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# app_2.py
# https://github.com/ayushkumarshah/Guitar-Chords-recognition
# https://github.com/ayushkumarshah/Guitar-Chords-recognition/blob/master/app.py
# https://raw.githubusercontent.com/ayushkumarshah/Guitar-Chords-recognition/master/app.py
import time, os
import logging
import streamlit as st
import numpy as np
import librosa, librosa.display
import matplotlib.pyplot as plt
from PIL import Image
from settings import IMAGE_DIR, DURATION, WAVE_OUTPUT_FILE
from src.sound import sound
from src.model import CNN
from setup_logging import setup_logging
setup_logging()
logger = logging.getLogger('app')
def init_model():
cnn = CNN((128, 87))
cnn.load_model()
return cnn
def get_spectrogram(type='mel'):
logger.info("Extracting spectrogram")
y, sr = librosa.load(WAVE_OUTPUT_FILE, duration=DURATION)
ps = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=128)
logger.info("Spectrogram Extracted")
format = '%+2.0f'
if type == 'DB':
ps = librosa.power_to_db(ps, ref=np.max)
format = ''.join[format, 'DB']
logger.info("Converted to DB scale")
return ps, format
def display(spectrogram, format):
plt.figure(figsize=(10, 4))
librosa.display.specshow(spectrogram, y_axis='mel', x_axis='time')
plt.title('Mel-frequency spectrogram')
plt.colorbar(format=format)
plt.tight_layout()
st.pyplot(clear_figure=False)
def main():
title = "Guitar Chord Recognition"
st.title(title)
image = Image.open(os.path.join(IMAGE_DIR, 'app_guitar.jpg'))
st.image(image, use_column_width=True)
if st.button('Record'):
with st.spinner(f'Recording for {DURATION} seconds ....'):
sound.record()
st.success("Recording completed")
if st.button('Play'):
# sound.play()
try:
audio_file = open(WAVE_OUTPUT_FILE, 'rb')
audio_bytes = audio_file.read()
st.audio(audio_bytes, format='audio/wav')
except:
st.write("Please record sound first")
if st.button('Classify'):
cnn = init_model()
with st.spinner("Classifying the chord"):
chord = cnn.predict(WAVE_OUTPUT_FILE, False)
st.success("Classification completed")
st.write("### The recorded chord is **", chord + "**")
if chord == 'N/A':
st.write("Please record sound first")
st.write("\n")
# Add a placeholder
if st.button('Display Spectrogram'):
# type = st.radio("Scale of spectrogram:",
# ('mel', 'DB'))
if os.path.exists(WAVE_OUTPUT_FILE):
spectrogram, format = get_spectrogram(type='mel')
display(spectrogram, format)
else:
st.write("Please record sound first")
if __name__ == '__main__':
main()
# for i in range(100):
# # Update the progress bar with each iteration.
# latest_iteration.text(f'Iteration {i+1}')
# bar.progress(i + 1)
# time.sleep(0.1) |