Music_Seperation_and_Visualization/model/another-try/Wave-U-Net/read_the_sound.py

import torch
import torchaudio
import matplotlib as plt
import musdb
import os
import numpy as np
import glob
import librosa
import soundfile



def load(path, sr=22050, mono=True, mode="numpy", offset=0.0, duration=None):
    y, curr_sr = librosa.load(path, sr=sr, mono=mono, res_type='kaiser_fast', offset=offset, duration=duration)

    if len(y.shape) == 1:
        # Expand channel dimension
        y = y[np.newaxis, :]

    if mode == "pytorch":
        y = torch.tensor(y)

    return y, curr_sr


def write_wav(path, audio, sr):
    soundfile.write(path, audio.T, sr, "PCM_16")

def get_musdbhq(database_path):
    '''
    Retrieve audio file paths for MUSDB HQ dataset
    :param database_path: MUSDB HQ root directory
    :return: dictionary with train and test keys, each containing list of samples, each sample containing all audio paths
    '''
    subsets = list()

    for subset in ["train", "test"]:
        print("Loading " + subset + " set...")
        tracks = glob.glob(os.path.join(database_path, subset, "*"))
        samples = list()

        # Go through tracks
        for track_folder in sorted(tracks):
            # Skip track if mixture is already written, assuming this track is done already
            example = dict()
            for stem in ["mix", "bass", "drums", "other", "vocals"]:
                filename = stem if stem != "mix" else "mixture"
                audio_path = os.path.join(track_folder, filename + ".wav")
                example[stem] = audio_path

            # Add other instruments to form accompaniment
            acc_path = os.path.join(track_folder, "accompaniment.wav")

            if not os.path.exists(acc_path):
                print("Writing accompaniment to " + track_folder)
                stem_audio = []
                for stem in ["bass", "drums", "other"]:
                    audio, sr = load(example[stem], sr=None, mono=False)
                    stem_audio.append(audio)
                acc_audio = np.clip(sum(stem_audio), -1.0, 1.0)
                write_wav(acc_path, acc_audio, sr)

            example["accompaniment"] = acc_path

            samples.append(example)

        subsets.append(samples)

    return subsets

path = "C:/Users/IAN/Desktop/Wave-U-Net/musdb18-hq/"

res = get_musdbhq(path)
print(res)