import os import csv import random import datasets import numpy as np from glob import glob _NAMES = { "chanyin": 0, "dianyin": 6, "shanghua": 2, "xiahua": 3, "huazhi": 4, "guazou": 4, "lianmo": 4, "liantuo": 4, "yaozhi": 5, "boxian": 1, } _NAME = [ "chanyin", # Vibrato "boxian", # Plucks "shanghua", # Upward Portamento "xiahua", # Downward Portamento "huazhi/guazou/lianmo/liantuo", # Glissando "yaozhi", # Tremolo "dianyin", # Point Note ] _HOMEPAGE = f"https://www.modelscope.cn/datasets/ccmusic-database/{os.path.basename(__file__)[:-3]}" _DOMAIN = f"{_HOMEPAGE}/resolve/master/data" _URLS = { "audio": f"{_DOMAIN}/audio.zip", "mel": f"{_DOMAIN}/mel.zip", "label": f"{_DOMAIN}/label.zip", } _TIME_LENGTH = 3 # seconds _SAMPLE_RATE = 44100 _HOP_LENGTH = 512 # SAMPLE_RATE * ZHEN_LENGTH // 1000 class Guzheng_Tech99(datasets.GeneratorBasedBuilder): def _info(self): return datasets.DatasetInfo( features=( datasets.Features( { "audio": datasets.Audio(sampling_rate=44100), "mel": datasets.Image(), "label": datasets.Sequence( feature={ "onset_time": datasets.Value("float32"), "offset_time": datasets.Value("float32"), "IPT": datasets.ClassLabel(num_classes=7, names=_NAME), "note": datasets.Value("int8"), } ), } ) if self.config.name == "default" else datasets.Features( { "data": datasets.features.Array3D( dtype="float32", shape=(88, 258, 1) ), "label": datasets.features.Array2D( dtype="float32", shape=(7, 258) ), } ) ), homepage=_HOMEPAGE, license="CC-BY-NC-ND", version="1.2.0", ) def _RoW_norm(self, data): common_sum = 0 square_sum = 0 tfle = 0 for i in range(len(data)): tfle += (data[i].sum(-1).sum(0) != 0).astype("int").sum() common_sum += data[i].sum(-1).sum(-1) square_sum += (data[i] ** 2).sum(-1).sum(-1) common_avg = common_sum / tfle square_avg = square_sum / tfle std = np.sqrt(square_avg - common_avg**2) return common_avg, std def _norm(self, avg, std, data, size): avg = np.tile(avg.reshape((1, -1, 1, 1)), (size[0], 1, size[2], size[3])) std = np.tile(std.reshape((1, -1, 1, 1)), (size[0], 1, size[2], size[3])) data = (data - avg) / std return data def _load(self, wav_dir, csv_dir, groups, avg=None, std=None): # Return all [(audio address, corresponding to csv file address), ( , ), ...] list if std is None: std = np.array([None]) if avg is None: avg = np.array([None]) def files(wav_dir, csv_dir, group): flacs = sorted(glob(os.path.join(wav_dir, group, "*.flac"))) if len(flacs) == 0: flacs = sorted(glob(os.path.join(wav_dir, group, "*.wav"))) csvs = sorted(glob(os.path.join(csv_dir, group, "*.csv"))) files = list(zip(flacs, csvs)) if len(files) == 0: raise RuntimeError(f"Group {group} is empty") result = [] for audio_path, csv_path in files: result.append((audio_path, csv_path)) return result # Returns the CQT of the input audio def logCQT(file): import librosa sr = _SAMPLE_RATE y, sr = librosa.load(file, sr=sr) # 帧长为32ms (1000ms/(16000/512) = 32ms), D2的频率是73.418 cqt = librosa.cqt( y, sr=sr, hop_length=_HOP_LENGTH, fmin=27.5, n_bins=88, bins_per_octave=12, ) return ( (1.0 / 80.0) * librosa.core.amplitude_to_db(np.abs(cqt), ref=np.max) ) + 1.0 def chunk_data(f): s = int(_SAMPLE_RATE * _TIME_LENGTH / _HOP_LENGTH) xdata = np.transpose(f) x = [] length = int(np.ceil((int(len(xdata) / s) + 1) * s)) app = np.zeros((length - xdata.shape[0], xdata.shape[1])) xdata = np.concatenate((xdata, app), 0) for i in range(int(length / s)): data = xdata[int(i * s) : int(i * s + s)] x.append(np.transpose(data[:s, :])) return np.array(x) def load_all(audio_path, csv_path): # Load audio features: The shape of cqt (88, 8520), 8520 is the number of frames on the time axis cqt = logCQT(audio_path) # Load the ground truth label hop = _HOP_LENGTH n_steps = cqt.shape[1] n_IPTs = 7 technique = _NAMES IPT_label = np.zeros([n_IPTs, n_steps], dtype=int) with open(csv_path, "r") as f: # csv file for each audio reader = csv.DictReader(f, delimiter=",") for label in reader: # each note onset = float(label["onset_time"]) offset = float(label["offset_time"]) IPT = int(technique[label["IPT"]]) left = int(round(onset * _SAMPLE_RATE / hop)) frame_right = int(round(offset * _SAMPLE_RATE / hop)) frame_right = min(n_steps, frame_right) IPT_label[IPT, left:frame_right] = 1 return dict( audiuo_path=audio_path, csv_path=csv_path, cqt=cqt, IPT_label=IPT_label ) data = [] # print(f"Loading {len(groups)} group{'s' if len(groups) > 1 else ''} ") for group in groups: for input_files in files(wav_dir, csv_dir, group): data.append(load_all(*input_files)) i = 0 for dic in data: x = dic["cqt"] x = chunk_data(x) y_i = dic["IPT_label"] y_i = chunk_data(y_i) if i == 0: Xtr = x Ytr_i = y_i i += 1 else: Xtr = np.concatenate([Xtr, x], axis=0) Ytr_i = np.concatenate([Ytr_i, y_i], axis=0) # Transform the shape of the input Xtr = np.expand_dims(Xtr, axis=3) # Calculate the mean and variance of the input if avg.all() == None and std.all() == None: avg, std = self._RoW_norm(Xtr) # Normalize Xtr = self._norm(avg, std, Xtr, Xtr.shape) return list(Xtr), list(Ytr_i) def _parse_csv_label(self, csv_file): label = [] with open(csv_file, mode="r", encoding="utf-8") as file: for row in csv.DictReader(file): label.append( { "onset_time": float(row["onset_time"]), "offset_time": float(row["offset_time"]), "IPT": _NAME[_NAMES[row["IPT"]]], "note": int(row["note"]), } ) return label def _split_generators(self, dl_manager): audio_files = dl_manager.download_and_extract(_URLS["audio"]) csv_files = dl_manager.download_and_extract(_URLS["label"]) trainset, validset, testset = [], [], [] if self.config.name == "default": files = {} mel_files = dl_manager.download_and_extract(_URLS["mel"]) for path in dl_manager.iter_files([audio_files]): fname: str = os.path.basename(path) if fname.endswith(".flac"): item_id = fname.split(".")[0] files[item_id] = {"audio": path} for path in dl_manager.iter_files([mel_files]): fname = os.path.basename(path) if fname.endswith(".jpg"): item_id = fname.split(".")[0] files[item_id]["mel"] = path for path in dl_manager.iter_files([csv_files]): fname = os.path.basename(path) if fname.endswith(".csv"): item_id = fname.split(".")[0] files[item_id]["label"] = self._parse_csv_label(path) for item in files.values(): if "train" in item["audio"]: trainset.append(item) elif "validation" in item["audio"]: validset.append(item) elif "test" in item["audio"]: testset.append(item) else: audio_dir = audio_files + "\\audio" csv_dir = csv_files + "\\label" X_train, Y_train = self._load(audio_dir, csv_dir, ["train"]) X_valid, Y_valid = self._load(audio_dir, csv_dir, ["validation"]) X_test, Y_test = self._load(audio_dir, csv_dir, ["test"]) for i in range(len(X_train)): trainset.append({"data": X_train[i], "label": Y_train[i]}) for i in range(len(X_valid)): validset.append({"data": X_valid[i], "label": Y_valid[i]}) for i in range(len(X_test)): testset.append({"data": X_test[i], "label": Y_test[i]}) random.shuffle(trainset) random.shuffle(validset) random.shuffle(testset) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"files": trainset} ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={"files": validset} ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"files": testset} ), ] def _generate_examples(self, files): for i, path in enumerate(files): yield i, path