app.py

import os
import spaces
import torch
import gradio as gr
from transformers import pipeline
from transformers.pipelines.audio_utils import ffmpeg_read

MODEL_NAME = "openai/whisper-large-v3"
BATCH_SIZE = 8
FILE_LIMIT_MB = 1000

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

pipe = pipeline(
    task="automatic-speech-recognition",
    model=MODEL_NAME,
    chunk_length_s=30,
    device=device,
)

@spaces.GPU
def respond_to_question(transcript, question):
    # Optionally, use OpenAI API to generate a response to the user's question
    # based on the transcript
    response = ""
    # Replace this with your OpenAI API key
    openai.api_key = os.environ["OPENAI_API_KEY"]
    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=f"Transcript: {transcript}\n\nUser: {question}\n\nAI:",
        temperature=0.3,
        max_tokens=60,
        top_p=1,
        frequency_penalty=0,
        presence_penalty=0
    ).choices[0].text
    return response
    
with gr.Blocks() as transcriberUI:
    gr.Markdown(
        """
        # Ola!
        Clicar no botao abaixo para selecionar o Audio a ser transcrito!
        Ambiente Demo disponivel 24x7. Running on ZeroGPU with openai/whisper-large-v3
        """
    )
    inp = gr.File(label="Arquivo de Audio", show_label=True, type="filepath", file_count="single", file_types=["mp3"])
    transcribe = gr.Textbox(label="Transcricao", show_label=True, show_copy_button=True)
    ask_question = gr.Textbox(label="Ask a question", visible=True)
    response_output = gr.Textbox(label="Response", visible=True)
    submit_question = gr.Button("Submit question", visible=True)

    @spaces.GPU
    def audio_transcribe(inputs):
        if inputs is None:
            raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
    
        text = pipe(inputs, batch_size=BATCH_SIZE, return_timestamps=True)["text"]
        ask_question.visible = True
        submit_question.visible = True
    
        return  text

    def ask_question_callback(transcribe_output,ask_question):
        if ask_question:
            response = respond_to_question(transcript_output, ask_question)
            response_output.visible = True
            response_output.value = response
        else:
            response_output.value = "No question asked"

        return response_output

    inp.upload(audio_transcribe, inputs=inp, outputs=transcribe)
    submit_question.click(ask_question_callback, outputs=[response_output], inputs=[transcribe, ask_question])


transcriberUI.queue().launch()