@ericedouard/vad-node-realtime
Version:
Powerful, user-friendly realtime voice activity detector (VAD) for node
53 lines • 1.98 kB
JavaScript
"use strict";
var _a;
Object.defineProperty(exports, "__esModule", { value: true });
exports.SileroLegacy = void 0;
const logging_1 = require("../logging");
class SileroLegacy {
constructor(ortInstance, _session, _h, _c, _sr) {
this.ortInstance = ortInstance;
this._session = _session;
this._h = _h;
this._c = _c;
this._sr = _sr;
this.reset_state = () => {
const zeroes = Array(2 * 64).fill(0);
this._h = new this.ortInstance.Tensor("float32", zeroes, [2, 1, 64]);
this._c = new this.ortInstance.Tensor("float32", zeroes, [2, 1, 64]);
};
this.process = async (audioFrame) => {
const t = new this.ortInstance.Tensor("float32", audioFrame, [
1,
audioFrame.length,
]);
const inputs = {
input: t,
h: this._h,
c: this._c,
sr: this._sr,
};
const out = await this._session.run(inputs);
this._h = out["hn"];
this._c = out["cn"];
const [isSpeech] = out["output"]?.data;
const notSpeech = 1 - isSpeech;
return { notSpeech, isSpeech };
};
}
}
exports.SileroLegacy = SileroLegacy;
_a = SileroLegacy;
SileroLegacy.new = async (ortInstance, modelFetcher) => {
logging_1.log.debug("initializing vad");
const modelArrayBuffer = await modelFetcher();
const _session = await ortInstance.InferenceSession.create(modelArrayBuffer);
// @ts-ignore
const _sr = new ortInstance.Tensor("int64", [16000n]);
const zeroes = Array(2 * 64).fill(0);
const _h = new ortInstance.Tensor("float32", zeroes, [2, 1, 64]);
const _c = new ortInstance.Tensor("float32", zeroes, [2, 1, 64]);
logging_1.log.debug("vad is initialized");
const model = new _a(ortInstance, _session, _h, _c, _sr);
return model;
};
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