model.py

import subprocess

import speech_recognition as sr
from transformers import Wav2Vec2FeatureExtractor, Wav2Vec2ForCTC, AutoModelForCTC
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from utils import WHITESPACE_HANDLER
from transformers import pipeline
from settings import settings
from transformers import AutoProcessor, AutoModelForCTC
import torchaudio
import requests


async def create_wav(audio_file):
    wav_audio_path = audio_file.replace(audio_file.split(".")[-1], '.wav')
    subprocess.run(['ffmpeg', '-i', audio_file, wav_audio_path])
    return wav_audio_path


async def speech2text(audio_file):
    if not audio_file.endswith(".wav"):
        audio_file = await create_wav()

    # recognizer = sr.Recognizer()
    # with sr.AudioFile(audio_file) as audio_file:
    #     audio = recognizer.record(audio_file)
    #     aligned_transcript = recognizer.recognize_google(audio, language=settings.LANGUAGE)

    url = settings.URL
    headers = {'Authorization': settings.API}
    files = {'file': (audio_file, open(audio_file, 'rb'))}
    response = requests.post(url, headers=headers, files=files)
    aligned_transcript = response.json()['result']["text"]

    return aligned_transcript

async def summerizer(aligned_transcript):
    model_name = settings.SUMMARIZER_MODEL
    tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

    input_ids = tokenizer(
        [WHITESPACE_HANDLER(aligned_transcript)],
        return_tensors="pt",
        padding="max_length",
        truncation=True,
        max_length=512)["input_ids"]
    output_ids = model.generate(
        input_ids=input_ids,
        max_length=84,
        no_repeat_ngram_size=2,
        num_beams=4
    )[0]
    summary = tokenizer.decode(
        output_ids,
        skip_special_tokens=True,
        clean_up_tokenization_spaces=False
    )
    return summary


async def STT_with_Summary(audio_file):
    aligned_transcript = await speech2text(audio_file)
    summary = await summerizer(aligned_transcript)
    return aligned_transcript, summary