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• net core and video transcoding on aws lambda

  14 septembre 2022, par user1765862

  I'm looking for a solution to :

  


  

  1. upload video to s3 bucket

  


  2. after video upload an aws lambda function will be triggered

  


  3. lambda function will use ffmpeg layer in order to transcode video (mainly cropping with other functionalities)

  


  4. save result (transcoded video into s3 bucket)

  


  


  My language of choice inside lambda is c# and net core runtime.

  


  I have found various resources for video manipulation with aws ffmpeg layer using lambda function but no examples in net core lambda.

  


  My question is :

  


  


  Can I use existing FFmpeg/FFprobe Lambda Layer for Amazon Linux such
as this one with lambda function written in c# and .net core ?

  


  


  Another question :

  


  


  Would you suggest Amazon Elastic Transcoder as a better choice with
lambda function .net core integration ?

  


  


• I tried to play the audio on Alexa skill from my S3 Bucket, from the test tab, **it show but in fact, I can't hear any sound

  19 avril 2022, par Siti Mayna

  So I tried to play the audio on Alexa skill from my S3 Bucket, from the test tab, it show but in fact, I can't hear any sound. Another fact is, that I tried to use the sample audio from https://developer.amazon.com/en-US/docs/alexa/custom-skills/ask-soundlibrary.html and it is worked, but why it won't work when it comes from my own S3 Bucket ?

  


  Notes :

  


  I've tried to test the skill using my mobile phone also.

  


  I've tried to encode the audio using FFmpeg.

  


  I've tried to use Jovo to convert the audio. https://v3.jovo.tech/audio-converter

  


  I don't know how to fix this error.

  


  There is no error message on cloud watch.

  


  Assumptions :
There is some problem related to the audio resources or there is more set to play audio from S3 Bucket since the sample audio is working.

  


  Steps to reproduce :

  


  


  Build the interaction model

  


  


  


  Encode the audio to make it Alexa skill friendly (fulfill the requirements, like sample rate, etc), I used and tried all of these :

  


  


  A :

  


  ffmpeg -i  -ac 2 -codec:a libmp3lame -b:a 48k -ar 16000 -write_xing 0 


  


  B :

  


  ffmpeg -i  -ac 2 -codec:a libmp3lame -b:a 48k -ar 24000 -write_xing 0 


  


  C :

  


  ffmpeg -y -i input.mp3 -ar 16000 -ab 48k -codec:a libmp3lame -ac 1 output.mp3


  


  


  Upload the audio resources on S3Bucket
Audio sample on s3 storage but none of them are produce any sounds

  


  


  


  Use the link and insert it to APLA.json

  


  


  
    {
      "type": "APLA",
      "version": "0.91",
      "description": "Simple document that generates speech",
      "mainTemplate": {
        "parameters": [
          "payload"
        ],
        "type": "Sequencer",
        "items": [
          {
            "type": "Audio",
            "source": "https://72578561-d9d8-47b4-811c-cafbcbc5ddb9-us-east-1.s3.amazonaws.com/Media/one-small-step-alexa-24.mp3"
          }
        ]
      }
    }



  


  notes : I change the link sources based on audio that I tried.

  


  


  the intent on lambda_function.py :

  


  


  def _load_apl_document(file_path):
    # type: (str) -> Dict[str, Any]
    """Load the apl json document at the path into a dict object."""
    with open(file_path) as f:
        return json.load(f)

class LaunchRequestHandler(AbstractRequestHandler):
    """Handler for Skill Launch."""
    def can_handle(self, handler_input):
        # type: (HandlerInput) -> bool

        return ask_utils.is_request_type("LaunchRequest")(handler_input)

    def handle(self, handler_input):
        # type: (HandlerInput) -> Response
        logger.info("In LaunchRequestHandler")

        # type: (HandlerInput) -> Response
        speak_output = "Hello World!"
        # .ask("add a reprompt if you want to keep the session open for the user to respond")

        return (
            handler_input.response_builder
                #.speak(speak_output)
                .add_directive(
                        RenderDocumentDirective(
                            token="pagerToken",
                            document=_load_apl_document("APLA.json"),
                            datasources={}
                        )
                    )
                .response
        )


  


  


  Deploy

  


  


  


  Test it

  


  


  


  The result of the test on my end :

The response for testing

  


  


  the JSON response :

  


  {
    "body": {
        "version": "1.0",
        "response": {
            "directives": [
                {
                    "type": "Alexa.Presentation.APLA.RenderDocument",
                    "token": "pagerToken",
                    "document": {
                        "type": "APLA",
                        "version": "0.91",
                        "description": "Simple document that generates speech",
                        "mainTemplate": {
                            "parameters": [
                                "payload"
                            ],
                            "type": "Sequencer",
                            "items": [
                                {
                                    "type": "Audio",
                                    "source": "https://72578561-d9d8-47b4-811c-cafbcbc5ddb9-us-east-1.s3.amazonaws.com/Media/one-small-step-alexa-24.mp3"
                                }
                            ]
                        }
                    },
                    "datasources": {}
                }
            ],
            "type": "_DEFAULT_RESPONSE"
        },
        "sessionAttributes": {},
        "userAgent": "ask-python/1.16.1 Python/3.7.12"
    }
}


  


  


  On my cloud Watch :
Cloud Watch

  


  


• swscale : aarch64 : Optimize the final summation in the hscale routine

  20 avril 2022, par Martin Storsjö

  swscale : aarch64 : Optimize the final summation in the hscale routine
  Before :                     Cortex A53      A72      A73  Graviton 2  Graviton 3
  
  hscale_8_to_15_width8_neon :     8273.0   4602.5   4289.5      2429.7      1629.1
  
  hscale_8_to_15_width16_neon :   12405.7   6803.0   6359.0      3549.0      2378.4
  
  hscale_8_to_15_width32_neon :   21258.7  11491.7  11469.2      5797.2      3919.6
  
  hscale_8_to_15_width40_neon :   25652.0  14173.7  12488.2      6893.5      4810.4
  After :
  
  hscale_8_to_15_width8_neon :     7633.0   3981.5   3350.2      1980.7      1261.1
  
  hscale_8_to_15_width16_neon :   11666.7   5951.0   5512.0      3080.7      2131.4
  
  hscale_8_to_15_width32_neon :   20900.7  10733.2   9481.7      5275.2      3862.1
  
  hscale_8_to_15_width40_neon :   24826.0  13536.2  11502.0      6397.2      4731.9
  Thus, this gives overall a 8-29% speedup for the smaller filter
  
  sizes, around 1-8% for the larger filter sizes.
  Inspired by a patch by Jonathan Swinney <jswinney@amazon.com>.
  Signed-off-by : Martin Storsjö <martin@martin.st>
  

  • [DH] libswscale/aarch64/hscale.S