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()