import time import numpy as np import torch import torch.nn as nn import yaml from ml_collections import ConfigDict from omegaconf import OmegaConf from tqdm import tqdm def get_model_from_config(model_type, config_path): with open(config_path) as f: if model_type == 'htdemucs': config = OmegaConf.load(config_path) else: config = ConfigDict(yaml.load(f, Loader=yaml.FullLoader)) if model_type == 'htdemucs': from models.demucs4ht import get_model model = get_model(config) elif model_type == 'mel_band_roformer': from models.bs_roformer import MelBandRoformer model = MelBandRoformer( **dict(config.model) ) elif model_type == 'bs_roformer': from models.bs_roformer import BSRoformer model = BSRoformer( **dict(config.model) ) elif model_type == 'scnet': from models.scnet import SCNet model = SCNet( **dict(config.model) ) else: print('Unknown model: {}'.format(model_type)) model = None return model, config def _getWindowingArray(window_size, fade_size): fadein = torch.linspace(0, 1, fade_size) fadeout = torch.linspace(1, 0, fade_size) window = torch.ones(window_size) window[-fade_size:] *= fadeout window[:fade_size] *= fadein return window def demix_track(config, model, mix, device, pbar=False): C = config.audio.chunk_size N = config.inference.num_overlap fade_size = C // 10 step = int(C // N) border = C - step batch_size = config.inference.batch_size length_init = mix.shape[-1] # Do pad from the beginning and end to account floating window results better if length_init > 2 * border and (border > 0): mix = nn.functional.pad(mix, (border, border), mode='reflect') # windowingArray crossfades at segment boundaries to mitigate clicking artifacts windowingArray = _getWindowingArray(C, fade_size) with torch.cuda.amp.autocast(enabled=config.training.use_amp): with torch.inference_mode(): if config.training.target_instrument is not None: req_shape = (1, ) + tuple(mix.shape) else: req_shape = (len(config.training.instruments),) + tuple(mix.shape) result = torch.zeros(req_shape, dtype=torch.float32) counter = torch.zeros(req_shape, dtype=torch.float32) i = 0 batch_data = [] batch_locations = [] progress_bar = tqdm(total=mix.shape[1], desc="Processing audio chunks", leave=False) if pbar else None while i < mix.shape[1]: # print(i, i + C, mix.shape[1]) part = mix[:, i:i + C].to(device) length = part.shape[-1] if length < C: if length > C // 2 + 1: part = nn.functional.pad(input=part, pad=(0, C - length), mode='reflect') else: part = nn.functional.pad(input=part, pad=(0, C - length, 0, 0), mode='constant', value=0) batch_data.append(part) batch_locations.append((i, length)) i += step if len(batch_data) >= batch_size or (i >= mix.shape[1]): arr = torch.stack(batch_data, dim=0) x = model(arr) window = windowingArray if i - step == 0: # First audio chunk, no fadein window[:fade_size] = 1 elif i >= mix.shape[1]: # Last audio chunk, no fadeout window[-fade_size:] = 1 for j in range(len(batch_locations)): start, l = batch_locations[j] result[..., start:start+l] += x[j][..., :l].cpu() * window[..., :l] counter[..., start:start+l] += window[..., :l] batch_data = [] batch_locations = [] if progress_bar: progress_bar.update(step) if progress_bar: progress_bar.close() estimated_sources = result / counter estimated_sources = estimated_sources.cpu().numpy() np.nan_to_num(estimated_sources, copy=False, nan=0.0) if length_init > 2 * border and (border > 0): # Remove pad estimated_sources = estimated_sources[..., border:-border] if config.training.target_instrument is None: return {k: v for k, v in zip(config.training.instruments, estimated_sources)} else: return {k: v for k, v in zip([config.training.target_instrument], estimated_sources)} def demix_track_demucs(config, model, mix, device, pbar=False): S = len(config.training.instruments) C = config.training.samplerate * config.training.segment N = config.inference.num_overlap batch_size = config.inference.batch_size step = C // N # print(S, C, N, step, mix.shape, mix.device) with torch.cuda.amp.autocast(enabled=config.training.use_amp): with torch.inference_mode(): req_shape = (S, ) + tuple(mix.shape) result = torch.zeros(req_shape, dtype=torch.float32) counter = torch.zeros(req_shape, dtype=torch.float32) i = 0 batch_data = [] batch_locations = [] progress_bar = tqdm(total=mix.shape[1], desc="Processing audio chunks", leave=False) if pbar else None while i < mix.shape[1]: # print(i, i + C, mix.shape[1]) part = mix[:, i:i + C].to(device) length = part.shape[-1] if length < C: part = nn.functional.pad(input=part, pad=(0, C - length, 0, 0), mode='constant', value=0) batch_data.append(part) batch_locations.append((i, length)) i += step if len(batch_data) >= batch_size or (i >= mix.shape[1]): arr = torch.stack(batch_data, dim=0) x = model(arr) for j in range(len(batch_locations)): start, l = batch_locations[j] result[..., start:start+l] += x[j][..., :l].cpu() counter[..., start:start+l] += 1. batch_data = [] batch_locations = [] if progress_bar: progress_bar.update(step) if progress_bar: progress_bar.close() estimated_sources = result / counter estimated_sources = estimated_sources.cpu().numpy() np.nan_to_num(estimated_sources, copy=False, nan=0.0) if S > 1: return {k: v for k, v in zip(config.training.instruments, estimated_sources)} else: return estimated_sources def sdr(references, estimates): # compute SDR for one song delta = 1e-7 # avoid numerical errors num = np.sum(np.square(references), axis=(1, 2)) den = np.sum(np.square(references - estimates), axis=(1, 2)) num += delta den += delta return 10 * np.log10(num / den)