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inference_v5.py
198
inference_v5.py
@ -1,7 +1,16 @@
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from functools import total_ordering
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import pprint
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import argparse
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import os
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import importlib
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from statistics import mode
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import sys
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import subprocess
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import contextlib
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from subprocess import run
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from tkinter.ttk import Progressbar
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from typing import _SpecialForm, overload
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from unittest.mock import _SpecState
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import cv2
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import librosa
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@ -20,9 +29,7 @@ from collections import defaultdict
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import tkinter as tk
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import traceback # Error Message Recent Calls
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import time # Timer
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import random
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class VocalRemover(object):
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@ -44,7 +51,10 @@ class VocalRemover(object):
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global args
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global model_params_d
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#progressb = tqdm
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p = argparse.ArgumentParser()
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p.add_argument('--paramone', type=str, default='lib_v5/modelparams/4band_44100.json')
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p.add_argument('--paramtwo', type=str, default='lib_v5/modelparams/4band_v2.json')
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@ -96,85 +106,6 @@ class VocalRemover(object):
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self.text_widget.write('Done!\n')
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def _execute(self, X_mag_pad, roi_size, n_window, device, model, aggressiveness):
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model.eval()
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with torch.no_grad():
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preds = []
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for i in tqdm(range(n_window)):
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start = i * roi_size
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X_mag_window = X_mag_pad[None, :, :, start:start + self.data['window_size']]
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X_mag_window = torch.from_numpy(X_mag_window).to(device)
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pred = model.predict(X_mag_window, aggressiveness)
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pred = pred.detach().cpu().numpy()
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preds.append(pred[0])
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pred = np.concatenate(preds, axis=2)
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return pred
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def preprocess(self, X_spec):
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X_mag = np.abs(X_spec)
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X_phase = np.angle(X_spec)
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return X_mag, X_phase
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def inference(self, X_spec, device, model, aggressiveness):
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X_mag, X_phase = self.preprocess(X_spec)
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coef = X_mag.max()
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X_mag_pre = X_mag / coef
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n_frame = X_mag_pre.shape[2]
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pad_l, pad_r, roi_size = dataset.make_padding(n_frame,
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self.data['window_size'], model.offset)
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n_window = int(np.ceil(n_frame / roi_size))
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X_mag_pad = np.pad(
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X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
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pred = self._execute(X_mag_pad, roi_size, n_window,
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device, model, aggressiveness)
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pred = pred[:, :, :n_frame]
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return pred * coef, X_mag, np.exp(1.j * X_phase)
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def inference_tta(self, X_spec, device, model, aggressiveness):
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X_mag, X_phase = self.preprocess(X_spec)
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coef = X_mag.max()
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X_mag_pre = X_mag / coef
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n_frame = X_mag_pre.shape[2]
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pad_l, pad_r, roi_size = dataset.make_padding(n_frame,
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self.data['window_size'], model.offset)
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n_window = int(np.ceil(n_frame / roi_size))
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X_mag_pad = np.pad(
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X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
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pred = self._execute(X_mag_pad, roi_size, n_window,
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device, model, aggressiveness)
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pred = pred[:, :, :n_frame]
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pad_l += roi_size // 2
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pad_r += roi_size // 2
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n_window += 1
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X_mag_pad = np.pad(
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X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
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pred_tta = self._execute(X_mag_pad, roi_size, n_window,
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device, model, aggressiveness)
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pred_tta = pred_tta[:, :, roi_size // 2:]
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pred_tta = pred_tta[:, :, :n_frame]
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return (pred + pred_tta) * 0.5 * coef, X_mag, np.exp(1.j * X_phase)
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data = {
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# Paths
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'input_paths': None,
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@ -230,9 +161,9 @@ def determineModelFolderName():
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return modelFolderName
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def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress_var: tk.Variable,
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**kwargs: dict):
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def save_files(wav_instrument, wav_vocals):
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"""Save output music files"""
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vocal_name = '(Vocals)'
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@ -350,23 +281,102 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
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step=0.1)
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text_widget.write(base_text + 'Stft of wave source...\n')
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text_widget.write(base_text + 'Done!\n')
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text_widget.write(base_text + "Please Wait..\n")
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X_spec_m = spec_utils.combine_spectrograms(X_spec_s, mp)
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del X_wave, X_spec_s
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if data['tta']:
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pred, X_mag, X_phase = vocal_remover.inference_tta(X_spec_m,
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device,
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model, {'value': args.aggressiveness,'split_bin': mp.param['band'][1]['crop_stop']})
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else:
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pred, X_mag, X_phase = vocal_remover.inference(X_spec_m,
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device,
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model, {'value': args.aggressiveness,'split_bin': mp.param['band'][1]['crop_stop']})
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def inference(X_spec, device, model, aggressiveness):
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def _execute(X_mag_pad, roi_size, n_window, device, model, aggressiveness):
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model.eval()
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with torch.no_grad():
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preds = []
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iterations = [n_window]
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total_iterations = sum(iterations)
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text_widget.write(base_text + "Length: "f"{total_iterations} Slices\n")
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for i in tqdm(range(n_window)):
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update_progress(**progress_kwargs,
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step=(0.1 + (0.8/n_window * i)))
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start = i * roi_size
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X_mag_window = X_mag_pad[None, :, :, start:start + data['window_size']]
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X_mag_window = torch.from_numpy(X_mag_window).to(device)
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pred = model.predict(X_mag_window, aggressiveness)
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pred = pred.detach().cpu().numpy()
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preds.append(pred[0])
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pred = np.concatenate(preds, axis=2)
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return pred
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def preprocess(X_spec):
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X_mag = np.abs(X_spec)
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X_phase = np.angle(X_spec)
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return X_mag, X_phase
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X_mag, X_phase = preprocess(X_spec)
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coef = X_mag.max()
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X_mag_pre = X_mag / coef
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n_frame = X_mag_pre.shape[2]
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pad_l, pad_r, roi_size = dataset.make_padding(n_frame,
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data['window_size'], model.offset)
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n_window = int(np.ceil(n_frame / roi_size))
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X_mag_pad = np.pad(
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X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
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pred = _execute(X_mag_pad, roi_size, n_window,
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device, model, aggressiveness)
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pred = pred[:, :, :n_frame]
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if data['tta']:
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pad_l += roi_size // 2
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pad_r += roi_size // 2
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n_window += 1
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X_mag_pad = np.pad(
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X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
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pred_tta = _execute(X_mag_pad, roi_size, n_window,
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device, model, aggressiveness)
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pred_tta = pred_tta[:, :, roi_size // 2:]
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pred_tta = pred_tta[:, :, :n_frame]
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return (pred + pred_tta) * 0.5 * coef, X_mag, np.exp(1.j * X_phase)
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else:
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return pred * coef, X_mag, np.exp(1.j * X_phase)
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aggressiveness = {'value': args.aggressiveness, 'split_bin': mp.param['band'][1]['crop_stop']}
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if data['tta']:
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text_widget.write(base_text + "Running Inferences (TTA)...\n")
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else:
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text_widget.write(base_text + "Running Inference...\n")
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pred, X_mag, X_phase = inference(X_spec_m,
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device,
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model, aggressiveness)
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text_widget.write(base_text + 'Done!\n')
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update_progress(**progress_kwargs,
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step=0.6)
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step=0.9)
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# Postprocess
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if data['postprocess']:
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text_widget.write(base_text + 'Post processing...\n')
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@ -375,7 +385,7 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
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text_widget.write(base_text + 'Done!\n')
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update_progress(**progress_kwargs,
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step=0.65)
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step=0.95)
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# Inverse stft
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text_widget.write(base_text + 'Inverse stft of instruments and vocals...\n') # nopep8
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