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Médias (10)
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Demon Seed
26 septembre 2011, par
Mis à jour : Septembre 2011
Langue : English
Type : Audio
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Demon seed (wav version)
26 septembre 2011, par
Mis à jour : Avril 2013
Langue : English
Type : Audio
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The four of us are dying (wav version)
26 septembre 2011, par
Mis à jour : Avril 2013
Langue : English
Type : Audio
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Corona radiata (wav version)
26 septembre 2011, par
Mis à jour : Avril 2013
Langue : English
Type : Audio
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Lights in the sky (wav version)
26 septembre 2011, par
Mis à jour : Avril 2013
Langue : English
Type : Audio
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Head down (wav version)
26 septembre 2011, par
Mis à jour : Avril 2013
Langue : English
Type : Audio
Autres articles (61)
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Websites made with MediaSPIP
2 mai 2011, parThis page lists some websites based on MediaSPIP.
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Amélioration de la version de base
13 septembre 2013Jolie sélection multiple
Le plugin Chosen permet d’améliorer l’ergonomie des champs de sélection multiple. Voir les deux images suivantes pour comparer.
Il suffit pour cela d’activer le plugin Chosen (Configuration générale du site > Gestion des plugins), puis de configurer le plugin (Les squelettes > Chosen) en activant l’utilisation de Chosen dans le site public et en spécifiant les éléments de formulaires à améliorer, par exemple select[multiple] pour les listes à sélection multiple (...) -
HTML5 audio and video support
13 avril 2011, parMediaSPIP uses HTML5 video and audio tags to play multimedia files, taking advantage of the latest W3C innovations supported by modern browsers.
The MediaSPIP player used has been created specifically for MediaSPIP and can be easily adapted to fit in with a specific theme.
For older browsers the Flowplayer flash fallback is used.
MediaSPIP allows for media playback on major mobile platforms with the above (...)
Sur d’autres sites (8252)
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lavc/libopenh264enc : add bit rate control select support
29 avril 2020, par Linjie Fulavc/libopenh264enc : add bit rate control select support
RC_BITRATE_MODE :
set BITS_EXCEEDED to iCurrentBitsLevel and allows QP adjust
in RcCalculatePictureQp().RC_BUFFERBASED_MODE :
use buffer status to adjust the video quality.RC_TIMESTAMP_MODE :
bit rate control based on timestamp, introduced in release 1.4.Default to use RC_QUALITY_MODE.
Signed-off-by : Linjie Fu <linjie.fu@intel.com>
Signed-off-by : Martin Storsjö <martin@martin.st> -
What determine number of cpu core usage of filter select=gt(scene,0.1) ?
15 décembre 2022, par kocoten1992I've notice that when using filter gt(scene,0.1), for example :


ffmpeg -i big_buck_bunny.mkv -filter:v "select='gt(scene,0.1)',showinfo" -f null -


Depends on the video, number of cpu cores usage varies extremely (sometimes it 3 cores usage - other time 12 cores usage in different video).


Would like to ask what determine that logic ?


I try to read ffmpeg source code but not familiar with it, a general explanation would be enough, but much appreciate if you point out the line/directory determine that logic in https://github.com/FFmpeg/FFmpeg.


(Also not asking how to reduce cpu usage, interested in the logic determine that).


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Detect volume via mic, start recording, end on silence, transcribe and sent to endpoint
15 juin 2023, par alphadmonI have been attempting to get this to work in many ways but I can't seem to get it right. Most of the time I get a part of it to work and then when I try to make other parts work, I generally break other things.


I am intercepting the volume coming from the mic and if it is louder than 50, I start a recording. I then keep recording until there is a silence, if the silence is equal to 5 seconds I then stop the recording.


I then send the recording to be transcribed by
whisper
using OpenAI API.

Once that is returned, I then want to send it to the open ai chat end point and get the response.


After that, I would like to start listening again.


Here is what I have that is sort of working so far, but the recording is an empty file always :


// DETECT SPEECH
const recorder = require('node-record-lpcm16');

// TRANSCRIBE
const fs = require("fs");
const ffmpeg = require("fluent-ffmpeg");
const mic = require("mic");
const { Readable } = require("stream");
const ffmpegPath = require("@ffmpeg-installer/ffmpeg").path;
require('dotenv').config();

// CHAT
const { Configuration, OpenAIApi } = require("openai");

// OPEN AI
const configuration = new Configuration({
 organization: process.env.OPENAI_ORG,
 apiKey: process.env.OPENAI_API_KEY,
});
const openai = new OpenAIApi(configuration);

// SETUP
ffmpeg.setFfmpegPath(ffmpegPath);

// VARS
let isRecording = false;
const audioFilename = 'recorded_audio.wav';
const micInstance = mic({
 rate: '16000',
 channels: '1',
 fileType: 'wav',
});

// DETECT SPEECH
const file = fs.createWriteStream('determine_speech.wav', { encoding: 'binary' });
const recording = recorder.record();
recording.stream().pipe(file);


recording.stream().on('data', async (data) => {
 let volume = parseInt(calculateVolume(data));
 if (volume > 50 && !isRecording) {
 console.log('You are talking.');
 await recordAudio(audioFilename);
 } else {
 setTimeout(async () => {
 console.log('You are quiet.');
 micInstance.stop();
 console.log('Finished recording');
 const transcription = await transcribeAudio(audioFilename);
 console.log('Transcription:', transcription);
 setTimeout(async () => {
 await askAI(transcription);
 }, 5000);
 }, 5000);
 }
});

function calculateVolume(data) {
 let sum = 0;

 for (let i = 0; i < data.length; i += 2) {
 const sample = data.readInt16LE(i);
 sum += sample * sample;
 }

 const rms = Math.sqrt(sum / (data.length / 2));

 return rms;
}

// TRANSCRIBE
function recordAudio(filename) {
 const micInputStream = micInstance.getAudioStream();
 const output = fs.createWriteStream(filename);
 const writable = new Readable().wrap(micInputStream);

 console.log('Listening...');

 writable.pipe(output);

 micInstance.start();

 micInputStream.on('error', (err) => {
 console.error(err);
 });
}

// Transcribe audio
async function transcribeAudio(filename) {
 const transcript = await openai.createTranscription(
 fs.createReadStream(filename),
 "whisper-1",
 );
 return transcript.data.text;
}

// CHAT
async function askAI(text) {
 let completion = await openai.createChatCompletion({
 model: "gpt-4",
 temperature: 0.2,
 stream: false,
 messages: [
 { role: "user", content: text },
 { role: "system", content: "Act like you are a rude person." }
 ],
 });

 completion = JSON.stringify(completion.data, null, 2);
 console.log(completion);
}