You can add Gemini text-to-speech to an Angular web app with Firebase AI Logic’s JavaScript SDK: send text to a TTS model, receive audio, then decode and play the returned PCM in the browser. For earlier playback, use the streaming method and process audio chunks as they arrive. This is streamed narration, not a live conversational voice session.
What “real-time” means for Gemini TTS
Firebase describes TTS as “a unidirectional, request-response interaction (text in, audio out).” It is suited to reading supplied text, such as an article or narration. Gemini Live API is the bidirectional option for an ongoing voice conversation, where the model can respond dynamically rather than simply recite a supplied passage. The two APIs serve different interaction patterns; see Firebase’s audio-generation guide.
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| Decision | Gemini TTS | Gemini Live API |
|---|---|---|
| Interaction | One-way: text in, generated speech out | Two-way, live voice interaction |
| What is spoken | Supplied text, with optional style or sound direction | Context-sensitive responses during a conversation |
| Typical use | Article reading, narration, audiobooks | Dynamic conversational voice experiences |
| Integration shape | generateContent or streamed generateContentStream; handle generated PCM |
Stateful live session with streamed audio input and output |
What you need in an Angular project
- A Firebase project with a registered web app and Firebase’s JavaScript SDK installed.
- Firebase AI Logic configured for the Gemini API provider you intend to use.
- An Angular TypeScript location for Firebase initialization and model calls. Firebase documents the web JavaScript SDK, not a dedicated Angular wrapper or a specific tested Angular version; integrating it in an Angular service is an application-level choice.
- A browser playback implementation for the raw PCM data returned by the model. Firebase’s example demonstrates decoding the data but leaves the playback function to the developer.
Follow Firebase’s current getting-started workflow to create or select the Firebase project, register the web app, configure the provider, and install the SDK. Keep project initialization consistent with the app’s existing Angular setup. Firebase AI Logic routes client requests through its proxy service, so do not put a Gemini Developer API key in Angular source.
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The Firebase web example imports the AI Logic helpers from firebase/ai and selects the Gemini Developer API backend. This is the documented JavaScript shape; adapt the Firebase app reference to your own initialization rather than treating the snippet as a complete Angular app.
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import { getAI, getGenerativeModel, GoogleAIBackend, ResponseModality } from "firebase/ai";
const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() });
const model = getGenerativeModel(ai, {
model: "gemini-3.1-flash-tts-preview",
generationConfig: {
responseModalities: [ResponseModality.AUDIO],
speechConfig: {
voiceConfig: {
prebuiltVoiceConfig: { voiceName: "Kore" }
}
}
}
});The example uses the Gemini Developer API backend. If your Firebase project uses the Agent Platform Gemini API, choose the corresponding provider setup in Firebase AI Logic instead of copying that backend choice unchanged. The documented Gemini 3.x TTS model ID is gemini-3.1-flash-tts-preview. Firebase’s model reference lists it as Preview with a release date of 2026-06-17; confirm that the model is available for your selected provider and project before relying on it. Preview features can change incompatibly and do not carry an SLA or deprecation policy. Check the supported-model reference for current status.
Send text and choose a voice
In the single-speaker configuration, specify a prebuilt voice name. The language code is optional; if omitted, the guide says the language can be auto-detected. For predictable language selection, configure a code such as en-US in the speech configuration, following Firebase’s TTS configuration guide.
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For a complete response, send the text with generateContent:
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const result = await model.generateContent("Read this text aloud: Welcome to the app.");
const response = result.response;The response contains inline audio data encoded as base64. Extract the audio part from the response, decode it, and pass the resulting bytes to your playback code. The exact response-part extraction and browser playback are application responsibilities; the SDK example does not provide a ready-made Angular audio component.
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Stream audio chunks in the browser
For Gemini 3.x TTS, Firebase documents generateContentStream so your app can handle response chunks as they arrive rather than waiting for the entire response. This can reduce the wait before playback begins, but Firebase’s cited guide does not establish a numeric latency guarantee.
const stream = await model.generateContentStream("Read this text aloud: Welcome to the app.");
for await (const chunk of stream.stream) {
// Find the audio part in this chunk, decode its base64 data,
// then enqueue the PCM bytes in your browser playback implementation.
}Each received audio chunk must be extracted and handled in order. Firebase’s browser example shows base64 decoding to an ArrayBuffer; it does not implement chunk buffering or playback. In an Angular UI, keep the stream and playback lifecycle under application control:
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- Show loading and playback state so users can tell whether speech is being generated or played.
- Preserve chunk order and buffer audio so the output does not race ahead of playback.
- Provide a stop or cancel action and discard pending chunks when a user starts another request or leaves the view.
- Handle request, decoding, and playback errors, and release audio resources when playback ends or the component is destroyed.
Those are implementation considerations for a responsive browser experience, not behavior supplied by the Firebase sample. Test playback in the browsers and devices your app supports.
Protect a client-side feature before launch
Client-side access means users can invoke the feature from their browsers, so configure safeguards before making it public. Firebase recommends App Check to help protect model-access APIs from unauthorized clients. Firebase AI Logic also provides configurable per-user rate limits. Review those limits and your provider configuration for the expected use of your app. These controls help manage access and abuse; they do not guarantee that every unauthorized request or unwanted cost is prevented.
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