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| # 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) |