Create mastering_transfer.py

inference/mastering_transfer.py

@@ -0,0 +1,378 @@
+"""
+    Inference code of music style transfer
+    of the work "Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio Effects"
+
+    Process : converts the mastering style of the input music recording to that of the refernce music.
+                files inside the target directory should be organized as follow 
+                    "path_to_data_directory"/"song_name_#1"/input.wav
+                    "path_to_data_directory"/"song_name_#1"/reference.wav
+                    ...
+                    "path_to_data_directory"/"song_name_#n"/input.wav
+                    "path_to_data_directory"/"song_name_#n"/reference.wav
+                where the 'input' and 'reference' should share the same names.
+"""
+import numpy as np
+from glob import glob
+import os
+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, TCNModel
+from data_loader import *
+import librosa
+
+
+
+class Mastering_Style_Transfer_Inference:
+    def __init__(self, args, trained_w_ddp=True):
+        if torch.cuda.is_available():
+            self.device = torch.device("cuda:0")
+        else:
+            self.device = torch.device("cpu")
+        
+        # inference computational hyperparameters
+        self.args = args
+        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)
+        self.models['mastering_converter'] = TCNModel(nparams=args.cfg_converter["condition_dimension"], \
+                                                    ninputs=2, \
+                                                    noutputs=2, \
+                                                    nblocks=args.cfg_converter["nblocks"], \
+                                                    dilation_growth=args.cfg_converter["dilation_growth"], \
+                                                    kernel_size=args.cfg_converter["kernel_size"], \
+                                                    channel_width=args.cfg_converter["channel_width"], \
+                                                    stack_size=args.cfg_converter["stack_size"], \
+                                                    cond_dim=args.cfg_converter["condition_dimension"], \
+                                                    causal=args.cfg_converter["causal"]).to(self.device)
+        
+        ckpt_paths = {'effects_encoder' : args.ckpt_path_enc, \
+                        'mastering_converter' : args.ckpt_path_conv}
+        # reload saved model weights
+        ddp = trained_w_ddp
+        self.reload_weights(ckpt_paths, ddp=ddp)
+
+        # load data loader for the inference procedure
+        inference_dataset = Song_Dataset_Inference(args)
+        self.data_loader = DataLoader(inference_dataset, \
+                                        batch_size=1, \
+                                        shuffle=False, \
+                                        num_workers=args.workers, \
+                                        drop_last=False)
+
+        ''' check stem-wise result '''
+        if not self.args.do_not_separate:
+            os.environ['MKL_THREADING_LAYER'] = 'GNU'
+            separate_file_names = [args.input_file_name, args.reference_file_name]
+            if self.args.interpolation:
+                separate_file_names.append(args.reference_file_name_2interpolate)
+            for cur_idx, cur_inf_dir in enumerate(sorted(glob(f"{args.target_dir}*/"))):
+                for cur_file_name in separate_file_names:
+                    cur_sep_file_path = os.path.join(cur_inf_dir, cur_file_name+'.wav')
+                    cur_sep_output_dir = os.path.join(cur_inf_dir, args.stem_level_directory_name)
+                    if os.path.exists(os.path.join(cur_sep_output_dir, self.args.separation_model, cur_file_name, 'drums.wav')):
+                        print(f'\talready separated current file : {cur_sep_file_path}')
+                    else:
+                        cur_cmd_line = f"demucs {cur_sep_file_path} -n {self.args.separation_model} -d {self.device} -o {cur_sep_output_dir}"
+                        os.system(cur_cmd_line)
+
+
+    # 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} ---")
+
+
+    # Inference whole song
+    def inference(self, input_track_path, reference_track_path):
+        print("\n======= Start to inference music mixing style transfer =======")
+        # normalized input
+        output_name_tag = 'output' if self.args.normalize_input else 'output_notnormed'
+
+        input_aud = load_wav_segment(input_track_path)
+        reference_aud = load_wav_segment(reference_track_path)
+
+        # input_stems, reference_stems, dir_name
+        print(f"---inference file name : {dir_name[0]}---")
+        cur_out_dir = dir_name[0].replace(self.target_dir, self.output_dir)
+        os.makedirs(cur_out_dir, exist_ok=True)
+        ''' stem-level inference '''
+        inst_outputs = []
+        for cur_inst_idx, cur_inst_name in enumerate(self.args.instruments):
+            print(f'\t{cur_inst_name}...')
+            ''' segmentize whole songs into batch '''
+            if len(input_stems[0][cur_inst_idx][0]) > self.args.segment_length:
+                cur_inst_input_stem = self.batchwise_segmentization(input_stems[0][cur_inst_idx], \
+                                                                            dir_name[0], \
+                                                                            segment_length=self.args.segment_length, \
+                                                                            discard_last=False)
+            else:
+                cur_inst_input_stem = [input_stems[:, cur_inst_idx]]
+            if len(reference_stems[0][cur_inst_idx][0]) > self.args.segment_length*2:
+                cur_inst_reference_stem = self.batchwise_segmentization(reference_stems[0][cur_inst_idx], \
+                                                                            dir_name[0], \
+                                                                            segment_length=self.args.segment_length_ref, \
+                                                                            discard_last=False)
+            else:
+                cur_inst_reference_stem = [reference_stems[:, cur_inst_idx]]
+
+            ''' inference '''
+            # first extract reference style embedding
+            infered_ref_data_list = []
+            for cur_ref_data in cur_inst_reference_stem:
+                cur_ref_data = cur_ref_data.to(self.device)
+                # Effects Encoder inference
+                with torch.no_grad():
+                    self.models["effects_encoder"].eval()
+                    reference_feature = self.models["effects_encoder"](cur_ref_data)
+                infered_ref_data_list.append(reference_feature)
+            # compute average value from the extracted exbeddings
+            infered_ref_data = torch.stack(infered_ref_data_list)
+            infered_ref_data_avg = torch.mean(infered_ref_data.reshape(infered_ref_data.shape[0]*infered_ref_data.shape[1], infered_ref_data.shape[2]), axis=0)
+
+            # mixing style converter
+            infered_data_list = []
+            for cur_data in cur_inst_input_stem:
+                cur_data = cur_data.to(self.device)
+                with torch.no_grad():
+                    self.models["mastering_converter"].eval()
+                    infered_data = self.models["mastering_converter"](cur_data, infered_ref_data_avg.unsqueeze(0))
+                infered_data_list.append(infered_data.cpu().detach())
+
+            # combine back to whole song
+            for cur_idx, cur_batch_infered_data in enumerate(infered_data_list):
+                cur_infered_data_sequential = torch.cat(torch.unbind(cur_batch_infered_data, dim=0), dim=-1)
+                fin_data_out = cur_infered_data_sequential if cur_idx==0 else torch.cat((fin_data_out, cur_infered_data_sequential), dim=-1)
+            # final output of current instrument
+            fin_data_out_inst = fin_data_out[:, :input_stems[0][cur_inst_idx].shape[-1]].numpy()
+
+            inst_outputs.append(fin_data_out_inst)
+            # save output of each instrument
+            if self.args.save_each_inst:
+                sf.write(os.path.join(cur_out_dir, f"{cur_inst_name}_{output_name_tag}.wav"), fin_data_out_inst.transpose(-1, -2), self.args.sample_rate, 'PCM_16')
+        # remix
+        fin_data_out_mix = sum(inst_outputs)
+        fin_output_path = os.path.join(cur_out_dir, f"mixture_{output_name_tag}.wav")
+        sf.write(fin_output_path, fin_data_out_mix.transpose(-1, -2), self.args.sample_rate, 'PCM_16')
+
+        return fin_output_path
+
+
+    # Inference whole song
+    def inference_interpolation(self, ):
+        print("\n======= Start to inference interpolation examples =======")
+        # normalized input
+        output_name_tag = 'output_interpolation' if self.args.normalize_input else 'output_notnormed_interpolation'
+
+        for step, (input_stems, reference_stems_A, reference_stems_B, dir_name) in enumerate(self.data_loader):
+            print(f"---inference file name : {dir_name[0]}---")
+            cur_out_dir = dir_name[0].replace(self.target_dir, self.output_dir)
+            os.makedirs(cur_out_dir, exist_ok=True)
+            ''' stem-level inference '''
+            inst_outputs = []
+            for cur_inst_idx, cur_inst_name in enumerate(self.args.instruments):
+                print(f'\t{cur_inst_name}...')
+                ''' segmentize whole song '''
+                # segmentize input according to number of interpolating segments
+                interpolate_segment_length = input_stems[0][cur_inst_idx].shape[1] // self.args.interpolate_segments + 1
+                cur_inst_input_stem = self.batchwise_segmentization(input_stems[0][cur_inst_idx], \
+                                                                                dir_name[0], \
+                                                                                segment_length=interpolate_segment_length, \
+                                                                                discard_last=False)
+                # batchwise segmentize 2 reference tracks
+                if len(reference_stems_A[0][cur_inst_idx][0]) > self.args.segment_length_ref:
+                    cur_inst_reference_stem_A = self.batchwise_segmentization(reference_stems_A[0][cur_inst_idx], \
+                                                                                        dir_name[0], \
+                                                                                        segment_length=self.args.segment_length_ref, \
+                                                                                        discard_last=False)
+                else:
+                    cur_inst_reference_stem_A = [reference_stems_A[:, cur_inst_idx]]
+                if len(reference_stems_B[0][cur_inst_idx][0]) > self.args.segment_length_ref:
+                    cur_inst_reference_stem_B = self.batchwise_segmentization(reference_stems_B[0][cur_inst_idx], \
+                                                                                        dir_name[0], \
+                                                                                        segment_length=self.args.segment_length, \
+                                                                                        discard_last=False)
+                else:
+                    cur_inst_reference_stem_B = [reference_stems_B[:, cur_inst_idx]]
+
+                ''' inference '''
+                # first extract reference style embeddings
+                # reference A
+                infered_ref_data_list = []
+                for cur_ref_data in cur_inst_reference_stem_A:
+                    cur_ref_data = cur_ref_data.to(self.device)
+                    # Effects Encoder inference
+                    with torch.no_grad():
+                        self.models["effects_encoder"].eval()
+                        reference_feature = self.models["effects_encoder"](cur_ref_data)
+                    infered_ref_data_list.append(reference_feature)
+                # compute average value from the extracted exbeddings
+                infered_ref_data = torch.stack(infered_ref_data_list)
+                infered_ref_data_avg_A = torch.mean(infered_ref_data.reshape(infered_ref_data.shape[0]*infered_ref_data.shape[1], infered_ref_data.shape[2]), axis=0)
+
+                # reference B
+                infered_ref_data_list = []
+                for cur_ref_data in cur_inst_reference_stem_B:
+                    cur_ref_data = cur_ref_data.to(self.device)
+                    # Effects Encoder inference
+                    with torch.no_grad():
+                        self.models["effects_encoder"].eval()
+                        reference_feature = self.models["effects_encoder"](cur_ref_data)
+                    infered_ref_data_list.append(reference_feature)
+                # compute average value from the extracted exbeddings
+                infered_ref_data = torch.stack(infered_ref_data_list)
+                infered_ref_data_avg_B = torch.mean(infered_ref_data.reshape(infered_ref_data.shape[0]*infered_ref_data.shape[1], infered_ref_data.shape[2]), axis=0)
+
+                # mixing style converter
+                infered_data_list = []
+                for cur_idx, cur_data in enumerate(cur_inst_input_stem):
+                    cur_data = cur_data.to(self.device)
+                    # perform linear interpolation on embedding space
+                    cur_weight = (self.args.interpolate_segments-1-cur_idx) / (self.args.interpolate_segments-1)
+                    cur_ref_emb = cur_weight * infered_ref_data_avg_A +  (1-cur_weight) * infered_ref_data_avg_B
+                    with torch.no_grad():
+                        self.models["mastering_converter"].eval()
+                        infered_data = self.models["mastering_converter"](cur_data, cur_ref_emb.unsqueeze(0))
+                    infered_data_list.append(infered_data.cpu().detach())
+
+                # combine back to whole song
+                for cur_idx, cur_batch_infered_data in enumerate(infered_data_list):
+                    cur_infered_data_sequential = torch.cat(torch.unbind(cur_batch_infered_data, dim=0), dim=-1)
+                    fin_data_out = cur_infered_data_sequential if cur_idx==0 else torch.cat((fin_data_out, cur_infered_data_sequential), dim=-1)
+                # final output of current instrument
+                fin_data_out_inst = fin_data_out[:, :input_stems[0][cur_inst_idx].shape[-1]].numpy()
+                inst_outputs.append(fin_data_out_inst)
+
+                # save output of each instrument
+                if self.args.save_each_inst:
+                    sf.write(os.path.join(cur_out_dir, f"{cur_inst_name}_{output_name_tag}.wav"), fin_data_out_inst.transpose(-1, -2), self.args.sample_rate, 'PCM_16')
+            # remix
+            fin_data_out_mix = sum(inst_outputs)
+            fin_output_path = os.path.join(cur_out_dir, f"mixture_{output_name_tag}.wav")
+            sf.write(fin_output_path, fin_data_out_mix.transpose(-1, -2), self.args.sample_rate, 'PCM_16')
+
+            return fin_output_path
+
+
+    # function that segmentize an entire song into batch
+    def batchwise_segmentization(self, target_song, song_name, segment_length, discard_last=False):
+        assert target_song.shape[-1] >= self.args.segment_length, \
+                f"Error : Insufficient duration!\n\t \
+                Target song's length is shorter than segment length.\n\t \
+                Song name : {song_name}\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] % segment_length
+            target_song = target_song[:, :target_length]
+        # pad last segment
+        else:
+            pad_length = segment_length - target_song.shape[-1] % segment_length
+            target_song = torch.cat((target_song, torch.zeros(2, pad_length)), axis=-1)
+
+        # segmentize according to the given segment_length
+        whole_batch_data = []
+        batch_wise_data = []
+        for cur_segment_idx in range(target_song.shape[-1]//segment_length):
+            batch_wise_data.append(target_song[..., cur_segment_idx*segment_length:(cur_segment_idx+1)*segment_length])
+            if len(batch_wise_data)==self.args.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
+
+
+
+def set_up_mastering(start_point_in_second=0, duration_in_second=30):
+    os.environ['MASTER_ADDR'] = '127.0.0.1'
+    os.environ["CUDA_VISIBLE_DEVICES"] = '0'
+    os.environ['MASTER_PORT'] = '8888'
+
+    def str2bool(v):
+        if v.lower() in ('yes', 'true', 't', 'y', '1'):
+            return True
+        elif v.lower() in ('no', 'false', 'f', 'n', '0'):
+            return False
+        else:
+            raise argparse.ArgumentTypeError('Boolean value expected.')
+
+    ''' Configurations for music mixing style transfer '''
+    currentdir = os.path.dirname(os.path.realpath(__file__))
+    default_ckpt_path_enc = os.path.join(os.path.dirname(currentdir), 'weights', 'FXencoder_ps.pt')
+    default_ckpt_path_conv = os.path.join(os.path.dirname(currentdir), 'weights', 'MixFXcloner_ps.pt')
+    default_ckpt_path_master = os.path.join(os.path.dirname(currentdir), 'weights', 'MasterFXcloner_ps.pt')
+    default_norm_feature_path = os.path.join(os.path.dirname(currentdir), 'weights', 'musdb18_fxfeatures_eqcompimagegain.npy')
+
+    import argparse
+    import yaml
+    parser = argparse.ArgumentParser()
+
+    directory_args = parser.add_argument_group('Directory args')
+    # directory paths
+    directory_args.add_argument('--target_dir', type=str, default='./yt_dir/')
+    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('--input_file_name', type=str, default='input')
+    directory_args.add_argument('--reference_file_name', type=str, default='reference')
+    directory_args.add_argument('--reference_file_name_2interpolate', type=str, default='reference_B')
+    # saved weights
+    directory_args.add_argument('--ckpt_path_enc', type=str, default=default_ckpt_path_enc)
+    directory_args.add_argument('--ckpt_path_conv', type=str, default=default_ckpt_path_master)
+    directory_args.add_argument('--precomputed_normalization_feature', type=str, default=default_norm_feature_path)
+
+    inference_args = parser.add_argument_group('Inference args')
+    inference_args.add_argument('--sample_rate', type=int, default=44100)
+    inference_args.add_argument('--segment_length', type=int, default=2**19)        # segmentize input according to this duration
+    inference_args.add_argument('--segment_length_ref', type=int, default=2**19)    # segmentize reference according to this duration
+    # stem-level instruments & separation
+    inference_args.add_argument('--instruments', type=str2bool, default=["drums", "bass", "other", "vocals"], help='instrumental tracks to perform style transfer')
+    inference_args.add_argument('--stem_level_directory_name', type=str, default='separated')
+    inference_args.add_argument('--save_each_inst', type=str2bool, default=False)
+    inference_args.add_argument('--do_not_separate', type=str2bool, default=False)
+    inference_args.add_argument('--separation_model', type=str, default='htdemucs')
+    # FX normalization
+    inference_args.add_argument('--normalize_input', type=str2bool, default=False)
+    inference_args.add_argument('--normalization_order', type=str2bool, default=['loudness', 'eq', 'compression', 'imager', 'loudness']) # Effects to be normalized, order matters
+    # interpolation
+    inference_args.add_argument('--interpolation', type=str2bool, default=False)
+    inference_args.add_argument('--interpolate_segments', type=int, default=30)
+
+    device_args = parser.add_argument_group('Device args')
+    device_args.add_argument('--workers', type=int, default=1)
+    device_args.add_argument('--batch_size', type=int, default=1)   # for processing long audio
+
+    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']
+    args.cfg_converter = configs['TCN']['default']
+
+    return args
+