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You can add Gemini text-to-speech (TTS) to an Angular app with Firebase AI Logic’s JavaScript SDK: send text to a TTS model, then decode and play the raw PCM audio returned by the request. To handle audio as it arrives, use generateContentStream. This is streamed narration, not a live, two-way voice conversation.
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. Streaming lets the app process generated audio chunks as they arrive instead of waiting for the complete response, but it does not turn TTS into a conversation.
For a voice interaction in which a person speaks and the model responds dynamically, use the Gemini Live API instead. It supports bidirectional audio interaction and uses a stateful live session. The two APIs solve different problems; see Firebase’s audio generation guide.
| Decision | Gemini TTS | Gemini Live API |
|---|---|---|
| Interaction | One-way: supplied text in, generated speech out | Two-way: live voice conversation |
| What is spoken | The text you provide, with optional speech direction | A context-sensitive response generated in the conversation |
| Typical use | Articles, narration, or audiobook-style reading | Dynamic conversational voice experiences |
| Output approach | generateContent or streamed generateContentStream; raw PCM audio |
Stateful live session with streamed audio input and output |
What you need before adding TTS
- A Firebase project with a registered web app and the Firebase JavaScript SDK installed.
- A configured Gemini API provider for Firebase AI Logic. The example below uses the Gemini Developer API backend; projects using the Vertex AI Gemini API need the matching provider setup.
- A browser-side playback implementation. Firebase’s sample shows how to decode audio data, but leaves the actual
playAudiofunction to the developer.
Follow Firebase’s current web getting-started guide for project and SDK setup. In Angular, keep Firebase initialization in your existing TypeScript configuration or an injectable service. This is an Angular integration approach, not a Firebase-documented Angular-specific wrapper or a claim of testing against a particular Angular version.
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Configure Firebase AI Logic for speech output
The Firebase web SDK exposes the same JavaScript APIs to TypeScript code in an Angular app. This example configures the Gemini Developer API backend, selects the documented TTS model, and requests audio using the prebuilt Kore voice. Replace firebaseApp with your initialized Firebase app.
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',
},
},
languageCode: 'en-US',
},
},
});The voice name is required for the single-speaker setup; the language code is optional and may be omitted for automatic detection. Check Firebase’s TTS guide for supported configuration options and the current example. The TTS model shown here, gemini-3.1-flash-tts-preview, is listed as Preview in Firebase’s supported models reference, with a release date of 2026-06-17. Preview features do not have an SLA or deprecation policy and may change incompatibly, so confirm that the model is available for your chosen provider before deployment.
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Generate a complete response or stream audio chunks
Wait for the complete generated audio
Use generateContent when it is acceptable to wait for the response before processing audio. The returned audio is inline data; extract its base64 payload and decode it before passing it to your browser playback code.
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const result = await model.generateContent('Read this text aloud: Hello from my Angular app.');
const parts = result.response.candidates?.[0]?.content?.parts ?? [];
for (const part of parts) {
const data = part.inlineData?.data;
if (data) {
const pcmBuffer = base64ToArrayBuffer(data);
await playPcmAudio(pcmBuffer);
}
}base64ToArrayBuffer and playPcmAudio above represent application-provided functions, not Firebase SDK methods. The Firebase web example demonstrates base64 decoding and delegates playback to developer code.
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Process generated chunks as they arrive
For streamed output, call generateContentStream and iterate over its stream. Each chunk can contain inline audio data to decode and hand to your playback layer. The TTS documentation supports streaming for Gemini 3.x TTS models; handling chunks earlier can reduce the wait before playback begins, but Firebase’s cited guide provides no numeric latency guarantee.
const result = await model.generateContentStream(
'Read this text aloud: Hello from my Angular app.'
);
for await (const chunk of result.stream) {
const parts = chunk.candidates?.[0]?.content?.parts ?? [];
for (const part of parts) {
const data = part.inlineData?.data;
if (data) {
const pcmBuffer = base64ToArrayBuffer(data);
await enqueuePcmChunk(pcmBuffer);
}
}
}The queue in this sketch is also application code. A usable Angular feature needs to preserve chunk order and buffer audio appropriately; it should also define what happens when a user stops playback or starts another request. Treat loading state, cancellation, errors, and cleanup as part of the component or service design rather than assuming the SDK supplies a complete audio player.
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Play the returned PCM in the browser
Gemini returns raw PCM audio data, not a ready-made audio player or Angular component. After base64 decoding, the app must provide browser playback for PCM and, for streamed output, feed chunks in order. The Firebase example does not prescribe a complete playback implementation or guarantee a particular perceived start time. Build and test the playback layer against your target browsers and the response format documented for the selected model.
Protect a client-side integration
Firebase AI Logic routes client SDK calls through its proxy service, so the browser app does not need a Gemini Developer API key embedded in Angular source. That architecture does not by itself prevent unauthorized use. Firebase recommends App Check to help protect model-access APIs, and Firebase AI Logic offers configurable per-user rate limits. Configure those controls and monitor usage as part of preparing a public feature; they reduce risk but are not a guarantee that abuse is impossible.
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Provider choice, App Check, and rate-limit configuration are covered in Firebase’s Firebase AI Logic documentation.
Quick Recap
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