Orca-iOS 0.2.2

Orca-iOS 0.2.2

Maintained by Kwangsoo Yeo, Albert Ho.

Orca-iOS 0.2.2

  • By
  • Picovoice




Made in Vancouver, Canada by Picovoice

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Orca is an on-device text-to-speech engine producing high-quality, realistic, spoken audio with zero latency. Orca is:

  • Private; All voice processing runs locally.
  • Cross-Platform:
    • Linux (x86_64), macOS (x86_64, arm64), Windows (x86_64)
    • Raspberry Pi (5, 4, 3) and NVIDIA Jetson Nano

Please note that Orca is currently in development. While we prioritize stability and compatibility, certain aspects of Orca may undergo changes as we continually enhance and refine the engine to provide the best user experience possible.

Table of Contents


Text input

Orca accepts the 26 lowercase (a-z) and 26 uppercase (A-Z) letters of the English alphabet, as well as common punctuation marks. You can get a list of all supported characters by calling the valid_characters() method provided in the Orca SDK you are using. Pronunciations of characters or words not supported by this list can be achieved with custom pronunciations.

Custom pronunciations

Orca supports custom pronunciations via a specific syntax embedded within the input text. This feature allows users to define unique pronunciations for words using the following format: {word|pronunciation}. The pronunciation is expressed in ARPAbet phonemes. The following are examples of sentences using custom pronunciations:

  • "This is a {custom|K AH S T AH M} pronunciation"
  • "{read|R IY D} this as {read|R EH D}, please."
  • "I {live|L IH V} in {Sevilla|S EH V IY Y AH}. We have great {live|L AY V} sports!"


Orca can synthesize speech with various voices, each of which is characterized by a model file located in lib/common. To synthesize speech with a specific voice, provide the associated model file as an argument to the orca init function. The following are the voices currently available:

Model name Sample rate (Hz)
orca_params_female.pv 22050
orca_params_male.pv 22050

Speech control

Orca provides a set of parameters to control the synthesized speech. The following table lists the available parameters:

Parameter Default Description
speech rate 1.0 Speed of generated speech. Valid values are within [0.7, 1.3].
Higher (lower) values generate faster (slower) speech.

Audio output

Orca's synthesized speech is delivered as either raw audio data or a WAV file. Output audio will be in single-channel 16-bit PCM format and can be directly fed into a playback audio system.


AccessKey is your authentication and authorization token for deploying Picovoice SDKs, including Orca. Anyone who is using Picovoice needs to have a valid AccessKey. You must keep your AccessKey secret. You will need internet connectivity to validate your AccessKey with Picovoice license servers even though the text-to-speech engine is running 100% offline.

AccessKey also verifies that your usage is within the limits of your account. Everyone who signs up for Picovoice Console receives the Free Tier usage rights described here. If you wish to increase your limits, you can purchase a subscription plan.


Python Demos

To run the Python demo, run the following in the console:

pip3 install pvorcademo
orca_demo --access_key ${ACCESS_KEY} --text ${TEXT} --output_path ${WAV_OUTPUT_PATH}

Replace ${ACCESS_KEY} with yours obtained from Picovoice Console, ${TEXT} with the text to be synthesized, and ${WAV_OUTPUT_PATH} with a path to an output WAV file.

iOS Demo

Run the following from demo/ios to install the Orca-iOS CocoaPod:

pod install

Replace let ACCESS_KEY = "..." inside ViewModel.swift with yours obtained from Picovoice Console.

Then, using Xcode, open the generated OrcaDemo.xcworkspace and run the application.

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

C Demos

Build the demo:

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

Run the demo:

./demo/c/build/orca_demo -l ${LIBRARY_PATH} -m ${MODEL_PATH} -a ${ACCESS_KEY} -t ${TEXT} -o ${OUTPUT_PATH}



Install the Python SDK:

pip3 install pvorca

Create an instance of the engine and generate speech:

import pvorca

orca = pvorca.create(access_key='${ACCESS_KEY}')
pcm = orca.synthesize('${TEXT}')

Replace ${ACCESS_KEY} with yours obtained from Picovoice Console and ${TEXT} with the text to be synthesized including potential custom pronunciations.

Finally, when done make sure to explicitly release the resources:


For more details see Python SDK.


Create an instance of the engine and synthesize:

import Orca

let modelPath = Bundle(for: type(of: self)).path(
        forResource: "${MODEL_FILE}", // Name of the model file name for Orca
        ofType: "pv")!

do {
  let orca = try Orca(accessKey: "${ACCESS_KEY}", modelPath: modelPath)
} catch {}

do {
    let pcm = try orca.synthesize(text: "${TEXT}")
} catch {}

Replace ${ACCESS_KEY} with yours obtained from Picovoice Console, ${MODEL_FILE} with the model file name for Orca and ${TEXT} with the text to be synthesized including potential custom pronunciations.

When done be sure to explicitly release the resources using orca.delete().


The header file include/pv_orca.h contains relevant information on Orca's C SDK.

Build an instance of the object:

pv_orca_t *handle = NULL;
const char *model_path = "${MODEL_PATH}";
pv_status_t status = pv_orca_init("${ACCESS_KEY}", model_path, &handle);
if (status != PV_STATUS_SUCCESS) {
    // error handling logic

Replace ${ACCESS_KEY} with the AccessKey obtained from Picovoice Console, and ${MODEL_PATH} with the path to the model file available under lib/common.

Create a synthesize_params object to control the synthesized speech:

pv_orca_synthesize_params_t *synthesize_params = NULL;
status = pv_orca_synthesize_params_init(&synthesize_params);
// change the default parameters of synthesize_params as desired

Now, the handle and synthesize_params object can be used to synthesize speech:

int32_t num_samples = 0;
int16_t *synthesized_pcm = NULL;
status = pv_orca_synthesize(

Replace ${TEXT} with the text to be synthesized including potential custom pronunciations.

Finally, when done make sure to release the acquired resources:



v0.1.0 - January 24th, 2024

  • Beta release


You can find the FAQ here.