import gc import hashlib import os import queue import threading import warnings import librosa import numpy as np import onnxruntime as ort import soundfile as sf import torch from tqdm import tqdm warnings.filterwarnings("ignore") stem_naming = {'Vocals': 'Instrumental', 'Other': 'Instruments', 'Instrumental': 'Vocals', 'Drums': 'Drumless', 'Bass': 'Bassless'} class MDXModel: def __init__(self, device, dim_f, dim_t, n_fft, hop=1024, stem_name=None, compensation=1.000): self.dim_f = dim_f self.dim_t = dim_t self.dim_c = 4 self.n_fft = n_fft self.hop = hop self.stem_name = stem_name self.compensation = compensation self.n_bins = self.n_fft // 2 + 1 self.chunk_size = hop * (self.dim_t - 1) self.window = torch.hann_window(window_length=self.n_fft, periodic=True).to(device) out_c = self.dim_c self.freq_pad = torch.zeros([1, out_c, self.n_bins - self.dim_f, self.dim_t]).to(device) def stft(self, x): x = x.reshape([-1, self.chunk_size]) x = torch.stft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True, return_complex=True) x = torch.view_as_real(x) x = x.permute([0, 3, 1, 2]) x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape([-1, 4, self.n_bins, self.dim_t]) return x[:, :, :self.dim_f] def istft(self, x, freq_pad=None): freq_pad = self.freq_pad.repeat([x.shape[0], 1, 1, 1]) if freq_pad is None else freq_pad x = torch.cat([x, freq_pad], -2) # c = 4*2 if self.target_name=='*' else 2 x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape([-1, 2, self.n_bins, self.dim_t]) x = x.permute([0, 2, 3, 1]) x = x.contiguous() x = torch.view_as_complex(x) x = torch.istft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True) return x.reshape([-1, 2, self.chunk_size]) class MDX: DEFAULT_SR = 44100 # Unit: seconds DEFAULT_CHUNK_SIZE = 0 * DEFAULT_SR DEFAULT_MARGIN_SIZE = 1 * DEFAULT_SR DEFAULT_PROCESSOR = 0 def __init__(self, model_path: str, params: MDXModel, processor=DEFAULT_PROCESSOR): # Set the device and the provider (CPU or CUDA) #self.device = torch.device(f'cuda:{processor}') if processor >= 0 else torch.device('cpu') self.device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu') #self.provider = ['CUDAExecutionProvider'] if processor >= 0 else ['CPUExecutionProvider'] self.provider = ['CPUExecutionProvider'] self.model = params # Load the ONNX model using ONNX Runtime self.ort = ort.InferenceSession(model_path, providers=self.provider) # Preload the model for faster performance self.ort.run(None, {'input': torch.rand(1, 4, params.dim_f, params.dim_t).numpy()}) self.process = lambda spec: self.ort.run(None, {'input': spec.cpu().numpy()})[0] self.prog = None @staticmethod def get_hash(model_path): try: with open(model_path, 'rb') as f: f.seek(- 10000 * 1024, 2) model_hash = hashlib.md5(f.read()).hexdigest() except: model_hash = hashlib.md5(open(model_path, 'rb').read()).hexdigest() return model_hash @staticmethod def segment(wave, combine=True, chunk_size=DEFAULT_CHUNK_SIZE, margin_size=DEFAULT_MARGIN_SIZE): """ Segment or join segmented wave array Args: wave: (np.array) Wave array to be segmented or joined combine: (bool) If True, combines segmented wave array. If False, segments wave array. chunk_size: (int) Size of each segment (in samples) margin_size: (int) Size of margin between segments (in samples) Returns: numpy array: Segmented or joined wave array """ if combine: processed_wave = None # Initializing as None instead of [] for later numpy array concatenation for segment_count, segment in enumerate(wave): start = 0 if segment_count == 0 else margin_size end = None if segment_count == len(wave) - 1 else -margin_size if margin_size == 0: end = None if processed_wave is None: # Create array for first segment processed_wave = segment[:, start:end] else: # Concatenate to existing array for subsequent segments processed_wave = np.concatenate((processed_wave, segment[:, start:end]), axis=-1) else: processed_wave = [] sample_count = wave.shape[-1] if chunk_size <= 0 or chunk_size > sample_count: chunk_size = sample_count if margin_size > chunk_size: margin_size = chunk_size for segment_count, skip in enumerate(range(0, sample_count, chunk_size)): margin = 0 if segment_count == 0 else margin_size end = min(skip + chunk_size + margin_size, sample_count) start = skip - margin cut = wave[:, start:end].copy() processed_wave.append(cut) if end == sample_count: break return processed_wave def pad_wave(self, wave): """ Pad the wave array to match the required chunk size Args: wave: (np.array) Wave array to be padded Returns: tuple: (padded_wave, pad, trim) - padded_wave: Padded wave array - pad: Number of samples that were padded - trim: Number of samples that were trimmed """ n_sample = wave.shape[1] trim = self.model.n_fft // 2 gen_size = self.model.chunk_size - 2 * trim pad = gen_size - n_sample % gen_size # Padded wave wave_p = np.concatenate((np.zeros((2, trim)), wave, np.zeros((2, pad)), np.zeros((2, trim))), 1) mix_waves = [] for i in range(0, n_sample + pad, gen_size): waves = np.array(wave_p[:, i:i + self.model.chunk_size]) mix_waves.append(waves) mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(self.device) return mix_waves, pad, trim def _process_wave(self, mix_waves, trim, pad, q: queue.Queue, _id: int): """ Process each wave segment in a multi-threaded environment Args: mix_waves: (torch.Tensor) Wave segments to be processed trim: (int) Number of samples trimmed during padding pad: (int) Number of samples padded during padding q: (queue.Queue) Queue to hold the processed wave segments _id: (int) Identifier of the processed wave segment Returns: numpy array: Processed wave segment """ mix_waves = mix_waves.split(1) with torch.no_grad(): pw = [] for mix_wave in mix_waves: self.prog.update() spec = self.model.stft(mix_wave) processed_spec = torch.tensor(self.process(spec)) processed_wav = self.model.istft(processed_spec.to(self.device)) processed_wav = processed_wav[:, :, trim:-trim].transpose(0, 1).reshape(2, -1).cpu().numpy() pw.append(processed_wav) processed_signal = np.concatenate(pw, axis=-1)[:, :-pad] q.put({_id: processed_signal}) return processed_signal def process_wave(self, wave: np.array, mt_threads=1): """ Process the wave array in a multi-threaded environment Args: wave: (np.array) Wave array to be processed mt_threads: (int) Number of threads to be used for processing Returns: numpy array: Processed wave array """ self.prog = tqdm(total=0) chunk = wave.shape[-1] // mt_threads waves = self.segment(wave, False, chunk) # Create a queue to hold the processed wave segments q = queue.Queue() threads = [] for c, batch in enumerate(waves): mix_waves, pad, trim = self.pad_wave(batch) self.prog.total = len(mix_waves) * mt_threads thread = threading.Thread(target=self._process_wave, args=(mix_waves, trim, pad, q, c)) thread.start() threads.append(thread) for thread in threads: thread.join() self.prog.close() processed_batches = [] while not q.empty(): processed_batches.append(q.get()) processed_batches = [list(wave.values())[0] for wave in sorted(processed_batches, key=lambda d: list(d.keys())[0])] assert len(processed_batches) == len(waves), 'Incomplete processed batches, please reduce batch size!' return self.segment(processed_batches, True, chunk) def run_mdx(model_params, output_dir, model_path, filename, exclude_main=False, exclude_inversion=False, suffix=None, invert_suffix=None, denoise=False, keep_orig=True, m_threads=2): device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu') #device_properties = torch.cuda.get_device_properties(device) print("Device", device) vram_gb = 12 #device_properties.total_memory / 1024**3 m_threads = 1 if vram_gb < 8 else 2 model_hash = MDX.get_hash(model_path) mp = model_params.get(model_hash) model = MDXModel( device, dim_f=mp["mdx_dim_f_set"], dim_t=2 ** mp["mdx_dim_t_set"], n_fft=mp["mdx_n_fft_scale_set"], stem_name=mp["primary_stem"], compensation=mp["compensate"] ) mdx_sess = MDX(model_path, model) wave, sr = librosa.load(filename, mono=False, sr=44100) # normalizing input wave gives better output peak = max(np.max(wave), abs(np.min(wave))) wave /= peak if denoise: wave_processed = -(mdx_sess.process_wave(-wave, m_threads)) + (mdx_sess.process_wave(wave, m_threads)) wave_processed *= 0.5 else: wave_processed = mdx_sess.process_wave(wave, m_threads) # return to previous peak wave_processed *= peak stem_name = model.stem_name if suffix is None else suffix main_filepath = None if not exclude_main: main_filepath = os.path.join(output_dir, f"{os.path.basename(os.path.splitext(filename)[0])}_{stem_name}.wav") sf.write(main_filepath, wave_processed.T, sr) invert_filepath = None if not exclude_inversion: diff_stem_name = stem_naming.get(stem_name) if invert_suffix is None else invert_suffix stem_name = f"{stem_name}_diff" if diff_stem_name is None else diff_stem_name invert_filepath = os.path.join(output_dir, f"{os.path.basename(os.path.splitext(filename)[0])}_{stem_name}.wav") sf.write(invert_filepath, (-wave_processed.T * model.compensation) + wave.T, sr) if not keep_orig: os.remove(filename) del mdx_sess, wave_processed, wave if torch.cuda.is_available(): torch.cuda.empty_cache() gc.collect() return main_filepath, invert_filepath