Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import pipeline
+
+# Load the emotion recognition pipeline
+emotion_pipeline = pipeline(model="j-hartmann/emotion-english-distilroberta-base", task="text-classification")
+# Load the asr pipeline
+asr = pipeline(task="automatic-speech-recognition",
+               model="distil-whisper/distil-small.en")
+#Creating a function to detect emotion from a speech. First we recognise the speech and output as a text and then we feed that to the emotion detection pipeline.
+def transcribe_speech(filepath):
+    if not filepath:
+        return "No audio found, please retry.", []
+
+    # Transcribe the speech
+    output = asr(filepath)
+    transcription = output["text"]
+
+    # Detect the emotion in the transcribed text
+    emotion_output = emotion_pipeline(transcription)
+
+    # Format emotion output
+    formatted_emotions = [
+        f"{emo['label']}: {emo['score']:.4f}" for emo in emotion_output
+    ]
+
+    return transcription, formatted_emotions
+# Gradio interfaces for microphone and file upload
+mic_transcribe = gr.Interface(
+    fn=transcribe_speech,
+    inputs=gr.Audio(sources="microphone", type="filepath"),
+    outputs=[
+        gr.Textbox(label="Transcription", lines=3),
+        gr.Textbox(label="Emotions", lines=5)
+    ],
+    allow_flagging="never"
+)
+
+file_transcribe = gr.Interface(
+    fn=transcribe_speech,
+    inputs=gr.Audio(sources="upload", type="filepath"),
+    outputs=[
+        gr.Textbox(label="Transcription", lines=3),
+        gr.Textbox(label="Emotions", lines=5)
+    ],
+    allow_flagging="never"
+)
+# Create the demo with tabbed interfaces
+demo = gr.Blocks()
+
+with demo:
+    gr.TabbedInterface(
+        [mic_transcribe, file_transcribe],
+        ["Transcribe Microphone", "Transcribe Audio File"],
+    )
+
+# Launch the Gradio demo
+if __name__ == "__main__":
+    demo.launch(debug=True)