Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+from subprocess import run
+from faster_whisper import WhisperModel
+import soundfile as sf
+import numpy as np
+
+# Inicialize o modelo NLLB
+tokenizer = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M")
+model = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
+
+# Inicialize o modelo Whisper
+model_size = "large-v2"
+whisper_model = WhisperModel(model_size, device="cuda", compute_type="float16")
+
+def process_video(radio, video, target_language, use_wav2lip):
+    # 1. Extraia o áudio do vídeo usando FFMPEG
+    run(["ffmpeg", "-i", video.name, "audio.wav"])
+
+    # 2. Transcrição usando Whisper
+    segments, _ = whisper_model.transcribe("audio.wav")
+    transcript = " ".join([segment.text for segment in segments])
+
+    # 3. Tradução usando NLLB
+    inputs = tokenizer(transcript, return_tensors="pt")
+    lang_code = {"English": "eng_Latn", "Spanish": "spa_Latn", "French": "fra_Latn"}  # Adicione mais idiomas conforme necessário
+    translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.lang_code_to_id[lang_code[target_language]], max_length=100)
+    translated_text = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
+
+    # 4. Queimar a legenda traduzida no vídeo
+    with open("subtitle.srt", "w") as f:
+        f.write("1\n00:00:00,000 --> 00:00:10,000\n" + translated_text)  # Este é um exemplo simples. Você pode dividir o texto em várias partes e ajustar os tempos.
+    
+    run(["ffmpeg", "-i", video.name, "-vf", "subtitles=subtitle.srt", "output_video.mp4"])
+
+    return "output_video.mp4"
+
+# Interface Gradio
+video = gr.Video()
+radio = gr.Radio(["Upload", "Record"], value="Upload", show_label=False)
+iface = gr.Interface(
+    fn=process_video,
+    inputs=[
+        radio,
+        video,
+        gr.Dropdown(choices=["English", "Spanish", "French"], label="Target Language for Dubbing", value="Spanish"),
+        gr.Checkbox(label="Video has a close-up face. Use Wav2lip.", value=False)
+    ],
+    outputs=gr.Video(),
+    live=False
+)
+
+iface.launch()