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• Video Conferencing in HTML5 : WebRTC via Web Sockets

  1er janvier 2014, par silvia

  A bit over a week ago I gave a presentation at Web Directions Code 2012 in Melbourne. Maxine and John asked me to speak about something related to HTML5 video, so I went for the new shiny : WebRTC – real-time communication in the browser.

  Presentation slides

  I only had 20 min, so I had to make it tight. I wanted to show off video conferencing without special plugins in Google Chrome in just a few lines of code, as is the promise of WebRTC. To a large extent, I achieved this. But I made some interesting discoveries along the way. Demos are in the slide deck.

  UPDATE : Opera 12 has been released with WebRTC support.

  Housekeeping : if you want to replicate what I have done, you need to install a Google Chrome Web Browser 19+. Then make sure you go to chrome ://flags and activate the MediaStream and PeerConnection experiment(s). Restart your browser and now you can experiment with this feature. Big warning up-front : it’s not production-ready, since there are still changes happening to the spec and there is no compatible implementation by another browser yet.

  Here is a brief summary of the steps involved to set up video conferencing in your browser :

  1. Set up a video element each for the local and the remote video stream.
  2. Grab the local camera and stream it to the first video element.
  3. (*) Establish a connection to another person running the same Web page.
  4. Send the local camera stream on that peer connection.
  5. Accept the remote camera stream into the second video element.

  Now, the most difficult part of all of this – believe it or not – is the signalling part that is required to build the peer connection (marked with (*)). Initially I wanted to run completely without a server and just enter the remote’s IP address to establish the connection. This is, however, not a functionality that the PeerConnection object provides [might this be something to add to the spec ?].

  So, you need a server known to both parties that can provide for the handshake to set up the connection. All the examples that I have seen, such as https://apprtc.appspot.com/, use a channel management server on Google’s appengine. I wanted it all working with HTML5 technology, so I decided to use a Web Socket server instead.

  I implemented my Web Socket server using node.js (code of websocket server). The video conferencing demo is in the slide deck in an iframe – you can also use the stand-alone html page. Works like a treat.

  While it is still using Google’s STUN server to get through NAT, the messaging for setting up the connection is running completely through the Web Socket server. The messages that get exchanged are plain SDP message packets with a session ID. There are OFFER, ANSWER, and OK packets exchanged for each streaming direction. You can see some of it in the below image :

  

  I’m not running a public WebSocket server, so you won’t be able to see this part of the presentation working. But the local loopback video should work.

  At the conference, it all went without a hitch (while the wireless played along). I believe you have to host the WebSocket server on the same machine as the Web page, otherwise it won’t work for security reasons.

  A whole new world of opportunities lies out there when we get the ability to set up video conferencing on every Web page – scary and exciting at the same time !

• Analytics for ePortfolios, Mahara hui conference

  20 mars 2014, par Matthieu Aubry — Community, Meta

  I was privileged to present at the Mahara Hui conference in Wellington, New Zealand.

  Here are the slides of my presentation “Analytics for ePortfolios” :

  Summary : by using an analytics tool that integrates well with Mahara, such as Piwik, Mahara users can benefit from a multitude of insightful analytics reports.

  Learn more

  Mahara is a web application to build your electronic portfolio. You can create journals, upload files, embed social media resources from the web and collaborate with other users in groups. Mahara is a popular open source project built by a passionate community, and used in universities, schools and companies all over the world.

  Mahara Hui is the first kiwi conference on Mahara, the open source ePortfolio system, in New Zealand. This 2-day conference was held at Te Papa in Wellington from 19 to 20 March 2014 (schedule)

  Next steps

  I’m excited to join the Mahara team at the Mahara Hui Hackfest organised today at Catalyst IT offices. We will brainstorm how to integrate Piwik beautifully within Mahara, and how to ultimately provide students and employees useful analytics on all the content they create !

• 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