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DEPLOYMENT.md

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Home | Previous - Testing and evaluating your trained model | Next - Real life examples of using DeepSpeech

Deployment

Contents

Now that you have trained and evaluated your model, you are ready to use it for inference - where spoken phrases - utterances - are assessed by your trained model and a text transcription provided.

There are some things to be aware of during this stage of the process.

Protocol buffer and memory mappable file formats

By default, DeepSpeech will export the trained model as a .pb file, such as:

$ sudo ls -las volumes/deepspeech-data/_data/exported-model

     4 drwxr-xr-x 2 root root      4096 Feb  1 22:13 .
     4 drwxr-xr-x 6 root root      4096 Feb  1 22:23 ..
     4 -rwxr-xr-x 1 root root      1586 Feb  1 22:13 author_model_0.0.1.md
184488 -rwxr-xr-x 1 root root 188915369 Feb  1 22:13 output_graph.pb

A .pb file is a protocol buffer file. Protocol buffer is a widely used file format for trained models, but it has a significant downsides. It is not memory mappable. Memory mappable files can be referenced by the operating system using a file descriptor, and they consume far less memory than non-memory-mappable files. Protocol buffer files also tend to be much larger than memory-mappable files.

Most inference libraries, such as TensorFlow, require a memory-mappable format.

There are two formats in particular that you should be familiar with.

Exporting a memory mappable protocol buffer file with graphdef

Using the graphdef tool which is built in to TensorFlow (but deprecated in TensorFlow 2.3), you can export a memory-mappable protocol buffer file using the following commands:

convert_graphdef_memmapped_format --in_graph=output_graph.pb --out_graph=output_graph.pbmm

where --in_graph is a path to your .pb file and --out_graph is a path to the exported memory-mappable protocol buffer file.

root@12a4ee8ce1ed:/DeepSpeech# ./convert_graphdef_memmapped_format \
  --in_graph="persistent-data/exported-model/output_graph.pb" \
  --out_graph="persistent-data/exported-model/output_graph.pbmm"
2021-02-03 21:13:09.516709: W tensorflow/core/framework/cpu_allocator_impl.cc:81] Allocation of 134217728 exceeds 10% of system memory.
2021-02-03 21:13:09.647395: I tensorflow/contrib/util/convert_graphdef_memmapped_format_lib.cc:171] Converted 7 nodes

For more information on creating a memory-mappable protocol buffer file, consult the documentation.

Be aware that this file format is likely to be deprecated in the future. We strongly recommend the use of tflite.

Exporting a tflite model

The tflite engine (more information on tflite) is designed to allow inference on mobile, IoT and embedded devices. If you have not yet trained a model, and you want to export a model compatible with tflite, you will need to use the --export_tflite flags with the DeepSpeech.py script. For example:

python3 DeepSpeech.py \
  --train_files deepspeech-data/cv-corpus-6.1-2020-12-11/id/clips/train.csv \
  --dev_files deepspeech-data/cv-corpus-6.1-2020-12-11/id/clips/dev.csv \
  --test_files deepspeech-data/cv-corpus-6.1-2020-12-11/id/clips/test.csv \
  --checkpoint_dir deepspeech-data/checkpoints \
  --export_dir deepspeech-data/exported-model \
  --export_tflite

If you have already trained a model, and wish to export to tflite format, you can re-export it by specifying the same checkpoint_dir that you used for training, and by passing the --export_tflite parameter.

Here is an example:


python3 DeepSpeech.py \
   --checkpoint_dir persistent-data/checkpoints \
   --export_dir persistent-data/exported-model \
   --export_tflite

   I Loading best validating checkpoint from persistent-data/checkpoints-1feb2021-id/best_dev-34064
   I Loading variable from checkpoint: cudnn_lstm/rnn/multi_rnn_cell/cell_0/cudnn_compatible_lstm_cell/bias
   I Loading variable from checkpoint: cudnn_lstm/rnn/multi_rnn_cell/cell_0/cudnn_compatible_lstm_cell/kernel
   I Loading variable from checkpoint: layer_1/bias
   I Loading variable from checkpoint: layer_1/weights
   I Loading variable from checkpoint: layer_2/bias
   I Loading variable from checkpoint: layer_2/weights
   I Loading variable from checkpoint: layer_3/bias
   I Loading variable from checkpoint: layer_3/weights
   I Loading variable from checkpoint: layer_5/bias
   I Loading variable from checkpoint: layer_5/weights
   I Loading variable from checkpoint: layer_6/bias
   I Loading variable from checkpoint: layer_6/weights
   I Models exported at persistent-data/exported-model
   I Model metadata file saved to persistent-data/exported-model/author_model_0.0.1.md. Before submitting the exported model for publishing make sure all information in the metadata file is correct, and complete the URL fields.

root@0913858a2868:/DeepSpeech/persistent-data/exported-model# ls -las
total 415220
     4 drwxr-xr-x 2 root root      4096 Feb  3 22:42 .
     4 drwxr-xr-x 7 root root      4096 Feb  3 21:54 ..
     4 -rwxr-xr-x 1 root root      1582 Feb  3 22:42 author_model_0.0.1.md
184488 -rwxr-xr-x 1 root root 188915369 Feb  1 11:13 output_graph.pb
184496 -rw-r--r-- 1 root root 188916323 Feb  3 21:13 output_graph.pbmm
 46224 -rw-r--r-- 1 root root  47332112 Feb  3 22:42 output_graph.tflite

For more information on exporting a tflite model, please consult the documentation.

Home | Previous - Testing and evaluating your trained model | Next - Real life examples of using DeepSpeech