app.py

import gradio as gr 
from transformers import RobertaForQuestionAnswering
from transformers import BertForQuestionAnswering
from transformers import AutoTokenizer
from transformers import pipeline
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import sox
import subprocess


def read_file_and_process(wav_file):
    filename = wav_file.split('.')[0]
    filename_16k = filename + "16k.wav"
    resampler(wav_file, filename_16k)
    speech, _ = sf.read(filename_16k)
    inputs = processor(speech, sampling_rate=16_000, return_tensors="pt", padding=True)
    
    return inputs


def resampler(input_file_path, output_file_path):
    command = (
        f"ffmpeg -hide_banner -loglevel panic -i {input_file_path} -ar 16000 -ac 1 -bits_per_raw_sample 16 -vn "
        f"{output_file_path}"
    )
    subprocess.call(command, shell=True)


def parse_transcription(logits):
    predicted_ids = torch.argmax(logits, dim=-1)
    transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
    return transcription


def parse(wav_file):
    input_values = read_file_and_process(wav_file)
    with torch.no_grad():
        logits = model(**input_values).logits
    user_question = parse_transcription(logits)
    return user_question


model_id = "jonatasgrosman/wav2vec2-large-xlsr-53-persian"
processor = Wav2Vec2Processor.from_pretrained(model_id)
model = Wav2Vec2ForCTC.from_pretrained(model_id)

model1 = RobertaForQuestionAnswering.from_pretrained("pedramyazdipoor/persian_xlm_roberta_large")
tokenizer1 = AutoTokenizer.from_pretrained("pedramyazdipoor/persian_xlm_roberta_large")


roberta_large = pipeline(task='question-answering', model=model1, tokenizer=tokenizer1)

def Q_A(text=None, audio=None, context):
    if text is None:
        question = parse(audio)
    elif audio is None:
        question = text
    answer_pedram = roberta_large({"question":question, "context":context})['answer']
    return answer_pedram



# Create title, description and article strings
title = "Question and answer based on Roberta model develop by nima asl toghiri"
description = "سیستم پردازش زبانی پرسش و پاسخ"
article = "آموزش داده شده با مدل زبانی روبرتا"


demo = gr.Interface(fn=Q_A, # mapping function from input to output
                    inputs=[gr.Textbox(label='پرسش خود را وارد کنید:', show_label=True, text_align='right', lines=2),
                            gr.Audio(source="microphone", type="filepath",
                                     label="لطفا دکمه ضبط صدا را بزنید و شروع به صحبت کنید و بعذ از اتمام صحبت دوباره دکمه ضبط را فشار دهید.",
                                     show_download_button=True,
                                     show_edit_button=True,),
                            gr.Textbox(label='متن منبع خود را وارد کنید', show_label=True, text_align='right', lines=8)], # what are the inputs?
                    outputs=gr.Text(show_copy_button=True), # what are the outputs?
                              # our fn has two outputs, therefore we have two outputs
                    # Create examples list from "examples/" directory
                    title=title,
                    description=description,
                    article=article)

# Launch the demo!
demo.launch(share=True)