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Update app.py
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app.py
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@@ -5,34 +5,20 @@ import shutil
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from pathlib import Path
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import argparse
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import gradio as gr
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# โ
ุงูุชูุธูู ุฃููุงู: ููุท ููู
ุฌูุฏุงุช ุงูู
ุคูุชุฉ
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folders_to_delete = ["./output", "./__pycache__", "./.cache", "./temp"]
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for folder in folders_to_delete:
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if os.path.exists(folder):
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print(f"๐๏ธ ุญุฐู {folder}")
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shutil.rmtree(folder)
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# โ
ุทุจุงุนุฉ ุญุงูุฉ ุงูุฐุงูุฑุฉ
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import psutil
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mem = psutil.virtual_memory()
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print(f"๐ RAM ุงูู
ุณุชุฎุฏู
ุฉ: {mem.used / 1e9:.2f} GB / {mem.total / 1e9:.2f} GB")
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#
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if not os.path.exists("./models/fantasytalking_model.ckpt"):
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print("๐ ๏ธ ุฌุงุฑู ุชุญู
ูู ุงููู
ุงุฐุฌ ุนุจุฑ download_models.py ...")
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subprocess.run(["python", "download_models.py"])
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# โ
ุฅุนุฏุงุฏ ุงูู
ุณุงุฑุงุช
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sys.path.append(os.path.abspath("."))
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# โ
ุงุณุชูุฑุงุฏ ุงูู
ูููุงุช
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from STT.sst import speech_to_text
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from LLM.llm import generate_reply
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from TTS_X.tts import generate_voice
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from FantasyTalking.infer import load_models, main
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args_template = argparse.Namespace(
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fantasytalking_model_path="./models/fantasytalking_model.ckpt",
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wav2vec_model_dir="./models/wav2vec2-base-960h",
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@@ -52,17 +38,13 @@ args_template = argparse.Namespace(
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seed=1111
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)
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print("๐ ุฌุงุฑู ุชุญู
ูู FantasyTalking ู Wav2Vec...")
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pipe, fantasytalking, wav2vec_processor, wav2vec = load_models(args_template)
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print("โ
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# โ
ุชูููุฏ ููุฏูู
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def generate_video(image_path, audio_path, prompt, output_dir="./output"):
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# ุงูุณุฎู args_template ุฅูู dict ุนุดุงู ูุนุฏู ุนููู ุจุณูููุฉ
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args_dict = vars(args_template).copy()
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# ูุญุฏุซ ููุท ุงููู ูุญุชุงุฌู
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args_dict.update({
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"image_path": image_path,
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"audio_path": audio_path,
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@@ -70,23 +52,15 @@ def generate_video(image_path, audio_path, prompt, output_dir="./output"):
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"output_dir": output_dir
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})
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# ูุญูู ู
ู dict ุฅูู argparse.Namespace
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args = argparse.Namespace(**args_dict)
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return main(args, pipe, fantasytalking, wav2vec_processor, wav2vec)
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def full_pipeline(user_audio, user_image):
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print("๐ค ุชุญููู ุงูุตูุช ุฅูู ูุต...")
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user_text = speech_to_text(user_audio)
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reply = generate_reply(user_text)
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print("๐ ุชุญููู ุงูุฑุฏ ุฅูู ุตูุช...")
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reply_audio_path = generate_voice(reply)
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print("๐ฝ๏ธ ุชูููุฏ ุงูููุฏูู...")
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Path("./output").mkdir(parents=True, exist_ok=True)
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video_path = generate_video(
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image_path=user_image,
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@@ -96,24 +70,25 @@ def full_pipeline(user_audio, user_image):
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return user_text, reply, reply_audio_path, video_path
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with gr.Blocks(
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gr.Markdown("
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with gr.Row():
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with gr.Column():
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audio_input = gr.Audio(label="
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image_input = gr.Image(label="
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btn = gr.Button("
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with gr.Column():
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user_text = gr.Textbox(label="
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reply_text = gr.Textbox(label="
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reply_audio = gr.Audio(label="
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video_output = gr.Video(label="
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btn.click(fn=full_pipeline,
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inputs=[audio_input, image_input],
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outputs=[user_text, reply_text, reply_audio, video_output])
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demo.launch(inbrowser=True, share=True)
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from pathlib import Path
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import argparse
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import gradio as gr
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from STT.sst import speech_to_text
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from LLM.llm import generate_reply
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from TTS_X.tts import generate_voice
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from FantasyTalking.infer import load_models, main
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# downloading of models if didn't exist
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if not os.path.exists("./models/fantasytalking_model.ckpt"):
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subprocess.run(["python", "download_models.py"])
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args_template = argparse.Namespace(
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fantasytalking_model_path="./models/fantasytalking_model.ckpt",
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wav2vec_model_dir="./models/wav2vec2-base-960h",
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seed=1111
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)
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pipe, fantasytalking, wav2vec_processor, wav2vec = load_models(args_template)
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print("โ
")
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def generate_video(image_path, audio_path, prompt, output_dir="./output"):
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args_dict = vars(args_template).copy()
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args_dict.update({
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"image_path": image_path,
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"audio_path": audio_path,
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"output_dir": output_dir
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})
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args = argparse.Namespace(**args_dict)
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return main(args, pipe, fantasytalking, wav2vec_processor, wav2vec)
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def full_pipeline(user_audio, user_image):
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user_text = speech_to_text(user_audio)
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reply = generate_reply(user_text)
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reply_audio_path = generate_voice(reply)
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Path("./output").mkdir(parents=True, exist_ok=True)
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video_path = generate_video(
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image_path=user_image,
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return user_text, reply, reply_audio_path, video_path
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with gr.Blocks() as demo:
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gr.Markdown(" Realtime Interactive Avatar ๐ญ")
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with gr.Row():
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with gr.Column():
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audio_input = gr.Audio(label="Upload Voice", type="filepath")
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image_input = gr.Image(label="Upload Image", type="filepath")
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btn = gr.Button("Generate")
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with gr.Column():
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user_text = gr.Textbox(label="Transcribed Text (Speech to Text)")
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reply_text = gr.Textbox(label="Assistant Response (LLM)")
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reply_audio = gr.Audio(label="Spoken Response (Text to Speech)")
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video_output = gr.Video(label="Final Generated Video")
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btn.click(fn=full_pipeline,
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inputs=[audio_input, image_input],
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outputs=[user_text, reply_text, reply_audio, video_output])
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demo.launch(inbrowser=True, share=True)
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