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• AWS lambda SAM deploy error - Template format error : Unresolved resource dependencies

  1er juin 2022, par mozenge

  I have am trying to deploy an aws lambda function using the SAM cli. I have some layers defined in the sam template. Testing locally using sam local start-api works quite well. The but deploying using the sam deploy --guided command throws the following error
Error: Failed to create changeset for the stack: sam-app, ex: Waiter ChangeSetCreateComplete failed: Waiter encountered a terminal failure state: For expression "Status" we matched expected path: "FAILED" Status: FAILED. Reason: Template format error: Unresolved resource dependencies [arn:aws:lambda:us-west-1:338231645678:layer:ffmpeg:1] in the Resources block of the template

  


  The SAM template is as follows

  


  AWSTemplateFormatVersion: '2010-09-09'
Transform: AWS::Serverless-2016-10-31
Description: >
  video-processor-functions

  Functions to generate gif and thumbnail from uploaded videos
  
# More info about Globals: https://github.com/awslabs/serverless-application-model/blob/master/docs/globals.rst
Globals:
  Function:
    Timeout: 3
    Tracing: Active

Resources:
  VideoProcessorFunctions:
    Type: AWS::Serverless::Function # More info about Function Resource: https://github.com/awslabs/serverless-application-model/blob/master/versions/2016-10-31.md#awsserverlessfunction
    Properties:
      CodeUri: src/
      Handler: app.lambdaHandler
      Runtime: nodejs14.x
      # timeout in seconds - 2 minutes
      Timeout: 120
      Layers:
        - !Ref VideoProcessorDepLayer
        - !Ref arn:aws:lambda:us-west-1:338231645678:layer:ffmpeg:1
      Architectures:
        - x86_64
      Events:
        HelloWorld:
          Type: Api # More info about API Event Source: https://github.com/awslabs/serverless-application-model/blob/master/versions/2016-10-31.md#api
          Properties:
            Path: /hello
            Method: get

  VideoProcessorDepLayer:
    Type: AWS::Serverless::LayerVersion
    Properties:
      LayerName: mh-video-processor-dependencies
      Description: Dependencies for sam app [video-processor-functions]
      ContentUri: dependencies/
      CompatibleRuntimes:
        - nodejs14.17
      LicenseInfo: 'MIT'
      RetentionPolicy: Retain

Outputs:
  # ServerlessRestApi is an implicit API created out of Events key under Serverless::Function
  # Find out more about other implicit resources you can reference within SAM
  # https://github.com/awslabs/serverless-application-model/blob/master/docs/internals/generated_resources.rst#api
  HelloWorldApi:
    Description: "API Gateway endpoint URL for Prod stage for Hello World function"
    Value: !Sub "https://${ServerlessRestApi}.execute-api.${AWS::Region}.amazonaws.com/Prod/hello/"
  VideoProcessorFunctions:
    Description: "Generate GIF and Thumnail from Video"
    Value: !GetAtt VideoProcessorFunctions.Arn
  VideoProcessorFunctionsIamRole:
    Description: "Implicit IAM Role created for MH Video Processor function"
    Value: !GetAtt VideoProcessorFunctionsRole.Arn



  


  Any ideas what i'm doing wrong ?

  


• 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

  


  


• FFMPEG and AWS : What's the most efficient way to handle this ?

  28 mai 2022, par Red Vic

  I'm new to AWS and I originally built the FFmpeg functions on my Node.JS API. But I realized this is the wrong way to do it in a real-world app, and that you need to use separate Lambda functions in AWS that handle the video editing separately from the main server.

  


  I'm mainly a front-end developer but I'm open to learning new things.

  


  I basically have the following process in my app :

  


  

  • User uploads video.

  


  • I need to take that video and add a watermark to it.

  


  • I then need a copy of the watermarked video in a smaller resolution.

  


  • I then need a 6 seconds GIF of the smaller resolution video.

  


  • Finally, I need to upload the 3 edited files (2 .mp4's and 1 .gif) to S3, and remove the original, non-watermarked video.

  


  


  Here are my questions to be clear :

  


  

  • Should I upload the original file to S3 or to the server ? And why ?

  


  • Is the process above doable in a single Lambda function ? Or do I need more Lambda functions ?

  


  • How would you handle this problem, personally ?

  


  


  I originally built it by chaining one function to the next with promises, but AWS seems like a different world of doing things and the way I originally built it would not work.

  


  Thanks a lot.

  


  Update
Here are some tests I did with a couple videos :

  


  





  


  


  


  


  


  


  


  


  



  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  


  	Test 1	Test 2	Test 3	Test 4	Test 5
  Original video resolution	1080p	1080p	1080p	1080p	480p
  Original video duration	23 minutes	15 minutes	11 minutes	3.5 minutes	5 minutes
  Step 1 duration (Watermarking original video)	30 minutes	18 minutes	14 minutes	4 minutes	2 minutes
  Step 2 duration (Watermarking lower resolution)	5 minutes	3 minutes	3 minutes	1 minute	skip (already low res)
  Step 3 duration (6 seconds GIF creation)	negligible (15 seconds)	negligible (10 seconds)	negligible (7 seconds)	negligible	negligible
  Total	35 minutes	21 minutes	17 minutes	5 minutes	2 minutes