import os import subprocess import cv2 import numpy as np from tqdm import tqdm from speakervid_data_talkinghead import SpeakerVidTalkingDataset S3_ENDPOINT_URL = "https://t3.storage.dev" AWS_ACCESS_KEY_ID = "tid_cqKPLHboixMUUQxq_ImANLFwrehWmWZHlEaPZXzXNbKxf_fugg" AWS_SECRET_ACCESS_KEY = "tsec_CXLclBpmOD2blVqdL+smpI52cOxQiXs-pH-INnfU6yfhc1MAajUTpI7xWO+5YAyLwyXjpq" def _visualize_face_mask(video, face_mask, out_path, fps=25, alpha=0.5) -> None: frames = ( ((video + 1.0) * 127.5) .clamp(0, 255) .byte() .permute(0, 2, 3, 1) .cpu() .numpy() ) mask = face_mask.squeeze(1).cpu().numpy() h, w = frames.shape[1], frames.shape[2] if mask.shape[1] != h or mask.shape[2] != w: resized = np.zeros((mask.shape[0], h, w), dtype=np.float32) for i in range(mask.shape[0]): resized[i] = cv2.resize(mask[i], (w, h), interpolation=cv2.INTER_NEAREST) mask = resized writer = cv2.VideoWriter( out_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h) ) for i, frame in enumerate(frames): overlay = np.zeros_like(frame) overlay[:, :, 2] = (mask[i] > 0.5).astype(np.uint8) * 255 blended = cv2.addWeighted(frame, 1.0, overlay, alpha, 0.0) writer.write(cv2.cvtColor(blended, cv2.COLOR_RGB2BGR)) writer.release() def main() -> None: config = { "jsonl_path": "/mnt/nfs/datasets/SpeakerVid-5M/metadb_code/talking_top15_syncc.jsonl", "existing_tsv_path": "/mnt/nfs/datasets/SpeakerVid-5M/dataprocess_code/output_top15/existing.tsv", "audio_feature_model_id": "facebook/wav2vec2-base-960h", "filter_enabled": True, "sync_d_threshold": 10, # Sync-D (lower is better) "sync_c_threshold": 6.5, # Sync-C (higher is better) "debug_audio": False, "use_placeholder_caption": True } # Optional: override creds via env or config if needed. # if os.getenv("AWS_ACCESS_KEY_ID") and os.getenv("AWS_SECRET_ACCESS_KEY"): config["aws_access_key_id"] = AWS_ACCESS_KEY_ID # os.getenv("AWS_ACCESS_KEY_ID") config["aws_secret_access_key"] = AWS_SECRET_ACCESS_KEY #os.getenv("AWS_SECRET_ACCESS_KEY") dataset = SpeakerVidTalkingDataset(config=config) out_dir = os.path.join(os.getcwd(), "visual_tmp") os.makedirs(out_dir, exist_ok=True) for idx in tqdm(range(min(50, len(dataset)))): sample = dataset[idx] print("json_name:", sample.get("json_name")) print("pixel_values_vid shape:", tuple(sample["pixel_values_vid"].shape)) print("audio_input_values shape:", tuple(sample["audio_input_values"].shape)) print("caption:", sample.get("caption_content")) # if sample.get("face_mask") is not None: # out_path = os.path.join(out_dir, f"sample_{idx:04d}_mask.mp4") # _visualize_face_mask(sample["pixel_values_vid"], sample["face_mask"], out_path) # video = sample["pixel_values_vid"] # frames = ((video + 1.0) * 127.5).clamp(0, 255).byte().permute(0, 2, 3, 1).cpu().numpy() # out_path = os.path.join(out_dir, f"sample_{idx:04d}.mp4") # h, w = frames.shape[1], frames.shape[2] # writer = cv2.VideoWriter(out_path, cv2.VideoWriter_fourcc(*"mp4v"), 25, (w, h)) # for frame in frames: # writer.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)) # writer.release() # audio_clip = sample.get("audio_clip") # if audio_clip is None: # continue # audio_path = os.path.join(out_dir, f"sample_{idx:04d}.wav") # audio_sr = int(sample.get("audio_sample_rate", 16000)) # audio_clip = np.asarray(audio_clip, dtype=np.float32) # try: # import soundfile as sf # sf.write(audio_path, audio_clip, audio_sr) # except Exception: # from scipy.io import wavfile # wavfile.write(audio_path, audio_sr, audio_clip) # mux_path = os.path.join(out_dir, f"sample_{idx:04d}_av.mp4") # subprocess.run( # [ # "ffmpeg", # "-y", # "-i", # out_path, # "-i", # audio_path, # "-c:v", # "copy", # "-c:a", # "aac", # "-shortest", # mux_path, # ], # check=True, # stdout=subprocess.DEVNULL, # stderr=subprocess.DEVNULL, # ) if __name__ == "__main__": main()