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 @โ€‹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
Clean Machine Learning, a Coding Kata

Kata: Clean Machine Learning From Dirty Code First, open the Kata in Google Colab (or else download it) You can clone this project and launch jupyter-

Neuraxio 13 Nov 03, 2022
NAS-FCOS: Fast Neural Architecture Search for Object Detection (CVPR 2020)

NAS-FCOS: Fast Neural Architecture Search for Object Detection This project hosts the train and inference code with pretrained model for implementing

Ning Wang 180 Dec 06, 2022
Implementation supporting the ICCV 2017 paper "GANs for Biological Image Synthesis"

GANs for Biological Image Synthesis This codes implements the ICCV-2017 paper "GANs for Biological Image Synthesis". The paper and its supplementary m

Anton Osokin 95 Nov 25, 2022
Research using Cirq!

ReCirq Research using Cirq! This project contains modules for running quantum computing applications and experiments through Cirq and Quantum Engine.

quantumlib 230 Dec 29, 2022
discovering subdomains, hidden paths, extracting unique links

python-website-crawler discovering subdomains, hidden paths, extracting unique links pip install -r requirements.txt discover subdomain: You can give

merve 4 Sep 05, 2022
Realtime_Multi-Person_Pose_Estimation

Introduction Multi Person PoseEstimation By PyTorch Results Require Pytorch Installation git submodule init && git submodule update Demo Download conv

tensorboy 1.3k Jan 05, 2023
Anderson Acceleration for Deep Learning

Anderson Accelerated Deep Learning (AADL) AADL is a Python package that implements the Anderson acceleration to speed-up the training of deep learning

Oak Ridge National Laboratory 7 Nov 24, 2022
A high-performance anchor-free YOLO. Exceeding yolov3~v5 with ONNX, TensorRT, NCNN, and Openvino supported.

YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and industrial communities. For more details, please refer to our rep

7.7k Jan 06, 2023
Global Rhythm Style Transfer Without Text Transcriptions

Global Prosody Style Transfer Without Text Transcriptions This repository provides a PyTorch implementation of AutoPST, which enables unsupervised glo

Kaizhi Qian 193 Dec 30, 2022
Single-Shot Motion Completion with Transformer

Single-Shot Motion Completion with Transformer ๐Ÿ‘‰ [Preprint] ๐Ÿ‘ˆ Abstract Motion completion is a challenging and long-discussed problem, which is of gr

FuxiCV 78 Dec 29, 2022
Freecodecamp Scientific Computing with Python Certification; Solution for Challenge 2: Time Calculator

Assignment Write a function named add_time that takes in two required parameters and one optional parameter: a start time in the 12-hour clock format

Hellen Namulinda 0 Feb 26, 2022
Mixed Transformer UNet for Medical Image Segmentation

MT-UNet Update 2022/01/05 By another round of training based on previous weights, our model also achieved a better performance on ACDC (91.61% DSC). W

dotman 92 Dec 25, 2022
TrackTech: Real-time tracking of subjects and objects on multiple cameras

TrackTech: Real-time tracking of subjects and objects on multiple cameras This project is part of the 2021 spring bachelor final project of the Bachel

5 Jun 17, 2022
a curated list of docker-compose files prepared for testing data engineering tools, databases and open source libraries.

data-services A repository for storing various Data Engineering docker-compose files in one place. How to use it ? Set the required settings in .env f

BigData.IR 525 Dec 03, 2022
Shuwa Gesture Toolkit is a framework that detects and classifies arbitrary gestures in short videos

Shuwa Gesture Toolkit is a framework that detects and classifies arbitrary gestures in short videos

Google 89 Dec 22, 2022
KITTI-360 Annotation Tool is a framework that developed based on python(cherrypy + jinja2 + sqlite3) as the server end and javascript + WebGL as the front end.

KITTI-360 Annotation Tool is a framework that developed based on python(cherrypy + jinja2 + sqlite3) as the server end and javascript + WebGL as the front end.

86 Dec 12, 2022
A Python library for working with arbitrary-dimension hypercomplex numbers following the Cayley-Dickson construction of algebras.

Hypercomplex A Python library for working with quaternions, octonions, sedenions, and beyond following the Cayley-Dickson construction of hypercomplex

7 Nov 04, 2022
Visual Memorability for Robotic Interestingness via Unsupervised Online Learning (ECCV 2020 Oral and TRO)

Visual Interestingness Refer to the project description for more details. This code based on the following paper. Chen Wang, Yuheng Qiu, Wenshan Wang,

Chen Wang 36 Sep 08, 2022
Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach

This repository holds the implementation for paper Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach Download our preproc

Qitian Wu 42 Dec 27, 2022
Waymo motion prediction challenge 2021: 3rd place solution

Waymo motion prediction challenge 2021: 3rd place solution ๐Ÿ“œ Technical report ๐Ÿ—จ๏ธ Presentation ๐ŸŽ‰ Announcement ๐Ÿ›†Motion Prediction Channel Website ๐Ÿ›†

158 Jan 08, 2023