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| #https://huggingface.co/spaces/Xuratron/abstract-speech-summarizer | |
| # Here are the imports | |
| import PyPDF2 | |
| import re | |
| import torch | |
| from transformers import pipeline | |
| import soundfile as sf | |
| from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub | |
| from fairseq.models.text_to_speech.hub_interface import TTSHubInterface | |
| import gradio as gr | |
| # Here is the code | |
| def extract_and_clean_abstract(uploaded_file): | |
| """ | |
| Extracts and cleans the abstract from the uploaded PDF file. | |
| """ | |
| reader = PyPDF2.PdfReader(uploaded_file.file) | |
| text = "" | |
| for page in reader.pages: | |
| text += page.extract_text() or "" | |
| # Regular expression pattern to find the abstract | |
| pattern = r"(Abstract|ABSTRACT|abstract)(.*?)(Introduction|INTRODUCTION|introduction|1|Keywords|KEYWORDS|keywords)" | |
| match = re.search(pattern, text, re.DOTALL) | |
| if match: | |
| abstract = match.group(2).strip() | |
| else: | |
| abstract = "Abstract not found." | |
| # Clean the abstract text | |
| cleaned_abstract = abstract.replace('\n', ' ').replace('- ', '') | |
| return cleaned_abstract | |
| def summarize_text(hf_model_name, text): | |
| """ | |
| Summarizes the given text using a Hugging Face model. | |
| """ | |
| summarizer = pipeline("summarization", model=hf_model_name) | |
| summary = summarizer(text, max_length=130, min_length=30, do_sample=False)[0]['summary_text'] | |
| return summary | |
| def text_to_speech(text): | |
| """ | |
| Converts text to speech using a Hugging Face model. | |
| """ | |
| models, cfg, task = load_model_ensemble_and_task_from_hf_hub( | |
| "facebook/fastspeech2-en-ljspeech", | |
| arg_overrides={"vocoder": "hifigan", "fp16": False} | |
| ) | |
| model = models[0] | |
| TTSHubInterface.update_cfg_with_data_cfg(cfg, task.data_cfg) | |
| generator = task.build_generator([model], cfg) | |
| sample = TTSHubInterface.get_model_input(task, text) | |
| wav, rate = TTSHubInterface.get_prediction(task, model, generator, sample) | |
| return wav, rate | |
| def process_pdf(uploaded_file, hf_model_name): | |
| """ | |
| Processes the uploaded PDF file to extract, summarize the abstract, and convert it to speech. | |
| """ | |
| abstract = extract_and_clean_abstract(uploaded_file) | |
| summary = summarize_text(hf_model_name, abstract) | |
| wav, rate = text_to_speech(summary) | |
| sf.write('/tmp/speech_output.wav', wav, rate) | |
| return '/tmp/speech_output.wav' | |
| iface = gr.Interface( | |
| fn=process_pdf, | |
| inputs=[ | |
| gr.inputs.File(label="Upload PDF", type="pdf"), | |
| gr.inputs.Textbox(label="Hugging Face Model Name for Summarization") | |
| ], | |
| outputs=gr.outputs.Audio(label="Audio Summary"), | |
| title="PDF Abstract to Speech", | |
| description="Extracts and summarizes the abstract from a PDF file and converts it to speech." | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() | |