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• 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')
    }
 


  


• 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')
    }
 


  


• Why is one ffmpeg webm dash stream much larger than the others ?

  5 janvier 2017, par ranvel

  Over the summer, I worked on putting together a script which took a x264 video/mp3 stream and broke it up into the different streams so that it would work via MSE-DASH. (Based heavily on the instructions on the webmproject.org website) Those same scripts have ceased to work, turning a 6GB video into several 25 Gb videos. I kept up with updates of ffmpeg and so I don’t know when it stopped working, but I am guessing it was due to the way that their DASH Webm implementation was updated.

  I found new method which works better, but still has a major problem with one stream. I was hoping someone could explain how this encoding works so that I could understand the underlying cause.

  #!/bin/bash
  
  COMMON_OPTS="-map 0:0 -an -threads 11 -cpu-used 4 -cmp chroma"
  
  WEBM_OPTS="-f webm -c:v vp9 -keyint_min 50 -g 50 -dash 1"
  
  
  
  ffmpeg -i $1 -vn -acodec libvorbis -ab 128k audio.webm &
  
  ffmpeg -i $1 $COMMON_OPTS $WEBM_OPTS -b:v 500k -vf scale=1280:720 -y vid-500k.webm &
  
  ffmpeg -i $1 $COMMON_OPTS $WEBM_OPTS -b:v 700k -vf scale=1280:720 -y vid-700k.webm & 
  
  ffmpeg -i $1 $COMMON_OPTS $WEBM_OPTS -b:v 1000k -vf scale=1280:720 -y vid-1000k.webm &
  
  ffmpeg -i $1 $COMMON_OPTS $WEBM_OPTS -b:v 1500k -vf scale=1280:720 -y vid-1500k.webm  

  The transcode is not yet complete, but you can see where this is headed :

  -rw-r--r--  1 user  staff    87M Jan  4 23:27 audio.webm
  
  -rw-r--r--  1 user  staff    27M Jan  4 23:42 vid-1000k.webm
  
  -rw-r--r--  1 user  staff   285M Jan  4 23:42 vid-1500k.webm
  
  -rw-r--r--  1 user  staff    15M Jan  4 23:42 vid-500k.webm
  
  -rw-r--r--  1 user  staff    20M Jan  4 23:42 vid-700k.webm

  The 1500k variant is disproportionately larger than the other streams.

  The other problem is that when I use a shorter video, lets say eight or nine minutes, the above configuration runs as expected and everything is perfect. I don’t know where the limit for this is since each test costs a lot of processing power and time, but if it’s less than ten minutes, it works and if its longer than an hour, it produces massive files.