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import gradio as gr
import os
import torch
import torchaudio
from transformers import (
WhisperProcessor, WhisperForConditionalGeneration,
SpeechT5Processor, SpeechT5ForTextToSpeech,
MarianMTModel, MarianTokenizer
)
import ffmpeg
import soundfile as sf
# Configurações
UPLOAD_FOLDER = "uploads"
OUTPUT_FOLDER = "outputs"
if not os.path.exists(UPLOAD_FOLDER):
os.makedirs(UPLOAD_FOLDER)
if not os.path.exists(OUTPUT_FOLDER):
os.makedirs(OUTPUT_FOLDER)
# Verificar se a GPU está disponível
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Usando dispositivo: {device}")
# Inicializar modelos
whisper_processor = WhisperProcessor.from_pretrained("openai/whisper-small")
whisper_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small").to(device)
tts_processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
tts_model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts").to(device)
# Usar um modelo alternativo de tradução
translation_model_name = "Helsinki-NLP/opus-mt-tc-big-en-pt"
translation_tokenizer = MarianTokenizer.from_pretrained(translation_model_name)
translation_model = MarianMTModel.from_pretrained(translation_model_name).to(device)
# Funções auxiliares
def transcribe_audio(audio_path):
waveform, sample_rate = torchaudio.load(audio_path)
waveform = waveform.to(device)
inputs = whisper_processor(waveform.squeeze().cpu().numpy(), sampling_rate=sample_rate, return_tensors="pt").to(device)
with torch.no_grad():
predicted_ids = whisper_model.generate(inputs.input_features)
transcription = whisper_processor.batch_decode(predicted_ids, skip_special_tokens=True)
return transcription[0]
def synthesize_speech(text, output_path):
inputs = tts_processor(text, return_tensors="pt").to(device)
with torch.no_grad():
speech = tts_model.generate_speech(inputs["input_ids"], tts_model.speaker_embeddings)
sf.write(output_path, speech.cpu().numpy(), samplerate=22050)
def translate_text(text, target_language="pt"):
inputs = translation_tokenizer(text, return_tensors="pt", truncation=True).to(device)
with torch.no_grad():
translated_ids = translation_model.generate(**inputs)
translated_text = translation_tokenizer.decode(translated_ids[0], skip_special_tokens=True)
return translated_text
def extract_audio(video_path, audio_path):
ffmpeg.input(video_path).output(audio_path, ac=1, ar="16000").run(overwrite_output=True)
def replace_audio_in_video(video_path, audio_path, output_path):
video = ffmpeg.input(video_path)
audio = ffmpeg.input(audio_path)
ffmpeg.output(video.video, audio.audio, output_path, vcodec="copy", acodec="aac").run(overwrite_output=True)
# Função principal para Gradio
def translate_video(video, progress=gr.Progress()):
try:
# O Gradio passa o caminho do arquivo como uma string
video_path = video
# Atualizar progresso
progress(0, desc="Extraindo áudio do vídeo...")
audio_path = os.path.join(UPLOAD_FOLDER, "audio.wav")
extract_audio(video_path, audio_path)
# Atualizar progresso
progress(0.25, desc="Transcrevendo áudio...")
transcribed_text = transcribe_audio(audio_path)
print("Texto transcrito:", transcribed_text)
# Atualizar progresso
progress(0.5, desc="Traduzindo texto...")
translated_text = translate_text(transcribed_text, target_language="pt")
print("Texto traduzido:", translated_text)
# Atualizar progresso
progress(0.75, desc="Sintetizando áudio em português...")
synthesized_audio_path = os.path.join(UPLOAD_FOLDER, "synthesized_audio.wav")
synthesize_speech(translated_text, synthesized_audio_path)
# Atualizar progresso
progress(0.9, desc="Substituindo áudio no vídeo...")
output_video_path = os.path.join(OUTPUT_FOLDER, "translated_video.mp4")
replace_audio_in_video(video_path, synthesized_audio_path, output_video_path)
# Finalizar progresso
progress(1.0, desc="Concluído!")
return output_video_path
except Exception as e:
return f"Erro: {str(e)}"
# Interface Gradio
iface = gr.Interface(
fn=translate_video,
inputs=gr.Video(),
outputs=gr.Video(),
title="Tradutor de Vídeo",
description="Carregue um vídeo em qualquer idioma e ele será traduzido para português."
)
iface.launch()
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