def __init__(self, config):
super(NSynthTFRecordDataset, self).__init__(config)
self._train_data_path = util.expand_path(config['train_data_path'])
self._train_meta_path = None
if 'train_meta_path' in config and config['train_meta_path']:
self._train_meta_path = util.expand_path(config['train_meta_path'])
else:
magic_meta_path = os.path.join(config['train_root_dir'],
'meta.json')
if os.path.exists(magic_meta_path):
self._train_meta_path = magic_meta_path
self._instrument_sources = config['train_instrument_sources']
self._min_pitch = config['train_min_pitch']
self._max_pitch = config['train_max_pitch']
def main(unused_argv):
absl.flags.FLAGS.alsologtostderr = True
# Set hyperparams from json args and defaults
flags = lib_flags.Flags()
# Config hparams
if FLAGS.config:
config_module = importlib.import_module(
'magenta.models.gansynth.configs.{}'.format(FLAGS.config))
flags.load(config_module.hparams)
# Command line hparams
flags.load_json(FLAGS.hparams)
# Set default flags
lib_model.set_flags(flags)
print('Flags:')
flags.print_values()
# Create training directory
flags['train_root_dir'] = util.expand_path(flags['train_root_dir'])
if not tf.gfile.Exists(flags['train_root_dir']):
tf.gfile.MakeDirs(flags['train_root_dir'])
# Save the flags to help with loading the model latter
fname = os.path.join(flags['train_root_dir'], 'experiment.json')
with tf.gfile.Open(fname, 'w') as f:
json.dump(flags, f)
# Run training
run(flags)
def main(unused_argv):
absl.flags.FLAGS.alsologtostderr = True
# Set hyperparams from json args and defaults
flags = lib_flags.Flags()
# Config hparams
if FLAGS.config:
config_module = importlib.import_module(
'magenta.models.gansynth.configs.{}'.format(FLAGS.config))
flags.load(config_module.hparams)
# Command line hparams
flags.load_json(FLAGS.hparams)
# Set default flags
lib_model.set_flags(flags)
print('Flags:')
flags.print_values()
# Create training directory
flags['train_root_dir'] = util.expand_path(flags['train_root_dir'])
if not tf.gfile.Exists(flags['train_root_dir']):
tf.gfile.MakeDirs(flags['train_root_dir'])
# Save the flags to help with loading the model latter
fname = os.path.join(flags['train_root_dir'], 'experiment.json')
with tf.gfile.Open(fname, 'w') as f:
json.dump(flags, f) # pytype: disable=wrong-arg-types
# Run training
run(flags)
def load_from_path(cls, path, flags=None):
"""Instantiate a Model for eval using flags and weights from a saved model.
Currently only supports models trained by the experiment runner, since
Model itself doesn't save flags (so we rely the runner's experiment.json)
Args:
path: Path to model directory (which contains stage folders).
flags: Additional flags for loading the model.
Raises:
ValueError: If folder of path contains no stage folders.
Returns:
model: Instantiated model with saved weights.
"""
# Read the flags from the experiment.json file
# experiment.json is in the folder above
# Remove last '/' if present
path = path.rstrip('/')
if not path.startswith('gs://'):
path = util.expand_path(path)
if flags is None:
flags = lib_flags.Flags()
flags['train_root_dir'] = path
experiment_json_path = os.path.join(path, 'experiment.json')
try:
# Read json to dict
with tf.gfile.GFile(experiment_json_path, 'r') as f:
experiment_json = json.load(f)
# Load dict as a Flags() object
flags.load(experiment_json)
except Exception as e: # pylint: disable=broad-except
print("Warning! Couldn't load model flags from experiment.json")
print(e)
# Set default flags
set_flags(flags)
flags.print_values()
# Get list_of_directories
train_sub_dirs = sorted([
sub_dir for sub_dir in tf.gfile.ListDirectory(path)
if sub_dir.startswith('stage_')
])
if not train_sub_dirs:
raise ValueError(
'No stage folders found, is %s the correct model path?' % path)
# Get last checkpoint
last_stage_dir = train_sub_dirs[-1]
stage_id = int(last_stage_dir.split('_')[-1].strip('/'))
weights_dir = os.path.join(path, last_stage_dir)
ckpt = tf.train.latest_checkpoint(weights_dir)
print('Load model from {}'.format(ckpt))
# Load the model, use eval_batch_size if present
batch_size = flags.get('eval_batch_size',
train_util.get_batch_size(stage_id, **flags))
model = cls(stage_id, batch_size, flags)
model.saver.restore(model.sess, ckpt)
return model
def main(unused_argv):
absl.flags.FLAGS.alsologtostderr = True
# Load the model
flags = lib_flags.Flags({'batch_size_schedule': [FLAGS.batch_size]})
model = lib_model.Model.load_from_path(FLAGS.ckpt_dir, flags)
# Make an output directory if it doesn't exist
output_dir = util.expand_path(FLAGS.output_dir)
if not tf.gfile.Exists(output_dir):
tf.gfile.MakeDirs(output_dir)
# generate 4 random latent vectors
z_instruments = model.generate_z(4)
instrument_names = list(
gen_instrument_name(random.randint(3, 8)) for _ in range(4))
# interpolate
res = FLAGS.resolution
pitches = parse_pitches(FLAGS.pitches)
xy_grid = make_grid(res)
print()
print("resolution =", res)
print("pitches =", pitches)
print("z_instruments.shape =", z_instruments.shape)
print("z_instruments =", z_instruments)
print("instrument_names =", instrument_names)
z_notes, note_metas = get_z_notes(z_instruments, instrument_names, xy_grid)
print("z_notes.shape =", z_notes.shape)
z_notes_rep = np.repeat(z_notes, len(pitches), axis=0)
print("z_notes_rep.shape =", z_notes_rep.shape)
pitches_rep = pitches * z_notes.shape[0]
print("len(pitches_rep) =", len(pitches_rep))
print("generating {} samples,,".format(len(z_notes_rep)))
audio_notes = model.generate_samples_from_z(z_notes_rep, pitches_rep)
audio_metas = []
for note_meta in note_metas:
for pitch in pitches:
meta = dict(note_meta)
meta["pitch"] = pitch
audio_metas.append(meta)
print("audio_notes.shape =", audio_notes.shape)
print("len(audio_metas) =", len(audio_metas))
for i, (wave, meta) in enumerate(zip(audio_notes, audio_metas)):
name = meta_to_name(meta)
fn = os.path.join(output_dir, "gen_{}.wav".format(name))
gu.save_wav(wave, fn)
def load_from_path(cls, path, flags=None):
"""Instantiate a Model for eval using flags and weights from a saved model.
Currently only supports models trained by the experiment runner, since
Model itself doesn't save flags (so we rely the runner's experiment.json)
Args:
path: Path to model directory (which contains stage folders).
flags: Additional flags for loading the model.
Raises:
ValueError: If folder of path contains no stage folders.
Returns:
model: Instantiated model with saved weights.
"""
# Read the flags from the experiment.json file
# experiment.json is in the folder above
# Remove last '/' if present
path = path[:-1] if path.endswith('/') else path
path = util.expand_path(path)
if flags is None:
flags = lib_flags.Flags()
flags['train_root_dir'] = path
experiment_json_path = os.path.join(path, 'experiment.json')
try:
# Read json to dict
with tf.gfile.GFile(experiment_json_path, 'r') as f:
experiment_json = json.load(f)
# Load dict as a Flags() object
flags.load(experiment_json)
except Exception as e: # pylint: disable=broad-except
print("Warning! Couldn't load model flags from experiment.json")
print(e)
# Set default flags
set_flags(flags)
flags.print_values()
# Get list_of_directories
train_sub_dirs = sorted([sub_dir for sub_dir in tf.gfile.ListDirectory(path)
if sub_dir.startswith('stage_')])
if not train_sub_dirs:
raise ValueError('No stage folders found, is %s the correct model path?'
% path)
# Get last checkpoint
last_stage_dir = train_sub_dirs[-1]
stage_id = int(last_stage_dir.split('_')[-1])
weights_dir = os.path.join(path, last_stage_dir)
ckpt = tf.train.latest_checkpoint(weights_dir)
print('Load model from {}'.format(ckpt))
# Load the model, use eval_batch_size if present
batch_size = flags.get('eval_batch_size',
train_util.get_batch_size(stage_id, **flags))
model = cls(stage_id, batch_size, flags)
model.saver.restore(model.sess, ckpt)
return model
def load_midi(midi_path):
"""Load midi as a notesequence."""
midi_path = util.expand_path(midi_path)
ns = mm.midi_file_to_sequence_proto(midi_path)
pitches = np.array([n.pitch for n in ns.notes])
start_times = np.array([n.start_time for n in ns.notes])
end_times = np.array([n.end_time for n in ns.notes])
notes = {'pitches': pitches,
'start_times': start_times,
'end_times': end_times}
# print(ns)
# print(notes)
return ns, notes
def load_midi(midi_path):
"""Load midi as a notesequence."""
midi_path = util.expand_path(midi_path)
ns = mm.midi_file_to_sequence_proto(midi_path)
pitches = np.array([n.pitch for n in ns.notes])
start_times = np.array([n.start_time for n in ns.notes])
end_times = np.array([n.end_time for n in ns.notes])
notes = {'pitches': pitches,
'start_times': start_times,
'end_times': end_times}
# print(ns)
# print(notes)
return ns, notes
def load_midi(midi_path, min_pitch=36, max_pitch=84):
"""Load midi as a notesequence."""
midi_path = util.expand_path(midi_path)
ns = mm.midi_file_to_sequence_proto(midi_path)
pitches = np.array([n.pitch for n in ns.notes])
velocities = np.array([n.velocity for n in ns.notes])
start_times = np.array([n.start_time for n in ns.notes])
end_times = np.array([n.end_time for n in ns.notes])
valid = np.logical_and(pitches >= min_pitch, pitches <= max_pitch)
notes = {'pitches': pitches[valid],
'velocities': velocities[valid],
'start_times': start_times[valid],
'end_times': end_times[valid]}
return ns, notes
def load_midi(midi_path, min_pitch=36, max_pitch=84):
"""Load midi as a notesequence."""
midi_path = util.expand_path(midi_path)
ns = mm.midi_file_to_sequence_proto(midi_path)
pitches = np.array([n.pitch for n in ns.notes])
velocities = np.array([n.velocity for n in ns.notes])
start_times = np.array([n.start_time for n in ns.notes])
end_times = np.array([n.end_time for n in ns.notes])
valid = np.logical_and(pitches >= min_pitch, pitches <= max_pitch)
notes = {'pitches': pitches[valid],
'velocities': velocities[valid],
'start_times': start_times[valid],
'end_times': end_times[valid]}
return ns, notes
def main(unused_argv):
absl.flags.FLAGS.alsologtostderr = True
# Load the model
flags = lib_flags.Flags(
{
'batch_size_schedule': [FLAGS.batch_size],
'tfds_data_dir': FLAGS.tfds_data_dir
})
model = lib_model.Model.load_from_path(FLAGS.ckpt_dir, flags)
# Make an output directory if it doesn't exist
output_dir = util.expand_path(FLAGS.output_dir)
if not tf.gfile.Exists(output_dir):
tf.gfile.MakeDirs(output_dir)
if FLAGS.midi_file:
# If a MIDI file is provided, synthesize interpolations across the clip
unused_ns, notes = gu.load_midi(FLAGS.midi_file)
# Distribute latent vectors linearly in time
z_instruments, t_instruments = gu.get_random_instruments(
model,
notes['end_times'][-1],
secs_per_instrument=FLAGS.secs_per_instrument)
# Get latent vectors for each note
z_notes = gu.get_z_notes(notes['start_times'], z_instruments, t_instruments)
# Generate audio for each note
print('Generating {} samples...'.format(len(z_notes)))
audio_notes = model.generate_samples_from_z(z_notes, notes['pitches'])
# Make a single audio clip
audio_clip = gu.combine_notes(audio_notes,
notes['start_times'],
notes['end_times'],
notes['velocities'])
# Write the wave files
fname = os.path.join(output_dir, 'generated_clip.wav')
gu.save_wav(audio_clip, fname)
else:
# Otherwise, just generate a batch of random sounds
waves = model.generate_samples(FLAGS.batch_size)
# Write the wave files
for i in range(len(waves)):
fname = os.path.join(output_dir, 'generated_{}.wav'.format(i))
gu.save_wav(waves[i], fname)
def main(unused_argv):
absl.flags.FLAGS.alsologtostderr = True
# Load the model
flags = lib_flags.Flags({'batch_size_schedule': [FLAGS.batch_size]})
model = lib_model.Model.load_from_path(FLAGS.ckpt_dir, flags)
# Make an output directory if it doesn't exist
output_dir = util.expand_path(FLAGS.output_dir)
if not tf.gfile.Exists(output_dir):
tf.gfile.MakeDirs(output_dir)
if FLAGS.midi_file:
# If a MIDI file is provided, synthesize interpolations across the clip
unused_ns, notes = gu.load_midi(FLAGS.midi_file)
# Distribute latent vectors linearly in time
z_instruments, t_instruments = gu.get_random_instruments(
model,
notes['end_times'][-1],
secs_per_instrument=FLAGS.secs_per_instrument)
# Get latent vectors for each note
z_notes = gu.get_z_notes(notes['start_times'], z_instruments, t_instruments)
# Generate audio for each note
print('Generating {} samples...'.format(len(z_notes)))
audio_notes = model.generate_samples_from_z(z_notes, notes['pitches'])
# Make a single audio clip
audio_clip = gu.combine_notes(audio_notes,
notes['start_times'],
notes['end_times'],
notes['velocities'])
# Write the wave files
fname = os.path.join(output_dir, 'generated_clip.wav')
gu.save_wav(audio_clip, fname)
else:
# Otherwise, just generate a batch of random sounds
waves = model.generate_samples(FLAGS.batch_size)
# Write the wave files
for i in range(len(waves)):
fname = os.path.join(output_dir, 'generated_{}.wav'.format(i))
gu.save_wav(waves[i], fname)
def __init__(self, config):
self._train_data_path = util.expand_path(config['train_data_path'])
for key, value in filemap.iteritems():
fname = os.path.join('/content/gansynth/midi', key)
with open(fname, 'w') as f:
f.write(value)
print('Writing {}'.format(fname))
file_list.append(fname)
return file_list
# GLOBALS
CKPT_DIR = '/content/gansynth/acoustic_only'
output_dir = '/content/gansynth/samples'
BATCH_SIZE = 16
SR = 16000
# Make an output directory if it doesn't exist
OUTPUT_DIR = util.expand_path(output_dir)
if not tf.gfile.Exists(OUTPUT_DIR):
tf.gfile.MakeDirs(OUTPUT_DIR)
# Load the model
tf.reset_default_graph()
flags = lib_flags.Flags({'batch_size_schedule': [BATCH_SIZE]})
model = lib_model.Model.load_from_path(CKPT_DIR, flags)
# Helper functions
def load_midi(midi_path, min_pitch=36, max_pitch=84):
"""Load midi as a notesequence."""
midi_path = util.expand_path(midi_path)
ns = mm.midi_file_to_sequence_proto(midi_path)
pitches = np.array([n.pitch for n in ns.notes])
velocities = np.array([n.velocity for n in ns.notes])
def main(unused_argv):
absl.flags.FLAGS.alsologtostderr = True
# Load the model
flags = lib_flags.Flags({'batch_size_schedule': [FLAGS.batch_size]})
model = lib_model.Model.load_from_path(FLAGS.ckpt_dir, flags)
# Make an output directory if it doesn't exist
output_dir = util.expand_path(FLAGS.output_dir)
if not tf.gfile.Exists(output_dir):
tf.gfile.MakeDirs(output_dir)
with open(FLAGS.pca_file, "rb") as fp:
pca = pickle.load(fp)
if FLAGS.seed != None:
np.random.seed(seed=FLAGS.seed)
tf.random.set_random_seed(FLAGS.seed)
edits_axis = np.linspace(FLAGS.min, FLAGS.max, FLAGS.steps)
edits_list = list(cartesian_product(edits_axis, edits_axis))
pitch_arr = np.array([FLAGS.pitch])
edits_batches = batch(FLAGS.batch_size, edits_list, [])
pitch_batches = [[FLAGS.pitch] * FLAGS.batch_size] * len(edits_batches)
fig, ax = plt.subplots(nrows=FLAGS.steps,
ncols=FLAGS.steps,
figsize=(15, 7.5))
fig.set_tight_layout(True)
def _plot():
j = 0
for edits_batch, pitch_batch in zip(edits_batches, pitch_batches):
waves = model.generate_samples_from_edits(pitch_batch, edits_batch,
pca)
for i, edits in enumerate(edits_batch):
if j >= len(edits_list):
return
row = j // FLAGS.steps
col = j % FLAGS.steps
wave = waves[i]
# wave = np.random.rand(64000)
x = edits[0]
y = edits[1]
# plot wave
subplot = ax[col][row]
subplot.title.set_text("({}, {})".format(
format_float(x), format_float(y)))
plotstft(subplot, wave, 16000, binsize=2**8, colormap="magma")
subplot.set_axis_off()
# save wave
gu.save_wav(
wave,
os.path.join(output_dir, "wave_{},{}.wav".format(x, y)))
j += 1
_plot()
plt.savefig(os.path.join(output_dir, "plot.svg"), bbox_inches="tight")
if FLAGS.show:
plt.show()
meta = [
"checkpoint name: {}".format(os.path.basename(FLAGS.ckpt_dir)),
"pca name: {}".format(os.path.basename(FLAGS.pca_file)),
"pitch: {}".format(FLAGS.pitch), "min: {}".format(FLAGS.min),
"max: {}".format(FLAGS.max), "steps: {}".format(FLAGS.steps)
]
with open(os.path.join(output_dir, "meta.txt"), "w") as fp:
fp.write("\n".join(meta) + "\n")
def main(unused_argv):
absl.flags.FLAGS.alsologtostderr = True
# Load the model
flags = lib_flags.Flags({
'batch_size_schedule': [FLAGS.batch_size],
**({
'tfds_data_dir': FLAGS.tfds_data_dir
} if FLAGS.tfds_data_dir else {})
})
model = lib_model.Model.load_from_path(FLAGS.ckpt_dir, flags)
# Make an output directory if it doesn't exist
output_dir = util.expand_path(FLAGS.output_dir)
if not tf.gfile.Exists(output_dir):
tf.gfile.MakeDirs(output_dir)
if FLAGS.seed != None:
np.random.seed(seed=FLAGS.seed)
tf.random.set_random_seed(FLAGS.seed)
layer_offsets = {}
if FLAGS.edits_file:
with open(FLAGS.edits_file, "rb") as fp:
edits_dict = pickle.load(fp)
assert "layer" in edits_dict
assert "comp" in edits_dict
directions = edits_dict["comp"]
amounts = np.zeros(edits_dict["comp"].shape[:1], dtype=np.float32)
amounts[:len(list(map(float, FLAGS.edits)))] = FLAGS.edits
scaled_directions = amounts.reshape(-1, 1, 1, 1) * directions
linear_combination = np.sum(scaled_directions, axis=0)
linear_combination_batch = np.repeat(linear_combination.reshape(
1, *linear_combination.shape),
FLAGS.batch_size,
axis=0)
layer_offsets[edits_dict["layer"]] = linear_combination_batch
if FLAGS.midi_file:
# If a MIDI file is provided, synthesize interpolations across the clip
unused_ns, notes = gu.load_midi(FLAGS.midi_file)
# Distribute latent vectors linearly in time
z_instruments, t_instruments = gu.get_random_instruments(
model,
notes['end_times'][-1],
secs_per_instrument=FLAGS.secs_per_instrument)
# Get latent vectors for each note
z_notes = gu.get_z_notes(notes['start_times'], z_instruments,
t_instruments)
# Generate audio for each note
print('Generating {} samples...'.format(len(z_notes)))
audio_notes = model.generate_samples_from_z(
z_notes, notes['pitches'], layer_offsets=layer_offsets)
# Make a single audio clip
audio_clip = gu.combine_notes(audio_notes, notes['start_times'],
notes['end_times'], notes['velocities'])
# Write the wave files
fname = os.path.join(output_dir, 'generated_clip.wav')
gu.save_wav(audio_clip, fname)
else:
# Otherwise, just generate a batch of random sounds
waves = model.generate_samples(FLAGS.batch_size,
pitch=FLAGS.pitch,
layer_offsets=layer_offsets)
# Write the wave files
for i in range(len(waves)):
fname = os.path.join(output_dir, 'generated_{}.wav'.format(i))
gu.save_wav(waves[i], fname)
def usage():
print("Usage: python3 gansynth.py <path_to_model> <path_to_output_dir> <path_to_midi_file:optional>")
if len(sys.argv) < 3:
usage()
raise SystemExit
## Variables ##
ckpt_dir, output_dir = sys.argv[1], sys.argv[2]
batch_size = 16
sample_rate = SAMPLE_RATE
# Make an output directory if it doesn't exist
output_dir = util.expand_path(output_dir)
if not tf.gfile.Exists(output_dir):
tf.gfile.MakeDirs(output_dir)
# Load the model
tf.reset_default_graph()
flags = lib_flags.Flags({'batch_size_schedule': [batch_size]})
model = lib_model.Model.load_from_path(ckpt_dir, flags)
# Helper functions
def load_midi(midi_path, min_pitch=36, max_pitch=84):
"""Load midi as a notesequence."""
midi_path = util.expand_path(midi_path)
ns = music.midi_file_to_sequence_proto(midi_path)
pitches = np.array([n.pitch for n in ns.notes])
velocities = np.array([n.velocity for n in ns.notes])
def __init__(self, config): self._train_data_path = util.expand_path(config['train_data_path'])
def main(unused_argv):
absl.flags.FLAGS.alsologtostderr = True
# Load the model
flags = lib_flags.Flags({'batch_size_schedule': [FLAGS.batch_size]})
model = lib_model.Model.load_from_path(FLAGS.ckpt_dir, flags)
# Make an output directory if it doesn't exist
output_dir = util.expand_path(FLAGS.output_dir)
if not tf.gfile.Exists(output_dir):
tf.gfile.MakeDirs(output_dir)
if FLAGS.midi_file:
# If a MIDI file is provided, synthesize interpolations across the clip
unused_ns, notes = gu.load_midi(FLAGS.midi_file)
# Distribute latent vectors linearly in time
z_instruments, t_instruments = gu.get_random_instruments(
model,
notes['end_times'][-1],
secs_per_instrument=FLAGS.secs_per_instrument)
# Get latent vectors for each note
z_notes = gu.get_z_notes(notes['start_times'], z_instruments,
t_instruments)
# Generate audio for each note
print('Generating {} samples...'.format(len(z_notes)))
audio_notes = model.generate_samples_from_z(z_notes, notes['pitches'])
# Make a single audio clip
audio_clip = gu.combine_notes(audio_notes, notes['start_times'],
notes['end_times'], notes['velocities'])
# Write the wave files
fname = os.path.join(output_dir, 'generated_clip.wav')
gu.save_wav(audio_clip, fname)
else:
# Otherwise, just generate a batch of random sounds
# waves = model.generate_samples(FLAGS.batch_size) # original
waves, z = model.generate_samples(
FLAGS.batch_size, pitch=44
) #DEBUG: generate on singular pitch (range: 24-84), return latent vectors
# Write the wave files
for i in range(len(waves)):
fname = os.path.join(output_dir, 'generated_{}.wav'.format(i))
gu.save_wav(waves[i], fname)
# DEBUG: write z to file for later analysis
fname = os.path.join(output_dir, 'z.p')
pickle.dump(z, open(fname, 'wb'))
# DEBUG: flag samples based on similar latent variables
flagged = get_flagged_latents(z, n=10)
print("\nflagged (z):")
for i in flagged:
print(i)
# DEBUG: flag samples based on similar waveforms
flagged = get_flagged_waves_par(waves, n=10, frac=0.01)
print("\nflagged (waves):")
for i in flagged:
print(i)
fname = os.path.join(output_dir,
'_sim_{}-{}.wav'.format(i[0][0], i[0][1]))
gu.save_wav(np.array(list(waves[i[0][0]]) + list(waves[i[0][1]])),
fname)
