Create app.py

app.py

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+from tensorflow import keras
+import os
+import soundfile as sf
+import numpy as np
+import librosa
+import gradio as gr
+import seaborn as sns
+import pandas as pd
+import plotly.express as px
+model = keras.models.load_model('/kaggle/input/emotions/emotion (1).h5')
+labels = ['Angry', 'Disgusted', 'Fearful', 'Happy', 'Neutral', 'Sad', 'Suprised']
+# def load()
+
+def predict(audio):
+#     audio_dir = ('/kaggle/input/sample-audios/')
+#     audio = os.path.join(audio_dir,audio_link)
+    wave, sr = librosa.load(audio, sr=None)
+    segment_dur_secs = 3  
+    segment_length = sr * segment_dur_secs
+    num_sections = int(np.ceil(len(wave) / segment_length))
+    split = []
+    paths =[]
+    for i in range(num_sections):
+        t = wave[i * segment_length: (i + 1) * segment_length]
+        split.append(t)
+        
+    out_dir = ('audio_data/splits/')
+    os.makedirs(out_dir, exist_ok=True)
+    for i in range(num_sections):
+        recording_name = os.path.basename(audio[:-4])
+        out_file = f"{recording_name}_{str(i)}.wav"
+        sf.write(os.path.join(out_dir, out_file), split[i], sr)
+        paths.append(os.path.join(out_dir, out_file))
+        
+            
+    predicted_features = pd.DataFrame(columns=['features'])
+    counter=0
+    for path in paths:
+        X, sample_rate = librosa.load(path
+                                      ,duration=2.5
+                                      ,sr=44100
+                                      ,offset=0.5
+                                     )
+        sample_rate = np.array(sample_rate)
+
+        # mean as the feature. Could do min and max etc as well. 
+        mfccs = np.mean(librosa.feature.mfcc(y=X, 
+                                            sr=sample_rate, 
+                                            n_mfcc=13),
+                        axis=0)
+        predicted_features.loc[counter] = [mfccs]
+        counter=counter+1
+    predicted_features = pd.DataFrame(predicted_features['features'].values.tolist())
+    predicted_features.dropna(inplace=True)
+    preds = model.predict(predicted_features)
+
+    preds=preds.argmax(axis=1)
+    df_preds = pd.DataFrame(preds,columns = ['prediction'])
+    emotions = []
+    for i in df_preds['prediction']:
+        emotion = labels[int(i)]
+        emotions.append(emotion)
+    df_preds['emotion'] = emotions
+    df_preds = df_preds.reset_index()
+    import plotly.io as pio
+    fig = px.line(df_preds, x="index", y="emotion", title='Life expectancy in Canada')
+#     plt = sns.lineplot(df_preds,x='index',y='emotion');
+#     plt.set_xlabel('samples(each in 3s interval)');
+    return fig
+
+# outputs = gr.Plot()
+title = "Emotion recognition"
+description = "This model can shows how speaker emotion changes over the speech"
+
+infr = gr.Interface(fn=predict,
+                    inputs=gr.Audio(type="filepath",),
+                    outputs=outputs,
+                    title=title,description=description,interpretation='default',)
+infr.launch()