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"""
Inference code of extracting embeddings from music recordings using FXencoder
of the work "Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio Effects"
Process : extracts FX embeddings of each song inside the target directory.
"""
from glob import glob
import os
import librosa
import numpy as np
import torch
import sys
currentdir = os.path.dirname(os.path.realpath(__file__))
sys.path.append(os.path.join(os.path.dirname(currentdir), "mixing_style_transfer"))
from networks import FXencoder
from data_loader import *
class FXencoder_Inference:
def __init__(self, args, trained_w_ddp=True):
if args.inference_device!='cpu' and torch.cuda.is_available():
self.device = torch.device("cuda:0")
else:
self.device = torch.device("cpu")
# inference computational hyperparameters
self.segment_length = args.segment_length
self.batch_size = args.batch_size
self.sample_rate = 44100 # sampling rate should be 44100
self.time_in_seconds = int(args.segment_length // self.sample_rate)
# directory configuration
self.output_dir = args.target_dir if args.output_dir==None else args.output_dir
self.target_dir = args.target_dir
# load model and its checkpoint weights
self.models = {}
self.models['effects_encoder'] = FXencoder(args.cfg_encoder).to(self.device)
ckpt_paths = {'effects_encoder' : args.ckpt_path_enc}
# reload saved model weights
ddp = trained_w_ddp
self.reload_weights(ckpt_paths, ddp=ddp)
# save current arguments
self.save_args(args)
# reload model weights from the target checkpoint path
def reload_weights(self, ckpt_paths, ddp=True):
for cur_model_name in self.models.keys():
checkpoint = torch.load(ckpt_paths[cur_model_name], map_location=self.device)
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in checkpoint["model"].items():
# remove `module.` if the model was trained with DDP
name = k[7:] if ddp else k
new_state_dict[name] = v
# load params
self.models[cur_model_name].load_state_dict(new_state_dict)
print(f"---reloaded checkpoint weights : {cur_model_name} ---")
# save averaged embedding from whole songs
def save_averaged_embeddings(self, ):
# embedding output directory path
emb_out_dir = f"{self.output_dir}"
print(f'\n\n=====Inference seconds : {self.time_in_seconds}=====')
# target_file_paths = glob(f"{self.target_dir}/**/*.wav", recursive=True)
target_file_paths = glob(os.path.join(self.target_dir, '**', '*.wav'), recursive=True)
for step, target_file_path in enumerate(target_file_paths):
print(f"\nInference step : {step+1}/{len(target_file_paths)}")
print(f"---current file path : {target_file_path}---")
''' load waveform signal '''
target_song_whole = load_wav_segment(target_file_path, axis=0)
# check if mono -> convert to stereo by duplicating mono signal
if len(target_song_whole.shape)==1:
target_song_whole = np.stack((target_song_whole, target_song_whole), axis=0)
# check axis dimension
# signal shape should be : [channel, signal duration]
elif target_song_whole.shape[1]==2:
target_song_whole = target_song_whole.transpose()
target_song_whole = torch.from_numpy(target_song_whole).float()
''' segmentize whole songs into batch '''
whole_batch_data = self.batchwise_segmentization(target_song_whole, target_file_path)
''' inference '''
# infer whole song
infered_data_list = []
infered_c_list = []
infered_z_list = []
for cur_idx, cur_data in enumerate(whole_batch_data):
cur_data = cur_data.to(self.device)
with torch.no_grad():
self.models["effects_encoder"].eval()
# FXencoder
out_c_emb = self.models["effects_encoder"](cur_data)
infered_c_list.append(out_c_emb.cpu().detach())
avg_c_feat = torch.mean(torch.cat(infered_c_list, dim=0), dim=0).squeeze().cpu().detach().numpy()
# save outputs
cur_output_path = target_file_path.replace(self.target_dir, self.output_dir).replace('.wav', '_fx_embedding.npy')
os.makedirs(os.path.dirname(cur_output_path), exist_ok=True)
np.save(cur_output_path, avg_c_feat)
# function that segmentize an entire song into batch
def batchwise_segmentization(self, target_song, target_file_path, discard_last=False):
assert target_song.shape[-1] >= self.segment_length, \
f"Error : Insufficient duration!\n\t \
Target song's length is shorter than segment length.\n\t \
Song name : {target_file_path}\n\t \
Consider changing the 'segment_length' or song with sufficient duration"
# discard restovers (last segment)
if discard_last:
target_length = target_song.shape[-1] - target_song.shape[-1] % self.segment_length
target_song = target_song[:, :target_length]
# pad last segment
else:
pad_length = self.segment_length - target_song.shape[-1] % self.segment_length
target_song = torch.cat((target_song, torch.zeros(2, pad_length)), axis=-1)
whole_batch_data = []
batch_wise_data = []
for cur_segment_idx in range(target_song.shape[-1]//self.segment_length):
batch_wise_data.append(target_song[..., cur_segment_idx*self.segment_length:(cur_segment_idx+1)*self.segment_length])
if len(batch_wise_data)==self.batch_size:
whole_batch_data.append(torch.stack(batch_wise_data, dim=0))
batch_wise_data = []
if batch_wise_data:
whole_batch_data.append(torch.stack(batch_wise_data, dim=0))
return whole_batch_data
# save current inference arguments
def save_args(self, params):
info = '\n[args]\n'
for sub_args in parser._action_groups:
if sub_args.title in ['positional arguments', 'optional arguments', 'options']:
continue
size_sub = len(sub_args._group_actions)
info += f' {sub_args.title} ({size_sub})\n'
for i, arg in enumerate(sub_args._group_actions):
prefix = '-'
info += f' {prefix} {arg.dest:20s}: {getattr(params, arg.dest)}\n'
info += '\n'
os.makedirs(self.output_dir, exist_ok=True)
record_path = f"{self.output_dir}feature_extraction_inference_configurations.txt"
f = open(record_path, 'w')
np.savetxt(f, [info], delimiter=" ", fmt="%s")
f.close()
if __name__ == '__main__':
''' Configurations for inferencing music effects encoder '''
currentdir = os.path.dirname(os.path.realpath(__file__))
default_ckpt_path = os.path.join(os.path.dirname(currentdir), 'weights', 'FXencoder_ps.pt')
import argparse
import yaml
parser = argparse.ArgumentParser()
directory_args = parser.add_argument_group('Directory args')
directory_args.add_argument('--target_dir', type=str, default='./samples/')
directory_args.add_argument('--output_dir', type=str, default=None, help='if no output_dir is specified (None), the results will be saved inside the target_dir')
directory_args.add_argument('--ckpt_path_enc', type=str, default=default_ckpt_path)
inference_args = parser.add_argument_group('Inference args')
inference_args.add_argument('--segment_length', type=int, default=44100*10) # segmentize input according to this duration
inference_args.add_argument('--batch_size', type=int, default=1) # for processing long audio
inference_args.add_argument('--inference_device', type=str, default='cpu', help="if this option is not set to 'cpu', inference will happen on gpu only if there is a detected one")
args = parser.parse_args()
# load network configurations
with open(os.path.join(currentdir, 'configs.yaml'), 'r') as f:
configs = yaml.full_load(f)
args.cfg_encoder = configs['Effects_Encoder']['default']
# Extract features using pre-trained FXencoder
inference_encoder = FXencoder_Inference(args)
inference_encoder.save_averaged_embeddings()
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