.ιo dᥱvs
# CodeChatConquer അടിച്ച് കേറി വാ.......! 🏃 A community with a goal to make everything free!! LETZ BUILD THE FUTURE
Show more📈 Analytical overview of Telegram channel .ιo dᥱvs
Channel .ιo dᥱvs in the English language segment is an active participant. Currently, the community unites 20 068 subscribers, ranking 6 741 in the Technologies & Applications category and 22 106 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 20 068 subscribers.
According to the latest data from 12 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 642 over the last 30 days and by 23 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 17.90%. Within the first 24 hours after publication, content typically collects N/A% reactions from the total number of subscribers.
- Post reach: On average, each post receives 3 593 views. Within the first day, a publication typically gains 0 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 10.
- Thematic interests: Content is focused on key topics such as insidead, pip, req, storage, ᴛʜᴇ.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“# CodeChatConquer
അടിച്ച് കേറി വാ.......! 🏃
A community with a goal to make everything free!!
LETZ BUILD THE FUTURE”
Thanks to the high frequency of updates (latest data received on 13 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
Will be Great-full if you all could put max reactions on all messages 😘Small help ya 😅
@botio_devs | @Appuz_007
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𝖚𝖕𝖉𝖆𝖙𝖊𝖘 | 𝓕𝓲𝓵𝓮 𝓽𝓸 𝓛𝓲𝓷𝓴 𝓑𝓸𝓽🔧 File Renewal Issue Resolved ▹ꜰɪʟᴇꜱ ᴛʜᴀᴛ ᴡᴇʀᴇ ᴘʀᴇᴠɪᴏᴜꜱʟʏ ᴇxᴘɪʀᴇᴅ ᴡᴏᴜʟᴅ ꜱᴛɪʟʟ ʀᴇᴛᴜʀɴ ᴀ “ꜰɪʟᴇ ᴇxᴘɪʀᴇᴅ” ᴍᴇꜱꜱᴀɢᴇ ᴇᴠᴇɴ ᴀꜰᴛᴇʀ ᴜꜱᴇʀꜱ ʀᴇ-ᴜᴘʟᴏᴀᴅᴇᴅ ᴛʜᴇᴍ. ᴛʜɪꜱ ʜᴀꜱ ɴᴏᴡ ʙᴇᴇɴ ꜰɪxᴇᴅ — ʀᴇ-ꜱᴇɴᴅɪɴɢ ᴀɴ ᴇxᴘɪʀᴇᴅ ꜰɪʟᴇ ᴡɪʟʟ ᴄᴏʀʀᴇᴄᴛʟʏ ʀᴇꜰʀᴇꜱʜ ᴀɴᴅ ᴇxᴛᴇɴᴅ ᴛʜᴇ ꜰɪʟᴇ’ꜱ ᴇxᴘɪʀʏ ᴛɪᴍᴇ. ⚙️ Minor Enhancements ▹ɪᴍᴘʀᴏᴠᴇᴅ ᴅᴏᴡɴʟᴏᴀᴅ ɴᴏᴅᴇ ʜᴀɴᴅʟɪɴɢ ꜰᴏʀ ꜰᴀꜱᴛᴇʀ ᴀɴᴅ ᴍᴏʀᴇ ꜱᴛᴀʙʟᴇ ᴘᴇʀꜰᴏʀᴍᴀɴᴄᴇ ▹ᴀᴅᴅᴇᴅ ᴀᴅᴅɪᴛɪᴏɴᴀʟ ɴᴏᴅᴇꜱ ꜰᴏʀ ʙᴇᴛᴛᴇʀ ᴅɪꜱᴛʀɪʙᴜᴛɪᴏɴ ᴀɴᴅ ʀᴇʟɪᴀʙɪʟɪᴛʏ
✕ COMMENT ANY OTHER BOTS ISSUES/ERRORS 💭@botio_devs | @Appuz_007
#MEGA_DOWNLOADEROne more step closer to our ultimate mega File/Folder Downloader Bot!! 😊 At Insane 60-100MbpS Download Speed 😱 🤤
Your Each reactions and comments make the process faster 💨@botio_devs | @Appuz_007
#CODE 5ᴀ ꜰᴀsᴛ ᴀᴜᴛᴏ-ᴄᴀᴘᴛɪᴏɴ ɢᴇɴᴇʀᴀᴛᴏʀ (ᴠɪᴅᴇᴏ → sʀᴛ + ᴛxᴛ) 🎬⚡️ Generate subtitles using Faster-Whisper (fast, accurate, low RAM).
from faster_whisper import WhisperModel
import subprocess
import os
#
# AUTO CAPTION GENERATOR (SRT + TXT)
#
VIDEO_PATH = "input.mp4"
AUDIO_PATH = "audio.wav"
SRT_PATH = "subtitles.srt"
TXT_PATH = "subtitles.txt"
# 1️⃣ Extract audio (WAV) from video
def extract_audio(video, audio):
cmd = [
"ffmpeg", "-y",
"-i", video,
"-vn",
"-acodec", "pcm_s16le",
"-ar", "16000",
"-ac", "1",
audio
]
subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
# 2️⃣ Run Whisper (fast + low RAM)
def generate_subtitles(audio_path):
model = WhisperModel("small", device="cpu") # change to "cuda" if GPU
segments, _ = model.transcribe(audio_path, beam_size=5)
srt_lines = []
txt_lines = []
index = 1
for seg in segments:
start = format_time(seg.start)
end = format_time(seg.end)
text = seg.text.strip()
# build SRT block
srt_lines.append(f"{index}")
srt_lines.append(f"{start} --> {end}")
srt_lines.append(text)
srt_lines.append("") # empty line
txt_lines.append(text)
index += 1
return "\n".join(srt_lines), "\n".join(txt_lines)
# time format → 00:00:00,000
def format_time(seconds):
ms = int((seconds % 1) * 1000)
h = int(seconds // 3600)
m = int((seconds % 3600) // 60)
s = int(seconds % 60)
return f"{h:02}:{m:02}:{s:02},{ms:03}"
# 3️⃣ Save files
def save_file(path, data):
with open(path, "w", encoding="utf-8") as f:
f.write(data)
# MAIN
if __name__ == "__main__":
print("Extracting audio...")
extract_audio(VIDEO_PATH, AUDIO_PATH)
print("Generating captions...")
srt, txt = generate_subtitles(AUDIO_PATH)
save_file(SRT_PATH, srt)
save_file(TXT_PATH, txt)
print("Done!")
print(f"Saved:\n → {SRT_PATH}\n → {TXT_PATH}")
────────────────────────────────────────────
ʀᴇǫᴜɪʀᴇᴍᴇɴᴛꜱ 🧩 ᴘɪᴘ: • pip install faster-whisper tqdm • pip install torch (CPU/GPU version as needed) ꜱʏꜱᴛᴇᴍ: • FFmpeg: apt install -y ffmpeg (Must be installed for audio extraction)────────────────────────────────────────────
sᴇᴛᴜᴘ ɢᴜɪᴅᴇ 🧭 1) ᴇxᴛʀᴀᴄᴛ ᴀᴜᴅɪᴏ ꜰʀᴏᴍ ᴠɪᴅᴇᴏ 🎧 The script converts the video into a clean 16kHz mono WAV for Whisper. 2) ʀᴜɴ ꜰᴀsᴛᴇʀ-ᴡʜɪsᴘᴇʀ ᴏɴ ᴛʜᴇ ᴀᴜᴅɪᴏ 🤖 Uses the small Whisper model → good accuracy + low RAM. Change device="cpu" → "cuda" for GPU boost. 3) ᴍᴀᴋᴇ sʀᴛ + ᴛxᴛ ꜰɪʟᴇs 📝 The script auto-builds proper SRT timestamps + clean text lines.────────────────────────────────────────────
ɴᴏᴛᴇs & ᴛɪᴘs 📝 ✔️ Works on CPU (slow but stable) ✔️ GPU gives huge speed boost (CUDA) ✔️ Output includes: • subtitles.srt → perfect for videos • subtitles.txt → plain transcript ✔️ You can swap model sizes: tiny, base, small, medium, large-v3 ✔️ Perfect for: • Caption generators • Video automation tools • YouTube/TikTok editors • Telegram bots──────────────────────────────────────────── ꜰᴇᴀᴛᴜʀᴇs: • ᴀᴜᴛᴏ ᴄᴀᴘᴛɪᴏɴs (sʀᴛ + ᴛxᴛ) 🎬 • ᴀᴄᴄᴜʀᴀᴛᴇ ᴡʜɪsᴘᴇʀ ᴍᴏᴅᴇʟ 🔊 • ʙᴀᴛᴄʜ ɢᴇɴᴇʀᴀᴛɪᴏɴ sᴜᴘᴘᴏʀᴛᴇᴅ ⚙️ • ʟᴏᴡ ʀᴀᴍ + ꜰᴀsᴛ ᴘᴇʀꜰᴏʀᴍᴀɴᴄᴇ ⚡️ • ᴘᴇʀꜰᴇᴄᴛ ꜰᴏʀ ᴀᴜᴛᴏᴍᴀᴛɪᴏɴ, ᴇᴅɪᴛɪɴɢ, ʙᴏᴛs 🤖
COMMENT🔹SHARE FOR MORE 💭@botio_devs | @Appuz_007
🥰 PUT MAXIMUM REACTIONS TO SHOW YOUR SUPPORT 😘
#CODE 4ᴀ sɪᴍᴘʟᴇ ᴘʏᴛʜᴏɴ ᴄᴏᴅᴇ ᴛᴏ ᴄᴏɴᴠᴇʀᴛ ᴀɴʏ ꜰᴏʟᴅᴇʀ ɪɴᴛᴏ ᴀ ᴢɪᴘ ꜰɪʟᴇ (ᴡɪᴛʜ ᴘʀᴏɢʀᴇss ʙᴀʀ) 📦⚡️
import os
import zipfile
from tqdm import tqdm # pip install tqdm
def folder_to_zip(folder_path, output_zip):
# Count total files for progress
file_count = sum(len(files) for _, _, files in os.walk(folder_path))
with zipfile.ZipFile(output_zip, "w", zipfile.ZIP_DEFLATED) as zipf:
with tqdm(total=file_count, desc="Zipping", unit="files") as progress:
for root, _, files in os.walk(folder_path):
for file in files:
full_path = os.path.join(root, file)
rel_path = os.path.relpath(full_path, folder_path)
zipf.write(full_path, rel_path)
progress.update(1)
print("ZIP file created:", output_zip)
# Usage
folder_to_zip("my_folder", "my_folder.zip")
────────────────────────────────────────────
ʀᴇǫᴜɪʀᴇᴍᴇɴᴛꜱ 🧩 ᴘɪᴘ: • pip install tqdm ꜱʏꜱᴛᴇᴍ: • No extra system packages needed (pure Python)────────────────────────────────────────────
sᴇᴛᴜᴘ ɢᴜɪᴅᴇ 🧭 1) ɪɴsᴛᴀʟʟ ᴛʜᴇ tqdm ᴘᴀᴄᴋᴀɢᴇ 📥 Used to show a clean and smooth progress bar. pip install tqdm 2) ᴘʀᴇᴘᴀʀᴇ ᴛʜᴇ ꜰᴏʟᴅᴇʀ ʏᴏᴜ ᴡᴀɴᴛ ᴛᴏ ᴢɪᴘ 📁 Example: my_folder/ ├ file1.txt ├ file2.jpg └ subfolder/ 3) ʀᴜɴ ᴛʜᴇ sᴄʀɪᴘᴛ ▶️ Edit this line with your folder name: folder_to_zip("my_folder", "my_folder.zip")──────────────────────────────────────────── ɴᴏᴛᴇs & ᴛɪᴘs 📝 ✔️ Works on Windows, Linux, macOS ✔️ Supports nested folders (recursively) ✔️ Clean progress bar with accurate file count ✔️ Uses ZIP_DEFLATED compression ✔️ Suitable for backups, bots, packaging, automation ──────────────────────────────────────────── ꜰᴇᴀᴛᴜʀᴇs: • ᴄᴏɴᴠᴇʀᴛs ᴀɴʏ ꜰᴏʟᴅᴇʀ ᴛᴏ ᴀ ᴄᴏᴍᴘʀᴇssᴇᴅ ᴢɪᴘ 📦 • sᴍᴏᴏᴛʜ ᴘʀᴏɢʀᴇss ʙᴀʀ (ᴛǫᴅᴍ) 📊 • ʀᴇᴄᴜʀsɪᴠᴇ ꜰɪʟᴇ ᴀᴅᴅɪᴛɪᴏɴ 🗂 • ʟɪɢʜᴛᴡᴇɪɢʜᴛ ᴀɴᴅ ꜰᴀsᴛ ⚡️ • ᴘᴇʀꜰᴇᴄᴛ ꜰᴏʀ ʙᴀᴄᴋᴜᴘs, ᴘᴀᴄᴋᴀɢᴇ ᴄʀᴇᴀᴛɪᴏɴ, ᴀᴜᴛᴏᴍᴀᴛɪᴏɴ ⚙️
COMMENT🔹SHARE FOR MORE 💭@botio_devs | @Appuz_007
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