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• Stopping FFMPEG from failing if one output fails

  10 septembre 2022, par Devin Dixon

  I am writing a service that is using FFMPEG to send an RTMP to multiple services. A simple example is such :

  


  ffmpeg -re -i rtmp://localhost:1935/live/xxxxxx -vcodec copy -acodec copy -attempt_recovery 1 -max_recovery_attempts 5 -drop_pkts_on_overflow 1 -f flv rtmp://a.rtmp.youtube.com/live2/xxxxxx -f flv rtmps://live-api-s.facebook.com:443/rtmp/xxxxx


  


  For the above example, occasionally the Facebook output fails and FFMPEG stops all streams. Is there a way to have ffmpeg not stop if one output fails ?

  


• Instagram Live API using Graph API

  16 août 2020, par Deepak Sharma

  I see Facebook has new graph API for live video. But I am not sure if it can used to go live on Instagram as well. I see third party softwares such as Yellow Duck being able to go live on Instagram. Not only that, a lot of softwares support streaming to any destination by just using an RTMP link. So does that mean any service that can generate an RTMP stream can broadcast to Instagram (with/without login to Instagram) ? How does Instagram live work if one can generate an RTMP stream ? Finally, if I can generate an RTMP/RTMPS stream locally on my desktop or phone using ffmpeg libraries, can I stream to Instagram ?

  


• Google Speech API + Go - Transcribing Audio Stream of Unknown Length

  14 février 2018, par Josh

  I have an rtmp stream of a video call and I want to transcribe it. I have created 2 services in Go and I’m getting results but it’s not very accurate and a lot of data seems to get lost.

  Let me explain.

  I have a transcode service, I use ffmpeg to transcode the video to Linear16 audio and place the output bytes onto a PubSub queue for a transcribe service to handle. Obviously there is a limit to the size of the PubSub message, and I want to start transcribing before the end of the video call. So, I chunk the transcoded data into 3 second clips (not fixed length, just seems about right) and put them onto the queue.

  The data is transcoded quite simply :

  var stdout Buffer
  
  
  
  cmd := exec.Command("ffmpeg", "-i", url, "-f", "s16le", "-acodec", "pcm_s16le", "-ar", "16000", "-ac", "1", "-")
  
  cmd.Stdout = &stdout
  
  
  
  if err := cmd.Start(); err != nil {
  
      log.Fatal(err)
  
  }
  
  
  
  ticker := time.NewTicker(3 * time.Second)
  
  
  
  for {
  
      select {
  
      case <-ticker.C:
  
          bytesConverted := stdout.Len()
  
          log.Infof("Converted %d bytes", bytesConverted)
  
  
  
          // Send the data we converted, even if there are no bytes.
  
          topic.Publish(ctx, &pubsub.Message{
  
              Data: stdout.Bytes(),
  
          })
  
  
  
          stdout.Reset()
  
      }
  
  }

  The transcribe service pulls messages from the queue at a rate of 1 every 3 seconds, helping to process the audio data at about the same rate as it’s being created. There are limits on the Speech API stream, it can’t be longer than 60 seconds so I stop the old stream and start a new one every 30 seconds so we never hit the limit, no matter how long the video call lasts for.

  This is how I’m transcribing it :

  stream := prepareNewStream()
  
  clipLengthTicker := time.NewTicker(30 * time.Second)
  
  chunkLengthTicker := time.NewTicker(3 * time.Second)
  
  
  
  cctx, cancel := context.WithCancel(context.TODO())
  
  err := subscription.Receive(cctx, func(ctx context.Context, msg *pubsub.Message) {
  
  
  
      select {
  
      case <-clipLengthTicker.C:
  
          log.Infof("Clip length reached.")
  
          log.Infof("Closing stream and starting over")
  
  
  
          err := stream.CloseSend()
  
          if err != nil {
  
              log.Fatalf("Could not close stream: %v", err)
  
          }
  
  
  
          go getResult(stream)
  
          stream = prepareNewStream()
  
  
  
      case <-chunkLengthTicker.C:
  
          log.Infof("Chunk length reached.")
  
  
  
          bytesConverted := len(msg.Data)
  
  
  
          log.Infof("Received %d bytes\n", bytesConverted)
  
  
  
          if bytesConverted > 0 {
  
              if err := stream.Send(&speechpb.StreamingRecognizeRequest{
  
                  StreamingRequest: &speechpb.StreamingRecognizeRequest_AudioContent{
  
                      AudioContent: transcodedChunk.Data,
  
                  },
  
              }); err != nil {
  
                  resp, _ := stream.Recv()
  
                  log.Errorf("Could not send audio: %v", resp.GetError())
  
              }
  
          }
  
  
  
          msg.Ack()
  
      }
  
  })

  I think the problem is that my 3 second chunks don’t necessarily line up with starts and end of phrases or sentences so I suspect that the Speech API is a recurrent neural network which has been trained on full sentences rather than individual words. So starting a clip in the middle of a sentence loses some data because it can’t figure out the first few words up to the natural end of a phrase. Also, I lose some data in changing from an old stream to a new stream. There’s some context lost. I guess overlapping clips might help with this.

  I have a couple of questions :

  1) Does this architecture seem appropriate for my constraints (unknown length of audio stream, etc.) ?

  2) What can I do to improve accuracy and minimise lost data ?

  (Note I’ve simplified the examples for readability. Point out if anything doesn’t make sense because I’ve been heavy handed in cutting the examples down.)