added wave and spectogram

README.md

@@ -1,6 +1,6 @@
 ---
 title: Heartbeat
-emoji: 🚀
+emoji: 💜
 colorFrom: purple
 colorTo: blue
 sdk: gradio

app.py

@@ -1,55 +1,107 @@
+from plotly.subplots import make_subplots
+import plotly.graph_objects as go
 import gradio as gr
 import numpy as np
 import librosa
 
 
+def getBeats(audiodata:np.ndarray, sr:int):
+    # Compute onset envelope
+    onset_env = librosa.onset.onset_strength(y=audiodata, sr=sr)
+    
+    # Detect beats
+    tempo, beats = librosa.beat.beat_track(onset_envelope=onset_env, sr=sr)
+    
+    # Convert beat frames to time
+    beattimes = librosa.frames_to_time(beats, sr=sr)
+    
+    return tempo[0], beattimes
+
+def plotCombined(audiodata, sr):
+    # Create subplots
+    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.1,
+                        subplot_titles=('Audio Waveform', 'Spectrogram'))
+
+    # Waveform plot
+    time = (np.arange(0, len(audiodata)) / sr)*2
+    fig.add_trace(
+        go.Scatter(x=time, y=audiodata, mode='lines', name='Waveform', line=dict(color='blue', width=1)),
+        row=1, col=1
+    )
+
+    # Spectrogram plot
+    D = librosa.stft(audiodata)
+    S_db = librosa.amplitude_to_db(np.abs(D), ref=np.max)
+    times = librosa.times_like(S_db)
+    freqs = librosa.fft_frequencies(sr=sr)
+
+    fig.add_trace(
+        go.Heatmap(z=S_db, x=times, y=freqs, colorscale='Viridis', 
+                   zmin=S_db.min(), zmax=S_db.max(), colorbar=dict(title='Magnitude (dB)')),
+        row=2, col=1
+    )
+
+    # Update layout
+    fig.update_layout(
+        height=800, width=900,
+        title_text="Audio Analysis",
+    )
+    fig.update_xaxes(title_text="Time (s)", row=2, col=1)
+    fig.update_yaxes(title_text="Amplitude", row=1, col=1)
+    fig.update_yaxes(title_text="Frequency (Hz)", type="log", row=2, col=1)
+
+    return fig
 
 def analyze(audio:gr.Audio):
     # Extract audio data and sample rate
-    sr, audio_data = audio
+    sr, audiodata = audio
 
-    # Ensure audio_data is a numpy array
-    if not isinstance(audio_data, np.ndarray):
-        audio_data = np.array(audio_data)
+    # Ensure audiodata is a numpy array
+    if not isinstance(audiodata, np.ndarray):
+        audiodata = np.array(audiodata)
 
     # Check if audio is mono or stereo
-    if len(audio_data.shape) > 1:
+    if len(audiodata.shape) > 1:
         # If stereo, convert to mono by averaging channels
-        audio_data = np.mean(audio_data, axis=1)
+        audiodata = np.mean(audiodata, axis=1)
 
-    audio_data = np.astype(audio_data, np.float16)
+    audiodata = np.astype(audiodata, np.float16)
 
     # Now you have:
-    # - audio_data: a 1D numpy array containing the audio samples
+    # - audiodata: a 1D numpy array containing the audio samples
     # - sr: the sample rate of the audio
 
     # Your analysis code goes here
     # For example, you could print basic information:
-    print(f"Audio length: {len(audio_data) / sr:.2f} seconds")
+    print(f"Audio length: {len(audiodata) / sr:.2f} seconds")
     print(f"Sample rate: {sr} Hz")
 
-    zcr = librosa.feature.zero_crossing_rate(audio_data)[0]
+    zcr = librosa.feature.zero_crossing_rate(audiodata)[0]
     print(f"Mean Zero Crossing Rate: {np.mean(zcr):.4f}")
 
     # Calculate RMS Energy
-    rms = librosa.feature.rms(y=audio_data)[0]
+    rms = librosa.feature.rms(y=audiodata)[0]
     print(f"Mean RMS Energy: {np.mean(rms):.4f}")
 
-    # Return your analysis results
+    tempo, beattimes = getBeats(audiodata, sr)
+    spectogram_wave = plotCombined(audiodata, sr)
 
+    # Return your analysis results
     results = f"""
-    - Audio length: {len(audio_data) / sr:.2f} seconds
+    - Audio length: {len(audiodata) / sr:.2f} seconds
     - Sample rate: {sr} Hz
     - Mean Zero Crossing Rate: {np.mean(zcr):.4f}
     - Mean RMS Energy: {np.mean(rms):.4f}
+    - Tempo: {tempo}
+    - Beats: {beattimes}
     """
-    return results
+    return results, spectogram_wave
 
 
 
 with gr.Blocks() as app:
 
-    gr.Markdown("🚨 This Project is still in works")
+    gr.Markdown("# 🚨 This Project is still in works")
 
     gr.Markdown("# Heartbeat")
     gr.Markdown("This App helps to analyze and extract Information from Heartbeats")
@@ -70,8 +122,9 @@ with gr.Blocks() as app:
     analyzebtn = gr.Button("analyze")
 
     results = gr.Markdown()
+    spectogram_wave = gr.Plot()
 
-    analyzebtn.click(analyze, audiofile, results)
+    analyzebtn.click(analyze, audiofile, [results, spectogram_wave])