VoiceTurn - Deployment - Edge Impulse

Alvaro Gonzalez-Vila / VoiceTurn Public

Target: Cortex-M4F 80MHz Clone this project

Configure your deployment

You can deploy your impulse to any device. This makes the model run without an internet connection, minimizes latency, and runs with minimal power consumption. Read more.

Run your model as a Docker container

To run your model as a container with an HTTP interface on NVIDIA Jetson's GPUs (JetPack 4.6.x), use:

Container:

public.ecr.aws/g7a8t7v6/inference-container-jetson:05926a492f8a440915266ea774513f4d7f7d69ad

Arguments:

--api-key ei_ac27acc15ec3d1165c9dcc942be2bde1b0d430e1b8382c77103df9fd0adb94e2 --run-http-server 1337

Ports to expose:

1337

For example, in a one-liner locally:

docker run --rm -it --runtime=nvidia --gpus all \
    -p 1337:1337 \
    public.ecr.aws/g7a8t7v6/inference-container-jetson:05926a492f8a440915266ea774513f4d7f7d69ad \
        --api-key ei_ac27acc15ec3d1165c9dcc942be2bde1b0d430e1b8382c77103df9fd0adb94e2 \
        --run-http-server 1337

This automatically builds and downloads the latest model (incl. hardware optimizations), and runs an HTTP endpoint at http://localhost:1337 with instructions.

Read the docs for information, like bundling in your model inside the container and selecting extra hardware optimizations.

Run your model as a Docker container

To run your model as a container with an HTTP interface on NVIDIA Jetson Orin's GPUs (JetPack 5.1.x), use:

Container:

public.ecr.aws/g7a8t7v6/inference-container-jetson-orin:1324212e2a5b32e59e31f3b959c45757960640d5

Arguments:

--api-key ei_ac27acc15ec3d1165c9dcc942be2bde1b0d430e1b8382c77103df9fd0adb94e2 --run-http-server 1337

Ports to expose:

1337

For example, in a one-liner locally:

docker run --rm -it --runtime=nvidia --gpus all \
    -p 1337:1337 \
    public.ecr.aws/g7a8t7v6/inference-container-jetson-orin:1324212e2a5b32e59e31f3b959c45757960640d5 \
        --api-key ei_ac27acc15ec3d1165c9dcc942be2bde1b0d430e1b8382c77103df9fd0adb94e2 \
        --run-http-server 1337

This automatically builds and downloads the latest model (incl. hardware optimizations), and runs an HTTP endpoint at http://localhost:1337 with instructions.

Read the docs for information, like bundling in your model inside the container and selecting extra hardware optimizations.

Run your model as a Docker container

To run your model as a container with an HTTP interface on NVIDIA Jetson Orin's GPUs (JetPack 6.0), use:

Container:

public.ecr.aws/g7a8t7v6/inference-container-jetson-orin-6-0:bb88b238be0bfbefadf3a517000ba2b2c80e185a

Arguments:

--api-key ei_ac27acc15ec3d1165c9dcc942be2bde1b0d430e1b8382c77103df9fd0adb94e2 --run-http-server 1337

Ports to expose:

1337

For example, in a one-liner locally:

docker run --rm -it --runtime=nvidia --gpus all \
    -p 1337:1337 \
    public.ecr.aws/g7a8t7v6/inference-container-jetson-orin-6-0:bb88b238be0bfbefadf3a517000ba2b2c80e185a \
        --api-key ei_ac27acc15ec3d1165c9dcc942be2bde1b0d430e1b8382c77103df9fd0adb94e2 \
        --run-http-server 1337

This automatically builds and downloads the latest model (incl. hardware optimizations), and runs an HTTP endpoint at http://localhost:1337 with instructions.

Read the docs for information, like bundling in your model inside the container and selecting extra hardware optimizations.

Model Optimizations

Model optimizations can increase on-device performance but may reduce accuracy.

Quantized (int8)


Latency
Ram
Flash
Accuracy

MFCC	NN Classifier	Total
156 ms.	4 ms.	160 ms.
16.8K	4.4K	16.8K
-	30.4K	-
		80.16%

Unoptimized (float32)


Latency
Ram
Flash
Accuracy

MFCC	NN Classifier	Total
156 ms.	18 ms.	174 ms.
16.8K	8.0K	16.8K
-	34.0K	-
		80.48%

Estimate for Cortex-M4F 80MHz.

Clone this project to deploy this impulse.

Configure your deployment

Deploy to any Linux-based development board

Run your model as a Docker container

Run your model as a Docker container

Run your model as a Docker container

Run your model as a Docker container

Model Optimizations

Quantized (int8)

Unoptimized (float32)

Latest build

Run this model

Build output

Model testing output

Configure your deployment

Default Deployment

C++ library

Deploy to any Linux-based development board

Run your model as a Docker container

Run your model as a Docker container

Run your model as a Docker container

Run your model as a Docker container

Model Optimizations

EON™ Compiler

EON™ Compiler

TensorFlow Lite

Quantized (int8)

Unoptimized (float32)

Latest build

Run this model

Build output

Model testing output