184 lines
7.2 KiB
Python
184 lines
7.2 KiB
Python
# import requests
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#
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# url = "https://ranking.rakuten.co.jp/search?stx=GBAmarket&smd=0&prl=&pru=&rvf=&arf=&vmd=0&ptn=1&srt=1&sgid="
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#
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# headers = {
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# "accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7",
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# "accept-language": "zh-CN,zh;q=0.9",
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# "priority": "u=0, i",
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# "referer": "https://ranking.rakuten.co.jp/search?stx=GBAmarket&smd=0&prl=&pru=&rvf=&arf=&vmd=0&ptn=1&srt=1&sgid=",
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# "sec-ch-ua": "\"Not)A;Brand\";v=\"8\", \"Chromium\";v=\"138\", \"Google Chrome\";v=\"138\"",
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# "sec-ch-ua-mobile": "?0",
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# "sec-ch-ua-platform": "\"macOS\"",
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# "sec-fetch-dest": "document",
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# "sec-fetch-mode": "navigate",
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# "sec-fetch-site": "same-origin",
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# "sec-fetch-user": "?1",
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# "upgrade-insecure-requests": "1",
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# "user-agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/138.0.0.0 Safari/537.36"
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# }
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#
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# cookies = {
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# "_ra": "1752116555230|376efdb7-8d68-468f-ab3b-b236a7fee8ab",
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# "Rp": "afb2f0411bbbb8f596a7324d3bf686f2d4d8c42e",
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# "rcxGlobal": "6ab617f6-e89a-4849-a17d-39346ceab779",
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# "_fbp": "fb.2.1752116561306.554477302923466861",
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# "__lt__cid.3df24f5b": "b28a713c-0c65-415e-a885-5de2abc1947d",
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# "_gcl_au": "1.1.852913923.1752116563",
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# "_tt_enable_cookie": "1",
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# "_ttp": "01JZS4J2SDCX5FARA1FBNTRQQX_.tt.2",
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# "s_pers": " s_mrcr=1100400000000000%7C4000000000000%7C4000000000000%7C4000000000000|1909796626962;",
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# "rcx": "ad34370f-13d0-4131-82c8-6edb6f41e8f8",
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# "_cc_id": "c13444ea89c20325d7c9f7a3cc7f1ffc",
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# "Re": "11.3.18.2.0.212416.1:35.4.5.3.0.564023.2-11.3.18.2.0.212416.1:35.4.5.3.0.564023.2",
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# "_uetvid": "58ee65a05d3a11f09ed13da392f5e26d",
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# "ttcsid_COAFPAJC77U4F0RAECNG": "1752128671110::neArZJuye17ZAl_sOSSX.2.1752128671110",
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# "ttcsid": "1752128671112::4tJK9XFbWPZlM0luW3dG.2.1752128671191",
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# "ttcsid_COAECTBC77U6F5DVOFS0": "1752128671186::Kc-BUymsV6Mgnf8-9p4j.2.1752128672359",
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# "rat_v": "e173160a11ee7f9bc722413162268762dff46f33",
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# "__gads": "ID=bc3203bc3f1cac41:T=1752116635:RT=1752575488:S=ALNI_MbfuXQosJcKAJqdmor0IpqLU52sAA",
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# "__gpi": "UID=00001158e9c20516:T=1752116635:RT=1752575488:S=ALNI_MZPOIso8ayWwZVhscaaB7rk4eERug",
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# "__eoi": "ID=411c6fdd85018b70:T=1752116635:RT=1752575488:S=AA-AfjbICu9yvBwUOq3Ua87yCQaw",
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# "panoramaId_expiry": "1752661888761",
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# "panoramaId": "c659c5f420e4e9748ea29913dff3a9fb927a13802d967d06ed67bdf7141ff3fc",
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# "panoramaIdType": "panoDevice",
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# "FCNEC": "[[\"AKsRol8ePxhzalKVzFIUlIuF-TIoX_n5Q0EORVJZ_-XTM6sIG2BpLffroHzKJWD2XpfVzXZK5Ez4dqmM3jq-x6jrQbUk1Ulvgmhvs_Nhg2mXWUEW1Ha9UXuCU7JjpeHsgDue7rWSvZYW_QcBeavPux3Qk5OOykBrwg==\"]]",
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# "cto_bundle": "cOve5191cElKb3EyM3Z1Y3p0WTBDb3FlUkhzWUJPcTVTOFVQRGxaTWZUaEFOYiUyQmIwR1REaTJIcUtiNlNUVW9mYmYwekZMNWZxZ3FKU2NiMDZtMTFBaDZSJTJGdFRGaFdtTGpZQkx0WE51d3BiT1p2c2pXeDZGdXZRekNVVlIlMkJnSG11amtxQWJydiUyRnlsdTMlMkJ5Z01XRURQTFhpT2ZBJTNEJTNE"
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# }
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#
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# response = requests.get(url)
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# response1 = requests.get(url, headers={
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# "accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7",
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# "accept-language": "zh-CN,zh;q=0.9",
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# "priority": "u=0, i",
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# "referer": "https://ranking.rakuten.co.jp/search?stx=GBAmarket&smd=0&prl=&pru=&rvf=&arf=&vmd=0&ptn=1&srt=1&sgid=",
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# "sec-ch-ua": "\"Not)A;Brand\";v=\"8\", \"Chromium\";v=\"138\", \"Google Chrome\";v=\"138\"",
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# "sec-ch-ua-mobile": "?0",
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# "sec-ch-ua-platform": "\"macOS\"",
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# "sec-fetch-dest": "document",
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# "sec-fetch-mode": "navigate",
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# "sec-fetch-site": "same-origin",
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# "sec-fetch-user": "?1",
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# "upgrade-insecure-requests": "1",
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# "user-agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/138.0.0.0 Safari/537.36"
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# })
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# print()
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import os
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import shutil
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import tempfile
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import tensorflow_hub as hub
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# def fix_yamnet_cache():
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# """清理并重新下载YAMNet模型"""
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#
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# # 1. 清理TensorFlow Hub缓存
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# cache_dir = os.path.join(tempfile.gettempdir(), 'tfhub_modules')
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# if os.path.exists(cache_dir):
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# print(f"🗑️ 清理缓存目录: {cache_dir}")
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# shutil.rmtree(cache_dir)
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# print("✅ 缓存清理完成")
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#
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# # 2. 设置新的缓存目录
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# new_cache_dir = os.path.expanduser("~/tfhub_cache")
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# os.makedirs(new_cache_dir, exist_ok=True)
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# os.environ['TFHUB_CACHE_DIR'] = new_cache_dir
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#
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# print(f"📁 设置新缓存目录: {new_cache_dir}")
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#
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# # 3. 重新下载YAMNet模型
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# try:
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# print("🔄 重新下载YAMNet模型...")
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# yamnet_model = hub.load('https://tfhub.dev/google/yamnet/1')
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# print("✅ YAMNet模型加载成功!")
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# return yamnet_model
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# except Exception as e:
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# print(f"❌ 模型加载仍然失败: {e}")
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# return None
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#
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#
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import os
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import mutagen
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from mutagen.mp3 import MP3
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from mutagen.wavpack import WavPack
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from mutagen.flac import FLAC
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from mutagen.wave import WAVE
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from mutagen.oggvorbis import OggVorbis
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def get_audio_duration(file_path):
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"""
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获取音频文件的时长（秒）
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参数:
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file_path (str): 音频文件路径
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返回:
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float: 音频时长（秒），如果无法解析则返回None
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"""
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try:
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# 根据文件扩展名选择合适的解析器
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ext = os.path.splitext(file_path)[1].lower()
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if ext == '.mp3':
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audio = MP3(file_path)
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elif ext == '.wav':
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audio = WAVE(file_path)
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elif ext == '.flac':
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audio = FLAC(file_path)
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elif ext == '.wv':
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audio = WavPack(file_path)
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elif ext == '.ogg':
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audio = OggVorbis(file_path)
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else:
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# 尝试通用解析器
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audio = mutagen.File(file_path)
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if not audio:
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print(f"不支持的文件格式: {file_path}")
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return None
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# 获取时长（秒）
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duration = audio.info.length
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return duration
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except Exception as e:
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print(f"处理文件 {file_path} 时出错: {str(e)}")
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return None
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def format_duration(seconds):
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"""将秒数格式化为时:分:秒"""
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if seconds is None:
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return "未知"
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hours = int(seconds // 3600)
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minutes = int((seconds % 3600) // 60)
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secs = int(seconds % 60)
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if hours > 0:
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return f"{hours}:{minutes:02d}:{secs:02d}"
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else:
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return f"{minutes}:{secs:02d}"
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def process_audio_files(directory):
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"""处理目录中的所有音频文件并显示时长"""
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# 支持的音频文件扩展名
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audio_extensions = ['.mp3', '.wav', '.flac', '.wv', '.ogg', '.m4a', '.aac']
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# 遍历目录中的所有文件
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for filename in os.listdir(directory):
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file_path = os.path.join(directory, filename)
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# 只处理文件，不处理目录
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if os.path.isfile(file_path):
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ext = os.path.splitext(filename)[1].lower()
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if ext in audio_extensions:
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duration_sec = get_audio_duration(file_path)
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duration_str = format_duration(duration_sec)
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print(f"{filename}: {duration_str}")
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if __name__ == "__main__":
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process_audio_files("data/cat_sounds_4")
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