Spaces:
Sleeping
Sleeping
File size: 5,610 Bytes
9178cf3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 |
import matplotlib.pyplot as plt
import librosa
import librosa.display
import numpy as np
import os,sys
import ruptures as rpt
from glob import glob
import soundfile
import csv
import gradio as gr
def fig_ax(figsize=(15, 5), dpi=150):
"""Return a (matplotlib) figure and ax objects with given size."""
return plt.subplots(figsize=figsize, dpi=dpi)
def get_sum_of_cost(algo, n_bkps) -> float:
"""Return the sum of costs for the change points `bkps`"""
bkps = algo.predict(n_bkps=n_bkps)
return algo.cost.sum_of_costs(bkps)
def variable_outputs(k):
k = int(k)
return [gr.Audio(visible=True)]*k + [gr.Audio(visible=False)]*(10-k)
def generate(wavfile,target_sampling_rate,hop_length_tempo,n_bkps_max):
if target_sampling_rate is not None:
signal2, sampling_rate = librosa.load(wavfile,sr=target_sampling_rate,mono=False)
else:
signal2, sampling_rate = librosa.load(wavfile,mono=False)
signal = signal2.sum(axis=0) / 2
# Compute the onset strength
hop_length_tempo = 512
oenv = librosa.onset.onset_strength(
y=signal, sr=sampling_rate, hop_length=hop_length_tempo
)
# Compute the tempogram
tempogram = librosa.feature.tempogram(
onset_envelope=oenv,
sr=sampling_rate,
hop_length=hop_length_tempo,
)
algo = rpt.KernelCPD(kernel="linear").fit(tempogram.T)
# Choose the number of changes (elbow heuristic)
n_bkps_max = 10 # K_max
# Start by computing the segmentation with most changes.
# After start, all segmentations with 1, 2,..., K_max-1 changes are also available for free.
_ = algo.predict(n_bkps_max)
array_of_n_bkps = np.arange(1, n_bkps_max + 1)
ex = [get_sum_of_cost(algo=algo, n_bkps=n_bkps) for n_bkps in array_of_n_bkps]
# print(ex[0])
biggiest=0
for i in range(1,len(ex)):
if abs(ex[i]- ex[i-1])>biggiest:
biggiest=abs(ex[i]- ex[i-1])
n_bkps=i+2
bkps = algo.predict(n_bkps=n_bkps)
# Convert the estimated change points (frame counts) to actual timestamps
bkps_times = librosa.frames_to_time(bkps, sr=sampling_rate, hop_length=hop_length_tempo)
# Compute change points corresponding indexes in original signal
bkps_time_indexes = (sampling_rate * bkps_times).astype(int).tolist()
bkps = [i//sampling_rate for i in bkps_time_indexes]
# print(bkps_time_indexes)
new_bkps_time_indexes =[]
if len(bkps_time_indexes)>2:
for i in range(len(bkps_time_indexes)):
if i==0:
if bkps_time_indexes[i]>=10*sampling_rate:
new_bkps_time_indexes.append(bkps_time_indexes[i])
elif i==len(bkps_time_indexes)-1:
if bkps_time_indexes[i]-bkps_time_indexes[i-1]<5*sampling_rate:
new_bkps_time_indexes.remove(new_bkps_time_indexes[-1])
new_bkps_time_indexes.append(bkps_time_indexes[i])
else:
if bkps_time_indexes[i]-bkps_time_indexes[i-1]>=10*sampling_rate:
new_bkps_time_indexes.append(bkps_time_indexes[i])
bkps_time_indexes = new_bkps_time_indexes
fig, ax = fig_ax()
_ = librosa.display.specshow(
tempogram,
ax=ax,
x_axis="s",
y_axis="tempo",
hop_length=hop_length_tempo,
sr=sampling_rate,
)
new_bkps_times = [ x/sampling_rate for x in bkps_time_indexes]
for b in new_bkps_times:
ax.axvline(b, ls="--", color="white", lw=4)
seg_list = []
for segment_number, (start, end) in enumerate(
rpt.utils.pairwise([0] + bkps_time_indexes), start=1
):
save_name= f"output_{segment_number}.mp3"
segment = signal2[:,start:end]
seg_list.append(save_name)
soundfile.write(save_name,
segment.T,
int(sampling_rate),
format='MP3'
)
seg_len = len(seg_list)
for i in range(10-seg_len):
seg_list.append("None")
return fig,seg_len,*seg_list
def list_map(lists):
print(len(lists), len(RESULTS))
for i in range(len(lists)):
RESULTS[i]= str(lists[i])
return RESULTS
with gr.Blocks() as demo:
gr.Markdown(
'''
# Demo of Music Segmentation(Intro, Verse, Outro..) using Change Detection Algoritm
'''
)
result_list = gr.State()
with gr.Column():
with gr.Row():
with gr.Column():
wavfile = gr.Audio(sources="upload", type="filepath")
btn_submit = gr.Button()
result_image = gr.Plot(label="result")
with gr.Accordion(label="Settings", open=False):
target_sampling_rate = gr.Number(label="target_sampling_rate", value=44100, interactive=True)
hop_length_tempo = gr.Number(label="hop_length_tempo", value=512, interactive=True)
n_bkps_max = gr.Number(label="n_bkps_max", value=10, interactive=True)
result_len = gr.Number(label="result_len",value=10,interactive=False)
RESULTS = []
with gr.Column():
for i in range(1,11):
w = gr.Audio(label=f"result part {i}",visible=False,type="filepath")
RESULTS.append(w)
result_len.change(variable_outputs,result_len,RESULTS)
# result_len.change(list_map,result_list,RESULTS)
btn_submit.click(
fn=generate,
inputs=[
wavfile,target_sampling_rate,hop_length_tempo,n_bkps_max
],
outputs=[
result_image,result_len,*RESULTS
],
)
demo.queue().launch(server_name="0.0.0.0")
|