Create app.py

app.py

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+from transformers import pipeline
+from sessions import sessions
+import torchaudio
+import torchaudio.transforms as T
+import gradio as gr
+
+pipe = pipeline(
+    "audio-classification",
+    model="BilalHasan/distilhubert-finetuned-ravdess",
+)
+
+audio_batch = []
+def split_audio(array):
+    len_of_each_array = 30 * 16000
+    arr1, arr2 = array[0: len_of_each_array], array[int(len_of_each_array / 2):]
+    audio_batch.append(arr1)
+    if len(arr2) > len_of_each_array:
+        split_audio(arr2)
+    else:
+        audio_batch.append(arr2)
+    return audio_batch
+
+
+def prediction(path):
+    predictions = []
+    array, sr = torchaudio.load(path)
+    resampler = T.Resample(sr, 16000)
+    resampled_audio = resampler(array)
+    audio_batch = split_audio(resampled_audio[0].numpy())
+    for i in range(len(audio_batch)):
+        predictions.append(pipe(audio_batch[i])[0]['label'])
+    mood = max(set(predictions), key = predictions.count)
+    if mood in ['neutral', 'calm', 'happy', 'surprised']:
+        mood = 'other'
+    session = sessions.mood2session[mood]
+    return mood, session
+
+
+demo = gr.Interface(
+    fn=prediction,
+    inputs=[gr.Audio(type="filepath")],
+    outputs=[gr.Textbox(label="Mood"), gr.Textbox(label="Session")]
+)
+demo.launch()