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from pathlib import Path
import time
import os
from contextlib import contextmanager
import random
import numpy as np
import audiotools as at
from audiotools import AudioSignal
import argbind
import shutil
import torch
import yaml
from vampnet.interface import Interface, signal_concat
from vampnet import mask as pmask
from ttutil import log
# TODO: incorporate discord bot (if mem allows)
# in a separate thread, send audio samples for listening
# and send back the results
# as well as the params for sampling
# also a command that lets you clear the current signal
# if you want to start over
device = "cuda" if torch.cuda.is_available() else "cpu"
VAMPNET_DIR = Path(".").resolve()
@contextmanager
def chdir(path):
old_dir = os.getcwd()
os.chdir(path)
try:
yield
finally:
os.chdir(old_dir)
def load_interface(model_choice="default") -> Interface:
with chdir(VAMPNET_DIR):
# populate the model choices with any interface.yml files in the generated confs
MODEL_CHOICES = {
"default": {
"Interface.coarse_ckpt": "models/vampnet/coarse.pth",
"Interface.coarse2fine_ckpt": "models/vampnet/c2f.pth",
"Interface.codec_ckpt": "models/vampnet/codec.pth",
}
}
generated_confs = Path("conf/generated")
for conf_file in generated_confs.glob("*/interface.yml"):
with open(conf_file) as f:
_conf = yaml.safe_load(f)
# check if the coarse, c2f, and codec ckpts exist
# otherwise, dont' add this model choice
if not (
Path(_conf["Interface.coarse_ckpt"]).exists() and
Path(_conf["Interface.coarse2fine_ckpt"]).exists() and
Path(_conf["Interface.codec_ckpt"]).exists()
):
continue
MODEL_CHOICES[conf_file.parent.name] = _conf
interface = Interface(
device=device,
coarse_ckpt=MODEL_CHOICES[model_choice]["Interface.coarse_ckpt"],
coarse2fine_ckpt=MODEL_CHOICES[model_choice]["Interface.coarse2fine_ckpt"],
codec_ckpt=MODEL_CHOICES[model_choice]["Interface.codec_ckpt"],
)
interface.model_choices = MODEL_CHOICES
interface.to("cuda" if torch.cuda.is_available() else "cpu")
return interface
def load_model(interface: Interface, model_choice: str):
interface.reload(
interface.model_choices[model_choice]["Interface.coarse_ckpt"],
interface.model_choices[model_choice]["Interface.coarse2fine_ckpt"],
)
def ez_variation(
interface,
sig: AudioSignal,
seed: int = None,
model_choice: str = None,
):
t0 = time.time()
if seed is None:
seed = int(torch.randint(0, 2**32, (1,)).item())
at.util.seed(seed)
# reload the model if necessary
if model_choice is not None:
load_model(interface, model_choice)
# SAMPLING MASK PARAMS, hard code for now, we'll prob want a more preset-ey thing for the actual thin
# we probably honestly just want to oscillate between the same 4 presets
# in a predictable order such that they have a predictable outcome
periodic_p = random.choice([3])
n_mask_codebooks = 3
sampletemp = random.choice([1.0,])
dropout = random.choice([0.0, 0.0])
top_p = None # NOTE: top p may be the culprit behind the collapse into single pitches.
# parameters for the build_mask function
build_mask_kwargs = dict(
rand_mask_intensity=1.0,
prefix_s=0.0,
suffix_s=0.0,
periodic_prompt=int(periodic_p),
periodic_prompt2=int(periodic_p),
periodic_prompt_width=1,
_dropout=dropout,
upper_codebook_mask=int(n_mask_codebooks),
upper_codebook_mask_2=int(n_mask_codebooks),
)
# parameters for the vamp function
vamp_kwargs = dict(
temperature=sampletemp,
typical_filtering=True,
typical_mass=0.15,
typical_min_tokens=64,
top_p=top_p,
seed=seed,
sample_cutoff=1.0,
)
# save the mask as a txt file
interface.set_chunk_size(10.0)
sig, mask, codes = interface.vamp(
sig,
batch_size=1,
feedback_steps=1,
time_stretch_factor=1,
build_mask_kwargs=build_mask_kwargs,
vamp_kwargs=vamp_kwargs,
return_mask=True,
)
log(f"vamp took {time.time() - t0} seconds")
return sig
def main():
import tqdm
interface = load_interface()
sig = AudioSignal.excerpt("assets/example.wav", duration=7.0)
sig = interface.preprocess(sig)
sig.write('ttout/in.wav')
insig = sig.clone()
fdbk_every = 4
fdbk = 0.5
for i in tqdm.tqdm(range(1000)):
sig = ez_variation(interface, sig, model_choice="orchestral")
sig.write(f'ttout/out{i}.wav')
if __name__ == "__main__":
main() |