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• 7 Ecommerce Metrics to Track and Improve in 2024

  12 avril 2024, par Erin

  You can invest hours into market research, create the best ads you’ve ever seen and fine-tune your budgets. But the only way to really know if your digital marketing campaigns move the needle is to track ecommerce metrics.

  It’s time to put your hopes and gut feelings aside and focus on the data. Ecommerce metrics are key performance indicators that can tell you a lot about the performance of a single campaign, a traffic source or your entire marketing efforts. 

  That’s why it’s essential to understand what ecommerce metrics are, key metrics to track and how to improve them. 

  Ready to do all of the above ? Then, let’s get started.

  What are ecommerce metrics ? 

  An ecommerce metric is any metric that helps you understand the effectiveness of your digital marketing efforts and the extent to which users are taking a desired action. Most ecommerce metrics focus on conversions, which could be anything from making a purchase to subscribing to your email list.

  You need to track ecommerce metrics to understand how well your marketing efforts are working. They are essential to helping you run a cost-effective marketing campaign that delivers a return on investment. 

  For example, tracking ecommerce metrics will help you identify whether your digital marketing campaigns are generating a return on investment or whether they are actually losing money. They also help you identify your most effective campaigns and traffic sources. 

  Ecommerce metrics also help you spot opportunities for improvement both in terms of your marketing campaigns and your site’s UX. 

  For instance, you can use ecommerce metrics to track the impact on revenue of A/B tests on your marketing campaigns. Or you can use them to understand how users interact with your website and what, if anything, you can do to make it more engaging.

  What’s the difference between conversion rate and conversion value ?

  The difference between a conversion rate and a conversion value is that the former is a percentage while the latter is a monetary value. 

  There can be confusion between the terms conversion rate and conversion value. Since conversions are core metrics in ecommerce, it’s worth taking a minute to clarify. 

  Conversion rates measure the percentage of people who take a desired action on your website compared to the total number of visitors. If you have 100 visitors and one of them converts, then your conversion rate is 1%. 

  Here’s the formula for calculating your conversion rate :

  Conversion Rate (%) = (Number of conversions / Total number of visitors) × 100

  

  Using the example above :

  Conversion Rate = (1 / 100) × 100 = 1%

  Conversion value is a monetary amount you assign to each conversion. In some cases, this is the price of the product a user purchases. In other conversion events, such as signing up for a free trial, you may wish to assign a hypothetical conversion value. 

  To calculate a hypothetical conversion value, let’s consider that you have estimated the average revenue generated from a paying customer is $300. If the conversion rate from free trial to paying customer is 20%, then the hypothetical conversion value for each free trial signup would be $300 multiplied by 20%, which equals $60. This takes into account the number of free trial users who eventually become paying customers.

  So the formula for hypothetical conversion value looks like this :

  

  Hypothetical conversion value = (Average revenue per paying customer) × (Conversion rate)

  Using the values from our example :

  Hypothetical conversion value = $300 × 20% = $60

  The most important ecommerce metrics and how to track them

  There are dozens of ecommerce metrics you could track, but here are seven of the most important. 

  Conversion rate

  Conversion rate is the percentage of visitors who take a desired action. It is arguably one of the most important ecommerce metrics and a great top-level indicator of the success of your marketing efforts. 

  You can measure the conversion rate of anything, including newsletter signups, ebook downloads, and product purchases, using the following formula :

  

  Conversion rate = (Number of people who took action / Total number of visitors) × 100

  You usually won’t have to manually calculate your conversion rate, though. Almost every web analytics or ad platform will track the conversion rate automatically.

  Matomo, for instance, automatically tracks any conversion you set in the Goals report.

  

  As you can see in the screenshot, your site’s conversions are plotted over a period of time and the conversion rate is tracked below the graph. You can change the time period to see how your conversion rate fluctuates.

  If you want to go even further, track your new visitor conversion rate to see how engaging your site is to first-time visitors. 

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  Cost per acquisition

  Cost per acquisition (CPA) is the average cost of acquiring a new user. You can calculate your overall CPA or you can break CPA down by email campaign, traffic source, or any other criteria. 

  Calculate CPA by dividing your total marketing cost by the number of new users you acquire.

  

  CPA = Total marketing cost​ / Number of new users acquired 

  So if your Google Ads campaign costs €1,000 and you acquire 100 new users, your CPA is €10 (1000/100=10).

  It’s important to note that CPA is not the same as customer acquisition cost. Customer acquisition cost considers the number of paying customers. CPA looks at the number of users taking a certain action, like subscribing to a newsletter, making a purchase, or signing up for a free trial.

  Cost per acquisition is a direct measure of your marketing efforts’ effectiveness, especially when comparing CPA to average customer spend and return on ad spend. 

  If your CPA is higher than the average customer spend, your marketing campaign is profitable. If not, then you can look at ways to either increase customer spend or decrease your cost per acquisition.

  Customer lifetime value

  Customer lifetime value (CLV) is the average amount of money a customer will spend with your ecommerce brand over their lifetime. 

  Customer value is the total worth of a customer to your brand based on their purchasing behaviour. To calculate it, multiply the average purchase value by the average number of purchases. For instance, if the average purchase value is €50 and customers make 5 purchases on average, the customer value would be €250.

  Use this formula to calculate customer value :

  

  Customer value = Average purchase value × Average number of purchases

  Then you can calculate customer lifetime value using the following formula :

  

  CLV = Customer value × Average customer lifespan

  In another example, let’s say you have a software company and customers pay you €500 per year for an annual subscription. If the average customer lifespan is 5 years, then the Customer Lifetime Value (CLV) would be €2,500.

  Customer lifetime value = €500 × 5 = €2,500

  Knowing how much potential customers are likely to spend helps you set accurate marketing budgets and optimise the price of your products. 

  Return on investment

  Return on investment (ROI) is the amount of revenue your marketing efforts generate compared to total spend. 

  It’s usually calculated as a percentage using the following formula :

  

  ROI = (Revenue / Total spend) × 100

  If you spend €1,000 on a paid ad campaign and your efforts bring in €5,000, then your ROI is 500% (5,000/1,000 × 100).

  With a web analytics tool like Matomo, you can quickly see the revenue generated from each traffic source and you can drill down further to compare different social media channels, search engines, referral websites and campaigns to get more granular view. 

  

  In the example above in Matomo’s Marketing Attribution feature, we can see that social networks are generating the highest amount of revenue in the year. To calculate ROI, we would need to compare the amount of investment to each channel. 

  Let’s say we invested $1,000 per year in search engine optimisation and content marketing, the return on investment (ROI) stands at approximately 2576%, based on a revenue of $26,763.48 per year. 

  Conversely, for organic social media campaigns, where $5,000 was invested and revenue amounted to $71,180.22 per year, the ROI is approximately 1323%. 

  Despite differences in revenue generation, both channels exhibit significant returns on investment, with SEO and content marketing demonstrating a much higher ROI compared to organic social media campaigns. 

  With that in mind, we might want to consider shifting our marketing budget to focus more on search engine optimisation and content marketing as it’s a greater return on investment.

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  Return on ad spend

  Return on ad spend (ROAS) is similar to return on investment, but it measures the profitability of a specific ad or campaign.

  Calculate ROAS using the following formula :

  

  ROAS = Revenue / Ad cost 

  A positive ROAS means you are making money. If you generate €3 for every €1 you spend on advertising, for example, there’s no reason to turn off that campaign. If you only make €1 for every €2 you spend, however, then you need to shut down the campaign or optimise it. 

  Bounce rate

  Bounce rate is the percentage of visitors who leave your site without taking another action. Calculate it using the following formula :

  

  Bounce rate = (Number of visitors who bounce / Total number of visitors) × 100

  Some portion of users will always leave your site immediately, but you should aim to make your bounce rate as low as possible. After all, every customer that bounces is a missed opportunity that you may never get again. 

  You can check the bounce rate for each one of your site’s pages using Matomo’s page analytics report. Web analytics tools like Google Analytics can track bounce rates for online stores also. 

  

  Bounce rate is calculated automatically. You can sort the list of pages by bounce rate allowing you to prioritise your optimisation efforts. 

  Don’t stop there, though. Explore bounce rate further by comparing your mobile bounce rate vs. desktop bounce rate by segmenting your traffic. This will highlight whether your mobile site needs improving. 

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

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  Click-through rate

  Your clickthrough rate (CTR) tells you the number of people who click on your ads as a percentage of total impressions. You can calculate it by dividing the number of clicks your ad gets by the total number of times people see it. 

  So the formula looks like this :

  

  CTR (%) = (Number of clicks / Total impressions​) × 100

  If an ad gets 1,000 impressions and 10 people click on it, then the CTR will be 10/1,000 × 100 = 1%

  You don’t usually need to calculate your clickthrough rate manually, however. Most ad platforms like Google Ads will automatically calculate CTR.

  What is considered a good ecommerce sales conversion rate ?

  This question is so broad it’s almost impossible to answer. The thing is, sales conversion rates vary massively depending on the conversion event and the industry. A good conversion rate in one industry might be terrible in another. 

  That being said, research shows that the average website conversion rate across all industries is 2.35%. Of course, some websites convert much better than this. The same study found that the top 25% of websites across all industries have a conversion rate of 5.31% or higher. 

  How can you improve your conversion rate ?

  Ecommerce metrics don’t just let you track your campaign’s ROI, they help you identify ways to improve your campaign. 

  Use these five tips to start improving your marketing campaign’s conversion rates today :

  Run A/B tests

  The most effective way to improve almost all of the ecommerce metrics you track is to test, test, and test again.

  A/B testing or multivariate testing compares two different versions of the same content, such as a landing page or blog post. Seeing which version performs better can help you squeeze as many conversions as possible from your website and ad campaigns. But only if you test as many things as possible. This should include :

  • Ad placement
  • Ad copy
  • CTAs
  • Headlines
  • Straplines
  • Colours
  • Design

  To create and analyse tests and their results effectively, you’ll need either an A/B testing platform or a web analytics solution like Matomo, which offers one out of the box.

  

  Matomo’s A/B Testing feature makes it easy to create and track tests over time, breaking down each test’s variations by the metrics that matter. It automatically calculates statistical significance, too, meaning you can be sure you’re making a change for the better. 

  Try Matomo for Free

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

• FFmpeg RTSP drop rate increases when frame rate is reduced

  13 avril 2024, par Avishka Perera

  I need to read an RTSP stream, process the images individually in Python, and then write the images back to an RTSP stream. As the RTSP server, I am using Mediamtx [1]. For streaming, I am using FFmpeg [2].

  


  I have the following code that works perfectly fine. For simplification purposes, I am streaming three generated images.

  


  import time
import numpy as np
import subprocess

width, height = 640, 480
fps = 25
rtsp_server_address = f"rtsp://localhost:8554/mystream"

ffmpeg_cmd = [
    "ffmpeg",
    "-re",
    "-f",
    "rawvideo",
    "-pix_fmt",
    "rgb24",
    "-s",
    f"{width}x{height}",
    "-i",
    "-",
    "-r",
    str(fps),
    "-avoid_negative_ts",
    "make_zero",
    "-vcodec",
    "libx264",
    "-threads",
    "4",
    "-f",
    "rtsp",
    rtsp_server_address,
]
colors = np.array(
    [
        [255, 0, 0],
        [0, 255, 0],
        [0, 0, 255],
    ]
).reshape(3, 1, 1, 3)
images = (np.ones((3, width, height, 3)) * colors).astype(np.uint8)

if __name__ == "__main__":

    process = subprocess.Popen(ffmpeg_cmd, stdin=subprocess.PIPE)
    start = time.time()
    exported = 0
    while True:
        exported += 1
        next_time = start + exported / fps
        now = time.time()
        if next_time > now:
            sleep_dur = next_time - now
            time.sleep(sleep_dur)

        image = images[exported % 3]
        image_bytes = image.tobytes()

        process.stdin.write(image_bytes)
        process.stdin.flush()

    process.stdin.close()
    process.wait()


  


  The issue is, that I need to run this at 10 fps because the processing step is heavy and can only afford 10 fps. Hence, as I reduce the frame rate from 25 to 10, the drop rate increases from 0% to 100%. And after a few iterations, I get a BrokenPipeError: [Errno 32] Broken pipe. Refer to the appendix for the complete log.

  


  As an alternative, I can use OpenCV compiled from source with GStreamer [3], but I prefer using FFmpeg to make the shipping process simple. Since compiling OpenCV from source can be tedious and dependent on the system.

  


  References

  


  [1] Mediamtx (formerly rtsp-simple-server) : https://github.com/bluenviron/mediamtx

  


  [2] FFmpeg : https://github.com/FFmpeg/FFmpeg

  


  [3] Compile OpenCV with GStreamer : https://github.com/bluenviron/mediamtx?tab=readme-ov-file#opencv

  


  Appendix

  


  Creating the source stream

  


  To instantiate the unprocessed stream, I use the following command. This streams the content of my webcam as and RTSP stream.

  


  ffmpeg -video_size 1280x720 -i /dev/video0  -avoid_negative_ts make_zero -vcodec libx264 -r 10 -f rtsp rtsp://localhost:8554/webcam


  


  Error log

  


  ffmpeg version 6.1.1 Copyright (c) 2000-2023 the FFmpeg developers&#xA;  built with gcc 12.3.0 (conda-forge gcc 12.3.0-5)&#xA;  configuration: --prefix=/home/conda/feedstock_root/build_artifacts/ffmpeg_1712656518955/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_plac --cc=/home/conda/feedstock_root/build_artifacts/ffmpeg_1712656518955/_build_env/bin/x86_64-conda-linux-gnu-cc --cxx=/home/conda/feedstock_root/build_artifacts/ffmpeg_1712656518955/_build_env/bin/x86_64-conda-linux-gnu-c&#x2B;&#x2B; --nm=/home/conda/feedstock_root/build_artifacts/ffmpeg_1712656518955/_build_env/bin/x86_64-conda-linux-gnu-nm --ar=/home/conda/feedstock_root/build_artifacts/ffmpeg_1712656518955/_build_env/bin/x86_64-conda-linux-gnu-ar --disable-doc --disable-openssl --enable-demuxer=dash --enable-hardcoded-tables --enable-libfreetype --enable-libharfbuzz --enable-libfontconfig --enable-libopenh264 --enable-libdav1d --enable-gnutls --enable-libmp3lame --enable-libvpx --enable-libass --enable-pthreads --enable-vaapi --enable-libopenvino --enable-gpl --enable-libx264 --enable-libx265 --enable-libaom --enable-libsvtav1 --enable-libxml2 --enable-pic --enable-shared --disable-static --enable-version3 --enable-zlib --enable-libopus --pkg-config=/home/conda/feedstock_root/build_artifacts/ffmpeg_1712656518955/_build_env/bin/pkg-config&#xA;  libavutil      58. 29.100 / 58. 29.100&#xA;  libavcodec     60. 31.102 / 60. 31.102&#xA;  libavformat    60. 16.100 / 60. 16.100&#xA;  libavdevice    60.  3.100 / 60.  3.100&#xA;  libavfilter     9. 12.100 /  9. 12.100&#xA;  libswscale      7.  5.100 /  7.  5.100&#xA;  libswresample   4. 12.100 /  4. 12.100&#xA;  libpostproc    57.  3.100 / 57.  3.100&#xA;Input #0, rawvideo, from &#x27;fd:&#x27;:&#xA;  Duration: N/A, start: 0.000000, bitrate: 184320 kb/s&#xA;  Stream #0:0: Video: rawvideo (RGB[24] / 0x18424752), rgb24, 640x480, 184320 kb/s, 25 tbr, 25 tbn&#xA;Stream mapping:&#xA;  Stream #0:0 -> #0:0 (rawvideo (native) -> h264 (libx264))&#xA;[libx264 @ 0x5e2ef8b01340] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2&#xA;[libx264 @ 0x5e2ef8b01340] profile High 4:4:4 Predictive, level 2.2, 4:4:4, 8-bit&#xA;[libx264 @ 0x5e2ef8b01340] 264 - core 164 r3095 baee400 - H.264/MPEG-4 AVC codec - Copyleft 2003-2022 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=4 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=10 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00&#xA;Output #0, rtsp, to &#x27;rtsp://localhost:8554/mystream&#x27;:&#xA;  Metadata:&#xA;    encoder         : Lavf60.16.100&#xA;  Stream #0:0: Video: h264, yuv444p(tv, progressive), 640x480, q=2-31, 10 fps, 90k tbn&#xA;    Metadata:&#xA;      encoder         : Lavc60.31.102 libx264&#xA;    Side data:&#xA;      cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A&#xA;[vost#0:0/libx264 @ 0x5e2ef8b01080] Error submitting a packet to the muxer: Broken pipe   &#xA;[out#0/rtsp @ 0x5e2ef8afd780] Error muxing a packet&#xA;[out#0/rtsp @ 0x5e2ef8afd780] video:1kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown&#xA;frame=    1 fps=0.1 q=-1.0 Lsize=N/A time=00:00:04.70 bitrate=N/A dup=0 drop=70 speed=0.389x    &#xA;[libx264 @ 0x5e2ef8b01340] frame I:16    Avg QP: 6.00  size:   147&#xA;[libx264 @ 0x5e2ef8b01340] frame P:17    Avg QP: 9.94  size:   101&#xA;[libx264 @ 0x5e2ef8b01340] frame B:17    Avg QP: 9.94  size:    64&#xA;[libx264 @ 0x5e2ef8b01340] consecutive B-frames: 50.0%  0.0% 42.0%  8.0%&#xA;[libx264 @ 0x5e2ef8b01340] mb I  I16..4: 81.3% 18.7%  0.0%&#xA;[libx264 @ 0x5e2ef8b01340] mb P  I16..4: 52.9%  0.0%  0.0%  P16..4:  0.0%  0.0%  0.0%  0.0%  0.0%    skip:47.1%&#xA;[libx264 @ 0x5e2ef8b01340] mb B  I16..4:  0.0%  5.9%  0.0%  B16..8:  0.1%  0.0%  0.0%  direct: 0.0%  skip:94.0%  L0:56.2% L1:43.8% BI: 0.0%&#xA;[libx264 @ 0x5e2ef8b01340] 8x8 transform intra:15.4% inter:100.0%&#xA;[libx264 @ 0x5e2ef8b01340] coded y,u,v intra: 0.0% 0.0% 0.0% inter: 0.0% 0.0% 0.0%&#xA;[libx264 @ 0x5e2ef8b01340] i16 v,h,dc,p: 97%  0%  3%  0%&#xA;[libx264 @ 0x5e2ef8b01340] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu:  0%  0% 100%  0%  0%  0%  0%  0%  0%&#xA;[libx264 @ 0x5e2ef8b01340] Weighted P-Frames: Y:52.9% UV:52.9%&#xA;[libx264 @ 0x5e2ef8b01340] ref P L0: 88.9%  0.0%  0.0% 11.1%&#xA;[libx264 @ 0x5e2ef8b01340] kb/s:8.27&#xA;Conversion failed!&#xA;Traceback (most recent call last):&#xA;  File "/home/avishka/projects/read-process-stream/minimal-ffmpeg-error.py", line 58, in <module>&#xA;    process.stdin.write(image_bytes)&#xA;BrokenPipeError: [Errno 32] Broken pipe&#xA;</module>

  


• ffmpeg failed to load audio file

  14 avril 2024, par Vaishnav Ghenge

  Failed to load audio: ffmpeg version 5.1.4-0+deb12u1 Copyright (c) Failed to load audio: ffmpeg version 5.1.4-0+deb12u1 Copyright (c) 2000-2023 the FFmpeg developers
  built with gcc 12 (Debian 12.2.0-14)
  configuration: --prefix=/usr --extra-version=0+deb12u1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libdav1d --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libglslang --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librabbitmq --enable-librist --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libsrt --enable-libssh --enable-libsvtav1 --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzimg --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --disable-sndio --enable-libjxl --enable-pocketsphinx --enable-librsvg --enable-libmfx --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libx264 --enable-libplacebo --enable-librav1e --enable-shared
  libavutil      57. 28.100 / 57. 28.100
  libavcodec     59. 37.100 / 59. 37.100
  libavformat    59. 27.100 / 59. 27.100
  libavdevice    59.  7.100 / 59.  7.100
  libavfilter     8. 44.100 /  8. 44.100
  libswscale      6.  7.100 /  6.  7.100
  libswresample   4.  7.100 /  4.  7.100
  libpostproc    56.  6.100 / 56.  6.100
/tmp/tmpjlchcpdm.wav: Invalid data found when processing input


  


  backend :

  


  
@app.route("/transcribe", methods=["POST"])
def transcribe():
    # Check if audio file is present in the request
    if 'audio_file' not in request.files:
        return jsonify({"error": "No file part"}), 400
    
    audio_file = request.files.get('audio_file')

    # Check if audio_file is sent in files
    if not audio_file:
        return jsonify({"error": "`audio_file` is missing in request.files"}), 400

    # Check if the file is present
    if audio_file.filename == '':
        return jsonify({"error": "No selected file"}), 400

    # Save the file with a unique name
    filename = secure_filename(audio_file.filename)
    unique_filename = os.path.join("uploads", str(uuid.uuid4()) + '_' + filename)
    # audio_file.save(unique_filename)
    
    # Read the contents of the audio file
    contents = audio_file.read()

    max_file_size = 500 * 1024 * 1024
    if len(contents) > max_file_size:
        return jsonify({"error": "File is too large"}), 400

    # Check if the file extension suggests it's a WAV file
    if not filename.lower().endswith('.wav'):
        # Delete the file if it's not a WAV file
        os.remove(unique_filename)
        return jsonify({"error": "Only WAV files are supported"}), 400

    print(f"\033[92m{filename}\033[0m")

    # Call Celery task asynchronously
    result = transcribe_audio.delay(contents)

    return jsonify({
        "task_id": result.id,
        "status": "pending"
    })


@celery_app.task
def transcribe_audio(contents):
    # Transcribe the audio
    try:
        # Create a temporary file to save the audio data
        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_audio:
            temp_path = temp_audio.name
            temp_audio.write(contents)

            print(f"\033[92mFile temporary path: {temp_path}\033[0m")
            transcribe_start_time = time.time()

            # Transcribe the audio
            transcription = transcribe_with_whisper(temp_path)
            
            transcribe_end_time = time.time()
            print(f"\033[92mTranscripted text: {transcription}\033[0m")

            return transcription, transcribe_end_time - transcribe_start_time

    except Exception as e:
        print(f"\033[92mError: {e}\033[0m")
        return str(e)


  


  frontend :

  


      useEffect(() => {
        const init = () => {
            navigator.mediaDevices.getUserMedia({audio: true})
                .then((audioStream) => {
                    const recorder = new MediaRecorder(audioStream);

                    recorder.ondataavailable = e => {
                        if (e.data.size > 0) {
                            setChunks(prevChunks => [...prevChunks, e.data]);
                        }
                    };

                    recorder.onerror = (e) => {
                        console.log("error: ", e);
                    }

                    recorder.onstart = () => {
                        console.log("started");
                    }

                    recorder.start();

                    setStream(audioStream);
                    setRecorder(recorder);
                });
        }

        init();

        return () => {
            if (recorder && recorder.state === 'recording') {
                recorder.stop();
            }

            if (stream) {
                stream.getTracks().forEach(track => track.stop());
            }
        }
    }, []);

    useEffect(() => {
        // Send chunks of audio data to the backend at regular intervals
        const intervalId = setInterval(() => {
            if (recorder && recorder.state === 'recording') {
                recorder.requestData(); // Trigger data available event
            }
        }, 8000); // Adjust the interval as needed


        return () => {
            if (intervalId) {
                console.log("Interval cleared");
                clearInterval(intervalId);
            }
        };
    }, [recorder]);

    useEffect(() => {
        const processAudio = async () => {
            if (chunks.length > 0) {
                // Send the latest chunk to the server for transcription
                const latestChunk = chunks[chunks.length - 1];

                const audioBlob = new Blob([latestChunk]);
                convertBlobToAudioFile(audioBlob);
            }
        };

        void processAudio();
    }, [chunks]);

    const convertBlobToAudioFile = useCallback((blob: Blob) => {
        // Convert Blob to audio file (e.g., WAV)
        // This conversion may require using a third-party library or service
        // For example, you can use the MediaRecorder API to record audio in WAV format directly
        // Alternatively, you can use a library like recorderjs to perform the conversion
        // Here's a simplified example using recorderjs:

        const reader = new FileReader();
        reader.onload = () => {
            const audioBuffer = reader.result; // ArrayBuffer containing audio data

            // Send audioBuffer to Flask server or perform further processing
            sendAudioToFlask(audioBuffer as ArrayBuffer);
        };

        reader.readAsArrayBuffer(blob);
    }, []);

    const sendAudioToFlask = useCallback((audioBuffer: ArrayBuffer) => {
        const formData = new FormData();
        formData.append('audio_file', new Blob([audioBuffer]), `speech_audio.wav`);

        console.log(formData.get("audio_file"));

        fetch('http://34.87.75.138:8000/transcribe', {
            method: 'POST',
            body: formData
        })
            .then(response => response.json())
            .then((data: { task_id: string, status: string }) => {
                pendingTaskIdsRef.current.push(data.task_id);
            })
            .catch(error => {
                console.error('Error sending audio to Flask server:', error);
            });
    }, []);


  


  I was trying to pass the audio from frontend to whisper model which is in flask app