/stt_models

Speech-to-text models in Pytorch

Primary LanguagePython

Speech-to-text models

Pytorch implementation and comparison speech-to-text (STT) models.

References:

Train on CPU/GPU

python main.py --num-workers 0 --batch-size 32 --train-data-urls train-clean-100 train-clean-360 --num-epochs 15 --window-stride 20 --optimizer adam --learning-rate 3e-4 --log-steps 100 --checkpoint test

Run on TPU

  • TPU needs to recompile the RNN graph for each training example
  • CTCLoss is currently (March 2021) is not supported on XLA (source) and here

Create a Google Cloud project

Install gcloud sdk on Mac OS

wget https://dl.google.com/dl/cloudsdk/channels/rapid/downloads/google-cloud-sdk-332.0.0-darwin-x86_64.tar.gz
tar -xf google-cloud-sdk-332.0.0-darwin-x86_64.tar.gz
./google-cloud-sdk/install.sh

Run gcloud init to initialize the SDK:

Create a new project:

gcloud projects create <PROJECT_ID>

Listing projects

gcloud projects list

Shutting down projects

gcloud projects delete <PROJECT_ID>

Turn on the Cloud TPU API for that project.

Set up compute zone

gcloud config set compute/zone <your-zone-here> --project <PROJECT_ID>

Set up a Compute Engine instance

Open a Cloud Shell window

https://console.cloud.google.com/?cloudshell=true

Create a variable for your project's ID

export PROJECT_ID=<project-id>

Configure gcloud command-line tool to use the project where you want to create Cloud TPU.

gcloud config set project ${PROJECT_ID}

From the Cloud Shell, launch the Compute Engine resource

gcloud compute instances create deepspeech-xla \
--zone=europe-west4-a \
--machine-type=n1-highmem-16 \
--image-family=torch-xla \
--image-project=ml-images  \
--boot-disk-size=300GB \
--scopes=https://www.googleapis.com/auth/cloud-platform

Connect to the new Compute Engine instance

gcloud compute ssh deepspeech-xla --zone=europe-west4-a

Launch a Cloud TPU resource

gcloud compute tpus create deepspeech-xla \
--zone=europe-west4-a \
--network=default \
--version=pytorch-1.8  \
--accelerator-type=v3-8

Identify the IP address for the Cloud TPU resource

gcloud compute tpus list --zone=europe-west4-a

Create and configure the PyTorch environment

Start a conda environment.

conda activate torch-xla-1.8

Configure environmental variables for the Cloud TPU resource

export TPU_IP_ADDRESS=<ip-address>
export XRT_TPU_CONFIG="tpu_worker;0;$TPU_IP_ADDRESS:8470"

Get TPU compatible model by running:

git clone https://github.com/discort/stt_models.git

Install dependencies

pip install -r requirements.txt

Run the model

python main.py \
--use-tpu 1 \
--world-size 1 \
--num-workers 0 \
--batch-size 128 \
--train-data-urls train-clean-100 train-clean-360 train-other-500 \
--val-data-urls dev-clean \
--num-epochs 1

List TPU Node+VM

gcloud compute tpus list --zone=europe-west4-a
gcloud compute instances list

To delete both TPU and the VM run

gcloud compute tpus delete deepspeech-xla --zone=europe-west4-a
gcloud compute instances delete deepspeech-xla --zone=europe-west4-a