bind()
+        sub(segdur_10secs)
+
+    # Medium models (1.5B)
+    launcher.bind_(fsdp)
+    launcher.slurm_(gpus=64).bind_(label='64gpus')
+    with launcher.job_array():
+        # 30 seconds
+        sub = launcher.bind()
+        sub(medium, adam)
+
+        # 10 seconds
+        sub = launcher.bind()
+        sub(segdur_10secs)

audiocraft/grids/magnet/magnet_pretrained_32khz_eval.py

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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.
+
+"""
+Evaluation with objective metrics for the pretrained MAGNeT models.
+This grid takes signature from the training grid and runs evaluation-only stage.
+
+When running the grid for the first time, please use:
+REGEN=1 dora grid magnet.magnet_pretrained_32khz_eval
+and re-use the REGEN=1 option when the grid is changed to force regenerating it.
+
+Note that you need the proper metrics external libraries setup to use all
+the objective metrics activated in this grid. Refer to the README for more information.
+"""
+
+import os
+
+from ..musicgen._explorers import GenerationEvalExplorer
+from ...environment import AudioCraftEnvironment
+from ... import train
+
+
+def eval(launcher, batch_size: int = 32):
+    opts = {
+        'dset': 'audio/musiccaps_32khz',
+        'solver/musicgen/evaluation': 'objective_eval',
+        'execute_only': 'evaluate',
+        '+dataset.evaluate.batch_size': batch_size,
+        '+metrics.fad.tf.batch_size': 16,
+    }
+    # binary for FAD computation: replace this path with your own path
+    metrics_opts = {
+        'metrics.fad.tf.bin': '/data/home/jadecopet/local/usr/opt/google-research'
+    }
+
+    sub = launcher.bind(opts)
+    sub.bind_(metrics_opts)
+
+    # base objective metrics
+    sub()
+
+
+@GenerationEvalExplorer
+def explorer(launcher):
+    partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
+    launcher.slurm_(gpus=4, partition=partitions)
+
+    if 'REGEN' not in os.environ:
+        folder = train.main.dora.dir / 'grids' / __name__.split('.', 2)[-1]
+        with launcher.job_array():
+            for sig in folder.iterdir():
+                if not sig.is_symlink():
+                    continue
+                xp = train.main.get_xp_from_sig(sig.name)
+                launcher(xp.argv)
+        return
+
+    with launcher.job_array():
+        magnet = launcher.bind(solver="magnet/magnet_32khz")
+
+        fsdp = {'autocast': False, 'fsdp.use': True}
+
+        segdur_10secs = {'dataset.segment_duration': 10,
+                         'generate.lm.decoding_steps': [20, 10, 10, 10]}
+
+        # 10-second magnet models
+        magnet_small_10secs = magnet.bind({'continue_from': '//pretrained/facebook/magnet-small-10secs'})
+        magnet_small_10secs.bind_(segdur_10secs)
+        eval(magnet_small_10secs, batch_size=128)
+
+        magnet_medium_10secs = magnet.bind({'continue_from': '//pretrained/facebook/magnet-medium-10secs'})
+        magnet_medium_10secs.bind_(segdur_10secs)
+        magnet_medium_10secs.bind_({'model/lm/model_scale': 'medium'})
+        magnet_medium_10secs.bind_(fsdp)
+        eval(magnet_medium_10secs, batch_size=128)
+
+        # 30-second magnet models
+        magnet_small_30secs = magnet.bind({'continue_from': '//pretrained/facebook/magnet-small-30secs'})
+        eval(magnet_small_30secs, batch_size=128)
+
+        magnet_medium_30secs = magnet.bind({'continue_from': '//pretrained/facebook/magnet-medium-30secs'})
+        magnet_medium_30secs.bind_({'model/lm/model_scale': 'medium'})
+        magnet_medium_30secs.bind_(fsdp)
+        eval(magnet_medium_30secs, batch_size=128)