get-ml-model-rnnt
Automatically generated README for this automation recipe: get-ml-model-rnnt
Category: AI/ML models
License: Apache 2.0
- CM meta description for this script: _cm.json
- Output cached? True
Reuse this script in your project
Install MLCommons CM automation meta-framework
Pull CM repository with this automation recipe (CM script)
cm pull repo mlcommons@cm4mlops
Print CM help from the command line
cmr "get ml-model rnnt raw librispeech speech-recognition" --help
Run this script
Run this script via CLI
cm run script --tags=get,ml-model,rnnt,raw,librispeech,speech-recognition[,variations]
Run this script via CLI (alternative)
cmr "get ml-model rnnt raw librispeech speech-recognition [variations]"
Run this script from Python
import cmind
r = cmind.access({'action':'run'
'automation':'script',
'tags':'get,ml-model,rnnt,raw,librispeech,speech-recognition'
'out':'con',
...
(other input keys for this script)
...
})
if r['return']>0:
print (r['error'])
Run this script via Docker (beta)
cm docker script "get ml-model rnnt raw librispeech speech-recognition[variations]"
Variations
-
No group (any combination of variations can be selected)
Click here to expand this section.
_weights
- ENV variables:
- CM_MODEL_WEIGHTS_FILE:
yes
- CM_MODEL_WEIGHTS_FILE:
- ENV variables:
-
Group "download-src"
Click here to expand this section.
_amazon-s3
_zenodo
(default)
-
Group "framework"
Click here to expand this section.
_pytorch
(default)- ENV variables:
- CM_ML_MODEL_FRAMEWORK:
pytorch
- CM_ML_MODEL_FRAMEWORK:
- ENV variables:
-
Group "precision"
Click here to expand this section.
_fp32
(default)- ENV variables:
- CM_ML_MODEL_INPUT_DATA_TYPES:
fp32
- CM_ML_MODEL_PRECISION:
fp32
- CM_ML_MODEL_WEIGHT_DATA_TYPES:
fp32
- CM_ML_MODEL_INPUT_DATA_TYPES:
- ENV variables:
Default variations
_fp32,_pytorch,_zenodo
Script output
cmr "get ml-model rnnt raw librispeech speech-recognition [variations]" -j