Spaces:
Build error
Build error
File size: 6,548 Bytes
bf35104 403f739 dccc366 4f526ed 403f739 45a9fe0 403f739 7bb7369 403f739 16fc878 8f0f72b 403f739 7879a97 0234904 7879a97 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 |
# https://huggingface.co/spaces/barser65/assessment3
def converti(path):
import pip
def install(package):
if hasattr(pip, 'main'):
pip.main(['install', package])
else:
pip._internal.main(['install', package])
install('git+https://github.com/huggingface/transformers.git')
install('datasets sentencepiece')
install('PyPDF2')
install('pdfminer.six')
install('pdfplumber')
install('poppler-utils')
install('tesseract-ocr')
install('libtesseract-dev')
# To read the PDF
import PyPDF2
# To analyze the PDF layout and extract text
from pdfminer.high_level import extract_pages, extract_text
from pdfminer.layout import LTTextContainer, LTChar, LTRect, LTFigure
# To extract text from tables in PDF
import pdfplumber
# To remove the additional created files
import os
# Create a function to extract text
def text_extraction(element):
# Extracting the text from the in-line text element
line_text = element.get_text()
# Find the formats of the text
# Initialize the list with all the formats that appeared in the line of text
line_formats = []
for text_line in element:
if isinstance(text_line, LTTextContainer):
# Iterating through each character in the line of text
for character in text_line:
if isinstance(character, LTChar):
# Append the font name of the character
line_formats.append(character.fontname)
# Append the font size of the character
line_formats.append(character.size)
# Find the unique font sizes and names in the line
format_per_line = list(set(line_formats))
# Return a tuple with the text in each line along with its format
return (line_text, format_per_line)
def read_pdf(pdf_path):
# create a PDF file object
pdfFileObj = open(pdf_path, 'rb')
# create a PDF reader object
pdfReaded = PyPDF2.PdfReader(pdfFileObj)
# Create the dictionary to extract text from each image
text_per_page = {}
# We extract the pages from the PDF
for pagenum, page in enumerate(extract_pages(pdf_path)):
print("Elaborating Page_" +str(pagenum))
# Initialize the variables needed for the text extraction from the page
pageObj = pdfReaded.pages[pagenum]
page_text = []
line_format = []
text_from_images = []
text_from_tables = []
page_content = []
# Initialize the number of the examined tables
table_num = 0
first_element= True
table_extraction_flag= False
# Open the pdf file
pdf = pdfplumber.open(pdf_path)
# Find the examined page
page_tables = pdf.pages[pagenum]
# Find the number of tables on the page
tables = page_tables.find_tables()
# Find all the elements
page_elements = [(element.y1, element) for element in page._objs]
# Sort all the elements as they appear in the page
page_elements.sort(key=lambda a: a[0], reverse=True)
# Find the elements that composed a page
for i,component in enumerate(page_elements):
# Extract the position of the top side of the element in the PDF
pos= component[0]
# Extract the element of the page layout
element = component[1]
# Check if the element is a text element
if isinstance(element, LTTextContainer):
# Check if the text appeared in a table
if table_extraction_flag == False:
# Use the function to extract the text and format for each text element
(line_text, format_per_line) = text_extraction(element)
# Append the text of each line to the page text
page_text.append(line_text)
# Append the format for each line containing text
line_format.append(format_per_line)
page_content.append(line_text)
else:
# Omit the text that appeared in a table
pass
# Create the key of the dictionary
dctkey = 'Page_'+str(pagenum)
# Add the list of list as the value of the page key
text_per_page[dctkey]= [page_text, line_format, text_from_images,text_from_tables, page_content]
# Closing the pdf file object
pdfFileObj.close()
return text_per_page
pdf_path = path
text_per_page = read_pdf(pdf_path)
abstr = ''
while len(abstr) == 0:
for par in range(len(text_per_page)):
for x in text_per_page['Page_'+str(par)]:
mystring = ' '.join(map(str,x))
if mystring.find('Abstract\n') > 0:
abstr0 = mystring[mystring.find('Abstract\n')+10:]
abstr = abstr0[:abstr0.find('1\n')]
from transformers import pipeline
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
summary = summarizer(abstr, max_length=56)
summary_text = summary[0]['summary_text']
import torch
import soundfile as sf
from IPython.display import Audio
from datasets import load_dataset
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
inputs = processor(text=summary_text, return_tensors="pt")
from datasets import load_dataset
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
spectrogram = model.generate_speech(inputs["input_ids"], speaker_embeddings)
from transformers import SpeechT5HifiGan
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
with torch.no_grad():
speech = vocoder(spectrogram)
speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
return Audio(speech, rate=16000)
import gradio as gr
iface = gr.Interface(fn=converti, inputs="file", outputs="audio")
iface.launch() |