On-device speech-to-index engine powered by deep learning.

Overview

Octopus

GitHub

PyPI Maven Central Cocoapods

Made in Vancouver, Canada by Picovoice

Twitter URL YouTube Channel Views

Octopus is Picovoice's Speech-to-Index engine. It directly indexes speech without relying on a text representation. This acoustic-only approach boosts accuracy by removing out-of-vocabulary limitation and eliminating the problem of competing hypothesis (e.g. homophones)

Table of Contents

Demos

Python Demos

Install the demo package:

sudo pip3 install pvoctopusdemo

Run the following in the terminal:

octopus_demo  --access_key {AccessKey} --audio_paths ${AUDIO_PATHS}

Replace ${AccessKey} with your AccessKey obtained from Picovoice Console and ${AUDIO_PATHS} with a space-separated list of audio files. Octopus starts processing the audio files and asks you for search phrases and shows results interactively.

For more information about the Python demos go to demo/python.

C Demos

Build the demo:

cmake -S demo/c/ -B demo/c/build && cmake --build demo/c/build

Index a given audio file:

./demo/c/build/octopus_index_demo ${LIBRARY_PATH} ${ACCESS_KEY} ${AUDIO_PATH} ${INDEX_PATH}

Then search the index for a given phrase:

./demo/c/build/octopus_search_demo ${LIBRARY_PATH} ${MODEL_PATH} ${ACCESS_KEY} ${INDEX_PATH} ${SEARCH_PHRASE}

Replace ${LIBRARY_PATH} with path to appropriate library available under lib, ${ACCESS_KEY} with AccessKey obtained from Picovoice Console, ${AUDIO_PATH} with the path to a given audio file and format, ${INDEX_PATH} with the path to cached index file and ${SEARCH_PHRASE} to a search phrase.

For more information about C demos go to demo/c.

Android Demos

Using Android Studio, open demo/android/OctopusDemo as an Android project.

Replace "${YOUR_ACCESS_KEY_HERE}" inside MainActivity.java with your AccessKey obtained from Picovoice Console. Then run the demo.

For more information about Android demos go to demo/android.

iOS Demos

From the demo/ios/OctopusDemo, run the following to install the Octopus CocoaPod:

pod install

Replace "{YOUR_ACCESS_KEY_HERE}" inside ViewModel.swift with your AccessKey obtained from Picovoice Console. Then, using Xcode, open the generated OctopusDemo.xcworkspace and run the application.

For more information about iOS demos go to demo/ios.

Web Demos

From demo/web run the following in the terminal:

yarn
yarn start

(or)

npm install
npm run start

Open http://localhost:5000 in your browser to try the demo.

SDKs

Python

Create an instance of the engine:

import pvoctopus
access_key = ""  # AccessKey provided by Picovoice Console (https://picovoice.ai/console/)
handle = pvoctopus.create(access_key=access_key)

Index your raw audio data or file:

audio_data = [..]
metadata = handle.index(audio_data)
# or 
audio_file_path = "/path/to/my/audiofile.wav"
metadata = handle.index_file(audio_file_path)

Then search the metadata for phrases:

{match.end_sec} ({match.probablity})") ">
avocado_matches = matches['avocado']
for match in avocado_matches:
    print(f"Match for `avocado`: {match.start_sec} -> {match.end_sec} ({match.probablity})")

When done the handle resources have to be released explicitly:

handle.delete()

C

pv_octopus.h header file contains relevant information. Build an instance of the object:

    const char *model_path = "..."; // absolute path to the model file available at `lib/common/octopus_params.pv`
    const char *access_key = "..." // AccessKey provided by Picovoice Console (https://picovoice.ai/console/)
    pv_octopus_t *handle = NULL;
    pv_status_t status = pv_octopus_init(access_key, model_path, &handle);
    if (status != PV_STATUS_SUCCESS) {
        // error handling logic
    }

Index audio data using constructed object:

const char *audio_path = "..."; // absolute path to the audio file to be indexed
void *indices = NULL;
int32_t num_indices_bytes = 0;
pv_status_t status = pv_octopus_index_file(handle, audio_path, &indices, &num_indices_bytes);
if (status != PV_STATUS_SUCCESS) {
    // error handling logic
}

Search the indexed data:

const char *phrase = "...";
pv_octopus_match_t *matches = NULL;
int32_t num_matches = 0;
pv_status_t status = pv_octopus_search(handle, indices, num_indices_bytes, phrase, &matches, &num_matches);
if (status != PV_STATUS_SUCCESS) {
    // error handling logic
}

When done be sure to release the acquired resources:

pv_octopus_delete(handle);

Android

Create an instance of the engine:

import ai.picovoice.octopus.*;

final String accessKey = "..."; // AccessKey provided by Picovoice Console (https://picovoice.ai/console/)
try {
    Octopus handle = new Octopus.Builder(accessKey).build(appContext);
} catch (OctopusException ex) { }

Index audio data using constructed object:

final String audioFilePath = "/path/to/my/audiofile.wav"
try {
    OctopusMetadata metadata = handle.indexAudioFile(audioFilePath);
} catch (OctopusException ex) { }

Search the indexed data:

HashMap <String, OctopusMatch[]> matches = handle.search(metadata, phrases);

for (Map.Entry<String, OctopusMatch[]> entry : map.entrySet()) {
    final String phrase = entry.getKey();
    for (OctopusMatch phraseMatch : entry.getValue()){
        final float startSec = phraseMatch.getStartSec();
        final float endSec = phraseMatch.getEndSec();
        final float probability = phraseMatch.getProbability();
    }
}

When done be sure to release the acquired resources:

metadata.delete();
handle.delete();

iOS

Create an instance of the engine:

import Octopus

let accessKey : String = // .. AccessKey provided by Picovoice Console (https://picovoice.ai/console/)
do {
    let handle = try Octopus(accessKey: accessKey)
} catch { }

Index audio data using constructed object:

let audioFilePath = "/path/to/my/audiofile.wav"
do {
    let metadata = try handle.indexAudioFile(path: audioFilePath)
} catch { }

Search the indexed data:

let matches: Dictionary<String, [OctopusMatch]> = try octopus.search(metadata: metadata, phrases: phrases)
for (phrase, phraseMatches) in matches {
    for phraseMatch in phraseMatches {
        var startSec = phraseMatch.startSec;
        var endSec = phraseMatch.endSec;
        var probability = phraseMatch.probability;
    }
}

When done be sure to release the acquired resources:

handle.delete();

Web

Octopus is available on modern web browsers (i.e., not Internet Explorer) via WebAssembly. Octopus is provided pre-packaged as a Web Worker to allow it to perform processing off the main thread.

Vanilla JavaScript and HTML (CDN Script Tag)

">
>
<html lang="en">

<head>
  <script src="https://unpkg.com/@picovoice/octopus-web-en-worker/dist/iife/index.js">script>
  <script type="application/javascript">
    // The metadata object to save the result of indexing for later searches
    let octopusMetadata = undefined

    function octopusIndexCallback(metadata) {
      octopusMetadata = metadata
    }

    function octopusSearchCallback(matches) {
      console.log(`Search results (${matches.length}):`)
      console.log(`Start: ${match.startSec}s -> End: ${match.endSec}s (Probability: ${match.probability})`)
    }

    async function startOctopus() {
      // Create an Octopus Worker
      // Note: you receive a Worker object, _not_ an individual Octopus instance
      const accessKey = ... // AccessKey string provided by Picovoice Console (https://picovoice.ai/console/)
      const OctopusWorker = await OctopusWorkerFactory.create(
        accessKey,
        octopusIndexCallback,
        octopusSearchCallback
      )
    }

    document.addEventListener("DOMContentLoaded", function () {
      startOctopus();
      // Send Octopus the audio signal
      const audioSignal = new Int16Array(/* Provide data with correct format*/)
      OctopusWorker.postMessage({
        command: "index",
        input: audioSignal,
      });
    });

    const searchText = ...
    OctopusWorker.postMessage({
      command: "search",
      metadata: octopusMetadata,
      searchPhrase: searchText,
    });
  script>
head>

<body>body>

html>

Vanilla JavaScript and HTML (ES Modules)

yarn add @picovoice/octopus-web-en-worker

(or)

npm install @picovoice/octopus-web-en-worker
End: ${match.endSec}s (Probability: ${match.probability})`); } async function startOctopus() { // Create an Octopus Worker // Note: you receive a Worker object, _not_ an individual Octopus instance const accessKey = // .. AccessKey provided by Picovoice Console (https://picovoice.ai/console/) const OctopusWorker = await OctopusWorkerFactory.create( accessKey, octopusIndexCallback, octopusSearchCallback ); } startOctopus() ... // Send Octopus the audio signal const audioSignal = new Int16Array(/* Provide data with correct format*/) OctopusWorker.postMessage({ command: "index", input: audioSignal, }); ... const searchText = ...; OctopusWorker.postMessage({ command: "search", metadata: octopusMetadata, searchPhrase: searchText, }); ">
import { OctopusWebEnWorker } from "@picovoice/octopus-web-en-worker";

// The metadata object to save the result of indexing for later searches
let octopusMetadata = undefined;

function octopusIndexCallback(metadata) {
  octopusMetadata = metadata;
}

function octopusSearchCallback(matches) {
  console.log(`Search results (${matches.length}):`);
  console.log(`Start: ${match.startSec}s -> End: ${match.endSec}s (Probability: ${match.probability})`);
}


async function startOctopus() {
  // Create an Octopus Worker
  // Note: you receive a Worker object, _not_ an individual Octopus instance
  const accessKey = // .. AccessKey provided by Picovoice Console (https://picovoice.ai/console/)
  const OctopusWorker = await OctopusWorkerFactory.create(
    accessKey,
    octopusIndexCallback,
    octopusSearchCallback
  );
}

startOctopus()

...

// Send Octopus the audio signal
const audioSignal = new Int16Array(/* Provide data with correct format*/)
OctopusWorker.postMessage({
  command: "index",
  input: audioSignal,
});

...

const searchText = ...;
OctopusWorker.postMessage({
  command: "search",
  metadata: octopusMetadata,
  searchPhrase: searchText,
});

Releases

v1.0.0 Oct 8th, 2021

  • Initial release.
You might also like...
A python script to lookup Passport Index Dataset

visa-cli A python script to lookup Passport Index Dataset Installation pip install visa-cli Usage usage: visa-cli [-h] [-d DESTINATION_COUNTRY] [-f]

This is a virtual picture dragging application. Users may virtually slide photos across the screen. The distance between the index and middle fingers determines the movement. Smaller distances indicate click and motion, whereas bigger distances indicate only hand movement.
A set of simple scripts to process the Imagenet-1K dataset as TFRecords and make index files for NVIDIA DALI.

Overview This is a set of simple scripts to process the Imagenet-1K dataset as TFRecords and make index files for NVIDIA DALI. Make TFRecords To run t

PPLNN is a Primitive Library for Neural Network is a high-performance deep-learning inference engine for efficient AI inferencing
PPLNN is a Primitive Library for Neural Network is a high-performance deep-learning inference engine for efficient AI inferencing

PPLNN is a Primitive Library for Neural Network is a high-performance deep-learning inference engine for efficient AI inferencing

Implementation of "A Deep Learning Loss Function based on Auditory Power Compression for Speech Enhancement" by pytorch

This repository is used to suspend the results of our paper "A Deep Learning Loss Function based on Auditory Power Compression for Speech Enhancement"

Implementation of "StrengthNet: Deep Learning-based Emotion Strength Assessment for Emotional Speech Synthesis"

StrengthNet Implementation of "StrengthNet: Deep Learning-based Emotion Strength Assessment for Emotional Speech Synthesis" https://arxiv.org/abs/2110

This is the implementation of "SELF SUPERVISED REPRESENTATION LEARNING WITH DEEP CLUSTERING FOR ACOUSTIC UNIT DISCOVERY FROM RAW SPEECH" submitted to ICASSP 2022

CPC_DeepCluster This is the implementation of "SELF SUPERVISED REPRESENTATION LEARNING WITH DEEP CLUSTERING FOR ACOUSTIC UNIT DISCOVERY FROM RAW SPEEC

A fast, dataset-agnostic, deep visual search engine for digital art history

imgs.ai imgs.ai is a fast, dataset-agnostic, deep visual search engine for digital art history based on neural network embeddings. It utilizes modern

Comments
  • Bump terser from 5.13.1 to 5.16.1 in /binding/web

    Bump terser from 5.13.1 to 5.16.1 in /binding/web

    Bumps terser from 5.13.1 to 5.16.1.

    Changelog

    Sourced from terser's changelog.

    v5.16.1

    • Properly handle references in destructurings (const { [reference]: val } = ...)
    • Allow parsing of .#privatefield in nested classes
    • Do not evaluate operations that return large strings if that would make the output code larger
    • Make collapse_vars handle block scope correctly
    • Internal improvements: Typos (#1311), more tests, small-scale refactoring

    v5.16.0

    • Disallow private fields in object bodies (#1011)
    • Parse #privatefield in object (#1279)
    • Compress #privatefield in object

    v5.15.1

    • Fixed missing parentheses around optional chains
    • Avoid bare let or const as the bodies of if statements (#1253)
    • Small internal fixes (#1271)
    • Avoid inlining a class twice and creating two equivalent but !== classes.

    v5.15.0

    • Basic support for ES2022 class static initializer blocks.
    • Add AudioWorkletNode constructor options to domprops list (#1230)
    • Make identity function inliner not inline id(...expandedArgs)

    v5.14.2

    • Security fix for RegExps that should not be evaluated (regexp DDOS)
    • Source maps improvements (#1211)
    • Performance improvements in long property access evaluation (#1213)

    v5.14.1

    • keep_numbers option added to TypeScript defs (#1208)
    • Fixed parsing of nested template strings (#1204)

    v5.14.0

    • Switched to @​jridgewell/source-map for sourcemap generation (#1190, #1181)
    • Fixed source maps with non-terminated segments (#1106)
    • Enabled typescript types to be imported from the package (#1194)
    • Extra DOM props have been added (#1191)
    • Delete the AST while generating code, as a means to save RAM
    Commits

    Dependabot compatibility score

    Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.


    Dependabot commands and options

    You can trigger Dependabot actions by commenting on this PR:

    • @dependabot rebase will rebase this PR
    • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
    • @dependabot merge will merge this PR after your CI passes on it
    • @dependabot squash and merge will squash and merge this PR after your CI passes on it
    • @dependabot cancel merge will cancel a previously requested merge and block automerging
    • @dependabot reopen will reopen this PR if it is closed
    • @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
    • @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
    • @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
    • @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
    • @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
    • @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

    You can disable automated security fix PRs for this repo from the Security Alerts page.

    dependencies 
    opened by dependabot[bot] 1
  • Convert c owned memory into ctypes owned memory for metadata objects

    Convert c owned memory into ctypes owned memory for metadata objects

    ctypes doesn't free the c_void_p from c, but if we convert it into a block of memory created from ctypes (serialize -> deserialize), then it will garbage collect and free when appropriate.

    opened by ErisMik 1
Releases(v1.2)
  • v1.2(Aug 12, 2022)

Owner
Picovoice
Edge Voice AI Platform
Picovoice
Official Pytorch implementation of Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference (ICLR 2022)

The Official Implementation of CLIB (Continual Learning for i-Blurry) Online Continual Learning on Class Incremental Blurry Task Configuration with An

NAVER AI 34 Oct 26, 2022
Tensorflow-Project-Template - A best practice for tensorflow project template architecture.

Tensorflow Project Template A simple and well designed structure is essential for any Deep Learning project, so after a lot of practice and contributi

Mahmoud G. Salem 3.6k Dec 22, 2022
This repository contains the DendroMap implementation for scalable and interactive exploration of image datasets in machine learning.

DendroMap DendroMap is an interactive tool to explore large-scale image datasets used for machine learning. A deep understanding of your data can be v

DIV Lab 33 Dec 30, 2022
Milano is a tool for automating hyper-parameters search for your models on a backend of your choice.

Milano (This is a research project, not an official NVIDIA product.) Documentation https://nvidia.github.io/Milano Milano (Machine learning autotuner

NVIDIA Corporation 147 Dec 17, 2022
Implementation of Shape Generation and Completion Through Point-Voxel Diffusion

Shape Generation and Completion Through Point-Voxel Diffusion Project | Paper Implementation of Shape Generation and Completion Through Point-Voxel Di

Linqi Zhou 103 Dec 29, 2022
Implementation of Geometric Vector Perceptron, a simple circuit for 3d rotation equivariance for learning over large biomolecules, in Pytorch. Idea proposed and accepted at ICLR 2021

Geometric Vector Perceptron Implementation of Geometric Vector Perceptron, a simple circuit with 3d rotation equivariance for learning over large biom

Phil Wang 59 Nov 24, 2022
Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set (CVPRW 2019). A PyTorch implementation.

Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set —— PyTorch implementation This is an unofficial offici

Sicheng Xu 833 Dec 28, 2022
Company clustering with K-means/GMM and visualization with PCA, t-SNE, using SSAN relation extraction

RE results graph visualization and company clustering Installation pip install -r requirements.txt python -m nltk.downloader stopwords python3.7 main.

Jieun Han 1 Oct 06, 2022
Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds (CVPR 2022)

Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds (CVPR2022)[paper] Authors: Chenhang He, Ruihuang Li, Shuai Li, L

Billy HE 141 Dec 30, 2022
The 2nd place solution of 2021 google landmark retrieval on kaggle.

Leaderboard, taxonomy, and curated list of few-shot object detection papers.

229 Dec 13, 2022
A deep learning based semantic search platform that computes similarity scores between provided query and documents

semanticsearch This is a deep learning based semantic search platform that computes similarity scores between provided query and documents. Documents

1 Nov 30, 2021
An open-source Deep Learning Engine for Healthcare that aims to treat & prevent major diseases

AlphaCare Background AlphaCare is a work-in-progress, open-source Deep Learning Engine for Healthcare that aims to treat and prevent major diseases. T

Siraj Raval 44 Nov 05, 2022
AITUS - An atomatic notr maker for CYTUS

AITUS an automatic note maker for CYTUS. 利用AI根据指定乐曲生成CYTUS游戏谱面。 效果展示:https://www

GradiusTwinbee 6 Feb 24, 2022
Hyperopt for solving CIFAR-100 with a convolutional neural network (CNN) built with Keras and TensorFlow, GPU backend

Hyperopt for solving CIFAR-100 with a convolutional neural network (CNN) built with Keras and TensorFlow, GPU backend This project acts as both a tuto

Guillaume Chevalier 103 Jul 22, 2022
Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations

Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations This is the repository for the paper Consumer Fairness in Recomm

7 Nov 30, 2022
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with ONNX, TensorRT, ncnn, and OpenVINO supported.

Introduction YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and ind

7.7k Jan 03, 2023
A cool little repl-based simulation written in Python

A cool little repl-based simulation written in Python planned to integrate machine-learning into itself to have AI battle to the death before your eye

Em 6 Sep 17, 2022
Code for the paper Language as a Cognitive Tool to Imagine Goals in Curiosity Driven Exploration

IMAGINE: Language as a Cognitive Tool to Imagine Goals in Curiosity Driven Exploration This repo contains the code base of the paper Language as a Cog

Flowers Team 26 Dec 22, 2022
Calibrate your listeners! Robust communication-based training for pragmatic speakers. Findings of EMNLP 2021.

Calibrate your listeners! Robust communication-based training for pragmatic speakers Rose E. Wang, Julia White, Jesse Mu, Noah D. Goodman Findings of

Rose E. Wang 3 Apr 02, 2022
Repository aimed at compiling code, papers, demos etc.. related to my PhD on 3D vision and machine learning for fruit detection and shape estimation at the university of Lincoln

PhD_3DPerception Repository aimed at compiling code, papers, demos etc.. related to my PhD on 3D vision and machine learning for fruit detection and s

lelouedec 2 Oct 06, 2022