import os import numpy as np from pydub import AudioSegment from scipy.ndimage import maximum_filter1d import json import hashlib import tqdm import os from scipy.interpolate import interp1d from scipy.signal import argrelmax def infer_tempo(beats, fps, hist_smooth=4, no_tempo=-1): import madmom ibis = np.diff(beats) * fps bins = np.bincount(np.round(ibis).astype(int)) if not bins.any(): return no_tempo if hist_smooth > 0: bins = madmom.audio.signal.smooth(bins, hist_smooth) intervals = np.arange(len(bins)) interpolation_fn = interp1d(intervals, bins, 'quadratic') intervals = np.arange(intervals[0], intervals[-1], 0.001) tempi = 60.0 * fps / intervals print(tempi) bins = interpolation_fn(intervals) peaks = argrelmax(bins, mode='wrap')[0] if len(peaks) == 0: return no_tempo else: sorted_peaks = peaks[np.argsort(bins[peaks])[::-1]] return tempi[sorted_peaks][0] def quantise(beats): return [int(round(b * 25)) / 25 for b in beats] def get_sample(excerpt_path, beats, existed_uuid_list, split="train", key="gtzan", type="beat"): # print(f'processing {excerpt_path} ...') # print(f'beats: {beats}') data_sample = { "instruction": "Identify and list the timestamps of all beats in this audio track. Use the format of `0.0s,0.54s,1.0ss, ...`", "input": f"<|SOA|>{excerpt_path[len(PATH)+1:]}<|EOA|>", "output": ",".join([f"{b}s" for b in beats]), "uuid": "", "audioid": excerpt_path[len(PATH)+1:], # exclude the '/' at the beginning, to enable os.join.path "split": [split], "task_type": {"major": ["global_MIR"], "minor": ["beat_tracking"]}, "domain": "music", "source": key, "other": {} } if type == "downbeat": data_sample["instruction"] = "Identify and list the timestamps of all downbeats in this audio track. Use the format of `0.0s,1.54s,3.0s, ...`" data_sample["task_type"]["minor"] = ["downbeat_tracking"] # change uuid uuid_string = f"{data_sample['instruction']}#{data_sample['input']}#{data_sample['output']}" unique_id = hashlib.md5(uuid_string.encode()).hexdigest()[:16] #只取前16位 if unique_id in existed_uuid_list: sha1_hash = hashlib.sha1(uuid_string.encode()).hexdigest()[:16] # 为了相加的时候位数对应上 # 将 MD5 和 SHA1 结果相加,并计算新的 MD5 作为最终的 UUID unique_id = hashlib.md5((unique_id + sha1_hash).encode()).hexdigest()[:16] existed_uuid_list.add(unique_id) data_sample["uuid"] = f"{unique_id}" return data_sample EXCERPT_LENGTH = 30 * 1000 # 30 seconds in milliseconds MIN_LENGTH = 5 * 1000 # 5 seconds in milliseconds PATH = '/work/fast_data_yinghao/Beat-Transformer/data' load_annotation = np.load(f'{PATH}/full_beat_annotation.npz', allow_pickle=True) for key in ["ballroom"]: #"rwc", "ballroom", "gtzan", "hainsworth", "carnetic", "smc" # ballroom, GTZAN 30s, beat & downbeat # hainsworth, (RWC,) carnetic: split audio, beat & downbeat # smc: split audio, beat annotation = load_annotation[key] with open(f'{PATH}/audio_lists/{key}.txt', 'r') as f: audio_root = f.readlines() audio_root = [item.replace('\n', '') for item in audio_root] audio_root = [f'{PATH}/{item[37:]}' for item in audio_root] assert(len(annotation) == len(audio_root)) existed_uuid_list = set() data_samples = [] for idx, ann in tqdm.tqdm(enumerate(annotation)): # print(f'processing {audio_root[idx]} ...') audio_path = audio_root[idx] if len(ann.shape) == 1: beats = quantise(ann) downbeats = None elif key != "rwc": beats = quantise(ann[:,0]) downbeats = quantise(ann[ann[:, 1] == 1, 0]) else: NotImplementedError # beat = madmom.utils.quantize_events(annotation[:, 0], fps=self.fps, length=len(song)) # beat = np.maximum(beat, maximum_filter1d(beat, size=3) * 0.5) # beat = np.maximum(beat, maximum_filter1d(beat, size=3) * 0.5) # downbeat = annotation[annotation[:, 1] == 1][:, 0] # downbeat = madmom.utils.quantize_events(downbeat, fps=self.fps, length=len(song)) # downbeat = np.maximum(downbeat, maximum_filter1d(downbeat, size=3) * 0.5) # downbeat = np.maximum(downbeat, maximum_filter1d(downbeat, size=3) * 0.5) # print(f'tempo: {tempo}') if key =="ballroom": # tempo = infer_tempo(beats, fps=100) sample = get_sample(audio_path, beats, existed_uuid_list, key=key) data_samples.append(sample) sample = get_sample(audio_path, downbeats, existed_uuid_list, key=key, type="downbeat") data_samples.append(sample) elif key == "gtzan": if "jazz.00054" in audio_path: continue sample = get_sample(audio_path, beats, existed_uuid_list, split="test", key=key) data_samples.append(sample) if downbeats: sample = get_sample(audio_path, downbeats, existed_uuid_list, split="test", key=key, type="downbeat") data_samples.append(sample) else: audio = AudioSegment.from_file(audio_path) for i in range(0, len(audio), EXCERPT_LENGTH): end = i + EXCERPT_LENGTH if end < len(audio): excerpt = audio[i:end] else: excerpt = audio[i:] # Discard short audio clips if len(excerpt) < MIN_LENGTH: break end = len(audio) # # Save the excerpt to the same directory with a new name excerpt_path = f"{audio_path[:-4]}_{i//EXCERPT_LENGTH}.wav" if not os.path.exists(excerpt_path): excerpt.export(excerpt_path, format="wav") excerpt_beats = [b%30 for b in beats if i * 30 <= b <= (i + 1) * 30] if downbeats: excerpt_downbeats = [db%30 for db in downbeats if i * 30 <= db <= (i + 1) * 30] else: excerpt_downbeats = None # tempo = infer_tempo(excerpt_beats, fps=100) sample = get_sample(excerpt_path, excerpt_beats, existed_uuid_list, key=key) data_samples.append(sample) if downbeats: sample = get_sample(excerpt_path, excerpt_downbeats, existed_uuid_list, key=key, type="downbeat") data_samples.append(sample) # Remove the original audio file # os.remove(audio_path) # break split = "test" if key == "gtzan" else "train" output_file_path = f'{PATH}/../{key}_{split}.jsonl' # Replace with the desired output path with open(output_file_path, 'w') as outfile: # for sample in data_samples: json.dump(data_samples, outfile) # outfile.write('\n') outfile.close()