core.py

import transformers 
from transformers import pipeline

import whisper

import datetime

transformers.utils.move_cache()

# ====================================
# Load speech recognition model
# speech_recognition_pipeline = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h")
speech_recognition_model = whisper.load_model("base")

# ====================================
# Load text summarization model English
# text_summarization_pipeline_En = pipeline("summarization", model="facebook/bart-large-cnn")
tokenizer_En = transformers.AutoTokenizer.from_pretrained("facebook/bart-large-cnn")  
text_summarization_model_En = transformers.AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-cnn")

# ====================================
# Load text summarization model Vietnamese
tokenizer_Vi = transformers.AutoTokenizer.from_pretrained("VietAI/vit5-large-vietnews-summarization")  
text_summarization_model_Vi = transformers.AutoModelForSeq2SeqLM.from_pretrained("VietAI/vit5-large-vietnews-summarization")

def asr_transcript(input_file):
    audio = whisper.load_audio(input_file)
    output = speech_recognition_model.transcribe(audio)
    text = output['text']
    lang = "English"
    if output["language"] == 'en':
        lang = "English"
    elif output["language"] == 'vi':
        lang = "Vietnamese"

    detail = ""
    for segment in output['segments']:
        start = str(datetime.timedelta(seconds=round(segment['start'])))
        end = str(datetime.timedelta(seconds=round(segment['end'])))
        small_text = segment['text']
        detail = detail + start + "-" + end + " " + small_text + "\n"
    return text, lang, detail

def text_summarize_en(text_input):
    encoding = tokenizer_En(text_input, truncation=True, return_tensors="pt")
    input_ids, attention_masks = encoding["input_ids"], encoding["attention_mask"]
    outputs = text_summarization_model_En.generate(
        input_ids=input_ids, attention_mask=attention_masks,
        max_length=256,
        early_stopping=True
    )
    text = ""
    for output in outputs:
        line = tokenizer_En.decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=True)
        text = text + line
    return text

def text_summarize_vi(text_input):
    encoding = tokenizer_Vi(text_input, truncation=True, return_tensors="pt")
    input_ids, attention_masks = encoding["input_ids"], encoding["attention_mask"]
    outputs = text_summarization_model_Vi.generate(
        input_ids=input_ids, attention_mask=attention_masks,
        max_length=256,
        early_stopping=True
    )
    text = ""
    for output in outputs:
        line = tokenizer_Vi.decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=True)
        text = text + line
    return text

def text_summarize(text_input, lang):
    if lang == 'English':
        return text_summarize_en(text_input)
    elif lang == 'Vietnamese':
        return text_summarize_vi(text_input)
    else:
        return ""