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• My SBC Collection

  31 décembre 2023, par Multimedia Mike — General

  Like many computer nerds in the last decade, I have accumulated more than a few single-board computers, or “SBCs”, which are small computers based around a system-on-a-chip (SoC) that nearly always features an ARM CPU at its core. Surprisingly few of these units are Raspberry Pi units, though that brand has come to exemplify and dominate the product category.

  Also, as is the case for many computer nerds, most of these SBCs lay fallow for years at a time. Equipped with an inexpensive lightbox that I procured in the last year, I decided I could at least create glamour shots of various units and catalog them in a blog post.

  While Raspberry Pi still enjoys the most mindshare far and away, and while I do have a few Raspberry Pi units in my inventory, I have always been a bigger fan of the ODROID brand, which works with convenient importers around the world (in the USA, I can vouch for Ameridroid, to whom I’ve forked over a fair amount of cash for these computing toys).

  As mentioned, Raspberry Pi undisputedly has the most mindshare of all these SBC brands and I often wonder why… and then I immediately remind myself that it has the biggest ecosystem, and has a variety of turnkey projects and applications (such as Pi-hole and PiVPN) that promise a lower barrier to entry — as well as a slightly lower price point — than some of these other options. ODROID had a decent ecosystem for awhile, especially considering the monthly ODROID Magazine, though that ceased publication in July 2020. The Raspberry Pi and its variants were famously difficult to come by due to the global chip shortage from 2021-2023. Meanwhile, I had no trouble procuring these boards during the same timeframe.

  So let’s delve into the collection…
  

  Cubieboard
  The Raspberry Pi came out in 2012 and by 2013 I was somewhat coveting one to hack on. Finally ! An accessible ARM platform to play with. I had heard of the BeagleBoard for years but never tried to get my hands on one. I was thinking about taking the plunge on a new Raspberry Pi, but a colleague told me I should skip that and go with this new hotness called the Cubieboard, based on an Allwinner SoC. The big value-add that this board had vs. a Raspberry Pi was that it had a SATA adapter. Although now that it has been a decade, it only now occurs to me to quander whether it was true SATA or a USB-to-SATA bridge. Looking it up now, I’m led to believe that the SoC supported the functionality natively.

  Anyway, I did get it up and running but never did much with it, thus setting the tone for future SBC endeavors. No photos because I gave it to another tech enthusiast years ago, whose SBC collection dwarfs my own.

  ODROID-XU4
  I can’t recall exactly when or how I first encountered the ODROID brand. I probably read about it on some enthusiast page or another circa 2014 and decided to try one out. I eventually acquired a total of 3 of these ODROID-XU4 units, each with a different case, 1 with a fan and 2 passively-cooled :

  

  Collection of ODROID-XU4 SBCs

  This is based on the Samsung Exynos 5422 SoC, the same series as was used in their Note 3 phone released in 2013. It has been a fun chip to play with. The XU4 was also my first introduction to the eMMC storage solution that is commonly supported on the ODROID SBCs (alongside micro-SD). eMMC offers many benefits over SD in terms of read/write speed as well as well as longevity/write cycles. That’s getting less relevant these days, however, as more and more SBCs are being released with direct NVMe SSD support.

  I had initially wanted to make a retro-gaming device built on this platform (see the handheld section later for more meditations on that). In support of this common hobbyist goal, there is this nifty case XU4 case which apes the aesthetic of the Nintendo N64 :

  

  ODROID-XU4 N64-style case

  It even has a cool programmable LCD screen. Maybe one day I’ll find a use for it.

  For awhile, one of these XU4 units (likely the noisy, fan-cooled one) was contributing results to the FFmpeg FATE system.

  While it features gigabit ethernet and a USB3 port, I once tried to see if I could get 2 Gbps throughput with the unit using a USB3-gigabit dongle. I had curious results in that the total amount of traffic throughput could never exceed 1 Gbps across both interfaces. I.e., if 1 interface was dealing with 1 Gbps and the other interface tried to run at 1 Gbps, they would both only run at 500 Mbps. That remains a mystery to me since I don’t see that limitation with Intel chips.

  Still, the XU4 has been useful for a variety of projects and prototyping over the years.

  ODROID-HC2 NAS
  I find that a lot of my fellow nerds massively overengineer their homelab NAS setups. I’ll explore this in a future post. For my part, people tend to find my homelab NAS solution slightly underengineered. This is the ODROID-HC2 (the “HC” stands for “Home Cloud”) :

  

  ODROID-HC2 NAS

  It has the same guts as the ODROID-XU4 except no video output and the USB3 function is leveraged for a SATA bridge. This allows you to plug a SATA hard drive directly into the unit :

  

  ODROID-HC2 NAS uncovered

  Believe it or not, this has been my home NAS solution for something like 6 or 7 years now– I don’t clearly remember when I purchased it and put it into service.

  But isn’t this sort of irresponsible ? What about a failure of the main drive ? That’s why I have an external drive connected for backing up the most important data via rsync :

  

  ODROID-HC2 NAS backup enclosure

  The power consumption can’t be beat– Profiling for a few weeks of average usage worked out to 4.5 kWh for the ODROID-HC2… per month.

  ODROID-C2
  I was on a kick of ordering more SBCs at one point. This is the ODROID-C2, equipped with a 64-bit Amlogic SoC :

  

  ODROID-C2

  I had this on the FATE farm for awhile, performing 64-bit ARM builds (vs. the XU4’s 32-bit builds). As memory serves, it was unreliable and would occasionally freeze up.

  Here is a view of the eMMC storage through the bottom of the translucent case :

  

  Bottom of ODROID-C2 with view of eMMC storage

  ODROID-N2+
  Out of all my ODROID SBCs, this is the unit that I long to “get back to” the most– the ODROID-N2+ :

  

  ODROID-N2+

  Very capable unit that makes a great little desktop. I have some projects I want to develop using it so that it will force me to have a focused development environment.

  Raspberry Pi
  Eventually, I did break down and get a Raspberry Pi. I had a specific purpose in mind and, much to my surprise, I have stuck to it :

  

  Original Raspberry Pi

  I was using one of the ODROID-XU4 units as a VPN gateway. Eventually, I wanted to convert the XU4 to something else and I decided to run the VPN gateway as an appliance on the simplest device I could. So I procured this complete hand-me-down unit from eBay and went to work. This was also the first time I discovered the DietPi distribution and this box has been in service running Wireguard via PiVPN for many years.

  I also have a Raspberry Pi 3B+ kicking around somewhere. I used it as a Steam Link device for awhile.

  SOPINE + Baseboard
  Also procured when I was on this “let’s buy random SBCs” kick. The Pine64 SOPINE is actually a compute module that comes in the form factor of a memory module.

  

  Pine64 SOPINE Compute Module

  Back to using Allwinner SoCs. In order to make this thing useful, you need to place it in something. It’s possible to get a mini-ITX form factor board that can accommodate 7 of these modules. Before going to that extreme, there is this much simpler baseboard which can also use eMMC for storage.

  

  Baseboard with SOPINE, eMMC, and heat sinks

  I really need to find an appropriate case for this one as it currently performs its duty while sitting on an anti-static bag.

  NanoPi NEO3
  I enjoy running the DietPi distribution on many of these SBCs (as it’s developed not just for Raspberry Pi). I have also found their website to be a useful resource for discovering new SBCs. That’s how I found the NanoPi series and zeroed in on this NEO3 unit, sporting a Rockchip SoC, and photographed here with some American currency in order to illustrate its relative size :

  

  NanoPi NEO3

  I often forget about this computer because it’s off in another room, just quietly performing its assigned duty.

  MangoPi MQ-Pro
  So far, I’ve heard of these fruits prepending the Greek letter pi for naming small computing products :

  • Raspberry – the O.G.
  • Banana – seems to be popular for hobbyist router/switches
  • Orange
  • Atomic
  • Nano
  • Mango

  Okay, so the AtomicPi and NanoPi names don’t really make sense considering the fruit convention.

  Anyway, the newest entry is the MangoPi. These showed up on Ameridroid a few months ago. There are 2 variants : the MQ-Pro and the MQ-Quad. I picked one and rolled with it.

  

  MangoPi MQ-Pro pieces arrive

  When it arrived, I unpacked it, assembled the pieces, downloaded a distro, tossed that on a micro-SD card, connected a monitor and keyboard to it via its USB-C port, got the distro up and running, configured the wireless networking with a static IP address and installed sshd, and it was ready to go as a headless server for an edge application.

  

  MangoPi MQ-Pro components, ready for assembly

  The unit came with no instructions that I can recall. After I got it set up, I remember thinking, “What is wrong with me ? Why is it that I just know how to do all of this without any documentation ?”

  

  MangoPi MQ-Pro in first test

  Only after I got it up and running and poked around a bit did I realize that this SBC doesn’t have an ARM SoC– it’s a RISC-V SoC. It uses the Allwinner D1, so it looks like I came full circle back to Allwinner.

  

  MangoPi MQ-Pro with more US coinage for scale

  So I now have my first piece of RISC-V hobbyist kit, although I learned recently from Kostya that it’s not that great for multimedia.

  Handheld Gaming Units
  The folks at Hardkernel have also produced a series of handheld retro-gaming devices called ODROID-GO. The first one resembled the original Nintendo Game Boy, came as a kit to be assembled, and emulated 5 classic consoles. It also had some hackability to it. Quite a cool little device, and inexpensive too. I have since passed it along to another gaming enthusiast.

  Later came the ODROID-GO Advance, also a kit, but emulating more devices. I was extremely eager to get my hands on this since it could emulate SNES in addition to NES. It also features a headphone jack, unlike the earlier model. True to form, after I received mine, it took me about 13 months before I got around to assembling it. After that, the biggest challenge I had was trying to find an appropriate case for it.

  

  ODROID-GO Advance with case and headphones

  Even though it may try to copy the general aesthetic and form factor of the Game Boy Advance, cases for the GBA don’t fit this correctly.

  Further, Hardkernel have also released the ODROID-GO Super and Ultra models that do more and more. The Advance, Super, and Ultra models have powerful SoCs and feature much more hackability than the first ODROID-GO model.

  I know that the guts of the Advance have been used in other products as well. The same is likely true for the Super and Ultra.

  Ultimately, the ODROID-GO Advance was just another project I assembled and then set aside since I like the idea of playing old games much more than actually doing it. Plus, the fact has finally crystalized in my mind over the past few years that I have never enjoyed handheld gaming and likely will never enjoy handheld gaming, even after I started wearing glasses. Not that I’m averse to old Game Boy / Color / Advance games, but if I’m going to play them, I’d rather emulate them on a large display.

  The Future
  In some of my weaker moments, I consider ordering up certain Banana Pi products (like the Banana Pi BPI-R2) with a case and doing my own router tricks using some open source router/firewall solution. And then I remind myself that my existing prosumer-type home router is doing just fine. But maybe one day…

  The post My SBC Collection first appeared on Breaking Eggs And Making Omelettes.

• A Complete Guide to Metrics in Google Analytics

  11 janvier 2024, par Erin

  There’s no denying that Google Analytics is the most popular web analytics solution today. Many marketers choose it to understand user behaviour. But when it offers so many different types of metrics, it can be overwhelming to choose which ones to focus on. In this article, we’ll dive into how metrics work in Google Analytics 4 and how to decide which metrics may be most useful to you, depending on your analytics needs.

  However, there are alternative web analytics solutions that can provide more accurate data and supplement GA’s existing features. Keep reading to learn how to overcome Google Analytics limitations so you can get the more out of your web analytics.

  What is a metric in Google Analytics ?

  In Google Analytics, a metric is a quantitative measurement or numerical data that provides insights into specific aspects of user behaviour. Metrics represent the counts or sums of user interactions, events or other data points. You can use GA metrics to better understand how people engage with a website or mobile app. 

  Unlike the previous Universal Analytics (the previous version of GA), GA4 is event-centric and has automated and simplified the event tracking process. Compared to Universal Analytics, GA4 is more user-centric and lets you hone in on individual user journeys. Some examples of common key metrics in GA4 are : 

  • Sessions : A group of user interactions on your website that occur within a specific time period. A session concludes when there is no user activity for 30 minutes.
  • Total Users : The cumulative count of individuals who accessed your site within a specified date range.
  • Engagement Rate : The percentage of visits to your website or app that included engagement (e.g., one more pageview, one or more conversion, etc.), determined by dividing engaged sessions by sessions.

  

  Metrics are invaluable when it comes to website and conversion optimisation. Whether you’re on the marketing team, creating content or designing web pages, understanding how your users interact with your digital platforms is essential.

  GA4 metrics vs. dimensions

  GA4 uses metrics to discuss quantitative measurements and dimensions as qualitative descriptors that provide additional context to metrics. To make things crystal clear, here are some examples of how metrics and dimensions are used together : 

  • “Session duration” = metric, “device type” = dimension 
    • In this situation, the dimension can segment the data by device type so you can optimise the user experience for different devices.
  • “Bounce rate” = metric, “traffic source/medium” = dimension 
    • Here, the dimension helps you segment by traffic source to understand how different acquisition channels are performing. 
  • “Conversion rate” = metric, “Landing page” = dimension 
    • When the conversion rate data is segmented by landing page, you can better see the most effective landing pages. 

  You can get into the nitty gritty of granular analysis by combining metrics and dimensions to better understand specific user interactions.

  How do Google Analytics metrics work ?

  Before diving into the most important metrics you should track, let’s review how metrics in GA4 work. 

  

  1. Tracking code implementation

  The process begins with implementing Google Analytics 4 tracking code into the HTML of web pages. This tracking code is JavaScript added to each website page — it collects data related to user interactions, events and other important tidbits.

  2. Data collection

  As users interact with the website or app, the Google Analytics 4 tracking code captures various data points (i.e., page views, clicks, form submissions, custom events, etc.). This raw data is compiled and sent to Google Analytics servers for processing.

  3. Data processing algorithms

  When the data reaches Google Analytics servers, data processing algorithms come into play. These algorithms analyse the incoming raw data to identify the dataset’s trends, relationships and patterns. This part of the process involves cleaning and organising the data.

  4. Segmentation and customisation

  As discussed in the previous section, Google Analytics 4 allows for segmentation and customisation of data with dimensions. To analyse specific data groups, you can define segments based on various dimensions (e.g., traffic source, device type). Custom events and user properties can also be defined to tailor the tracking to the unique needs of your website or app.

  5. Report generation

  Google Analytics 4 can make comprehensive reports and dashboards based on the processed and segmented data. These reports, often in the form of graphs and charts, help identify patterns and trends in the data.

  What are the most important Google Analytics metrics to track ? 

  In this section, we’ll identify and define key metrics for marketing teams to track in Google Analytics 4. 

  

  1. Pageviews are the total number of times a specific page or screen on your website or app is viewed by visitors. Pageviews are calculated each time a web page is loaded or reloaded in a browser. You can use this metric to measure the popularity of certain content on your website and what users are interested in. 
  2. Event tracking monitors user interactions with content on a website or app (i.e., clicks, downloads, video views, etc.). Event tracking provides detailed insights into user engagement so you can better understand how users interact with dynamic content. 
  3. Retention rate can be analysed with a pre-made overview report that Google Analytics 4 provides. This user metric measures the percentage of visitors who return to your website or app after their first visit within a specific time period. Retention rate = (users with subsequent visits / total users in the initial cohort) x 100. Use this information to understand how relevant or effective your content, user experience and marketing efforts are in retaining visitors. You probably have more loyal/returning buyers if you have a high retention rate. 
  4. Average session duration calculates the average time users spend on your website or app per session. Average session duration = total duration of all sessions / # of sessions. A high average session duration indicates how interested and engaged users are with your content. 
  5. Site searches and search queries on your website are automatically tracked by Google Analytics 4. These metrics include search terms, number of searches and user engagement post-search. You can use site search metrics to better understand user intent and refine content based on users’ searches. 
  6. Entrance and exit pages show where users first enter and leave your site. This metric is calculated by the percentage of sessions that start or end on a specific page. Knowing where users are entering and leaving your site can help identify places for content optimisation. 
  7. Device and browser info includes data about which devices and browsers websites or apps visitors use. This is another metric that Google Analytics 4 automatically collects and categorises during user sessions. You can use this data to improve the user experience on relevant devices and browsers. 
  8. Bounce rate is the percentage of single-page sessions where users leave your site or app without interacting further. Bounce rate = (# of single-page sessions / total # of sessions) x 100. Bounce rate is useful for determining how effective your landing pages are — pages with high bounce rates can be tweaked and optimised to enhance user engagement.

  Examples of how Matomo can elevate your web analytics

  Although Google Analytics is a powerful tool for understanding user behaviour, it also has privacy concerns, limitations and a list of issues. Another web analytics solution like Matomo can help fill those gaps so you can get the most out of your analytics.

  

  1. Cross-verify and validate your observations from Google Analytics by comparing data from Matomo’s Heatmaps and Session Recordings for the same pages. This process grants you access to these advanced features that GA4 does not offer.

  

  1. Matomo provides you with greater accuracy thanks to its privacy-friendly design. Unlike GA4, Matomo can be configured to operate without cookies. This means increased accuracy without intrusive cookie consent screens interrupting the user experience. It’s a win for you and for your users. Matomo also doesn’t apply data sampling so you can rest assured that the data you see is 100% accurate.

  3. Unlike GA4, Matomo offers direct access to customer support so you can save time sifting through community forum threads and online documentation. Gain personalised assistance and guidance for your analytics questions, and resolve issues efficiently.

  

  4. Matomo’s Form Analytics and Media Analytics extend your analytics capabilities beyond just pageviews and event tracking.

     Tracking user interactions with forms can tell you which fields users struggle with, common drop-off points, in addition to which parts of the form successfully guide visitors towards submission.

     See first-hand how Concrete CMS 3x their leads using Matomo’s Form Analytics.

     Media Analytics can provide insight into how users interact with image, video, or audio content on your website. You can use this feature to assess the relevance and popularity of specific content by knowing what your audience is engaged by.

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

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  Final thoughts

  Although Google Analytics is a powerful tool on its own, Matomo can elevate your web analytics by offering advanced features, data accuracy and a privacy-friendly design. Don’t play a guessing game with your data — Matomo provides 100% accurate data so you don’t have to rely on AI or machine learning to fill in the gaps. Matomo can be configured cookieless which also provides you with more accurate data and a better user experience. 

  Lastly, Matomo is fully compliant with some of the world’s strictest privacy regulations like GPDR. You won’t have to sacrifice compliance for accurate, high quality data. 

  Start your 21-day free trial of Matomo — no credit card required.

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• "FFmpeg : Error not transitioning to the next song in Discord Bot's queue."

  1er avril 2024, par noober

  I have 3 modules, but I'm sure the error occurs within this module, and here is the entire code within that module :

  


  import asyncio
import discord
from discord import FFmpegOpusAudio, Embed
import os

async def handle_help(message):
    embed = discord.Embed(
        title="Danh sách lệnh cho Bé Mèo",
        description="Dưới đây là các lệnh mà chủ nhân có thể bắt Bé Mèo phục vụ:",
        color=discord.Color.blue()
    )
    embed.add_field(name="!play", value="Phát một bài hát từ YouTube.", inline=False)
    embed.add_field(name="!pause", value="Tạm dừng bài hát đang phát.", inline=False)
    embed.add_field(name="!resume", value="Tiếp tục bài hát đang bị tạm dừng.", inline=False)
    embed.add_field(name="!skip", value="Chuyển đến bài hát tiếp theo trong danh sách chờ.", inline=False)
    embed.add_field(name="!stop", value="Dừng phát nhạc và cho phép Bé Mèo đi ngủ tiếp.", inline=False)
    # Thêm các lệnh khác theo cùng mẫu trên
    await message.channel.send(embed=embed)

class Song:
    def __init__(self, title, player):
        self.title = title  # Lưu trữ tiêu đề bài hát ở đây
        self.player = player

# Thêm đối tượng Song vào hàng đợi
def add_song_to_queue(guild_id, queues, song):
    queues.setdefault(guild_id, []).append(song)

async def handle_list(message, queues):
    log_file_path = "C:\\Bot Music 2\\song_log.txt"
    if os.path.exists(log_file_path):
        with open(log_file_path, "r", encoding="utf-8") as f:
            song_list = f.readlines()

        if song_list:
            embed = discord.Embed(
                title="Danh sách bài hát",
                description="Danh sách các bài hát đã phát:",
                color=discord.Color.blue()
            )

            for i, song in enumerate(song_list, start=1):
                if i == 1:
                    song = "- Đang phát: " + song.strip()
                embed.add_field(name=f"Bài hát {i}", value=song, inline=False)

            await message.channel.send(embed=embed)
        else:
            await message.channel.send("Hiện không có dữ liệu trong file log.")
    else:
        await message.channel.send("File log không tồn tại.")

async def handle_commands(message, client, queues, voice_clients, yt_dl_options, ytdl, ffmpeg_options=None, guild_id=None, data=None):
    # Nếu không có ffmpeg_options, sử dụng các thiết lập mặc định
    if ffmpeg_options is None:
        ffmpeg_options = {
            'before_options': '-reconnect 1 -reconnect_streamed 1 -reconnect_delay_max 5',
            'options': '-vn -filter:a "volume=0.25"'
        }
    
    # Khởi tạo voice_client
    if guild_id is None:
        guild_id = message.guild.id

    if guild_id in voice_clients:
        voice_client = voice_clients[guild_id]
    else:
        voice_client = None

    # Xử lý lệnh !play
    if message.content.startswith("!play"):
        try:
            # Kiểm tra xem người gửi tin nhắn có đang ở trong kênh voice không
            voice_channel = message.author.voice.channel
            # Kiểm tra xem bot có đang ở trong kênh voice của guild không
            if voice_client and voice_client.is_connected():
                await voice_client.move_to(voice_channel)
            else:
                voice_client = await voice_channel.connect()
                voice_clients[guild_id] = voice_client
        except Exception as e:
            print(e)

        try:
            query = ' '.join(message.content.split()[1:])
            if query.startswith('http'):
                url = query
            else:
                query = 'ytsearch:' + query
                loop = asyncio.get_event_loop()
                data = await loop.run_in_executor(None, lambda: ytdl.extract_info(query, download=False))
                if not data:
                    raise ValueError("Không có dữ liệu trả về từ YouTube.")
                url = data['entries'][0]['url']

            player = FFmpegOpusAudio(url, **ffmpeg_options)
            # Lấy thông tin của bài hát mới đang được yêu cầu
            title = data['entries'][0]['title']
            duration = data['entries'][0]['duration']
            creator = data['entries'][0]['creator'] if 'creator' in data['entries'][0] else "Unknown"
            requester = message.author.nick if message.author.nick else message.author.name
                    
            # Tạo embed để thông báo thông tin bài hát mới
            embed = discord.Embed(
                title="Thông tin bài hát mới",
                description=f"**Bài hát:** *{title}*\n**Thời lượng:** *{duration}*\n**Tác giả:** *{creator}*\n**Người yêu cầu:** *{requester}*",
                color=discord.Color.green()
            )
            await message.channel.send(embed=embed)
            
            # Sau khi lấy thông tin của bài hát diễn ra, gọi hàm log_song_title với title của bài hát
            # Ví dụ:
            title = data['entries'][0]['title']
            await log_song_title(title)

            # Thêm vào danh sách chờ nếu có bài hát đang phát
            if voice_client.is_playing():
                queues.setdefault(guild_id, []).append(player)
            else:
                voice_client.play(player)
                
        except Exception as e:
            print(e)
            
    if message.content.startswith("!link"):
            try:
                voice_client = await message.author.voice.channel.connect()
                voice_clients[voice_client.guild.id] = voice_client
            except Exception as e:
                print(e)

            try:
                url = message.content.split()[1]

                loop = asyncio.get_event_loop()
                data = await loop.run_in_executor(None, lambda: ytdl.extract_info(url, download=False))

                song = data['url']
                player = discord.FFmpegOpusAudio(song, **ffmpeg_options)

                voice_clients[message.guild.id].play(player)
            except Exception as e:
                print(e)

    # Xử lý lệnh !queue
    elif message.content.startswith("!queue"):
        queue = queues.get(guild_id, [])
        if queue:
            await message.channel.send("Danh sách chờ:")
            for index, item in enumerate(queue, 1):
                await message.channel.send(f"{index}. {item.title}")
        else:
            await message.channel.send("Không có bài hát nào trong danh sách chờ.")

    # Xử lý lệnh !skip
    elif message.content.startswith("!skip"):
        try:
            if voice_client and voice_client.is_playing():
                voice_client.stop()
                await play_next_song(guild_id, queues, voice_client, skip=True)
                await remove_first_line_from_log()
        except Exception as e:
            print(e)

    # Xử lý các lệnh như !pause, !resume, !stop
    elif message.content.startswith("!pause"):
        try:
            if voice_client and voice_client.is_playing():
                voice_client.pause()
        except Exception as e:
            print(e)

    elif message.content.startswith("!resume"):
        try:
            if voice_client and not voice_client.is_playing():
                voice_client.resume()
        except Exception as e:
            print(e)

    elif message.content.startswith("!stop"):
        try:
            if voice_client:
                voice_client.stop()
                await voice_client.disconnect()
                del voice_clients[guild_id]  # Xóa voice_client sau khi dừng
        except Exception as e:
            print(e)

async def log_song_title(title):
    log_file_path = "C:\\Bot Music 2\\song_log.txt"
    try:
        # Kiểm tra xem tệp tin log đã tồn tại chưa
        if not os.path.exists(log_file_path):
            # Nếu chưa tồn tại, tạo tệp tin mới và ghi title vào tệp tin đó
            with open(log_file_path, 'w', encoding='utf-8') as file:
                file.write(title + '\n')
        else:
            # Nếu tệp tin log đã tồn tại, mở tệp tin và chèn title vào cuối tệp tin
            with open(log_file_path, 'a', encoding='utf-8') as file:
                file.write(title + '\n')
    except Exception as e:
        print(f"Error logging song title: {e}")

async def remove_first_line_from_log():
    log_file_path = "C:\\Bot Music 2\\song_log.txt"
    try:
        with open(log_file_path, "r", encoding="utf-8") as f:
            lines = f.readlines()
        # Xóa dòng đầu tiên trong list lines
        lines = lines[1:]
        with open(log_file_path, "w", encoding="utf-8") as f:
            for line in lines:
                f.write(line)
    except Exception as e:
        print(f"Error removing first line from log: {e}")
        
async def clear_log_file():
    log_file_path = "C:\\Bot Music 2\\song_log.txt"
    try:
        with open(log_file_path, "w", encoding="utf-8") as f:
            f.truncate(0)
    except Exception as e:
        print(f"Error clearing log file: {e}")


async def play_next_song(guild_id, queues, voice_client, skip=False):
    queue = queues.get(guild_id, [])
    if queue:
        player = queue.pop(0)
        voice_client.play(player, after=lambda e: asyncio.run_coroutine_threadsafe(play_next_song(guild_id, queues, voice_client, skip=False), voice_client.loop))
        if skip:
            return
        else:
            await remove_first_line_from_log()  # Xóa dòng đầu tiên trong file log
    elif skip:
        await remove_first_line_from_log()  # Xóa dòng đầu tiên trong file log
        await voice_client.disconnect()
        del voice_client[guild_id]  # Xóa voice_client sau khi dừng
    else:
        await clear_log_file()  # Xóa dòng đầu tiên trong file log
        await voice_client.disconnect()
        del voice_client[guild_id]  # Xóa voice_client sau khi dừng


  


  I have tried asking ChatGPT, Gemini, or Bing, and they always lead me into a loop of errors that cannot be resolved. This error only occurs when the song naturally finishes playing due to its duration. If the song is playing and I use the command !skip, the next song in the queue will play and function normally. I noticed that it seems like if a song ends naturally, the song queue is also cleared immediately. I hope someone can help me with this