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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
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