def __init__(self, sample_rate, frame_rate, run_f0_crepe=True):
super().__init__(sample_rate, frame_rate)
self._loudness_metrics = metrics.LoudnessMetrics(
sample_rate=sample_rate, frame_rate=frame_rate)
self._f0_metrics = metrics.F0Metrics(sample_rate=sample_rate,
frame_rate=frame_rate)
self._run_f0_crepe = run_f0_crepe
if self._run_f0_crepe:
self._f0_crepe_metrics = metrics.F0CrepeMetrics(
sample_rate=sample_rate, frame_rate=frame_rate)
def __init__(self,
sample_rate,
frame_rate,
processor_name='sinusoidal',
noisy=False):
super().__init__(sample_rate, frame_rate)
self._noisy = noisy
self._processor_name = processor_name
self._f0_twm_metrics = metrics.F0Metrics(
sample_rate=sample_rate, frame_rate=frame_rate, name='f0_twm')
def test_f0_metrics_resamples_f0_hz_predictions_to_expected_length(self):
f0_metrics = ddsp_metrics.F0Metrics(self.sample_rate, self.frame_rate)
expected_len = self.frame_rate * self.audio_len_sec
shorter_len = int(expected_len * 0.8)
# Batch 1: known sin features vs. batch of shorter f0_hz at different f0
f0_metrics.update_state(
self.batch_of_sin_feats,
3 * self.batch_of_sin_feats['f0_hz'][:, :shorter_len])
self.assertGreater(f0_metrics.metrics['f0_dist'].result(), 0)
f0_metrics.flush(step=1)
def test_f0_metrics_has_expected_values(self):
f0_metrics = ddsp_metrics.F0Metrics(self.sample_rate, self.frame_rate)
# Batch 1: known sin features vs. batch of known sin f0_hz
f0_metrics.update_state(self.batch_of_sin_feats,
self.batch_of_sin_feats['f0_hz'])
self.assertAllClose(f0_metrics.metrics['f0_dist'].result(), 0)
# Batch 2: known sin features vs. batch of f0_hz at different f0
f0_metrics.update_state(self.batch_of_sin_feats,
self.batch_of_sin_feats['f0_hz'] * 3)
self.assertGreater(f0_metrics.metrics['f0_dist'].result(), 0)
f0_metrics.flush(step=1)
def evaluate_or_sample(data_provider,
model,
mode='eval',
save_dir='/tmp/ddsp/training',
restore_dir='',
batch_size=32,
num_batches=50,
ckpt_delay_secs=0,
run_once=False,
run_until_step=0):
"""Run evaluation loop.
Args:
data_provider: DataProvider instance.
model: Model instance.
mode: Whether to 'eval' with metrics or create 'sample' s.
save_dir: Path to directory to save summary events.
restore_dir: Path to directory with checkpoints, defaults to save_dir.
batch_size: Size of each eval/sample batch.
num_batches: How many batches to eval from dataset. -1 denotes all batches.
ckpt_delay_secs: Time to wait when a new checkpoint was not detected.
run_once: Only run evaluation or sampling once.
run_until_step: Run until we see a checkpoint with a step greater or equal
to the specified value. Ignored if <= 0.
Returns:
If the mode is 'eval', then returns a dictionary of Tensors keyed by loss
type. Otherwise, returns None.
"""
# Default to restoring from the save directory.
restore_dir = save_dir if not restore_dir else restore_dir
# Set up the summary writer and metrics.
summary_dir = os.path.join(save_dir, 'summaries', 'eval')
summary_writer = tf.summary.create_file_writer(summary_dir)
# Sample continuously and load the newest checkpoint each time
checkpoints_iterator = tf.train.checkpoints_iterator(
restore_dir, ckpt_delay_secs)
# Get the dataset.
dataset = data_provider.get_batch(batch_size=batch_size,
shuffle=False,
repeats=-1)
# Get audio sample rate
sample_rate = data_provider.sample_rate
# Get feature frame rate
frame_rate = data_provider.frame_rate
latest_losses = None
with summary_writer.as_default():
for checkpoint_path in checkpoints_iterator:
step = int(checkpoint_path.split('-')[-1])
# Redefine thte dataset iterator each time to make deterministic.
dataset_iter = iter(dataset)
# Load model.
model.restore(checkpoint_path)
# Iterate through dataset and make predictions
checkpoint_start_time = time.time()
for batch_idx in range(1, num_batches + 1):
try:
start_time = time.time()
logging.info('Predicting batch %d of size %d', batch_idx,
batch_size)
# Predict a batch of audio.
batch = next(dataset_iter)
if isinstance(data_provider, data.SyntheticNotes):
batch['audio'] = model.generate_synthetic_audio(batch)
batch['f0_confidence'] = tf.ones_like(
batch['f0_hz'])[:, :, 0]
batch[
'loudness_db'] = ddsp.spectral_ops.compute_loudness(
batch['audio'])
elif isinstance(data_provider, data.ZippedProvider):
batch, unused_ss_batch = model.parse_zipped_features(
batch)
# TODO(jesseengel): Find a way to add losses with training=False.
audio = batch['audio']
audio_gen, losses = model(batch,
return_losses=True,
training=True)
outputs = model.get_controls(batch, training=True)
# Create metrics on first batch.
if mode == 'eval' and batch_idx == 1:
loudness_metrics = metrics.LoudnessMetrics(
sample_rate=sample_rate, frame_rate=frame_rate)
f0_metrics = metrics.F0Metrics(sample_rate=sample_rate,
frame_rate=frame_rate,
name='f0_harm')
f0_crepe_metrics = metrics.F0CrepeMetrics(
sample_rate=sample_rate, frame_rate=frame_rate)
f0_twm_metrics = metrics.F0Metrics(
sample_rate=sample_rate,
frame_rate=frame_rate,
name='f0_twm')
avg_losses = {
name: tf.keras.metrics.Mean(name=name,
dtype=tf.float32)
for name in list(losses.keys())
}
processor_group = getattr(model, 'processor_group', None)
if processor_group is not None:
for processor in processor_group.processors:
# If using a sinusoidal model, infer f0 with two-way mismatch.
if isinstance(processor, ddsp.synths.Sinusoidal):
# Run on CPU to avoid running out of memory (not expensive).
with tf.device('CPU'):
processor_controls = outputs[
processor.name]['controls']
amps = processor_controls['amplitudes']
freqs = processor_controls['frequencies']
twm = ddsp.losses.TWMLoss()
# Treat all freqs as candidate f0s.
outputs['f0_hz_twm'] = twm.predict_f0(
freqs, freqs, amps)
logging.info(
'Added f0 estimate from sinusoids.')
break
# If using a noisy sinusoidal model, infer f0 w/ two-way mismatch.
elif isinstance(processor,
ddsp.synths.NoisySinusoidal):
# Run on CPU to avoid running out of memory (not expensive).
with tf.device('CPU'):
processor_controls = outputs[
processor.name]['controls']
amps = processor_controls['amplitudes']
freqs = processor_controls['frequencies']
noise_ratios = processor_controls[
'noise_ratios']
amps = amps * (1.0 - noise_ratios)
twm = ddsp.losses.TWMLoss()
# Treat all freqs as candidate f0s.
outputs['f0_hz_twm'] = twm.predict_f0(
freqs, freqs, amps)
logging.info(
'Added f0 estimate from sinusoids.')
break
has_f0_twm = ('f0_hz_twm' in outputs and 'f0_hz' in batch)
has_f0 = ('f0_hz' in outputs and 'f0_hz' in batch)
logging.info('Prediction took %.1f seconds',
time.time() - start_time)
if mode == 'sample':
start_time = time.time()
logging.info('Writing summmaries for batch %d',
batch_idx)
if audio_gen is not None:
audio_gen = np.array(audio_gen)
# Add audio.
summaries.audio_summary(audio_gen,
step,
sample_rate,
name='audio_generated')
summaries.audio_summary(audio,
step,
sample_rate,
name='audio_original')
# Add plots.
summaries.waveform_summary(audio, audio_gen, step)
summaries.spectrogram_summary(
audio, audio_gen, step)
if has_f0:
summaries.f0_summary(batch['f0_hz'],
outputs['f0_hz'],
step,
name='f0_harmonic')
if has_f0_twm:
summaries.f0_summary(batch['f0_hz'],
outputs['f0_hz_twm'],
step,
name='f0_twm')
logging.info(
'Writing batch %i with size %i took %.1f seconds',
batch_idx, batch_size,
time.time() - start_time)
elif mode == 'eval':
start_time = time.time()
logging.info('Calculating metrics for batch %d',
batch_idx)
if audio_gen is not None:
loudness_metrics.update_state(batch, audio_gen)
if has_f0:
f0_metrics.update_state(
batch, outputs['f0_hz'])
else:
f0_crepe_metrics.update_state(batch, audio_gen)
if has_f0_twm:
f0_twm_metrics.update_state(
batch, outputs['f0_hz_twm'])
# Loss.
for k, v in losses.items():
avg_losses[k].update_state(v)
logging.info(
'Metrics for batch %i with size %i took %.1f seconds',
batch_idx, batch_size,
time.time() - start_time)
except tf.errors.OutOfRangeError:
logging.info('End of dataset.')
break
logging.info('All %d batches in checkpoint took %.1f seconds',
num_batches,
time.time() - checkpoint_start_time)
if mode == 'eval':
loudness_metrics.flush(step)
if has_f0:
f0_metrics.flush(step)
else:
f0_crepe_metrics.flush(step)
if has_f0_twm:
f0_twm_metrics.flush(step)
latest_losses = {}
for k, metric in avg_losses.items():
latest_losses[k] = metric.result()
tf.summary.scalar('losses/{}'.format(k),
metric.result(),
step=step)
metric.reset_states()
summary_writer.flush()
if run_once:
break
if 0 < run_until_step <= step:
logging.info(
'Saw checkpoint with step %d, which is greater or equal to'
' `run_until_step` of %d. Exiting.', step, run_until_step)
break
return latest_losses
def evaluate_or_sample(data_provider,
model,
mode='eval',
save_dir='~/tmp/ddsp/training',
restore_dir='',
batch_size=32,
num_batches=50,
ckpt_delay_secs=0,
run_once=False,
run_until_step=0):
"""Run evaluation loop.
Args:
data_provider: DataProvider instance.
model: Model instance.
mode: Whether to 'eval' with metrics or create 'sample' s.
save_dir: Path to directory to save summary events.
restore_dir: Path to directory with checkpoints, defaults to save_dir.
batch_size: Size of each eval/sample batch.
num_batches: How many batches to eval from dataset. -1 denotes all batches.
ckpt_delay_secs: Time to wait when a new checkpoint was not detected.
run_once: Only run evaluation or sampling once.
run_until_step: Run until we see a checkpoint with a step greater or equal
to the specified value. Ignored if <= 0.
Returns:
If the mode is 'eval', then returns a dictionary of Tensors keyed by loss
type. Otherwise, returns None.
"""
# Default to restoring from the save directory.
restore_dir = save_dir if not restore_dir else restore_dir
# Set up the summary writer and metrics.
summary_dir = os.path.join(save_dir, 'summaries', 'eval')
summary_writer = tf.summary.create_file_writer(summary_dir)
# Sample continuously and load the newest checkpoint each time
checkpoints_iterator = tf.train.checkpoints_iterator(
restore_dir, ckpt_delay_secs)
# Get the dataset.
dataset = data_provider.get_batch(batch_size=batch_size,
shuffle=False,
repeats=-1)
# Get audio sample rate
sample_rate = data_provider.sample_rate
# Get feature frame rate
frame_rate = data_provider.frame_rate
latest_losses = None
with summary_writer.as_default():
for checkpoint_path in checkpoints_iterator:
step = int(checkpoint_path.split('-')[-1])
# Redefine thte dataset iterator each time to make deterministic.
dataset_iter = iter(dataset)
# Load model.
model.restore(checkpoint_path)
# Iterate through dataset and make predictions
checkpoint_start_time = time.time()
for batch_idx in range(1, num_batches + 1):
try:
start_time = time.time()
logging.info('Predicting batch %d of size %d', batch_idx,
batch_size)
# Predict a batch of audio.
batch = next(dataset_iter)
# TODO(jesseengel): Find a way to add losses with training=False.
audio = batch['audio']
audio_gen, losses = model(batch,
return_losses=True,
training=True)
audio_gen = np.array(audio_gen)
outputs = model.get_controls(batch, training=True)
# Create metrics on first batch.
if mode == 'eval' and batch_idx == 1:
loudness_metrics = metrics.LoudnessMetrics(
sample_rate=sample_rate, frame_rate=frame_rate)
f0_metrics = metrics.F0Metrics(sample_rate=sample_rate,
frame_rate=frame_rate,
name='f0_harm')
f0_crepe_metrics = metrics.F0CrepeMetrics(
sample_rate=sample_rate, frame_rate=frame_rate)
avg_losses = {
name: tf.keras.metrics.Mean(name=name,
dtype=tf.float32)
for name in list(losses.keys())
}
has_f0 = ('f0_hz' in outputs and 'f0_hz' in batch)
logging.info('Prediction took %.1f seconds',
time.time() - start_time)
if mode == 'sample':
start_time = time.time()
logging.info('Writing summmaries for batch %d',
batch_idx)
# Add audio.
summaries.audio_summary(audio_gen,
step,
sample_rate,
name='audio_generated')
summaries.audio_summary(audio,
step,
sample_rate,
name='audio_original')
# Add plots.
summaries.waveform_summary(audio, audio_gen, step)
summaries.spectrogram_summary(audio, audio_gen, step)
if has_f0:
summaries.f0_summary(batch['f0_hz'],
outputs['f0_hz'],
step,
name='f0_harmonic')
logging.info(
'Writing batch %i with size %i took %.1f seconds',
batch_idx, batch_size,
time.time() - start_time)
elif mode == 'eval':
start_time = time.time()
logging.info('Calculating metrics for batch %d',
batch_idx)
loudness_metrics.update_state(batch, audio_gen)
if has_f0:
f0_metrics.update_state(batch, outputs['f0_hz'])
else:
f0_crepe_metrics.update_state(batch, audio_gen)
# Loss.
for k, v in losses.items():
avg_losses[k].update_state(v)
logging.info(
'Metrics for batch %i with size %i took %.1f seconds',
batch_idx, batch_size,
time.time() - start_time)
except tf.errors.OutOfRangeError:
logging.info('End of dataset.')
break
logging.info('All %d batches in checkpoint took %.1f seconds',
num_batches,
time.time() - checkpoint_start_time)
if mode == 'eval':
loudness_metrics.flush(step)
if has_f0:
f0_metrics.flush(step)
else:
f0_crepe_metrics.flush(step)
latest_losses = {}
for k, metric in avg_losses.items():
latest_losses[k] = metric.result()
tf.summary.scalar('losses/{}'.format(k),
metric.result(),
step=step)
metric.reset_states()
summary_writer.flush()
if run_once:
break
if 0 < run_until_step <= step:
logging.info(
'Saw checkpoint with step %d, which is greater or equal to'
' `run_until_step` of %d. Exiting.', step, run_until_step)
break
return latest_losses
def test_rpa_has_expected_values_error_outside_threshold(self):
rpa = ddsp_metrics.F0Metrics(self.sample_rate, self.frame_rate)
f0 = self.batch_of_sin_feats['f0_hz']
rpa.update_state(self.batch_of_sin_feats, f0 + 220)
self.assertEqual(rpa.metrics['raw_pitch_accuracy'].result(), 0.0)
self.assertEqual(rpa.metrics['raw_chroma_accuracy'].result(), 0.0)
