def handle_hallucinate_noz(model, stdin, stdout, state):
max_note_length = model.config['audio_length']
sample_rate = model.config['sample_rate']
pca = state["ganspace_components"]
stdevs = pca["stdev"]
layer_dtype = stdevs.dtype
hallucinate_msg = read_msg(stdin, protocol.hallucinate_struct.size)
step_count, interpolation_steps, spacing, start_trim, attack, sustain, release = protocol.from_hallucinate_msg(
hallucinate_msg)
edit_count_msg = read_msg(stdin, protocol.count_struct.size)
edit_count = protocol.from_count_msg(edit_count_msg)
pitch = min(model.pitch_counts.keys())
steps = []
for i in range(step_count):
edits = []
for j in range(edit_count):
edit_msg = read_msg(stdin, protocol.f64_struct.size)
edit = protocol.from_f64_msg(edit_msg)
edits.append(edit)
steps.append(np.array(edits, dtype=layer_dtype))
steps = list(interpolate_edits(steps, interpolation_steps))
layer_steps = np.array(list(
map(lambda edits: make_layer(pca, edits), steps)),
dtype=layer_dtype)
pitch_steps = np.repeat([pitch], len(steps))
audios = model.generate_samples_from_layers({pca["layer"]: layer_steps},
pitch_steps)
final_audio = combine_notes(audios,
spacing=spacing,
start_trim=start_trim,
attack=attack,
sustain=sustain,
release=release,
max_note_length=max_note_length,
sr=sample_rate)
final_audio = final_audio.astype("float32")
stdout.write(protocol.to_tag_msg(protocol.OUT_TAG_AUDIO))
stdout.write(
protocol.to_audio_size_msg(final_audio.size * final_audio.itemsize))
stdout.write(protocol.to_audio_msg(final_audio))
stdout.flush()
def handle_gen_audio(model, stdin, stdout, state):
count_msg = read_msg(stdin, protocol.count_struct.size)
count = protocol.from_count_msg(count_msg)
pitches = []
zs = []
for i in range(count):
gen_msg = read_msg(stdin, protocol.gen_audio_struct.size)
pitch, z = protocol.from_gen_msg(gen_msg)
pitches.append(pitch)
zs.append(z)
layer_offsets = {}
if 'ganspace_component_amplitudes' in state:
components = state['ganspace_components']['comp']
std_devs = state['ganspace_components']['stdev']
edits = state['ganspace_component_amplitudes']
amounts = np.zeros(components.shape[:1], dtype=np.float32)
amounts[:len(list(map(float, edits)))] = edits * std_devs
scaled_directions = amounts.reshape(-1, 1, 1, 1) * components
linear_combination = np.sum(scaled_directions, axis=0)
linear_combination_batch = np.repeat(linear_combination.reshape(
1, *linear_combination.shape),
8,
axis=0)
layer_offsets[state['ganspace_components']
['layer']] = linear_combination_batch
z_arr = np.array(zs)
try:
audios = model.generate_samples_from_z(z_arr,
pitches,
layer_offsets=layer_offsets)
except KeyError as e:
print_err(
"can't synthesize - model was not trained on pitch {}".format(
e.args[0]))
audios = []
stdout.write(protocol.to_tag_msg(protocol.OUT_TAG_AUDIO))
stdout.write(protocol.to_count_msg(len(audios)))
for audio in audios:
stdout.write(protocol.to_audio_size_msg(audio.size * audio.itemsize))
stdout.write(protocol.to_audio_msg(audio))
stdout.flush()
def handle_generate(state):
stdin = state.stdin
stdout = state.stdout
model = state.model
config = state.config
num_seqs = state.num_seqs
generate_msg = read_msg(stdin, protocol.generate_struct.size)
seed_sr, out_sr, num_outs, dur, seed_len = protocol.from_generate_msg(
generate_msg)
print_err("seed_sr =", seed_sr)
print_err("out_sr =", out_sr)
print_err("num_outs =", num_outs)
print_err("dur =", dur)
print_err("seed_len =", seed_len)
if seed_len > 0:
seed_msg = read_msg(stdin, seed_len * protocol.f32_struct.size)
seed_audio = protocol.from_audio_msg(seed_msg)
else:
seed_audio = np.array([], dtype=np.float32)
print_err("seed_audio size*itemsize =",
seed_audio.size * seed_audio.itemsize)
temps = []
for i in range(num_outs):
temp_len_msg = read_msg(stdin, protocol.size_struct.size)
temp_len = protocol.from_size_msg(temp_len_msg)
temp_str_msg = read_msg(stdin, temp_len)
temp_str = protocol.from_str_msg(temp_str_msg)
temp = gen.check_temperature(temp_str)
temps.append(temp)
print_err("temps =", temps)
# out_audios = [np.random.uniform(0.0, 1.0, out_len).astype(np.float32)]*num_outs
out_audios = list(generate(state, out_sr, dur, temps, seed_audio))
print_err("generated")
stdout.write(protocol.to_tag_msg(protocol.OUT_TAG_GENERATED))
stdout.write(
protocol.to_generated_msg(out_sr, len(out_audios), out_audios[0].size))
for audio in out_audios:
stdout.write(protocol.to_audio_msg(audio))
stdout.flush()
def handle_load_ganspace_components(model, stdin, stdout, state):
size_msg = read_msg(stdin, protocol.int_struct.size)
size = protocol.from_int_msg(size_msg)
msg = read_msg(stdin, size)
file = msg.decode('utf-8')
print_err("Opening components file '{}'".format(file))
with open(file, "rb") as fp:
state['ganspace_components'] = pickle.load(fp)
print_err("Components file loaded.")
component_count = len(state['ganspace_components']["comp"])
state['ganspace_component_count'] = component_count
stdout.write(protocol.to_tag_msg(protocol.OUT_TAG_LOAD_COMPONENTS))
stdout.write(protocol.to_count_msg(component_count))
stdout.flush()
def handle_set_component_amplitudes(model, stdin, stdout, state):
amplitudes = []
for i in range(0, state['ganspace_component_count']):
msg = read_msg(stdin, protocol.f64_struct.size)
value = protocol.from_float_msg(msg)
amplitudes.append(value)
state['ganspace_component_amplitudes'] = amplitudes
def handle_slerp_z(model, stdin, stdout, state):
slerp_z_msg = read_msg(stdin, protocol.slerp_z_struct.size)
z0, z1, amount = protocol.from_slerp_z_msg(slerp_z_msg)
z = gu.slerp(z0, z1, amount)
stdout.write(protocol.to_tag_msg(protocol.OUT_TAG_Z))
stdout.write(protocol.to_count_msg(1))
stdout.write(protocol.to_z_msg(z))
stdout.flush()
def handle_synthesize_noz(model, stdin, stdout, state):
print_err("handle_synthesize_noz")
count_msg = read_msg(stdin, protocol.count_struct.size)
count = protocol.from_count_msg(count_msg)
pitches = []
for i in range(count):
gen_msg = read_msg(stdin, protocol.synthesize_noz_struct.size)
pitch = protocol.from_synthesize_noz_msg(gen_msg)
pitches.append(pitch)
pca = state["ganspace_components"]
stdevs = pca["stdev"]
layer_dtype = stdevs.dtype
edits = np.array(state["ganspace_component_amplitudes"], dtype=layer_dtype)
layer = make_layer(pca, edits)
layers = np.repeat([layer], len(pitches), axis=0)
try:
audios = model.generate_samples_from_layers({pca["layer"]: layers},
pitches)
except KeyError as e:
print_err(
"can't synthesize - model was not trained on pitch {}".format(
e.args[0]))
audios = []
stdout.write(protocol.to_tag_msg(protocol.OUT_TAG_AUDIO))
stdout.write(protocol.to_count_msg(len(audios)))
for audio in audios:
stdout.write(protocol.to_audio_size_msg(audio.size * audio.itemsize))
stdout.write(protocol.to_audio_msg(audio))
stdout.flush()
def handle_rand_z(model, stdin, stdout, state):
"""
Generates a given number of new Z coordinates.
"""
count_msg = read_msg(stdin, protocol.count_struct.size)
count = protocol.from_count_msg(count_msg)
zs = model.generate_z(count)
stdout.write(protocol.to_tag_msg(protocol.OUT_TAG_Z))
stdout.write(protocol.to_count_msg(len(zs)))
for z in zs:
stdout.write(protocol.to_z_msg(z))
stdout.flush()
def handle_timbre_transfer(stdin, stdout):
transfer_msg = read_msg(stdin, protocol.timbre_transfer_struct.size)
h = protocol.from_timbre_transfer_msg(transfer_msg)
print_err(repr(h))
in_sample_rate, out_sample_rate, f0_octave_shift, f0_confidence_threshold, loudness_db_shift, adjust, quiet, autotune, ckpt_dir_len, in_audio_len = h
print_err("ckpt_dir_len =", ckpt_dir_len)
print_err("in_audio_len =", in_audio_len)
ckpt_dir_msg = stdin.read(ckpt_dir_len)
ckpt_dir = protocol.from_str_msg(ckpt_dir_msg)
print_err("ckpt_dir =", ckpt_dir)
in_audio_msg = stdin.read(in_audio_len)
print_err("len(in_audio_msg) =", len(in_audio_msg))
in_audio = protocol.from_audio_msg(in_audio_msg)
print_err("in_audio.size =", in_audio.size)
out_audio = timbre_transfer(
ckpt_dir=ckpt_dir,
audio=in_audio,
in_sample_rate=in_sample_rate,
out_sample_rate=out_sample_rate,
f0_octave_shift=f0_octave_shift,
f0_confidence_threshold=f0_confidence_threshold,
loudness_db_shift=loudness_db_shift,
adjust=adjust,
quiet=quiet,
autotune=autotune,
log=print_err)
out_audio = out_audio.numpy().ravel()
out_audio_len = out_audio.size * out_audio.itemsize
print_err("out_audio.shape =", out_audio.shape)
print_err("out_audio_len =", out_audio_len)
stdout.write(protocol.to_tag_msg(protocol.OUT_TAG_TIMBRE_TRANSFERRED))
print_err("wrote tag_timbre_transferred")
stdout.write(protocol.to_timbre_transferred_msg(out_audio_len))
print_err("wrote size")
bytez = protocol.to_audio_msg(out_audio)
print_err("len(bytez) =", len(bytez))
stdout.write(bytez)
print_err("wrote out_audio")
stdout.flush()
def handle_hallucinate(model, stdin, stdout, state):
max_note_length = model.config['audio_length']
sample_rate = model.config['sample_rate']
hallucinate_msg = read_msg(stdin, protocol.hallucinate_struct.size)
args = protocol.from_hallucinate_msg(hallucinate_msg)
note_count, interpolation_steps, spacing, start_trim, attack, sustain, release = args
print_err(
"note_count = {} interpolation_steps = {}, spacing = {}s, start_trim = {}s, attack = {}s, sustain = {}s, release = {}s"
.format(*args))
initial_notes = model.generate_z(note_count)
initial_piches = np.array(
[32] * len(initial_notes)
) # np.floor(30 + np.random.rand(len(initial_notes)) * 30)
final_notes, final_pitches = interpolate_notes(initial_notes,
initial_piches,
interpolation_steps)
audios = synthesize(model, final_notes, final_pitches)
final_audio = combine_notes(audios,
spacing=spacing,
start_trim=start_trim,
attack=attack,
sustain=sustain,
release=release,
max_note_length=max_note_length,
sr=sample_rate)
final_audio = final_audio.astype('float32')
stdout.write(protocol.to_tag_msg(protocol.OUT_TAG_AUDIO))
stdout.write(
protocol.to_audio_size_msg(final_audio.size * final_audio.itemsize))
stdout.write(protocol.to_audio_msg(final_audio))
stdout.flush()
ckpt_dir = sys.argv[1]
batch_size = int(sys.argv[2])
except IndexError:
print_err("usage: {} checkpoint_dir batch_size".format(
os.path.basename(__file__)))
sys.exit(1)
flags = lib_flags.Flags({"batch_size_schedule": [batch_size]})
model = lib_model.Model.load_from_path(ckpt_dir, flags)
stdin = os.fdopen(sys.stdin.fileno(), "rb", 0)
stdout = os.fdopen(sys.stdout.fileno(), "wb", 0)
stdout.write(gss.to_tag_msg(gss.OUT_TAG_INIT))
audio_length = model.config['audio_length']
sample_rate = model.config['sample_rate']
info_msg = gss.to_info_msg(audio_length=audio_length, sample_rate=sample_rate)
stdout.write(info_msg)
stdout.flush()
state = {}
while True:
in_tag_msg = read_msg(stdin, gss.tag_struct.size)
in_tag = gss.from_tag_msg(in_tag_msg)
if in_tag not in handlers:
raise ValueError("unknown input message tag: {}".format(in_tag))
handlers[in_tag](model, stdin, stdout, state)
model = gen.create_inference_model(ckpt_path, args.num_seqs, config)
print_err("hello :)")
# open standard input/output handles
stdin = sys.stdin.buffer
stdout = sys.stdout.buffer
# write init message
stdout.write(protocol.to_tag_msg(protocol.OUT_TAG_INIT))
stdout.flush()
print_err("it begins @_@")
state = SimpleNamespace(stdin=stdin,
stdout=stdout,
config=config,
num_seqs=args.num_seqs,
model=model)
while True:
in_tag_msg = read_msg(stdin, protocol.tag_struct.size)
in_tag = protocol.from_tag_msg(in_tag_msg)
if in_tag not in handlers:
raise ValueError("unknown input message tag: {}".format(in_tag))
handlers[in_tag](state)
