.ipynb_checkpoints/inference-checkpoint.py

import argparse
import os

import librosa
import numpy as np
import soundfile as sf
import torch
from tqdm import tqdm

from lib import dataset
from lib import nets
from lib import spec_utils
from lib import utils


class Separator(object):

    def __init__(self, model, device=None, batchsize=1, cropsize=256, postprocess=False):
        self.model = model
        self.offset = model.offset
        self.device = device
        self.batchsize = batchsize
        self.cropsize = cropsize
        self.postprocess = postprocess

    def _postprocess(self, X_spec, mask):
        if self.postprocess:
            mask_mag = np.abs(mask)
            mask_mag = spec_utils.merge_artifacts(mask_mag)
            mask = mask_mag * np.exp(1.j * np.angle(mask))

        y_spec = X_spec * mask
        v_spec = X_spec - y_spec

        return y_spec, v_spec

    def _separate(self, X_spec_pad, roi_size):
        X_dataset = []
        patches = (X_spec_pad.shape[2] - 2 * self.offset) // roi_size
        for i in range(patches):
            start = i * roi_size
            X_spec_crop = X_spec_pad[:, :, start:start + self.cropsize]
            X_dataset.append(X_spec_crop)

        X_dataset = np.asarray(X_dataset)

        self.model.eval()
        with torch.no_grad():
            mask_list = []
            # To reduce the overhead, dataloader is not used.
            for i in tqdm(range(0, patches, self.batchsize)):
                X_batch = X_dataset[i: i + self.batchsize]
                X_batch = torch.from_numpy(X_batch).to(self.device)

                mask = self.model.predict_mask(X_batch)

                mask = mask.detach().cpu().numpy()
                mask = np.concatenate(mask, axis=2)
                mask_list.append(mask)

            mask = np.concatenate(mask_list, axis=2)

        return mask

    def separate(self, X_spec):
        n_frame = X_spec.shape[2]
        pad_l, pad_r, roi_size = dataset.make_padding(n_frame, self.cropsize, self.offset)
        X_spec_pad = np.pad(X_spec, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
        X_spec_pad /= np.abs(X_spec).max()

        mask = self._separate(X_spec_pad, roi_size)
        mask = mask[:, :, :n_frame]

        y_spec, v_spec = self._postprocess(X_spec, mask)

        return y_spec, v_spec

    def separate_tta(self, X_spec):
        n_frame = X_spec.shape[2]
        pad_l, pad_r, roi_size = dataset.make_padding(n_frame, self.cropsize, self.offset)
        X_spec_pad = np.pad(X_spec, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
        X_spec_pad /= X_spec_pad.max()

        mask = self._separate(X_spec_pad, roi_size)

        pad_l += roi_size // 2
        pad_r += roi_size // 2
        X_spec_pad = np.pad(X_spec, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
        X_spec_pad /= X_spec_pad.max()

        mask_tta = self._separate(X_spec_pad, roi_size)
        mask_tta = mask_tta[:, :, roi_size // 2:]
        mask = (mask[:, :, :n_frame] + mask_tta[:, :, :n_frame]) * 0.5

        y_spec, v_spec = self._postprocess(X_spec, mask)

        return y_spec, v_spec




def main(gpu=-1, pretrained_model='models/baseline.pth', input_file='', sr=44100, n_fft=2048, 
         hop_length=1024, batchsize=4, cropsize=256, output_image=False, tta=False, output_dir=""):

    print('loading model...', end=' ')
    device = torch.device('cpu')
    if gpu >= 0:
        if torch.cuda.is_available():
            device = torch.device('cuda:{}'.format(gpu))
        elif torch.backends.mps.is_available() and torch.backends.mps.is_built():
            device = torch.device('mps')
    model = nets.CascadedNet(n_fft, hop_length, 32, 128, True)
    model.load_state_dict(torch.load(pretrained_model, map_location='cpu'))
    model.to(device)
    print('done')

    print('loading wave source...', end=' ')
    print('loading wave source...', end=' ')
    print("Chemin du fichier audio :", input_file)  # Ajoutez cette ligne pour déboguer
    X, sr = librosa.load(input_file, sr=sr, mono=False, dtype=np.float32, res_type='kaiser_fast')
    basename = os.path.splitext(os.path.basename(input_file))[0]
    print('done')
   
    if X.ndim == 1:
        X = np.asarray([X, X])

    print('stft of wave source...', end=' ')
    X_spec = spec_utils.wave_to_spectrogram(X, hop_length, n_fft)
    print('done')

    sp = Separator(
        model=model,
        device=device,
        batchsize=batchsize,
        cropsize=cropsize,
    )

    if tta:
        y_spec, v_spec = sp.separate_tta(X_spec)
    else:
        y_spec, v_spec = sp.separate(X_spec)

    print('validating output directory...', end=' ')
    if output_dir != "":
        output_dir = output_dir.rstrip('/') + '/'
        os.makedirs(output_dir, exist_ok=True)
    print('done')

    print('inverse stft of instruments...', end=' ')
    wave = spec_utils.spectrogram_to_wave(y_spec, hop_length=hop_length)
    print('done')
    sf.write('{}{}_Instruments.wav'.format(output_dir, basename), wave.T, sr)

    print('inverse stft of vocals...', end=' ')
    wave = spec_utils.spectrogram_to_wave(v_spec, hop_length=hop_length)
    print('done')
    sf.write('{}{}_Vocals_finale.wav'.format(output_dir, basename), wave.T, sr)

    if output_image:
        image = spec_utils.spectrogram_to_image(y_spec)
        utils.imwrite('{}{}_Instruments.jpg'.format(output_dir, basename), image)

        image = spec_utils.spectrogram_to_image(v_spec)
        utils.imwrite('{}{}_Vocals.jpg'.format(output_dir, basename), image)



import os
# Appel de la fonction avec des paramètres

main(input_file=os.getcwd()+'/audio_gnu.wav')