app.py

import os
os.system("pip install git+https://github.com/openai/whisper.git")
import gradio as gr
import whisper
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import pipeline


#call tokenizer and NLP model for text classification
tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment-latest")
model_nlp = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment-latest")


# call whisper model for audio/speech processing
model = whisper.load_model("small")


def inference_audio(audio):
    audio = whisper.load_audio(audio)
    audio = whisper.pad_or_trim(audio)
    
    mel = whisper.log_mel_spectrogram(audio).to(model.device)
    
    _, probs = model.detect_language(mel)
    
    options = whisper.DecodingOptions(fp16 = False)
    result = whisper.decode(model, mel, options)
    
    return result.text

def inference_text(audio):
    text =inference_audio(audio)

    sentiment_task = pipeline("sentiment-analysis", model=model_nlp, tokenizer=tokenizer)
    res=sentiment_task(text)[0]

    return res['label'],res['score']

audio = gr.Audio(
                    label="Input Audio",
                    show_label=False,
                    source="microphone",
                    type="filepath"
                )


app=gr.Interface(title="Sentiment Audio Analysis",fn=inference_text,inputs=[audio], outputs=["text","text"])



app.launch()