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RNNT Training

RNNT Training best known configurations with Intel® Extension for PyTorch.

Model Information

Use Case Framework Model Repo Branch/Commit/Tag Optional Patch
Training Pytorch https://github.com/mlcommons/training/tree/master/rnn_speech_recognition/pytorch - -

Pre-Requisite

Prepare Dataset

##Get Dataset Downloading and preprocessing the fully dataset requires 500GB of free disk space and can take several hours to complete.

This repository provides scripts to download, and extract the following datasets:

bash scripts/download_librispeech.sh

Once the data download is complete, the following folders should exist:

  • datasets/LibriSpeech/
    • train-clean-100/
    • dev-clean/
    • dev-other/
    • test-clean/
    • test-other/

Next, convert the data into WAV files:

bash scripts/preprocess_librispeech.sh

Once the data is converted, the following additional files and folders should exist:

  • datasets/LibriSpeech/
    • librispeech-train-clean-100-wav.json
    • librispeech-train-clean-360-wav.json
    • librispeech-train-other-500-wav.json
    • librispeech-dev-clean-wav.json
    • librispeech-dev-other-wav.json
    • librispeech-test-clean-wav.json
    • librispeech-test-other-wav.json
    • train-clean-100-wav/
    • train-clean-360-wav/
    • train-other-500-wav/
    • dev-clean-wav/
    • dev-other-wav/
    • test-clean-wav/
    • test-other-wav/

For training, the following manifest files are used:

  • librispeech-train-clean-100-wav.json
  • librispeech-train-clean-360-wav.json
  • librispeech-train-other-500-wav.json please specify the folder path which contains datasets/LibriSpeech as parameter DATASET_DIR

Training

  1. git clone https://github.com/IntelAI/models.git
  2. cd models/models_v2/pytorch/rnnt/training/gpu
  3. Create virtual environment venv and activate it: 
    python3 -m venv venv
. ./venv/bin/activate
  4. Run setup.sh 
    ./setup.sh
  5. Install the latest GPU versions of torch, torchvision and intel_extension_for_pytorch: 
    python -m pip install torch==<torch_version> torchvision==<torchvision_version> intel-extension-for-pytorch==<ipex_version> --extra-index-url https://pytorch-extension.intel.com/release-whl-aitools/
  6. Set environment variables for Intel® oneAPI Base Toolkit: Default installation location {ONEAPI_ROOT} is /opt/intel/oneapi for root account, ${HOME}/intel/oneapi for other accounts 
    source {ONEAPI_ROOT}/compiler/latest/env/vars.sh
source {ONEAPI_ROOT}/mkl/latest/env/vars.sh
source {ONEAPI_ROOT}/tbb/latest/env/vars.sh
source {ONEAPI_ROOT}/mpi/latest/env/vars.sh
source {ONEAPI_ROOT}/ccl/latest/env/vars.sh
  7. Setup required environment paramaters
Parameter export command
MULTI_TILE export MULTI_TILE=True (True or False)
PLATFORM export PLATFORM=Max (Max)
DATASET_DIR export DATASET_DIR=
BATCH_SIZE (optional) export BATCH_SIZE=512
PRECISION (optional) export PRECISION=BF16 (BF16 FP32 and TF32 for Max)
OUTPUT_DIR (optional) export OUTPUT_DIR=$PWD
  1. Run run_model.sh

Output

Single-tile output will typically looks like:

Done in 172.56718969345093
total samples tested:  10240
total time (excluded audio processing):  88.60093999199307 s
rnnt_train throughput: 115.574 sample per second
rnnt_train latency:  4.430046999599654
Saving module RNNT in /home/frameworks.ai.models.intel-models/models/language_modeling/pytorch/rnnt/training/gpu/RNNT_1703175379.689953-epoch-0.pt
Saved.

Multi-tile output will typically looks like:

Done in 395.0894150733948
[0] total samples tested:  [0] 10240[0]
[0] total time (excluded audio processing):  [0] 89.35191706901242 [0] s
[0] rnnt_train throughput: 114.603 sample per second
[0] rnnt_train latency: [0]  [0] 4.467595853450621
[0] Saving module DistributedDataParallel in /home/frameworks.ai.models.intel-models/models/language_modeling/pytorch/rnnt/training/gpu/out/DistributedDataParallel_1703176458.9948287-epoch-0.pt
[0] Saved.
[1] sample id  22   | cost time is [1]  4.505866523002624
 79%|███████▊  | 22/28 [06:41<01:49, 18.25s/it]
[1] total samples tested:  10240
[1] total time (excluded audio processing):  91.75176179101254 [1] s
[1] rnnt_train throughput: 111.605 sample per second[1]
[1] rnnt_train latency:  [1] 4.587588089550628
[1] Saving module DistributedDataParallel in /home/frameworks.ai.models.intel-models/models/language_modeling/pytorch/rnnt/training/gpu/out/DistributedDataParallel_1703176465.345023-epoch-0.pt
[1] Saved.

Final results of the inference run can be found in results.yaml file.

results:
 - key: throughput
   value: 226.208
   unit: fps
 - key: latency
   value: N/A
   unit: s
 - key: accuracy
   value: None
   unit: loss