HeadAudio

Introduction

HeadAudio is an audio worklet node/processor for audio-driven, real-time viseme detection and lip-sync in browsers. It uses MFCC feature vectors and Gaussian prototypes with a Mahalanobis-distance classifier. As output, it generates Oculus viseme blend-shape values in real time and can be integrated into an existing 3D animation loop.

• Pros: Audio-driven lip-sync works with any audio stream or TTS output without requiring text transcripts or timestamps. It is fast, fully in-browser, and requires no server.

• Cons: Voice activity detection (VAD) and prediction accuracy are far from optimal, especially when the signal-to-noise ratio (SNR) is low. In general, the audio-driven approach is less accurate and computationally more demanding than TalkingHead's text-driven approach.

The solution is fully compatible with the TalkingHead. It doesn't have any external dependencies, and it is MIT licensed.

HeadTTS, webpack, and jest were used during development, training, and testing.

The implementation has been tested with the latest versions of Chrome, Edge, Firefox, and Safari desktop browsers, as well as on iPad/iPhone.

Important

The model's accuracy will hopefully improve over time. However, since all audio processing occurs fully in-browser and in real time, it will never be perfect and may not be suitable for all use cases. Some precision will always need to be sacrificed to stay within the real-time processing budget.

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Demo / Test App

App	Description
	A demo web app using HeadAudio, TalkingHead, and OpenAI Realtime API (WebRTC). It supports speech-to-speech, moods, hand gestures, and facial expressions through function calling. [Run] [Code] Note: The app uses OpenAI's gpt-realtime-mini model and requires an OpenAI API key. The “mini” model is a cost-effective version of GPT Realtime, but still relatively expensive for extended use.
	A test app for HeadAudio that lets you experiment with audio-stream processing and various parameters using HeadTTS (in-browser neural text-to-speech engine), your own audio file(s), or microphone input. [Run] [Code]

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Using the HeadAudio Worklet Node/Processor

The steps needed to setup and use HeadAudio:

1. Import the Audio Worklet Node HeadAudio from "./modules/headaudio.mjs". Alternatively, use the minified version "./dist/headaudio.min.mjs" or a CDN build.

2. Register the Audio Worklet Processor from "./modules/headworklet.mjs". Alternatively, use the minified version "./dist/headworklet.min.mjs" or a CDN build.

3. Create a new HeadAudio instance.

4. Load a pre-trained viseme model containing Gaussian prototypes, e.g., "./dist/model-en-mixed.bin".

5. Connect your speech audio node to the HeadAudio node. The node node has a single mono input and does not output any audio.

6. Optional: To compensate for processing latency (50–100 ms), add delay to your speech-audio path using the browser's standard DelayNode.

7. Assing onvalue callback function (key, value) that updates your avatar's blend shape key (Oculus viseme name, e.g, "viseme_aa") to the given value in the range [0,1].

8. Call the node's update method inside your 3D animation loop, passing the delta time (in milliseconds).

9. Optional: Set up any additional user event handlers as needed.

Here is a simplified code example using the above steps with a TalkingHead class instance head:

// 1. Import
import { TalkingHead } from "talkinghead";
import { HeadAudioNode } from "./modules/headaudio.mjs";

// 2. Register processor
const head = new TalkingHead( /* Your normal parameters */ );
await head.audioCtx.audioWorklet.addModule("./modules/headworklet.mjs");

// 3. Create new HeadAudio node
const headaudio = new HeadAudio(head.audioCtx, {
  processorOptions: { },
  parameterData: {
    vadGateActiveDb: -40,
    vadGateInactiveDb: -60
  }
});

// 4. Load a pre-trained model
await headaudio.loadModel("./dist/model-en-mixed.mjs");

// 5. Connect TalkingHead's speech gain node to HeadAudio node
head.audioSpeechGainNode.connect(headaudio);

// 6. OPTIONAL: Add some delay between gain and reverb nodes
const delayNode = new DelayNode( head.audioCtx, { delayTime: 0.1 });
head.audioSpeechGainNode.disconnect(head.audioReverbNode);
head.audioSpeechGainNode.connect(delayNode);
delayNode.connect(head.audioReverbNode);

// 7. Register callback function to set blend shape values
headaudio.onvalue = (key,value) => {
  Object.assign( head.mtAvatar[ key ],{ newvalue: value, needsUpdate: true });
};

// 8. Link node's `update` method to TalkingHead's animation loop
head.opt.update = headaudio.update.bind(headaudio);

// 9. OPTIONAL: Take eye contact and make a hand gesture when new sentence starts
let lastEnded = 0;
headaudio.onended = () => {
  lastEnded = Date.now();
};

headaudio.onstarted = () => {
  const duration = Date.now() - lastEnded;
  if ( duration > 150 ) { // New sentence, if 150 ms pause (adjust, if needed)
    head.lookAtCamera(500);
    head.speakWithHands();
  }
};

See the test app source code for more details.

The supported processerOptions are:

Option	Description	Default
frameEventsEnabled	If true, sends frame user-event objects containing a downsampled samples array and timestamp: { event: 'frame', frame, t }. NOTE: Mainly for testing.	false
vadEventsEnabled	If true, sends vad user-event objects with status counters and current log-energy in decibels: { event: 'vad', active, inactive, db, t }. NOTE: Mainly for testing.	false
featureEventsEnabled	If true, send feature user-event objects with the normalized feature vector, log-energy, timestamp, and duration: { event: 'feature', vector, le, t, d }. NOTE: Mainly for testing.	false
visemeEventsEnabled	If true, sends viseme user-event objects containing extended viseme information, including the predicted viseme, feature vector, distance array, timestamp, and duration: { event: 'viseme', viseme, vector, distances, t, d }. NOTE: Mainly for testing.	false

The supported parameterData are:

Parameter	Description	Default
vadMode	0 = Disabled, 1 = Gate. If disabled, processing relies only on silence prototypes (see silMode). Gate mode is a simple energy-based VAD suitable for low and stable noise floors with high SNR.	1
vadGateActiveDb	Decibel threshold above which audio is classified as active.	-40
vadGateActiveMs	Duration (ms) required before switching from inactive to active.	10
vadGateInactiveDb	Decibel threshold below which audio is classified as inactive.	-50
vadGateInactiveMs	Duration (ms) required before switching from active to inactive.	10
silMode	0 = Disabled, 1 = Manual calibration, 2 = Auto (NOT IMPLEMENTED). If disabled, only trained SIL prototypes are used. In manual mode, the app must perform silence calibration. Auto mode is currently not implemented.	1
silCalibrationWindowSec	Silence-calibration window in seconds.	3.0
silSensitivity	Sensitivity to silence.	1.2
speakerMeanHz	Estimated speaker mean frequency in Hz [50–500]. Adjusting this gently stretches/compresses the Mel spacing and frequency range to better match the speaker’s vocal-tract resonances and harmonic structure. Typical values: adult male 100–130, adult female 200–250, child 300–400. EXPERIMENTAL	150

Tip

All audio parameters can be changed in real-time, e.g.: headaudio.parameters.get("vadMode").value = 0;

Supported HeadAudio class events:

Event	Description
onvalue(key, value)	Called when a viseme blend-shape value is updated. key is one of: 'viseme_aa', 'viseme_E', 'viseme_I', 'viseme_O', 'viseme_U', 'viseme_PP', 'viseme_SS', 'viseme_TH' 'viseme_DD', 'viseme_FF', 'viseme_kk', 'viseme_nn', 'viseme_RR', 'viseme_CH', 'viseme_sil'. value is in the range [0,1].
onstarted(data)	Speech start event { event: "start", t }.
onended(data)	Speech end event { event: "end", t }.
onframe(data)	Frame event { event: "frame", frame, t }. Contains 32-bit float 16 kHz mono samples. Requires frameEventEnabled to be true.
onvad(data)	VAD event { event: "vad", t, db, active, inactive }. Requires vadEventEnabled to be true.
onfeature(data)	Feature event { event: "feature", vector, t, d }. Requires featureEventEnabled to be true.
onviseme(data)	Viseme event { event: "viseme", viseme, t, d, vector, distances }. Requires visemeEventEnabled to be true.
oncalibrated(data)	Calibration event { event: "calibrated", t, \[error] }.
onprocessorerror(event)	Fired when an internal processor error occurs.

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Training

Important

You do NOT need to train your own model as a pre-trained model is provided. However, if you want to train a custom model, the process below describes how the existing model was created.

The lip-sync model ./models/model-en-mixed.bin was trained with four HeadTTS voices using Harvard Sentences as input text. HeadTTS is ideal for generating training data because it can produce audio, phonemes, visemes, and highly accurate phoneme-level timestamps.

1. Install and start HeadTTS text-to-speech REST service locally (requires Node.js v20+):

git clone https://github.com/met4citizen/HeadTTS
cd HeadTTS
npm install
npm start

Note: Before using the HeadTTS server, download all the voices that you will be using from onnx-community/Kokoro-82M-v1.0-ONNX-timestamped to your HeadTTS ./voices directory.

2. Generate training data (.wav and .json)

In a separate console window, install HeadAudio (if you haven't already), then generate the training files from the text prompts (.txt):

git clone https://github.com/met4citizen/HeadAudio
cd HeadAudio
npm install
cd training
node precompile-headtts.mjs -i "./headtts/headtts-1.txt" -v "af_bella"
node precompile-headtts.mjs -i "./headtts/headtts-2.txt" -v "af_heart"
node precompile-headtts.mjs -i "./headtts/headtts-3.txt" -v "am_adam"
node precompile-headtts.mjs -i "./headtts/headtts-4.txt" -v "am_fenrir"

Each voice takes about 2 minutes to process and generates roughly 10 minutes of training audio (.wav) along with corresponding phoneme/viseme timestamp data (.json).

For Mahalanobis classification with 12 features, you should aim for at least 60–120 samples per phoneme. The process above will generate more than enough training data.

3. Compile the Gaussian prototypes into a binary model

Once the .wav and .json files are ready, build the final model (.bin):

node compile.mjs -i "./headtts" -o "model-en-mixed.bin"

Compilation takes about 30 seconds, and the resulting binary file will be approximately 14 kB.

For all console apps, use the --help option to view available arguments.

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Technical Overview

The viseme-detection process uses Mel-Frequency Cepstral Coefficient (MFCC) feature vectors, Gaussian prototypes, and a Mahalanobis distance classifier.

SSince only the relative ordering of distances matters, the squared Mahalanobis distance $d_M^2$ is used:

$\displaystyle\qquad d_{M}^2 = (\vec{x} - \vec{\mu})^\top \Sigma^{-1} (\vec{x} - \vec{\mu})$

The real-time processing steps are as follows:

• Audio input: The audio worklet processor receives real-time speech input on a dedicated audio thread. The incoming audio typically consists of 128 mono float-32 samples at 44.1 kHz or 48 kHz.

• Pre-emphasis and downsampling: A pre-emphasis filter is applied to emphasize high-frequency components, after which the signal is downsampled to 16 kHz using a polyphase low-pass filter.

• MFCC feature vector: The downsampled audio is processed in 512-sample frames with a 256-sample hop. Each frame is converted into MFCC features through the following steps: (1) Apply a Hamming window, (2) Compute the FFT, (3) Calculate the power spectrum, (4) Apply Mel filters, (5) Perform a Discrete Cosine Transform (DCT). The output is a normalized log-energy value and a 12-coefficient MFCC feature vector.

• Compression: MFCC features are compressed using tanh to improve robustness across speakers and recording conditions. If feature events are enabled, each feature vector is posted to the main thread via onfeature.

• Voice Activity Detection (VAD): A simple gate-based VAD model determines active vs. inactive speech based on energy thresholds. If vad events are enabled, active/inactive status and dB value are posted to the main thread via onvad.

• Classification: Each pre-trained Gaussian prototype contains a mean vector $\vec{\mu}$ and an inverse covariance matrix $\Sigma^{-1}$ for a given phoneme. The classifier computes the Mahalanobis distance for each prototype and selects the viseme with the lowest distance. The result is posted to the main thread. If the viseme events are enabled, the viseme, MFCC vector, and the distance array are sent via onviseme.

• Lip animation: Based on the detected viseme, lip movements are calculated with easing and blending/cross-fading. Real-time blend-shape values are delivered via the onvalue callback, using the Oculus morph-target name and the computed value.

The common parameters are set in ./modules/parameters.mjs:

Parameter	Description	Default
AUDIO_SAMPLE_RATE	Sample rate used in downsampling and processing.	16000
AUDIO_DOWNSAMPLE_FILTER_N	Number of taps per phase.	32
AUDIO_DOWNSAMPLE_PHASE_N	Number of fractional phases.	64
AUDIO_PREEMPHASIS_ENABLED	Apply pre-emphasis to boost higher frequencies.	true
AUDIO_PREEMPHASIS_ALPHA	Pre-emphasis alpha.	0.97
MFCC_SAMPLES_N	Number of samples per feature vector.	512
MFCC_SAMPLES_HOP	Number of sample hops, If the same as MFCC_SAMPLES_N, no overlap.	256
MFCC_COEFF_N	The number of MFCC coefficients ignoring log energy c0 and possible deltas.	12
MFCC_MEL_BANDS_N	The number of Mel bands.	40
MFCC_LIFTER	Lifter parameter.	22
MFCC_DELTAS_ENABLED	Calculate and include MFCC deltas.	false
MFCC_DELTA_DELTAS_ENABLED	Calculate and include MFCC delta-deltas. Requires that MFCC_DELTAS_ENABLED is true.	false
MFCC_COEFF_N_WITH_DELTAS	Derived constant.
MFCC_COMPRESSION_ENABLED	If true, apply tanh compression.	true
MFCC_COMPRESSION_TANH_R	Tanh compression range.	1.0
MODEL_VISEMES_N	The number of visemes.	15
MODEL_VISEME_SIL	Silence viseme ID.	14

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Appendix A: Oculus visemes

The used Oculus viseme numbering, naming and phoneme mapping:

ID	Oculus viseme	Phonemes
0	"viseme_aa"	"aa" (open / low): a, aː, ɑ, ɑː, ɐ, aɪ, aʊ, ä.
1	"viseme_E"	"E" (mid) + Central vowels: ɛ, ɛː, e, eː, eɪ, œ, ɜ, ʌ, ə, ɚ, ɘ.
2	"viseme_I"	"I" (close front): i, iː, ɪ, ɨ, y, yː, ʏ.
3	"viseme_O"	"O" (mid back): o, oː, ɔ, ɔː, ɒ, ø, øː.
4	"viseme_U"	"U" (close back): u, uː, ʊ, ɯ, ɯː, ɤ.
5	"viseme_PP"	Plosives / bilabials: p, b, m.
6	"viseme_SS"	Fricatives / sibilants: s, z, ʃ, ʒ, ɕ, ʑ, ç, ʝ, x, ɣ, h,
7	"viseme_TH"	Dentals: θ, ð.
8	"viseme_DD"	Alveolar stops: t, d.
9	"viseme_FF"	Labiodentals: f, v.
10	"viseme_kk"	Velar stops: k, g, q, ɢ.
11	"viseme_nn"	Nasals: n, ŋ, ɲ, ɳ, m̩.
12	"viseme_RR"	Liquids / approximants: ɹ, r, ɾ, ɽ, l, ɫ, j, w.
13	"viseme_CH"	Affricates: tʃ, dʒ, ts, dz.
14	"viseme_sil"	Silence / pause markers: ``, ˈ, `ˌ`, `‖`, `|`.

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Appendix B: Data Structures

Models such as ./dist/model-en-mixed.bin are binary files. Each model contains multiple Gaussian prototypes, typically one or more per viseme ID.

The byte layout of each prototype within the .bin file is as follows:

Field	Length in bytes	Description
phoneme	4	IPA phoneme stored as 1–2 UTF-16 characters.
N/A	1	Reserved for future use.
group	1	Group ID
N/A	1	Reserved for future use.
viseme	1	Viseme ID as an unsigned integer.
$\vec{\mu}$	12 * 4	Mean feature vector (12 × float-32).
$\Sigma^{-1}$	78 * 4	Lower-triangular part of the inverse covariance matrix (78 × float-32).

The file structure of each JSON (.json) viseme data is the following:

[
  {
    "section": "A word or sentence", // the word/sentence (optional)
    "ps": [ // Phonemes
      {
        "p": "ɪ", // Phoneme, 1-2 letters
        "v": 0, // Viseme ID
        "t": 100, // Start time (ms)
        "d": 50  // Duration (ms)
      },
      ...
    ]
  },
  {
    // Next word/sentence
  }
]

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Appendix C: Performance

In theory, the processing window for each 128-sample audio block is 2.9 ms at 44.1 kHz or 2.7 ms at 48 kHz. However, we must allow generous headroom for overhead, jitter, CPU spikes, and lower-end hardware. If we don't, the browser will begin dropping audio frames.

In practice, the processing should finish within 15-20% of the block duration. In our test environment^[1], we targeted a typical execution time of < 0.4 ms.

Below are measurement results for real-time processing inside the HeadAudio processor:

Step	Duration[1]	Notes
MFCC/FFT	0.025 ms	Computes a single MFCC feature vector (including FFT) from a 512-sample frame.
Classifier	0.005 ms	Computes a viseme prediction by evaluating Mahalanobis distances against 50 Gaussian prototypes.
Processor	~0.035 ms	Total time to process one 128-sample block (MFCC/FFT + classification). Represents typical peak values during a 21-second speech test (44.1 kHz mono).
TOTAL	<0.1 ms	Estimated max processing time per 128-sample block for 99.9% of frames.

Analysis of test-run statistics:

Assuming processing time per prediction ~0.5 ms, MFCC/FFT window size: 512 samples @ 16 kHz, hop size: 256 samples @ 16 kHz, and requiring three consecutive identical predictions before changing viseme, the total end-to-end latency is approximately 50 ms.

Training performance:

Training step	Duration[1]	Notes
Prototype	0.286 ms	Computes one Gaussian prototype from 1000 MFCC vectors.
TOTAL	<30 s	Training 50 prototypes including audio processing.

Distance matrix for ./dist/model-en-mixed.bin:

^[1] Test/training setup: Macbook Air M2 laptop, 8 cores, 16GB memory, latest desktop Chrome browser.