Create app.py

app.py

@@ -0,0 +1,59 @@
+from PyPDF2 import PdfReader
+from transformers import pipeline, SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
+import torch
+import soundfile as sf
+from IPython.display import Audio
+from datasets import load_dataset
+import gradio as gr
+import os, re
+import shutil
+
+first_model = pipeline(task='summarization',model='pszemraj/long-t5-tglobal-base-16384-book-summary')
+processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
+model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
+
+def readAbstract(pdf):
+  # Extract text from PDF
+  reader = PdfReader(pdf)
+  # Extract each page to variable.
+  abstract = reader.pages[0]
+  abstract = abstract.extract_text()
+  # Removing all before 'abstract' for cleaning
+  abstract = abstract[abstract.find('Abstract'):]
+  abstract = abstract.split('Introduction', 1)[0]
+  return abstract
+
+title = 'PDF Abstracter'
+description = 'The model takes a PDF with an abstract as input and summarises it in one sentence that can be read and listened to. Please note that only PDFs with an abstract will work, otherwise there will be an error'
+def abstract_summary(file):
+    # Set file path for uploaded file
+    file_path = "/content/" + os.path.basename(file)
+    shutil.copyfile(file.name, file_path)
+    # Extract Abstract from PDF
+    pdf = readAbstract(file_path)
+    # Run Summarisation Model
+    abstract = first_model(pdf)
+
+    # Text cleaning
+    abstract = str(abstract)
+    abstract = abstract.replace("[","").replace("]","").replace("{","").replace("}","").replace("'","").replace("summary_text: ","")
+
+    inputs = processor(text=str(abstract), return_tensors="pt")
+    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)
+    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)
+    audio = Audio(speech, rate=16000)
+    with open('/content/abstract.wav', 'wb') as f:
+        f.write(audio.data)
+    audio = os.path.join('/content/abstract.wav')
+    return abstract, audio
+
+gui = gr.Interface(fn=abstract_summary,inputs=["file",],outputs=["text","audio"],title=title,description=description)
+gui.launch(debug=True)
+
+gui.close()