app.py

import gradio as gr
import torch
import whisper

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

title="Whisper to Emotion"

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whisper_model = whisper.load_model("small")

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

def translate(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)
        
    return transcription.language


record_input = gr.Audio(source="microphone",type="filepath", show_label=False)

iface = gr.Interface(fn=translate, inputs=record_input, outputs="text")
iface.launch()