app.py

import gradio as gr
import torch
import whisper
from transformers import pipeline

### ————————————————————————————————————————

title="Whisper to Emotion"

### ————————————————————————————————————————

whisper_model = whisper.load_model("small")

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

emotion_classifier = pipeline("text-classification",model='bhadresh-savani/distilbert-base-uncased-emotion')

def translate_and_classify(audio):
    
    print("""
    —
    Sending audio to Whisper ...
    —
    """)
    audio = whisper.load_audio(audio)
    audio = whisper.pad_or_trim(audio)
    
    mel = whisper.log_mel_spectrogram(audio).to(whisper_model.device)
    
    _, probs = whisper_model.detect_language(mel)
    
    transcript_options = whisper.DecodingOptions(task="transcribe", fp16 = False)
    translate_options = whisper.DecodingOptions(task="translate", fp16 = False)
    
    transcription = whisper.decode(whisper_model, mel, transcript_options)
    translation = whisper.decode(whisper_model, mel, translate_options)
    
    print("Language Spoken: " + transcription.language)
    print("Transcript: " + transcription.text)  
    print("Translated: " + translation.text)
    
    emotion = emotion_classifier(translation.text)
    detected_emotion = emotion[0]["label"]
    return transcription.text, detected_emotion

with gr.Blocks() as demo:
    gr.Markdown("""
                ## Emotion Detection From Speech with Whisper
            """)
            
     gr.HTML(
     <p style="margin-bottom: 10px; font-size: 94%">
                Whisper is a general-purpose speech recognition model released by OpenAI that can perform multilingual speech recognition as well as speech translation and language identification. This allows for detecting emotion directly from speech in multiple languages 
              </p>
              )
    
    with gr.Row():
        with gr.Column():
            #gr.Markdown(""" ### Record audio """)
            audio_input = gr.Audio(label = 'Record Audio Input',source="microphone",type="filepath")
            with gr.Row():
                transcribe_audio = gr.Button('Transcribe')
                
        with gr.Column():
            with gr.Row():
                transcript_output = gr.Textbox(label="Transcription in the language you spoke", lines = 3)
                emotion_output = gr.Textbox(label = "Detected Emotion")
    
    transcribe_audio.click(translate_and_classify, inputs = audio_input, outputs = [transcript_output,emotion_output])
    
    gr.HTML('''
        <div class="footer">
                    <p>Whisper Model by <a href="https://github.com/openai/whisper" style="text-decoration: underline;" target="_blank">OpenAI</a> - Emotion Detection Model  
                    <a href="https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion" style="text-decoration: underline;" target="_blank">OpenAI</a>
                    </p>
        </div>
        ''')
    
    
demo.launch()