From b97c6d46707f68fc267490f9c939214f55260c79 Mon Sep 17 00:00:00 2001 From: Alireza Kenarsari Date: Fri, 20 Jul 2018 11:27:07 -0700 Subject: [PATCH] v1.4 (#52) v.14 comes with improved accuracy (especially tiny variant) and runtime optimizations. 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.../pv_porcupine_optimizer.REMOVED.git-id | 2 +- .../pv_porcupine_optimizer.REMOVED.git-id | 2 +- .../pv_porcupine_optimizer.REMOVED.git-id | 2 +- .../pv_porcupine_optimizer.REMOVED.git-id | 2 +- .../pv_porcupine_optimizer.exe.REMOVED.git-id | 2 +- .../pv_porcupine_optimizer.exe.REMOVED.git-id | 2 +- 316 files changed, 239 insertions(+), 213 deletions(-) delete mode 100644 resources/images/description.png diff --git a/README.md b/README.md index f4ed594f3..74fcaf992 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,3 @@ -![Description](resources/images/description.png) - # Porcupine   [![Tweet](https://img.shields.io/twitter/url/http/shields.io.svg?style=social)](https://twitter.com/intent/tweet?text=On-device%20wake%20word%20detection%20engine%20powered%20by%20deep%20learning&url=https://github.com/Picovoice/Porcupine&hashtags=deeplearning,voice,AI,privacy) [![Build Status](https://travis-ci.org/Picovoice/Porcupine.svg?branch=master)](https://travis-ci.org/Picovoice/Porcupine) @@ -12,7 +10,7 @@ build always-listening voice-enabled applications/platforms. Porcupine is * self-service. Developers are empowered to **choose any wake word** and build its model **within seconds**. * using **deep neural networks** trained in **real-world situations** (i.e. noise and reverberation). * compact and computationally-efficient making it suitable for **IoT** applications. -* **cross platform**. It is implemented in pure ANSI C. Currently **Raspberry Pi**, **Android**, **iOS**, **watchOS**, **Linux**, +* **cross platform**. It is implemented in pure fixed-point ANSI C. Currently **Raspberry Pi**, **Android**, **iOS**, **watchOS**, **Linux**, **Mac**, and **Windows** are supported. * **scalable**. It can detect tens of wake-words concurrently with virtually no added CPU/memory footprint. * **open-source**. Anything you find in this repository is Apache 2.0 licensed. @@ -22,6 +20,7 @@ build always-listening voice-enabled applications/platforms. Porcupine is * [Try It Out](#try-it-out) * [Getting Started](#getting-started) * [Performance](#performance) +* [Model Variants](#model-variants) * [Structure of Repository](#structure-of-repository) * [Running Demo Applications](#running-demo-applications) * [Python Demo Application](#python-demo-application) @@ -51,13 +50,22 @@ The demo application allows you to test Porcupine on a variety of wake words in ## Performance -A scientific comparison between accuracy of Porcupine and two other widely-used libraries, PocketSphinx and Snowboy, is provided -[here](https://github.com/Picovoice/wakeword-benchmark). Below is miss rate measurements for different engines given a fixed -false alarm rate in noisy conditions. It can be seen that Porcupine achieves much lower miss rate for any given false alarm rate. +A comparison between accuracy and runtime metrics of Porcupine and two other widely-used libraries, PocketSphinx and Snowboy, is provided +[here](https://github.com/Picovoice/wakeword-benchmark). Compared to best performing engine, Porcupine's standard model is +2.53 times more accurate, 2.6 times faster (on Raspberry Pi 3), and consumes 45% less memory. + +## Model Variants + +Porcupine comes in two different variations: **standard** and **tiny**. The tiny model is specifically designed for +deeply-embedded applications. Its accuracy is slightly lower than the standard model but it consumes considerably less +resources. Below is a comparison of runtime measurements for different variants of Porcupine on Raspberry Pi3 -![Accuracy](https://raw.githubusercontent.com/Picovoice/wakeword-benchmark/master/doc/img/benchmark_noisy_bar.png?raw=true ) +| Model Variant | CPU Usage | Memory Usage | +:---: | :---: | :---: +Standard | 7.39% | 1380 KB | +Tiny | 3.42% | 240 KB | -Porcupine is lightweight. It uses less than 1.4 MB of RAM. On a Raspberry Pi 3, it uses less than 7% of one of the CPU cores. The [tiny model](/lib/common/porcupine_tiny_params.pv) uses 2% of one of the CPU cores and less than 200 KB of RAM. +For accuracy comparison of different variants refer to [benchmark repository](https://github.com/Picovoice/wakeword-benchmark). ## Structure of Repository @@ -366,10 +374,16 @@ If you like to contribute to Porcupine, please read through [CONTRIBUTING.md](CO ## Releases +### v1.4.0 July 20, 2018 + +* Improved accuracy across all models (specifically tiny variant) +* Runtime optimizations +* Updated documentation + ### v1.3.0 June 19, 2018 -* Added tiny model for deeply embedded platforms. -* Improved model's accuracy. +* Added tiny model (200 KB) for deeply embedded platforms. +* Improved accuracy. * Runtime optimization and bug fixes. ### v1.2.0 April 21, 2018 @@ -389,5 +403,7 @@ Initial release. ## License Anything you find in this repository is licensed under Apache 2.0. This allows running the library on all supported platforms. Furthermore, -custom wake-words can be generated for Linux, Mac, and Windows. Custom wake-words for other platforms are provided with -the purchase of commercial license. In order to purchase a commercial license contact contact@picovoice.ai. +custom wake-words can be generated for Linux, Mac, and Windows. + +Custom wake-words for other platforms are provided with the purchase of commercial license. In order to purchase a +commercial license send an email to contact@picovoice.ai with a brief description of your use case. diff --git a/demo/python/README.md b/demo/python/README.md index f6fb9e6fe..7e913f409 100644 --- a/demo/python/README.md +++ b/demo/python/README.md @@ -38,7 +38,7 @@ JackShmReadWritePtr::~JackShmReadWritePtr - Init not done for 4294967295, skippi ```` There are few lines of output generated by [ALSA](https://en.wikipedia.org/wiki/Advanced_Linux_Sound_Architecture). Don't -be alarm this is normal! In order to detect a different wake word change the keyword file. +be alarmed this is normal! In order to detect a different wake word change the keyword file. Also, you can detect multiple wake words concurrently (e.g. Blueberry and Raspberry) using @@ -47,6 +47,13 @@ python demo/python/porcupine_demo.py --keyword_file_paths \ resources/keyword_files/blueberry_${SYSTEM}.ppn,resources/keyword_files/raspberry_${SYSTEM}.ppn ``` +Finally, in order to run Porcupine's tiny variant pass in tiny keyword file(s) along with path to tiny model file + +```bash +python demo/python/porcupine_demo.py --keyword_file_paths resources/keyword_files/blueberry_${SYSTEM}_tiny.ppn \ +--model_file_path lib/common/porcupine_tiny_params.pv +``` + # FAQ #### The demo application does not detect anything. 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