app.py

"""

This application processes audio files, transcribes them using a pretrained model (Whisper), and provides multiple summarization options for the transcribed text. The application also includes a PDF generation feature and is built with Gradio for the user interface

Webapp, transkribering (norsk), NbAiLab/nb-whisper-large, oppsummering, pdf-download.
"""

import time
import os
import warnings
from pydub import AudioSegment
import torch
from transformers import pipeline
from huggingface_hub import model_info
import spacy
#import nltk
#from nltk.tokenize import word_tokenize
#from nltk.corpus import stopwords
# from nltk.sem.logic import *
from nltk.tokenize import PunktTokenizer
import networkx as nx
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import pandas as pd
import numpy as np
import re
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import gradio as gr
from fpdf import FPDF
from PIL import Image

# Suppress warnings
warnings.filterwarnings("ignore")

#nltk.download('punkt', quiet=True)
#nltk.download('stopwords', quiet=True)
#word_tokenize = PunktTokenizer()

# Convert m4a audio to wav format
def convert_to_wav(audio_file):
    audio = AudioSegment.from_file(audio_file, format="m4a")
    wav_file = "temp.wav"
    audio.export(wav_file, format="wav")
    return wav_file

# Initialize device for torch
device = "cuda" if torch.cuda.is_available() else "cpu"

# Load smoking-big-pipe
MODEL_NAME = "NbAiLab/nb-whisper-medium"
lang = "no"

task = "transcribe"
forced_decoder_ids = None
# get_decoder_prompt_ids =

device = 0 if torch.cuda.is_available() else "cpu"
pipe = pipeline(
    task="automatic-speech-recognition",
    model=MODEL_NAME,
    chunk_length_s=30,
    device=device,
)

#pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=lang, task="transcribe")


def transcribe_audio(audio_file):
    if audio_file.endswith(".m4a"):
        audio_file = convert_to_wav(audio_file)

    start_time = time.time()

    text = pipe(audio_file)["text"]

    # Load the audio file using torchaudio
    #waveform, sample_rate = torchaudio.load(audio_file)

    # Process the waveform with Whisper's processor
    #input_features = whisper_processor(waveform, sampling_rate=sample_rate, return_tensors="pt").input_features.to(device)

    # Generate the transcription
    #output = whisper_model.generate(input_features=input_features)

    # Decode the output
    #text = whisper_processor.batch_decode(output, skip_special_tokens=True)[0]

    output_time = time.time() - start_time

    # # Calculate audio duration using the pipeline's internal method
    audio_duration = pipe.feature_extractor.sampling_rate * len(pipe.feature_extractor(audio_file)["input_features"][0]) / pipe.feature_extractor.sampling_rate

    # Real-time Factor calculation
    rtf = output_time / audio_duration

    # Format of the result
    result = (
        f"Time taken: {output_time:.2f} seconds\n"
        f"Audio duration: {audio_duration / 60:.2f} minutes ({audio_duration:.2f} seconds)\n"
        f"Real-time Factor (RTF): {rtf:.2f}\n"
        f"Number of words: {len(text.split())}\n\n"
        "Real-time Factor (RTF) is a measure often used to evaluate the speed of speech recognition systems. "
        "It is the ratio of transcription time to the duration of the audio.\n\n"
        "An RTF of less than 1 means the transcription process is faster than real-time."
    )

    # result = f"Time taken: {output_time:.2f} seconds\nNumber of words: {len(text.split())}"

    return text, result

# 'punkt' is used for tokenizing sentences and stopwords for filtering, currently only for English(?)

# Clean and preprocess text for summarization
def clean_text(text):
    text = re.sub(r'https?:\/\/.*[\r\n]*', '', text)
    text = re.sub(r'[^\w\s]', '', text)
    text = re.sub(r'\s+', ' ', text).strip()
    return text

nlp = spacy.blank("nb")  # 'nb' is code for Norwegian Bokmål
spacy_stop_words = spacy.lang.nb.stop_words.STOP_WORDS

def preprocess_text(text):
    # Process the text with SpaCy
    doc = nlp(text)
    # Use SpaCy's stop words directly
    stop_words = spacy_stop_words
    # Filter out stop words
    words = [token.text for token in doc if token.text.lower() not in stop_words]
    return ' '.join(words)

# Summarize text using the T5 model
def summarize_text(text):
    preprocessed_text = preprocess_text(text)
    inputs = summarization_tokenizer(preprocessed_text, max_length=1024, return_tensors="pt", truncation=True)
    inputs = inputs.to(device)
    summary_ids = summarization_model.generate(inputs.input_ids, num_beams=5, max_length=150, early_stopping=True)
    return summarization_tokenizer.decode(summary_ids[0], skip_special_tokens=True)

# Build similarity matrix for graph-based summary
def build_similarity_matrix(sentences, stop_words):
    similarity_matrix = nx.Graph()
    for i, tokens_a in enumerate(sentences):
        for j, tokens_b in enumerate(sentences):
            if i != j:
                common_words = set(tokens_a) & set(tokens_b)
                similarity_matrix.add_edge(i, j, weight=len(common_words))
    return similarity_matrix

# Graph-based summarization
def graph_based_summary(text, num_paragraphs=3):
    doc = nlp(text)
    sentences = [sent.text for sent in doc.sents]
    if len(sentences) < num_paragraphs:
        return sentences

    sentence_tokens = [nlp(sent) for sent in sentences]
    stop_words = spacy_stop_words
    filtered_tokens = [[token.text for token in tokens if token.text.lower() not in stop_words] for tokens in sentence_tokens]
    similarity_matrix = build_similarity_matrix(filtered_tokens, stop_words)

    scores = nx.pagerank(similarity_matrix)
    ranked_sentences = sorted(((scores[i], sent) for i, sent in enumerate(sentences)), reverse=True)
    return ' '.join([sent for _, sent in ranked_sentences[:num_paragraphs]])

# LexRank summarization
def lex_rank_summary(text, num_paragraphs=3, threshold=0.1):
    doc = nlp(text)
    sentences = [sent.text for sent in doc.sents]
    if len(sentences) < num_paragraphs:
        return sentences

    stop_words = spacy_stop_words
    vectorizer = TfidfVectorizer(stop_words=list(stop_words))
    X = vectorizer.fit_transform(sentences)
    similarity_matrix = cosine_similarity(X, X)

    # Apply threshold to the similarity matrix
    similarity_matrix[similarity_matrix < threshold] = 0
    nx_graph = nx.from_numpy_array(similarity_matrix)
    scores = nx.pagerank(nx_graph)
    ranked_sentences = sorted(((scores[i], s) for i, s in enumerate(sentences)), reverse=True)
    return ' '.join([ranked_sentences[i][1] for i in range(num_paragraphs)])

# TextRank summarization
def text_rank_summary(text, num_paragraphs=3):
    doc = nlp(text)
    sentences = [sent.text for sent in doc.sents]
    if len(sentences) < num_paragraphs:
        return sentences

    stop_words = spacy_stop_words
    vectorizer = TfidfVectorizer(stop_words=list(stop_words))
    X = vectorizer.fit_transform(sentences)
    similarity_matrix = cosine_similarity(X, X)

    nx_graph = nx.from_numpy_array(similarity_matrix)
    scores = nx.pagerank(nx_graph)
    ranked_sentences = sorted(((scores[i], s) for i, s in enumerate(sentences)), reverse=True)
    return ' '.join([ranked_sentences[i][1] for i in range(num_paragraphs)])


# Save text and summary to PDF
def save_to_pdf(text, summary):
    pdf = FPDF()
    pdf.add_page()
    pdf.set_font("Arial", size=12)

    if text:
        pdf.multi_cell(0, 10, "Text:\n" + text)

    pdf.ln(10)  # Paragraph space

    if summary:
        pdf.multi_cell(0, 10, "Summary:\n" + summary)

    pdf_output_path = "transcription.pdf"
    pdf.output(pdf_output_path)
    return pdf_output_path

def _return_img_html_embed(img_url):
    HTML_str = (
        f'<center> <img src="{img_url}" alt="Image" width="100%" height="auto"> </center>'
    )
    return HTML_str

# Gradio Interface
def display_image():
    img_url = "https://huggingface.co/spaces/camparchimedes/transcription_app/blob/main/picture.png"
    html_embed_str = _return_img_html_embed(img_url)
    return html_embed_str

iface = gr.Blocks()

with iface:
    gr.HTML(display_image())
    gr.Markdown("# Vi har nå muligheten til å oversette lydfiler til norsk skrift.")

    with gr.Tabs():
        with gr.TabItem("Transcription"):
            audio_input = gr.Audio(type="filepath")
            text_output = gr.Textbox(label="Text")
            result_output = gr.Textbox(label="Time taken and Number of words")
            transcribe_button = gr.Button("Transcribe")

            transcribe_button.click(fn=transcribe_audio, inputs=[audio_input], outputs=[text_output, result_output])

        with gr.TabItem("Summary | Graph-based"):
            summary_output = gr.Textbox(label="Summary | Graph-based")
            summarize_button = gr.Button("Summarize")

            summarize_button.click(fn=lambda text: graph_based_summary(text), inputs=[text_output], outputs=[summary_output])

        with gr.TabItem("Summary | LexRank"):
            summary_output = gr.Textbox(label="Summary | LexRank")
            summarize_button = gr.Button("Summarize")

            summarize_button.click(fn=lambda text: lex_rank_summary(text), inputs=[text_output], outputs=[summary_output])

        with gr.TabItem("Summary | TextRank"):
            summary_output = gr.Textbox(label="Summary | TextRank")
            summarize_button = gr.Button("Summarize")

            summarize_button.click(fn=lambda text: text_rank_summary(text), inputs=[text_output], outputs=[summary_output])

        with gr.TabItem("Download PDF"):
            pdf_text_only = gr.Button("Download PDF with Text Only")
            pdf_summary_only = gr.Button("Download PDF with Summary Only")
            pdf_both = gr.Button("Download PDF with Both")

            pdf_output = gr.File(label="Download PDF")

            pdf_text_only.click(fn=lambda text: save_to_pdf(text, ""), inputs=[text_output], outputs=[pdf_output])
            pdf_summary_only.click(fn=lambda summary: save_to_pdf("", summary), inputs=[summary_output], outputs=[pdf_output])
            pdf_both.click(fn=lambda text, summary: save_to_pdf(text, summary), inputs=[text_output, summary_output], outputs=[pdf_output])

iface.launch(share=True, debug=True)