Update app.py

app.py

@@ -1,24 +1,23 @@
 import gradio as gr
-import librosa
-import librosa.display
 import matplotlib.pyplot as plt
+import numpy as np
+import os
+import soundfile as sf
 
 def create_spectrogram_and_get_info(audio_file):
-    # Read the audio data from the file
-    audio_data, sample_rate = librosa.load(audio_file)
 
-    # Compute the mel-scaled spectrogram
-    spectrogram = librosa.feature.melspectrogram(y=audio_data, sr=sample_rate)
+    # Clear figure in case it has data in it
+    plt.clf()
+    
+    # Read the audio data from the file
+    audio_data, sample_rate = sf.read(audio_file)
 
-    # Convert the power spectrogram to decibel (dB) units
-    spectrogram_db = librosa.power_to_db(spectrogram, ref=np.max)
+    # Flatten the audio data if it's not mono
+    audio_data = audio_data.flatten() if len(audio_data.shape) > 1 else audio_data
 
-    # Display the spectrogram
-    plt.figure(figsize=(10, 4))
-    librosa.display.specshow(spectrogram_db, x_axis='time', y_axis='mel', sr=sample_rate, fmax=8000)
-    plt.colorbar(format='%+2.0f dB')
-    plt.title('Mel spectrogram')
-    plt.tight_layout()
+    # Create the spectrogram
+    plt.specgram(audio_data, Fs=sample_rate / 1, NFFT=4096, sides='onesided',
+                 cmap="Reds_r", scale_by_freq=True, scale='dB', mode='magnitude')
 
     # Save the spectrogram to a PNG file
     plt.savefig('spectrogram.png')
@@ -41,3 +40,8 @@ def create_spectrogram_and_get_info(audio_file):
 
     # Return the PNG file of the spectrogram and the info table
     return info_table, 'spectrogram.png'
+
+# Create the Gradio interface
+iface = gr.Interface(fn=create_spectrogram_and_get_info, inputs=gr.Audio(type="filepath"), outputs=["markdown", "image"])
+iface.launch()
+