def get_signal(self, amplitudes, harmonic_distribution, f0_hz):
"""Synthesize audio with additive synthesizer from controls.
Args:
amplitudes: Amplitude tensor of shape [batch, n_frames, 1]. Expects
float32 that is strictly positive.
harmonic_distribution: Tensor of shape [batch, n_frames, n_harmonics].
Expects float32 that is strictly positive and normalized in the last
dimension.
f0_hz: The fundamental frequency in Hertz. Tensor of shape [batch,
n_frames, 1].
Returns:
signal: A tensor of harmonic waves of shape [batch, n_samples].
"""
signal = core.harmonic_synthesis(
frequencies=f0_hz,
amplitudes=amplitudes,
harmonic_distribution=harmonic_distribution,
n_samples=self.n_samples,
sample_rate=self.sample_rate)
return signal
def test_harmonic_synthesis_is_accurate_one_frequency(self,
batch_size,
fundamental_frequency,
amplitude,
n_frames):
"""Tests generating a single sine wave with different frame parameters.
Generates sine waveforms with tensorflow and numpy and tests that they are
the same. Test over a range of inputs provided by the parameterized
inputs.
Args:
batch_size: Size of the batch to synthesize.
fundamental_frequency: Base frequency of the oscillator in Hertz.
amplitude: Amplitude of each harmonic in the waveform.
n_frames: Number of amplitude envelope frames.
"""
frequencies = fundamental_frequency * np.ones([batch_size, n_frames, 1])
amplitudes = amplitude * np.ones([batch_size, n_frames, 1])
frequencies_np = fundamental_frequency * np.ones([batch_size,
self.n_samples, 1])
amplitudes_np = amplitude * np.ones([batch_size, self.n_samples, 1])
# Create np test signal.
wav_np = create_wave_np(batch_size, frequencies_np, amplitudes_np,
self.seconds, self.n_samples)
with self.cached_session() as sess:
wav_tf = sess.run(core.harmonic_synthesis(
frequencies,
amplitudes,
n_samples=self.n_samples,
sample_rate=self.sample_rate))
pad = self.n_samples // n_frames # Ignore edge effects.
self.assertAllClose(wav_np[pad:-pad], wav_tf[pad:-pad])