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Autres articles (109)
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L’agrémenter visuellement
10 avril 2011MediaSPIP est basé sur un système de thèmes et de squelettes. Les squelettes définissent le placement des informations dans la page, définissant un usage spécifique de la plateforme, et les thèmes l’habillage graphique général.
Chacun peut proposer un nouveau thème graphique ou un squelette et le mettre à disposition de la communauté. -
Publier sur MédiaSpip
13 juin 2013Puis-je poster des contenus à partir d’une tablette Ipad ?
Oui, si votre Médiaspip installé est à la version 0.2 ou supérieure. Contacter au besoin l’administrateur de votre MédiaSpip pour le savoir -
Ajouter des informations spécifiques aux utilisateurs et autres modifications de comportement liées aux auteurs
12 avril 2011, parLa manière la plus simple d’ajouter des informations aux auteurs est d’installer le plugin Inscription3. Il permet également de modifier certains comportements liés aux utilisateurs (référez-vous à sa documentation pour plus d’informations).
Il est également possible d’ajouter des champs aux auteurs en installant les plugins champs extras 2 et Interface pour champs extras.
Sur d’autres sites (10713)
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ffmpeg pipe process ends right after writing first buffer data to input stream and does not keep running
6 mai, par Taketo MatsunagaI have been trying to convert 16bit PCM (s16le) audio data to webm using ffmpeg in C#.
But the process ends right after the writing the first buffer data to standard input.
I has exited with the status 0, meaning success. But do not know why....
Could anyone tell me why ?


I apprecite it if you could support me.


public class SpeechService : ISpeechService
 {
 
 /// <summary>
 /// Defines the _audioInputStream
 /// </summary>
 private readonly MemoryStream _audioInputStream = new MemoryStream();

 public async Task SendPcmAsWebmViaWebSocketAsync(
 MemoryStream pcmAudioStream,
 int sampleRate,
 int channels) 
 {
 string inputFormat = "s16le";

 var ffmpegProcessInfo = new ProcessStartInfo
 {
 FileName = _ffmpegPath,
 Arguments =
 $"-f {inputFormat} -ar {sampleRate} -ac {channels} -i pipe:0 " +
 $"-f webm pipe:1",
 RedirectStandardInput = true,
 RedirectStandardOutput = true,
 RedirectStandardError = true,
 UseShellExecute = false,
 CreateNoWindow = true,
 };

 _ffmpegProcess = new Process { StartInfo = ffmpegProcessInfo };

 Console.WriteLine("Starting FFmpeg process...");
 try
 {

 if (!await Task.Run(() => _ffmpegProcess.Start()))
 {
 Console.Error.WriteLine("Failed to start FFmpeg process.");
 return;
 }
 Console.WriteLine("FFmpeg process started.");

 }
 catch (Exception ex)
 {
 Console.Error.WriteLine($"Error starting FFmpeg process: {ex.Message}");
 throw;
 }

 var encodeAndSendTask = Task.Run(async () =>
 {
 try
 {
 using var ffmpegOutputStream = _ffmpegProcess.StandardOutput.BaseStream;
 byte[] buffer = new byte[8192]; // Temporary buffer to read data
 byte[] sendBuffer = new byte[8192]; // Buffer to accumulate data for sending
 int sendBufferIndex = 0; // Tracks the current size of sendBuffer
 int bytesRead;

 Console.WriteLine("Reading WebM output from FFmpeg and sending via WebSocket...");
 while (true)
 {
 if ((bytesRead = await ffmpegOutputStream.ReadAsync(buffer, 0, buffer.Length)) > 0)
 {
 // Copy data to sendBuffer
 Array.Copy(buffer, 0, sendBuffer, sendBufferIndex, bytesRead);
 sendBufferIndex += bytesRead;

 // If sendBuffer is full, send it via WebSocket
 if (sendBufferIndex >= sendBuffer.Length)
 {
 var segment = new ArraySegment<byte>(sendBuffer, 0, sendBuffer.Length);
 _ws.SendMessage(segment);
 sendBufferIndex = 0; // Reset the index after sending
 }
 }
 }
 }
 catch (OperationCanceledException)
 {
 Console.WriteLine("Encode/Send operation cancelled.");
 }
 catch (IOException ex) when (ex.InnerException is ObjectDisposedException)
 {
 Console.WriteLine("Stream was closed, likely due to process exit or cancellation.");
 }
 catch (Exception ex)
 {
 Console.Error.WriteLine($"Error during encoding/sending: {ex}");
 }
 });

 var errorReadTask = Task.Run(async () =>
 {
 Console.WriteLine("Starting to read FFmpeg stderr...");
 using var errorReader = _ffmpegProcess.StandardError;
 try
 {
 string? line;
 while ((line = await errorReader.ReadLineAsync()) != null) 
 {
 Console.WriteLine($"[FFmpeg stderr] {line}");
 }
 }
 catch (OperationCanceledException) { Console.WriteLine("FFmpeg stderr reading cancelled."); }
 catch (TimeoutException) { Console.WriteLine("FFmpeg stderr reading timed out (due to cancellation)."); }
 catch (Exception ex) { Console.Error.WriteLine($"Error reading FFmpeg stderr: {ex.Message}"); }
 Console.WriteLine("Finished reading FFmpeg stderr.");
 });

 }

 public async Task AppendAudioBuffer(AudioMediaBuffer audioBuffer)
 {
 try
 {
 // audio for a 1:1 call
 var bufferLength = audioBuffer.Length;
 if (bufferLength > 0)
 {
 var buffer = new byte[bufferLength];
 Marshal.Copy(audioBuffer.Data, buffer, 0, (int)bufferLength);

 _logger.Info("_ffmpegProcess.HasExited:" + _ffmpegProcess.HasExited);
 using var ffmpegInputStream = _ffmpegProcess.StandardInput.BaseStream;
 await ffmpegInputStream.WriteAsync(buffer, 0, buffer.Length);
 await ffmpegInputStream.FlushAsync(); // バッファをフラッシュ
 _logger.Info("Wrote buffer data.");

 }
 }
 catch (Exception e)
 {
 _logger.Error(e, "Exception happend writing to input stream");
 }
 }

</byte>

Starting FFmpeg process...
FFmpeg process started.
Starting to read FFmpeg stderr...
Reading WebM output from FFmpeg and sending via WebSocket...
[FFmpeg stderr] ffmpeg version 7.1.1-essentials_build-www.gyan.dev Copyright (c) 2000-2025 the FFmpeg developers
[FFmpeg stderr] built with gcc 14.2.0 (Rev1, Built by MSYS2 project)
[FFmpeg stderr] configuration: --enable-gpl --enable-version3 --enable-static --disable-w32threads --disable-autodetect --enable-fontconfig --enable-iconv --enable-gnutls --enable-libxml2 --enable-gmp --enable-bzlib --enable-lzma --enable-zlib --enable-libsrt --enable-libssh --enable-libzmq --enable-avisynth --enable-sdl2 --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxvid --enable-libaom --enable-libopenjpeg --enable-libvpx --enable-mediafoundation --enable-libass --enable-libfreetype --enable-libfribidi --enable-libharfbuzz --enable-libvidstab --enable-libvmaf --enable-libzimg --enable-amf --enable-cuda-llvm --enable-cuvid --enable-dxva2 --enable-d3d11va --enable-d3d12va --enable-ffnvcodec --enable-libvpl --enable-nvdec --enable-nvenc --enable-vaapi --enable-libgme --enable-libopenmpt --enable-libopencore-amrwb --enable-libmp3lame --enable-libtheora --enable-libvo-amrwbenc --enable-libgsm --enable-libopencore-amrnb --enable-libopus --enable-libspeex --enable-libvorbis --enable-librubberband
[FFmpeg stderr] libavutil 59. 39.100 / 59. 39.100
[FFmpeg stderr] libavcodec 61. 19.101 / 61. 19.101
[FFmpeg stderr] libavformat 61. 7.100 / 61. 7.100
[FFmpeg stderr] libavdevice 61. 3.100 / 61. 3.100
[FFmpeg stderr] libavfilter 10. 4.100 / 10. 4.100
[FFmpeg stderr] libswscale 8. 3.100 / 8. 3.100
[FFmpeg stderr] libswresample 5. 3.100 / 5. 3.100
[FFmpeg stderr] libpostproc 58. 3.100 / 58. 3.100

[2025-05-06 15:44:43,598][INFO][XbLogger.cs:85] _ffmpegProcess.HasExited:False
[2025-05-06 15:44:43,613][INFO][XbLogger.cs:85] Wrote buffer data.
[2025-05-06 15:44:43,613][INFO][XbLogger.cs:85] Wrote buffer data.
[FFmpeg stderr] [aist#0:0/pcm_s16le @ 0000025ec8d36040] Guessed Channel Layout: mono
[FFmpeg stderr] Input #0, s16le, from 'pipe:0':
[FFmpeg stderr] Duration: N/A, bitrate: 256 kb/s
[FFmpeg stderr] Stream #0:0: Audio: pcm_s16le, 16000 Hz, mono, s16, 256 kb/s
[FFmpeg stderr] Stream mapping:
[FFmpeg stderr] Stream #0:0 -> #0:0 (pcm_s16le (native) -> opus (libopus))
[FFmpeg stderr] [libopus @ 0000025ec8d317c0] No bit rate set. Defaulting to 64000 bps.
[FFmpeg stderr] Output #0, webm, to 'pipe:1':
[FFmpeg stderr] Metadata:
[FFmpeg stderr] encoder : Lavf61.7.100
[FFmpeg stderr] Stream #0:0: Audio: opus, 16000 Hz, mono, s16, 64 kb/s
[FFmpeg stderr] Metadata:
[FFmpeg stderr] encoder : Lavc61.19.101 libopus
[FFmpeg stderr] [out#0/webm @ 0000025ec8d36200] video:0KiB audio:1KiB subtitle:0KiB other streams:0KiB global headers:0KiB muxing overhead: 67.493113%
[FFmpeg stderr] size= 1KiB time=00:00:00.04 bitrate= 243.2kbits/s speed=2.81x
Finished reading FFmpeg stderr.
[2025-05-06 15:44:44,101][INFO][XbLogger.cs:85] _ffmpegProcess.HasExited:True
[2025-05-06 15:44:44,132][ERROR][XbLogger.cs:67] Exception happend writing to input stream
System.ObjectDisposedException: Cannot access a closed file.
 at System.IO.FileStream.WriteAsync(Byte[] buffer, Int32 offset, Int32 count, CancellationToken cancellationToken)
 at System.IO.Stream.WriteAsync(Byte[] buffer, Int32 offset, Int32 count)
 at EchoBot.Media.SpeechService.AppendAudioBuffer(AudioMediaBuffer audioBuffer) in C:\Users\tm068\Documents\workspace\myprj\xbridge-teams-bot\src\EchoBot\Media\SpeechService.cs:line 242


I am expecting the ffmpeg process keep running.


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Introducing the Data Warehouse Connector feature
30 janvier, par Matomo Core TeamMatomo is built on a simple truth : your data belongs to you, and you should have complete control over it. That’s why we’re excited to launch our new Data Warehouse Connector feature for Matomo Cloud, giving you even more ways to work with your analytics data.
Until now, getting raw data from Matomo Cloud required APIs and custom scripts, or waiting for engineering help.
Our new Data Warehouse Connector feature removes those barriers. You can now access your raw, unaggregated data and schedule regular exports straight to your data warehouse.
The feature works with all major data warehouses including (but not limited to) :
- Google BigQuery
- Amazon Redshift
- Snowflake
- Azure Synapse Analytics
- Apache Hive
- Teradata
You can schedule exports, combine your Matomo data with other data sources in your data warehouse, and easily query data with SQL-like queries.
Direct raw data access for greater data portability
Waiting for engineering support can delay your work. Managing API connections and writing scripts can be time-consuming. This keeps you from focusing on what you do best—analysing data.
With the Data Warehouse Connector feature, you get direct access to your raw Matomo data without the technical setup. So, you can spend more time analysing data and finding insights that matter.
Bringing your data together
Answering business questions often requires data from multiple sources. A single customer interaction might span your CRM, web analytics, sales systems, and more. Piecing this data together manually is time-consuming—what starts as a seemingly simple question from stakeholders can turn into hours of work collecting and comparing data across different tools.
This feature lets you combine your Matomo data with data from other business systems in your data warehouse. Instead of switching between tools or manually comparing spreadsheets, you can analyse all your data in one place to better understand how customers interact with your business.
Easy, custom analysis with SQL-like queries
Standard, pre-built reports often don’t address the specific, detailed questions that analysts need to answer.
When you use the Data Warehouse Connector feature, you can use SQL-like queries in your data warehouse to do detailed, customised analysis. This flexibility allows you to explore your data in depth and uncover specific insights that aren’t possible with pre-built reports.
Here is an example of how you might use SQL-like query to compare the behaviours of paying vs. non-paying users :
<xmp>SELECT
custom_dimension_value AS user_type, -- Assuming 'user_type' is stored in a custom dimension
COUNT(*) AS total_visits,
AVG(visit_total_time) AS avg_duration,
SUM(conversion.revenue) AS total_spent
FROM
`your_project.your_dataset.matomo_log_visit` AS visit
LEFT JOIN
`your_project.your_dataset.matomo_log_conversion` AS conversion
ON
visit.idvisit = conversion.idvisit
GROUP BY
custom_dimension_value; </xmp>
This query helps you compare metrics such as the number of visits, average session duration, and total amount spent between paying and non-paying users. It provides a full view of behavioural differences between these groups.
Advanced data manipulation and visualisation
When you need to create detailed reports or dive deep into data analysis, working within the constraints of a fixed user interface (UI) can limit your ability to draw insights.
Exporting your Matomo data to a data warehouse like BigQuery provides greater flexibility for in-depth manipulation and advanced visualisations, enabling you to uncover deeper insights and tailor your reports more effectively.
Getting started
To set up data warehouse exports in your Matomo :
- Go to System Admin (cog icon in the top right corner)
- Select ‘Export’ from the left-hand menu
- Choose ‘Data Warehouse Connector’
You’ll find detailed instructions in our data warehouse exports guide.
Please note, enabling this feature will cost an additional 10% of your current subscription. You can view the exact cost by following the steps above.
New to Matomo ? Start your 21-day free trial now (no credit card required), or request a demo.
Try Matomo for Free
21 day free trial. No credit card required.
-
FFmpeg error : ratecontrol_init : can't open stats file
6 octobre 2017, par oldo.nichoI’ve setup an AWS EC2 instance running Ubuntu 14.04 and have installed FFmpeg so that I can compress and transcode video.
I’m trying to do a two pass conversion with the following code :
ffmpeg -i input-file.avi -codec:v libx264 -profile:v high -preset slow -b:v 500k -maxrate 500k -bufsize 1000k -vf scale=702:-1 -threads 0 -pass 1 -an -f mp4 ~/encoded/nulland second pass :
ffmpeg -i input-file.avi -codec:v libx264 -profile:v high -preset slow -b:v 500k -maxrate 500k -bufsize 1000k -vf scale=702:-1 -threads 0 -pass 2 -codec:a libfdk_aac -b:a 128k -f mp4 output-file.mp4However I get the following error :
ffmpeg version N-77283-g91c2a33 Copyright (c) 2000-2015 the FFmpeg developers
built with gcc 4.8 (Ubuntu 4.8.4-2ubuntu1~14.04)
configuration: --prefix=/home/ubuntu/ffmpeg_build --pkg-config-flags=--static --extra-cflags=-I/home/ubuntu/ffmpeg_build/include --extra-ldflags=-L/home/ubuntu/ffmpeg_build/lib --bindir=/home/ubuntu/bin --enable-gpl --enable-libass --enable-libfdk-aac --enable-libfreetype --enable-libmp3lame --enable-libopus --enable-libtheora --enable-libvorbis --enable-libx264 --enable-nonfree
libavutil 55. 11.100 / 55. 11.100
libavcodec 57. 17.100 / 57. 17.100
libavformat 57. 20.100 / 57. 20.100
libavdevice 57. 0.100 / 57. 0.100
libavfilter 6. 21.100 / 6. 21.100
libswscale 4. 0.100 / 4. 0.100
libswresample 2. 0.101 / 2. 0.101
libpostproc 54. 0.100 / 54. 0.100
Input #0, avi, from 'input-file.avi':
Duration: 01:18:05.29, start: 0.000000, bitrate: 2025 kb/s
Stream #0:0: Video: mpeg4 (Simple Profile) (XVID / 0x44495658), yuv420p, 720x480 [SAR 1:1 DAR 3:2], 1789 kb/s, 29.97 fps, 29.97 tbr, 29.97 tbn, 29.97 tbc
Stream #0:1: Audio: ac3 ([0] [0][0] / 0x2000), 48000 Hz, stereo, fltp, 224 kb/s
[libx264 @ 0x1e04240] using SAR=1/1
[libx264 @ 0x1e04240] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX AVX2 FMA3 LZCNT BMI2
[libx264 @ 0x1e04240] ratecontrol_init: can't open stats file
Output #0, mp4, to '/home/ubuntu/encoded/null':
Stream #0:0: Video: h264, none, q=2-31, 128 kb/s, SAR 1:1 DAR 0:0, 29.97 fps
Metadata:
encoder : Lavc57.17.100 libx264
Stream mapping:
Stream #0:0 -> #0:0 (mpeg4 (native) -> h264 (libx264))
Error while opening encoder for output stream #0:0 - maybe incorrect parameters such as bit_rate, rate, width or heightThe command as written above works fine on my local computer (running OSX). Would anyone have any suggestions as to how to fix this problem ?
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