app.py

#             -----------------COPY OF NEW EDITION[app.py]-----------------

# check if still the case...........??*********************************************
# "The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results."


import time
import os
import spaces
import warnings
warnings.filterwarnings("ignore")
from pydub import AudioSegment

# If m4a audio, convert to wav (Python)
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

import torch
from transformers import pipeline, AutoProcessor # AutoModelForSpeechSeq2Seq

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
torch_dtype = torch.float32

pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-large", device=device, torch_dtype=torch_dtype)

# @spaces.GPU(queue=True)

# Initialize processor and pipeline
processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-large")
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
torch_dtype = torch.float32

language = "no"
task = "transcribe"

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

    start_time = time.time()

    # forced_decoder_ids@the correct context
    forced_decoder_ids = processor.get_decoder_prompt_ids(language=language, task=task)

    with torch.no_grad():
        # CUDA@function -->
        with torch.cuda.device(device) if torch.cuda.is_available() else contextlib.nullcontext():
            output = pipe(audio_file, chunk_length_s=30, generate_kwargs={"forced_decoder_ids": forced_decoder_ids})

    text = output["text"]
    end_time = time.time()
    output_time = end_time - start_time
    word_count = len(text.split())

    result = f"Time taken: {output_time:.2f} seconds\nNumber of words: {word_count}"

    return text, result


# [VERSION 3: full-on w/ 3 styles for summarization]
import nltk
from nltk.tokenize import word_tokenize, sent_tokenize
from nltk.corpus import stopwords
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

nltk.download('punkt')
nltk.download('stopwords')

WHITESPACE_HANDLER = lambda k: re.sub('\s+', ' ', re.sub('\n+', ' ', k.strip()))

def clean_text(text):
    text = re.sub(r'https?:\/\/.*[\r\n]*', '', str(text), flags=re.MULTILINE)
    text = re.sub(r'\<a href', ' ', str(text))
    text = re.sub(r'&amp;', '', str(text))
    text = re.sub(r'\(s+', '(', str(text))
    text = re.sub(r's+\)', ')', str(text))
    text = re.sub(r'\(\)', '', str(text))
    text = re.sub(r'\s+', ' ', str(text))
    text = re.sub(r'[_"\-;%|+&=*%!?:#$@\[\]]', ' ', str(text))
    text = re.sub(r'<br />', ' ', str(text))
    text = re.sub(r'\'', '', str(text))
    text = re.sub(r'«', '', str(text))
    text = re.sub(r'»', '', str(text))
    text = re.sub(r'–', '-', str(text))
    text = re.sub(r'…', '.', str(text))
    text = re.sub(r'[^\x00-\x7F]+', ' ', str(text))
    return text

def preprocess_text(text):
    try:
        words = word_tokenize(text)
        stop_words = set(stopwords.words('norwegian'))
        words_without_stopwords = [word for word in words if word.lower() not in stop_words]
        processed_text = ' '.join(words_without_stopwords)
        return processed_text
    except Exception as e:
        st.error(f"Error during text preprocessing: {e}")
        return None

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

summarization_model = AutoModelForSeq2SeqLM.from_pretrained("t5-base", return_dict=True, torch_dtype=torch.float16)
summarization_tokenizer = AutoTokenizer.from_pretrained("t5-base")
summarization_model.to(device)

# @spaces.GPU(queue=True)
def summarize_text(text):
    preprocessed_text = preprocess_text(text)
    if preprocessed_text is None:
        return None
    inputs = summarization_tokenizer([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)
    summary = summarization_tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    return 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

def graph_based_summary(text, num_paragraphs=3):
    sentences = text.strip().split(".")
    if len(sentences) < num_paragraphs:
        return sentences
    sentence_tokens = [word_tokenize(sent) for sent in sentences]
    stop_words = set(stopwords.words('norwegian'))
    filtered_tokens = [[word for word in tokens if word.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)
    summary = [sent for _, sent in ranked_sentences[:num_paragraphs]]
    return summary

def lex_rank_summary(text, num_paragraphs=3, threshold=0.1):
    sentences = nltk.sent_tokenize(text)
    if len(sentences) < num_paragraphs:
        return sentences
    stop_words = set(stopwords.words('norwegian'))
    vectorizer = TfidfVectorizer(stop_words=list(stop_words))
    X = vectorizer.fit_transform(sentences)
    similarity_matrix = cosine_similarity(X, X)


    for i in range(len(similarity_matrix)): # threshold
        for j in range(len(similarity_matrix[i])):
            if similarity_matrix[i][j] < threshold:
                similarity_matrix[i][j] = 0.0

    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)
    summary = [ranked_sentences[i][1] for i in range(num_paragraphs)]
    return summary


def text_rank_summary(text, num_paragraphs=3):
    sentences = nltk.sent_tokenize(text)
    if len(sentences) < num_paragraphs:
        return sentences

    stop_words = set(stopwords.words('norwegian'))
    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)  # graph, nodes (i.e sentences) & edges are similarity scores (is cool)
    scores = nx.pagerank(nx_graph) # PageRank algorithm, scoring sentences
    ranked_sentences = sorted(((scores[i], s) for i, s in enumerate(sentences)), reverse=True) # rank by PageRank scores

    summary = [ranked_sentences[i][1] for i in range(num_paragraphs)] # top sentences for summary
    return ' '.join(summary)


banner_html = """
<div style="text-align: center;">
    <img src="https://huggingface.co/spaces/camparchimedes/transcription_app/resolve/main/Olas%20AudioSwitch%20Shop.png" alt="Banner Image" width="100%" height="auto">
</div>
"""


import gradio as gr
from fpdf import FPDF
from PIL import Image

def save_to_pdf(text, summary):
    pdf = FPDF()
    pdf.add_page()alt="Banner Image"
    pdf.set_font("Arial", size=12)

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

    # paragraph space
    pdf.ln(10)

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

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



iface = gr.Interface(
    fn=transcribe_audio,
    inputs=gr.Audio(type="filepath"),
    outputs=gr.Textbox(label="Transcription"),
    title="SW Transcription App",
    description="Upload an audio file to get the text",
    theme="default",
    live=False
)


iface = gr.Blocks()

with iface:
    gr.HTML(banner_html)
    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_t1"):
            summary_output = gr.Textbox(label="Summary | Graph-based")
            summarize_button = gr.Button("Summarize")

            def summarize(text):
                if not text:
                    return "Warning: a text must be available."
                summary = graph_based_summary(text)
                return summary

            summarize_button.click(
                fn=summarize,
                inputs=[text_output],
                outputs=summary_output
            )

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

            def summarize(text):
                if not text:
                    return "Warning: a text must be available."
                summary = lex_rank_summary(text)
                return summary

            summarize_button.click(
                fn=summarize,
                inputs=[text_output],
                outputs=summary_output
            )

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

            def summarize(text):
                if not text:
                    return "Warning: a text must be available."
                summary = text_rank_summary(text)
                return summary

            summarize_button.click(
                fn=summarize,
                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_text_only = gr.File(label="Download PDF")
            pdf_output_summary_only = gr.File(label="Download PDF")
            pdf_output_both = gr.File(label="Download PDF")

            def generate_pdf_text_only(text):
                return save_to_pdf(text, "")

            def generate_pdf_summary_only(summary):
                return save_to_pdf("", summary)

            def generate_pdf_both(text, summary):
                return save_to_pdf(text, summary)

            pdf_text_only.click(
                fn=generate_pdf_text_only,
                inputs=[text_output],
                outputs=[pdf_output_text_only]
            )

            pdf_summary_only.click(
                fn=generate_pdf_summary_only,
                inputs=[summary_output],
                outputs=[pdf_output_summary_only]
            )

            pdf_both.click(
                fn=generate_pdf_both,
                inputs=[text_output, summary_output],
                outputs=[pdf_output_both]
            )

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