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import os |
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import cv2 |
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import numpy as np |
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import torch |
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ffmpeg_path = os.getenv('FFMPEG_PATH') |
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if ffmpeg_path is None: |
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print("please download ffmpeg-static and export to FFMPEG_PATH. \nFor example: export FFMPEG_PATH=/musetalk/ffmpeg-4.4-amd64-static") |
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elif ffmpeg_path not in os.getenv('PATH'): |
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print("add ffmpeg to path") |
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os.environ["PATH"] = f"{ffmpeg_path}:{os.environ['PATH']}" |
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from musetalk.whisper.audio2feature import Audio2Feature |
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from musetalk.models.vae import VAE |
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from musetalk.models.unet import UNet,PositionalEncoding |
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def load_all_model(): |
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audio_processor = Audio2Feature(model_path="./models/whisper/tiny.pt") |
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vae = VAE(model_path = "./models/sd-vae-ft-mse/") |
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unet = UNet(unet_config="./models/musetalk/musetalk.json", |
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model_path ="./models/musetalk/pytorch_model.bin") |
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pe = PositionalEncoding(d_model=384) |
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return audio_processor,vae,unet,pe |
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def get_file_type(video_path): |
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_, ext = os.path.splitext(video_path) |
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if ext.lower() in ['.jpg', '.jpeg', '.png', '.bmp', '.tif', '.tiff']: |
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return 'image' |
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elif ext.lower() in ['.avi', '.mp4', '.mov', '.flv', '.mkv']: |
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return 'video' |
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else: |
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return 'unsupported' |
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def get_video_fps(video_path): |
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video = cv2.VideoCapture(video_path) |
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fps = video.get(cv2.CAP_PROP_FPS) |
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video.release() |
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return fps |
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def datagen(whisper_chunks,vae_encode_latents,batch_size=8,delay_frame = 0): |
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whisper_batch, latent_batch = [], [] |
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for i, w in enumerate(whisper_chunks): |
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idx = (i+delay_frame)%len(vae_encode_latents) |
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latent = vae_encode_latents[idx] |
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whisper_batch.append(w) |
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latent_batch.append(latent) |
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if len(latent_batch) >= batch_size: |
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whisper_batch = np.asarray(whisper_batch) |
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latent_batch = torch.cat(latent_batch, dim=0) |
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yield whisper_batch, latent_batch |
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whisper_batch, latent_batch = [], [] |
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if len(latent_batch) > 0: |
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whisper_batch = np.asarray(whisper_batch) |
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latent_batch = torch.cat(latent_batch, dim=0) |
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yield whisper_batch, latent_batch |
