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• 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

• How to "extend" aws docker base image (.net core from scratch) by ... libs/ubuntu/ffmpeg ?

  26 avril 2022, par Nigrimmist

  i would like to use AWS Lambda through the image containers using .net core 3.1 and it is works fine for me in simplest code case. But i stucked with next scenario :

  


  By default, aws provide base image with .net core with aws libs based on "from scratch". So as i know, it minimal Linux that does not contains even package manager.

  


  I need to work with ffmpeg in the code, but to do it i need to install few packages and ... fmpeg. I have working code on image

  


  FROM mcr.microsoft.com/dotnet/runtime:3.1-bionic


  


  It is ubuntu with .net core runtime. But what is the right strategic in case of AWS Lambda image ? How can i ... merge them ?.

  


  Have few ideas, but not sure :

  


  

  1. use as is FROM public.ecr.aws/lambda/dotnet:core3.1 and try to install package manager, all depenendencies to use ffmpeg and so on ?

  


  2. Use mcr.microsoft.com/dotnet/runtime:3.1-bionic, somehow add required by amazon dependencies (how ? download content and attach from local ?) and configure it to run in Lambda runtime ?

  


  3. ... ?

  


  


  Will be glad to hear where is the solution here. Thanks !