Files
ai-spel-frontend/piper-worker.js
T
Ein Anderssono 7f77b904ac
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Track Piper model bytes in the worker
2026-10-16 08:02:17 +02:00

66 lines
2.4 KiB
JavaScript

import {TtsSession} from '@mintplex-labs/piper-tts-web';
import {trackModelDownload} from './piper-download-progress.mjs';
import {localPiperModelUrl} from './piper-model-routing.mjs';
import {createOnnxWasmPaths} from './piper-onnx-config.mjs';
const VOICE_ID = 'sv_SE-nst-medium';
const ASSET_ROOT = '/static/piper-wasm-v1';
const localFetch = self.fetch.bind(self);
// Keep the TTS library's OPFS cache logic, but serve models/config from the
// same-origin static asset service instead of its built-in external model URL.
self.fetch = async (resource, options) => {
const requestedUrl = resource instanceof Request ? resource.url : String(resource);
const localModelUrl = localPiperModelUrl(requestedUrl, self.location.origin);
if (!localModelUrl) return localFetch(resource, options);
const redirected = resource instanceof Request ? new Request(localModelUrl, resource) : localModelUrl;
const response = await localFetch(redirected, options);
if (!activeRequestId || !localModelUrl.endsWith('.onnx')) return response;
return trackModelDownload(response, {
id: activeRequestId,
postMessage: message => self.postMessage(message),
});
};
let sessionPromise;
let inferenceQueue = Promise.resolve();
let activeRequestId = null;
async function getSession() {
if (!sessionPromise) {
sessionPromise = TtsSession.create({
voiceId: VOICE_ID,
wasmPaths: {
onnxWasm: createOnnxWasmPaths(self.location.origin),
piperData: `${ASSET_ROOT}/piper/piper_phonemize.data`,
piperWasm: `${ASSET_ROOT}/piper/piper_phonemize.wasm`,
},
}).catch(error => {
sessionPromise = null;
throw error;
});
}
return sessionPromise;
}
async function synthesizeMessage(event) {
const {id, text} = event.data || {};
if (!Number.isSafeInteger(id) || typeof text !== 'string') return;
activeRequestId = id;
self.postMessage({id, type: 'progress', progress: {stage: 'starting'}});
try {
const session = await getSession();
const blob = await session.predict(text);
const audio = await blob.arrayBuffer();
self.postMessage({id, type: 'audio', audio}, [audio]);
} catch (error) {
self.postMessage({id, type: 'error', error: String(error?.message || error)});
} finally {
activeRequestId = null;
}
}
self.addEventListener('message', event => {
inferenceQueue = inferenceQueue.then(() => synthesizeMessage(event));
});