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