Create app_2.py

app_2.py

@@ -0,0 +1,92 @@
+# 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)