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import numpy as np
import tensorflow as tf
from scipy.io.wavfile import write
import keras.backend as K
import librosa.display
import cv2
import librosa
import matplotlib.pyplot as plt
import librosa.display
import numpy as np
from keras.applications import VGG16
import os
import scipy
import gradio as gr
import shutil
# Load the tune recognition model
model = tf.keras.models.load_model('embdmodel_1.hdf5')
embedding_model=model.layers[2]
DURATION = 10
WAVE_OUTPUT_FILE = "my_audio.wav"
# Define function to preprocess input audio
#convert song to mel spectogram as siamese network doesn't work on sound directly
def create_spectrogram(clip,sample_rate,save_path):
plt.interactive(False)
fig=plt.figure(figsize=[0.72,0.72])
S=librosa.feature.melspectrogram(y=clip,sr=sample_rate)
librosa.display.specshow(librosa.power_to_db(S,ref=np.max))
fig.savefig(save_path,dpi=400,bbox_inches='tight',pad_inches=0)
plt.close()
fig.clf()
plt.close(fig)
plt.close('all')
del save_path,clip,sample_rate,fig,S
def load_img(path):
img=cv2.imread(path)
img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
img=cv2.resize(img,(150,150))
return img
import pickle
with open('dict.pickle', 'rb') as handle:
songspecdict = pickle.load(handle)
def list_file_sizes():
path = "."
# Get list of all files only in the given directory
fun = lambda x : os.path.isfile(os.path.join(path,x))
files_list = filter(fun, os.listdir(path))
# Create a list of files in directory along with the size
size_of_file = [
(f,os.stat(os.path.join(path, f)).st_size)
for f in files_list
]
# Iterate over list of files along with size
# and print them one by one.
for f,s in size_of_file:
print("{} : {}MB".format(f, round(s/(1024*1024),3)))
def main(audio):
print(audio)
shutil.copy(audio, "./my_audio.wav")
list_file_sizes()
# Load the song to match
song, sr = librosa.load("my_audio.wav")
to_match = np.copy(song[0:220500])
print("Loaded data into librosa...")
# Create spectrogram image of the song to match
create_spectrogram(to_match, sr, 'test.png')
print("Created spectogram...")
# Load the spectrogram image of the song to match
to_match_img = load_img('test.png')
to_match_img = np.expand_dims(to_match_img, axis=0)
print("Loaded spectrum image...")
# Get the embedding of the song to match
to_match_emb = embedding_model.predict(to_match_img)
print("Get song embedding...")
# Calculate the distances between the song to match and the songs in the database
songsdistdict = {}
for key, values in songspecdict.items():
dist_array = []
for embd in values:
dist_array.append(np.linalg.norm(to_match_emb - embd))
songsdistdict[key] = min(dist_array)
song_titles=list(songsdistdict.keys())
distances=list(songsdistdict.values())
# Get the title and artist of the recognized song
recognized_song_artist, recognized_song_title = song_titles[distances.index(min(distances))].split('-')
recognized_song_title = os.path.splitext(recognized_song_title)[0]
print(f'Artist: {recognized_song_artist}')
print(f'Title: {recognized_song_title}')
from musixmatch import Musixmatch
# Initialize Musixmatch API
musixmatch = Musixmatch(apikey='2b0d0615efa782e95598a0e99bda4a60')
# Search for the recognized song
track_search_results = musixmatch.track_search(q_track=recognized_song_title, q_artist=recognized_song_artist, page_size=1, page=1, s_track_rating='desc')
if track_search_results['message']['header']['status_code'] == 200:
# Get the track ID for the top result
track_id = track_search_results['message']['body']['track_list'][0]['track']['track_id']
# Get the lyrics for the recognized song
lyrics_result = musixmatch.track_lyrics_get(track_id=track_id)
if lyrics_result['message']['header']['status_code'] == 200:
# Get the lyrics
lyrics = lyrics_result['message']['body']['lyrics']['lyrics_body']
# Remove the annotation tags from the lyrics
lyrics = lyrics.replace('******* This Lyrics is NOT for Commercial use *******', '').strip()
print("Lyrics:\n", lyrics)
else:
print("Couldn't find lyrics for the recognized song.")
# Play the recognized song
recognized_song_file = f'https://huggingface.co/spaces/prerna9811/Chord/tree/main/seismese_net_songs/{song_titles[distances.index(min(distances))]}'
recognized_song_audio, recognized_song_sr = librosa.load(recognized_song_file)
audio_file = open(recognized_song_file, 'rb') # enter the filename with filepath
audio_bytes = audio_file.read() # reading the file
return audio_bytes
import asyncio
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
demo = gr.Blocks()
mf_transcribe = gr.Interface(
fn=main,
inputs=gr.inputs.Audio(source="microphone", type="filepath"),
outputs="audio",
layout="horizontal",
theme="huggingface",
)
mf_transcribe.launch()
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