436 lines
17 KiB
Python
Executable File
436 lines
17 KiB
Python
Executable File
#!/usr/bin/env python3
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# Delta modulation audio encoder for playback via Uthernet II streaming.
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#
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# Simulates the Apple II speaker at 1MHz (i.e. cycle-level) resolution,
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# by modeling it as a damped harmonic oscillator.
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#
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# On the Apple II side we use an audio player that is able to toggle the
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# speaker with 1MHz precision, i.e. on any CPU clock cycle, although with a
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# lower limit of 10 cycles between toggles (i.e. 102KHz maximum
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# frequency).
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#
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# In order to reproduce a target audio waveform, we upscale it to 1MHz sample
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# rate, i.e. to determine the desired speaker position at every CPU clock
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# cycle, and compute the sequence of player operations on the Apple II side to
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# best reproduce this waveform.
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#
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# This means that we are able to control the Apple II speaker with cycle-level
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# precision, which results in high audio fidelity with low noise.
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#
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# To further optimize the audio quality we look ahead some defined number of
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# cycles and choose a speaker trajectory that minimizes errors over this range.
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# e.g. this allows us to anticipate large amplitude changes by pre-moving
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# the speaker to better approximate them.
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#
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# This also needs to take into account scheduling the "end of frame" opcode
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# every 2048 output bytes, where the Apple II will manage the TCP socket buffer
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# while ticking the speaker at a regular (a, b) cadence to attempt to
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# continue tracking the waveform as best we can. Since we are stepping away
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# from cycle-level management of the speaker during this period, it does
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# introduce some quality degradation (manifesting as a slight background
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# "crackle" to the audio)
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import argparse
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import collections
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import contextlib
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import functools
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import librosa
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import numpy
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import soundfile as sf
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from eta import ETA
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import lookahead
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import opcodes
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import opcodes_generated
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# How many bytes to use per frame in the audio stream. At the end of each
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# frame we need to switch to special end-of-frame operations to cause the
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# Apple II to manage the TCP socket buffers (ACK data received so far, and
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# check we have at least another frame of data available)
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#
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# With an 8KB socket buffer this seems to be about the maximum we can get away
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# with - it has to be page aligned, and 4KB causes stuttering even from a
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# local playback source.
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FRAME_SIZE = 2048
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class Speaker:
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"""Simulates the response of the Apple II speaker."""
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# TODO: move lookahead.evolve into Speaker method
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def __init__(self, sample_rate: float, freq: float, damping: float,
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scale: float):
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"""Initialize the Speaker object
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:arg sample_rate The sample rate of the simulated speaker (Hz)
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:arg freq The resonant frequency of the speaker
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:arg damping The exponential decay factor of the speaker response
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:arg scale Scale factor to normalize speaker position to desired range
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"""
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self.sample_rate = sample_rate
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self.freq = freq
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self.damping = damping
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self.scale = numpy.float64(scale) # TODO: analytic expression
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# See _Signal Processing in C_, C. Reid, T. Passin
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# https://archive.org/details/signalprocessing0000reid/
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dt = numpy.float64(1 / sample_rate)
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w = numpy.float64(freq * 2 * numpy.pi * dt)
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d = damping * dt
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e = numpy.exp(d)
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c1 = 2 * e * numpy.cos(w)
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c2 = e * e
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# Square wave impulse response parameters
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b2 = 0.0
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b1 = 1.0
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self.c1 = c1
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self.c2 = c2
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self.b1 = b1
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self.b2 = b2
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def total_error(positions: numpy.ndarray, data: numpy.ndarray) -> numpy.ndarray:
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"""Computes the total squared error for speaker position matrix vs data."""
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# Deal gracefully with the case where our speaker operation would slightly
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# run past the end of data
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min_len = min(len(positions), len(data))
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return numpy.sum(numpy.square(positions[:min_len] - data[:min_len]),
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axis=-1)
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@functools.lru_cache(None)
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def frame_horizon(frame_offset: int, lookahead_steps: int):
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"""Optimize frame_offset when more than lookahead_steps from end of frame.
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Candidate opcodes for all values of frame_offset are equal, until the
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end-of-frame opcode comes within our lookahead horizon. This avoids
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needing to recompute many copies of the same candidate opcodes.
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TODO: this is a bit of a hack and we should be able to make the candidate
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opcode selection itself smarter about avoiding duplicate work
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"""
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# TODO: This could be made tighter because a step is always at least 5
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# cycles towards lookahead_steps.
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if frame_offset < (FRAME_SIZE - lookahead_steps):
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return 0
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return frame_offset
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def audio_bytestream(data: numpy.ndarray, step: int, lookahead_steps: int,
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sample_rate: int):
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"""Computes optimal sequence of player opcodes to reproduce audio data."""
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# At resonance freq the scale is about 22400 but we can only access about 7%
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# of it across the frequency range. This is also the equilibrium speaker
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# position when voltage is held constant. Normalize to this working
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# range for convenience.
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inv_scale = 22400 * 0.07759626164027278 # XXX
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# Speaker response parameters can be fitted in several ways. First, by
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# recording audio of
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# - a single speaker click (impulse response)
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# - a frequency sweep over the entire audible spectrum
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#
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# Resonant frequency can be read off from the frequency spectrum. For my
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# speaker there were two primary frequencies, at ~3875 and ~480Hz.
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# Looking at the click waveform the higher frequency mode dominates at
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# short time scales, and the lower frequency mode dominates at late
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# times. Since we're interested in short timescale speaker control we
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# can ignore the latter.
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#
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# Damping factor can be estimated by fitting against the speaker click
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# waveform or the spectrum, e.g. by simulating a speaker click and then
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# computing its spectrum.
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#
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# TODO: other Apple II speakers almost certainly have different response
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# characteristics, but hopefully not too widely varying.
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sp = Speaker(sample_rate, freq=3875, damping=-1210, scale=1 / inv_scale)
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# Starting speaker applied voltage.
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voltage1 = voltage2 = 1.0
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# last 2 speaker positions.
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# XXX 0.0?
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y1 = y2 = 1.0
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# Leave enough padding at the end to look ahead from the last data value,
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# and in case we schedule an end-of-frame opcode towards the end.
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# TODO: avoid temporarily doubling memory footprint to concatenate
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data = numpy.ascontiguousarray(numpy.concatenate(
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[data, numpy.zeros(max(lookahead_steps, opcodes.cycle_length(
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opcodes_generated.PlayerOps.TICK_00)), dtype=numpy.float32)]))
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# index within input audio data
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i = 0
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# Position in 2048-byte TCP frame
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frame_offset = 0
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# Total squared error of audio output
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total_err = 0.0
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# Keep track of how many large deviations we see from the target waveform
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# as another measure of audio quality
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clicks = 0
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# total squared error threshold to consider an operation as producing a
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# click
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click_threshold = 0.3
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# Keep track of how many opcodes we schedule, so we can print summary
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# statistics at the end
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opcode_counts = collections.defaultdict(int)
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# Always look ahead at least this many cycles. We pick a higher value
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# when approaching end of frame, so we can maximize the
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min_lookahead_steps = lookahead_steps
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# Progress tracker
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# TODO: find a more adaptive ETA tracker, this one doesn't estimate well
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# if the processing rate changes (which it does for us, since first few
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# steps do extra work to precompute values that are cached for later
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# steps)
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eta = ETA(total=1000, min_ms_between_updates=0)
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# Value of i at which we should next update eta
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next_eta_tick = 0
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while i < len(data):
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if i >= next_eta_tick:
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eta.print_status()
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next_eta_tick = int(eta.i * len(data) / 1000)
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if frame_offset >= (FRAME_SIZE - 5): # XXX
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lookahead_steps = min_lookahead_steps + 130 # XXX parametrize
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else:
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lookahead_steps = min_lookahead_steps
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# The EOF opcodes need to act as a matched pair, so if we're emitting
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# the second one we need to pass in its predecessor
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last_opcode = opcode if frame_offset == FRAME_SIZE - 1 else None
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# Compute all possible opcode sequences we could emit starting at this
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# frame offset
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next_candidate_opcodes, voltages, lookahead_steps = \
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opcodes.candidate_opcodes(
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frame_horizon(frame_offset, lookahead_steps),
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lookahead_steps, last_opcode)
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# Simulate speaker trajectory for all of these candidate opcode
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# sequences and pick the one that minimizes total error
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opcode_idx = lookahead.evolve_return_best(
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sp, y1, y2, voltage1, voltage2, voltage1 * voltages,
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data[i:i + lookahead_steps])
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opcode = next_candidate_opcodes[opcode_idx]
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opcode_length = opcodes.cycle_length(opcode)
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opcode_counts[opcode] += 1
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# Apply this opcode to evolve the speaker position
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opcode_voltages = (voltage1 * opcode.voltages).reshape((1, -1))
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all_positions = lookahead.evolve(
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sp, y1, y2, voltage1, voltage2, opcode_voltages)
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assert all_positions.shape[0] == 1
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assert all_positions.shape[1] == opcode_length
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# Update to new speaker state
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voltage1 = opcode_voltages[0, -1]
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voltage2 = opcode_voltages[0, -2]
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y1 = all_positions[0, -1]
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y2 = all_positions[0, -2]
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# Track accumulated error between desired and actual speaker trajectory
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new_error = total_error(
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all_positions[0] * sp.scale, data[i:i + opcode_length]).item()
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total_err += new_error
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if new_error > click_threshold:
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clicks += 1
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# XXX
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print(frame_offset, i / sample_rate, opcode, new_error,
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numpy.mean(data[i:i + opcode_length]))
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# Emit chosen operation and simulated audio samples for recording
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yield opcode, numpy.array(
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all_positions * sp.scale, dtype=numpy.float32).reshape(-1)
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# Update input and output stream positions
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i += opcode_length
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frame_offset = (frame_offset + 1) % FRAME_SIZE
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# Make sure we have at least 2k left in stream so the player will do a
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# complete read of the last frame.
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# for _ in range(frame_offset % 2048, 2048):
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# yield opcodes.Opcode.EXIT
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eta.done()
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# Print summary statistics
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print("Total error %f" % total_err)
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print("%d clicks" % clicks)
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print("Opcodes used:")
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for v, k in sorted(list(opcode_counts.items()), key=lambda kv: kv[1],
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reverse=True):
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print("%s: %d" % (v, k))
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def preprocess_audio(
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filename: str, target_sample_rate: int, normalize: float,
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normalization_percentile: int) -> numpy.ndarray:
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"""Upscale input audio to target sample rate and normalize signal."""
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data, _ = librosa.load(filename, sr=target_sample_rate, mono=True)
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max_value = numpy.percentile(data, normalization_percentile)
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data /= max_value
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data *= normalize
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return data
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def downsample_audio(simulated_audio, original_audio, input_rate, output_rate,
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noise_output=False):
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"""Downscale the 1MHz simulated audio output suitable for writing as .wav
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:arg simulated_audio The simulated audio data to downsample
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:arg original_audio The original audio data that was simulated
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:arg input_rate Sample rate of input audio
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:arg output_rate Desired sample rate of output audio
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:arg noise_output Whether to also produce a noise waveform, i.e. difference
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between input and output audio
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:returns Tuple of downsampled audio and noise data (or None
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if noise_output==False)
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"""
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downsampled_output = librosa.resample(
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numpy.array(simulated_audio, dtype=numpy.float32),
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orig_sr=input_rate,
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target_sr=output_rate)
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downsampled_noise = None
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if noise_output:
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noise_len = min(len(simulated_audio), len(original_audio))
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downsampled_noise = librosa.resample(
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numpy.array(
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simulated_audio[:noise_len] - original_audio[:noise_len]),
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orig_sr=input_rate,
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target_sr=output_rate)
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return downsampled_output, downsampled_noise
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--clock", choices=['pal', 'ntsc'],
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help="Whether target machine clock speed is PAL ("
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"1015657Hz) or NTSC (1020484)",
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required=True)
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# TODO: implement 6502 - JMP indirect takes 5 cycles instead of 6
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parser.add_argument("--step_size", type=int,
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help="Delta encoding step size")
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# TODO: if we're not looking ahead beyond the longest (non-end-of-frame)
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# opcode then this will reduce quality, e.g. two opcodes may truncate to
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# the same prefix, but have different results when we apply them
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# fully.
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parser.add_argument("--lookahead_cycles", type=int,
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help="Number of clock cycles to look ahead in audio "
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"stream.")
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parser.add_argument("--normalization", default=0.8, type=float,
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help="Overall multiplier to rescale input audio "
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"values.")
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parser.add_argument("--norm_percentile", default=100,
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help="Normalize to specified percentile value of input "
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"audio")
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parser.add_argument("--wav_output", type=str, help="output audio file")
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parser.add_argument("--noise_output", type=str, help="output audio file")
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parser.add_argument("input", type=str, help="input audio file to convert")
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parser.add_argument("output", type=str, help="output audio file")
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args = parser.parse_args()
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# Effective clock rate, including every-65 cycle "long cycle" that takes
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# 16/14 as long.
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cpu_clock_rate = 1015657 if args.clock == 'pal' else 1020484 # NTSC
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input_audio = preprocess_audio(
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args.input, cpu_clock_rate, args.normalization, args.norm_percentile)
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print("Done preprocessing audio")
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# Sample rate for output .wav files
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# TODO: flag
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output_rate = 44100
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# Buffers simulated audio output so we can downsample it in suitably
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# large chunks for writing to the output .wav file
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output_buffer = []
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# Python contexts for writing output files if requested
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opcode_context = open(args.output, "wb+")
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if args.wav_output:
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wav_context = sf.SoundFile(
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args.wav_output, "w", output_rate, channels=1, format='WAV')
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else:
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# We're not creating a file but still need a context
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# XXX does this work?
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wav_context = contextlib.nullcontext
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if args.noise_output:
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noise_context = sf.SoundFile(
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args.noise_output, "w", output_rate, channels=1,
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format='WAV')
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else:
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# We're not creating a file but still need a context
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noise_context = contextlib.nullcontext
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with wav_context as wav_f, noise_context as noise_f, opcode_context \
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as opcode_f:
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# Tracks current position in input audio waveform
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input_offset = 0
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# Process input audio, writing output to ][-Sound audio file
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# and (if requested) .wav files of simulated speaker audio and
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# noise (difference between original and simulated audio)
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for idx, sample_data in enumerate(audio_bytestream(
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input_audio, args.step_size, args.lookahead_cycles,
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cpu_clock_rate)):
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opcode, samples = sample_data
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opcode_f.write(bytes([opcode.byte]))
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output_buffer.extend(samples)
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input_offset += len(samples)
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# Keep accumulating as long as we have <1MB in the buffer, or are
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# within 1MB from the end. This ensures we have enough samples to
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# downsample, including the last (partial) buffer.
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if (
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len(output_buffer) < 1 * 1024 * 1024 or (
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len(input_audio) - input_offset) < 1 * 1024 * 1024
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):
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continue
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# TODO: don't bother computing if we're not writing wavs
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downsampled_audio, downsampled_noise = downsample_audio(
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output_buffer, input_audio[input_offset - len(output_buffer):],
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cpu_clock_rate, output_rate, bool(args.noise_output)
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)
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if args.wav_output:
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wav_f.write(downsampled_audio)
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wav_f.flush()
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if args.noise_output:
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noise_f.write(downsampled_noise)
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noise_f.flush()
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output_buffer = []
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# TODO: handle last buffer more cleanly than duplicating this code
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if output_buffer:
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downsampled_audio, downsampled_noise = downsample_audio(
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output_buffer, input_audio[input_offset - len(output_buffer):],
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cpu_clock_rate, output_rate, bool(args.noise_output)
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)
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if args.wav_output:
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wav_f.write(downsampled_audio)
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if args.noise_output:
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noise_f.write(downsampled_noise)
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if __name__ == "__main__":
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main()
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