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import numpy as np
from librosa.core.audio import get_duration
from ...data.clip.clip_process import insert_endclip, insert_startclip
from .clip_process import filter_clipseq_target_point
from .music_clip import MusicClip, MusicClipSeq
def beatnet2TMEType(beat: np.array, duration: float) -> MusicClipSeq:
"""conver beatnet beat to tme beat type
Args:
beat (np.array): Nx2,
1st column is time,
2rd is type,
0, end point
1, strong beat
2,3,4 weak beat
-1 lyric
duration (float): audio time length
Returns:
MusicClipSeq:
"""
n = len(beat)
beat = np.insert(beat, 0, 0, axis=0)
beat = np.insert(beat, n + 1, [duration, 0], axis=0)
clips = []
for i in range(n + 1):
beat_type = int(beat[i + 1, 1])
clip = MusicClip(
time_start=beat[i, 0], # 开始时间
duration=round(beat[i + 1, 0] - beat[i, 0], 3), # 片段持续时间
clipid=i, # 片段序号，
timepoint_type=beat_type,
)
clips.append(clip)
clipseq = MusicClipSeq(clips=clips)
return clipseq
def generate_beatseq_with_beatnet(audio_path: str) -> np.array:
"""使用beatnet生成beat序列
Args:
audio_path (str):
Returns:
np.array: beat序列 Nx2,
1st column is time,
2rd is type,
0, end point
1, strong beat
2,3,4 weak beat
"""
from BeatNet.BeatNet import BeatNet
estimator = BeatNet(1, mode="offline", inference_model="DBN", plot=[], thread=False)
output = estimator.process(audio_path=audio_path)
return output
def generate_music_map_with_beatnet(
audio_path: str, target: list = [0, 1]
) -> MusicClipSeq:
"""使用beatnet生成beat MusicClipseq
Args:
audio_path (str):
target (list, optional): 只保留相应的拍点. Defaults to [0, 1].
Returns:
MusicClipSeq: 返回的beat序列
beat: np.array, 原始的beat检测结果
"""
output = generate_beatseq_with_beatnet(audio_path)
duration = get_duration(filename=audio_path)
clipseq = beatnet2TMEType(output, duration)
clipseq = insert_startclip(clipseq)
clipseq = insert_endclip(clipseq, duration)
clipseq = filter_clipseq_target_point(clipseq, target=target)
return clipseq, output
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