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• how to allow a worker to run a ffmpeg command on heroku for my python/django app ?

  10 mars 2013, par GetItDone

  I've been stuck trying to figure this out for weeks. I previously asked a similar question found here but I never got any replies. I really cannot find any good documentation anywhere. All I need to do is use a worker (don't care what worker have django-celery and rq installed) to convert a file to flv when it is uploaded from a form. I was able to get this done easily locally, but after over a week I haven't been able to get it to work no matter what I have tried. I tried adding a tasks.py file for celery, or a worker.py file for rq, and I have no idea what else (if anything) needs to be done, such as in my settings.py or Procfile. My procfile looks like :

  web: gunicorn lftv.wsgi -b 0.0.0.0:$PORT
  
  celeryd: celery -A tasks worker --loglevel=info
  
  worker: python worker.py

  My requirements.txt showing what I have installed looks like this :

  Django==1.4.3
  
  Logbook==0.4.1
  
  amqp==1.0.6
  
  anyjson==0.3.3
  
  billiard==2.7.3.19
  
  boto==2.6.0
  
  celery==3.0.13
  
  celery-with-redis==3.0
  
  distribute==0.6.31
  
  dj-database-url==0.2.1
  
  django-celery==3.0.11
  
  django-s3-folder-storage==0.1
  
  django-storages==1.1.6
  
  gunicorn==0.16.1
  
  kombu==2.5.4
  
  pil==1.1.7
  
  psycopg2==2.4.5
  
  python-dateutil==1.5
  
  pytz==2012j
  
  redis==2.7.2
  
  requests==1.1.0
  
  rq==0.3.2
  
  six==1.2.0
  
  times==0.6

  The only thing relevant in my settings.py are as follows :

  BROKER_BACKEND = 'django'
  
  BROKER_URL = #For this I copy/pasted the code from my redistogo add-on from heroku. Not sure if correct
  
  BROKER_TRANSPORT_OPTIONS = {'visibility_timeout': 1800}

  Without trying to take up too much more space, my tasks.py looks like this :

  import subprocess
  
  
  
  @task
  
  def ffmpeg_conversion(input_file):
  
      converted_file = subprocess.call(input_file)
  
      return converted_file

  I use S3 to store my static and media files, and the upload works (adding uploads to my bucket), however no matter what I try the conversion never will. Is there a good tutorial for absolute beginners ? I followed the heroku redis tutorial, celery docs, rq docs, and whatever else I can find, and got the examples to work, but the worker will not execute the command from my view. For example one of the many things I tried :

  ...
  
  ffmpeg = "ffmpeg -i %s -acodec mp3 -ar 22050 -f flv -s 320x240 %s" % (sourcefile, targetfile)
  
  ffmpegresult = ffmpeg_conversion.delay(ffmpeg)
  
  ...

  or using rq

  ...
  
  q = Queue(connection=conn)
  
  result = q.enqueue(ffmpeg_conversion, ffmpeg)
  
  ...

  I seems like it should be simple, however I am completely self-taught and have never deployed a project whatsoever, and there just doesn't seem to be any good documentation or tutorial available for what I am trying to do. I can't judge whether I am completely off and completely missing something significant or relatively close to getting this to work. I really do appreciate any input whatsoever, this is driving me nuts. Thanks in advance.

• ffmpeg file conversion AWS Lamda

  10 avril 2021, par eartoolbox

  I want a .webm file to be converted to a .wav file after it hits my S3 bucket. I followed this tutorial and tried to adapt it from my use case using the .webm -> .wav ffmpeg command described here.

  


  My AWS Lambda function generally works, in that when my .webm file hits the source bucket, it is converted to .wav and ends up in the destination bucket. However, the resulting file .wav is always 0 bytes (though the .webm not, including the appropriate audio). Did I adapt the code wrong ? I only changed the ffmpeg_cmd line from the first link.

  


  import json
import os
import subprocess
import shlex
import boto3

S3_DESTINATION_BUCKET = "hmtm-out"
SIGNED_URL_TIMEOUT = 60

def lambda_handler(event, context):

    s3_source_bucket = event['Records'][0]['s3']['bucket']['name']
    s3_source_key = event['Records'][0]['s3']['object']['key']

    s3_source_basename = os.path.splitext(os.path.basename(s3_source_key))[0]
    s3_destination_filename = s3_source_basename + ".wav"

    s3_client = boto3.client('s3')
    s3_source_signed_url = s3_client.generate_presigned_url('get_object',
        Params={'Bucket': s3_source_bucket, 'Key': s3_source_key},
        ExpiresIn=SIGNED_URL_TIMEOUT)
    
    ffmpeg_cmd = "/opt/bin/ffmpeg -i \"" + s3_source_signed_url + "\" -c:a pcm_f32le " + s3_destination_filename + " -"
    
    
    command1 = shlex.split(ffmpeg_cmd)
    p1 = subprocess.run(command1, stdout=subprocess.PIPE, stderr=subprocess.PIPE)

    resp = s3_client.put_object(Body=p1.stdout, Bucket=S3_DESTINATION_BUCKET, Key=s3_destination_filename)

    return {
        'statusCode': 200,
        'body': json.dumps('Processing complete successfully')
    }
 


  


• ffmpeg file conversion AWS Lambda

  10 avril 2021, par eartoolbox

  I want a .webm file to be converted to a .wav file after it hits my S3 bucket. I followed this tutorial and tried to adapt it from my use case using the .webm -> .wav ffmpeg command described here.

  


  My AWS Lambda function generally works, in that when my .webm file hits the source bucket, it is converted to .wav and ends up in the destination bucket. However, the resulting file .wav is always 0 bytes (though the .webm not, including the appropriate audio). Did I adapt the code wrong ? I only changed the ffmpeg_cmd line from the first link.

  


  import json
import os
import subprocess
import shlex
import boto3

S3_DESTINATION_BUCKET = "hmtm-out"
SIGNED_URL_TIMEOUT = 60

def lambda_handler(event, context):

    s3_source_bucket = event['Records'][0]['s3']['bucket']['name']
    s3_source_key = event['Records'][0]['s3']['object']['key']

    s3_source_basename = os.path.splitext(os.path.basename(s3_source_key))[0]
    s3_destination_filename = s3_source_basename + ".wav"

    s3_client = boto3.client('s3')
    s3_source_signed_url = s3_client.generate_presigned_url('get_object',
        Params={'Bucket': s3_source_bucket, 'Key': s3_source_key},
        ExpiresIn=SIGNED_URL_TIMEOUT)
    
    ffmpeg_cmd = "/opt/bin/ffmpeg -i \"" + s3_source_signed_url + "\" -c:a pcm_f32le " + s3_destination_filename + " -"
    
    
    command1 = shlex.split(ffmpeg_cmd)
    p1 = subprocess.run(command1, stdout=subprocess.PIPE, stderr=subprocess.PIPE)

    resp = s3_client.put_object(Body=p1.stdout, Bucket=S3_DESTINATION_BUCKET, Key=s3_destination_filename)

    return {
        'statusCode': 200,
        'body': json.dumps('Processing complete successfully')
    }