| 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() |
