app.py

import gradio as gr
import subprocess
import os
from transformers import WhisperProcessor, WhisperForConditionalGeneration
import language_tool_python
from pydub import AudioSegment
from docx import Document

def extract_audio(video_path, audio_path):
    command = f"ffmpeg -i '{video_path}' -ar 16000 -ac 1 -c:a pcm_s16le '{audio_path}' -y"
    subprocess.run(command, shell=True, check=True)
    return audio_path

def transcribe_audio(audio_path):
    processor = WhisperProcessor.from_pretrained("openai/whisper-base")
    model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-base")
    
    audio_input = processor(audio_path, return_tensors="pt", sampling_rate=16000)
    result = model.generate(**audio_input)
    transcription = processor.decode(result[0], skip_special_tokens=True)
    
    return transcription

def correct_text(text):
    tool = language_tool_python.LanguageTool('es')
    matches = tool.check(text)
    return language_tool_python.utils.correct(text, matches)

def process_video(video_file):
    video_path = video_file.name
    audio_path = os.path.splitext(video_path)[0] + '.wav'
    
    extract_audio(video_path, audio_path)
    transcribed_text = transcribe_audio(audio_path)
    corrected_text = correct_text(transcribed_text)
    
    doc = Document()
    doc.add_paragraph(corrected_text)
    doc_path = "transcription.docx"
    doc.save(doc_path)
    
    return corrected_text, doc_path

demo = gr.Interface(
    fn=process_video,
    inputs=gr.File(label="Sube un archivo de video"),
    outputs=[
        gr.Textbox(label="Texto transcrito y corregido"),
        gr.File(label="Descargar transcripción Word")
    ]
)

demo.launch()