| from transformers import pipeline | |
| from fpdf import FPDF | |
| import librosa | |
| import gradio as gr | |
| def transcribe_and_generate_pdf(audio_file, chunk_duration=30): | |
| try: | |
| transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-small") | |
| audio, sr = librosa.load(audio_file, sr=None) | |
| num_chunks = int(len(audio) / (sr * chunk_duration)) + 1 | |
| transcription = "" | |
| for i in range(num_chunks): | |
| start = i * sr * chunk_duration | |
| end = min((i + 1) * sr * chunk_duration, len(audio)) | |
| chunk = audio[start:end] | |
| chunk_transcription = transcriber(chunk, return_timestamps=True)["text"] | |
| transcription += chunk_transcription + " " | |
| output_pdf = "transcription.pdf" | |
| pdf = FPDF() | |
| pdf.add_page() | |
| pdf.set_font("Arial", size=12) | |
| pdf.multi_cell(0, 10, transcription) | |
| pdf.output(output_pdf) | |
| return transcription, output_pdf | |
| except Exception as e: | |
| return f"An error occurred: {e}", None | |
| interface = gr.Interface( | |
| fn=transcribe_and_generate_pdf, | |
| inputs=gr.Audio(type="filepath"), | |
| outputs=[ | |
| gr.Textbox(label="Transcription"), | |
| gr.File(label="Download PDF") | |
| ], | |
| title="Audio-to-Text and PDF Generator", | |
| description="Upload an audio file to get its transcription and download the PDF." | |
| ) | |
| if __name__ == "__main__": | |
| interface.launch() |