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framework for synchronous batch speech-to-text transcription using backends like AWS, Watson, etc.
Simple library for running batch transcribe jobs in AWS. Implemented on the py-transcribe framework to make your code transcribe-platform agnostic and easy to test.
pip install py_transcribe_aws
Set ENV var TRANSCRIBE_MODULE_PATH
, e.g.
export TRANSCRIBE_MODULE_PATH=transcribe_aws
or pass the module path at service-creation time, e.g.
from transcribe import init_transcription_service
service = init_transcription_service(
module_path="transcribe_aws"
)
Your code generally should not need to access any of the implementations in this module directly. See py-transcribe for docs on usage of the framework.
The following config vars can be set in ENV or passed in code, e.g. init_transcription_service(config={})
. Most env vars have two accepted versions and the version with a TRANSCRIBE_
prefix has higher precedence.
TRANSCRIBE_AWS_REGION|AWS_REGION
(required)
The region hosting the S3 bucket to which source audio (or video) files will be uploaded for transcription
TRANSCRIBE_AWS_ACCESS_KEY_ID|AWS_ACCESS_KEY_ID
(required)
TRANSCRIBE_AWS_SECRET_ACCESS_KEY|AWS_SECRET_ACCESS_KEY
(required)
TRANSCRIBE_AWS_S3_BUCKET_SOURCE
(required)
Bucket where source will be uploaded and then passed to AWS Transcribe
This repo includes a terraform module for setting up all the necessary infrastructure to run transcribe.
You can include the terraform module, like this:
module "transcribe_aws" {
source = "git::https://github.com/ICTLearningSciences/py-transcribe-aws.git?ref=tags/{CHANGE_TO_LATEST_VERSION}"
transcribe_namespace = "YOUR_NAMESPACE"
}
...and then the module exposes all the (sensitive) env vars for running transcribe in an output map, which you can use like
resource "some_server_type" {
# set TRANSCRIBE_AWS_ACCESS_KEY_ID, TRANSCRIBE_AWS_SECRET_ACCESS_KEY, etc. in some server-resource env
env = module.transcribe_aws.transcribe_env_vars
}
If you setting up AWS infrastructure manually (as opposed to using the terraform aboice), the AWS IAM used must have permissions to read/write/delete from the configured source bucket and also use AWS Transcribe
A minimal(ish) policy to allow the above might look like this:
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": ["s3:*Object"],
"Resource": "arn:aws:s3:::${YOUR_S3_BUCKET_NAME}/*"
},
{
"Effect": "Allow",
"Action": ["transcribe:*"],
"Resource": "*"
}
]
}
Run tests during development with
make test-all
Once ready to release, create a release tag, currently using semver-ish numbering, e.g. 1.0.0(-alpha.1)
FAQs
framework for synchronous batch speech-to-text transcription using backends like AWS, Watson, etc.
We found that py-transcribe-aws demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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