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import os
import json
import numpy as np
import pandas as pd
from tqdm import tqdm
import librosa
# Example parameters
stride = 20
motion_length = 64
speaker_target = 2
use_additional = False
root_dir = './beat_english_v2.0.0/'
output_dir = "./datasets/data_json/"
os.makedirs(output_dir, exist_ok=True)
train_test_split_path = './beat_english_v2.0.0/train_test_split.csv'
df = pd.read_csv(train_test_split_path)
filtered_df = df[(df['id'].str.split('_').str[0].astype(int) == speaker_target) & (df['type'] != 'additional')]
clips = []
for idx, row_item in tqdm(filtered_df.iterrows()):
video_id = row_item['id']
mode = row_item['type']
# check exist
npz_path = os.path.join(root_dir, "smplxflame_30", video_id + ".npz")
wav_path = os.path.join(root_dir, "wave16k", video_id + ".wav")
try:
motion_data = np.load(npz_path, allow_pickle=True)
except:
print(f"cant open {npz_path}")
try:
wave_data, _ = librosa.load(wav_path, sr=None)
except:
print(f"cant open {wav_path}")
motion = motion_data['poses']
total_len = motion.shape[0]
for i in range(0, total_len - motion_length, stride):
clip = {
"video_id": video_id,
"motion_path": npz_path,
"audio_path": wav_path,
"mode": mode,
"start_idx": i,
"end_idx": i + motion_length
}
clips.append(clip)
output_json = os.path.join(output_dir, f"beat2_s{stride}_l{motion_length}_speaker{speaker_target}.json")
with open(output_json, 'w') as f:
json.dump(clips, f, indent=4)
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