Another timestamp bugfix

Added cosine distance function

nodejs/example/test-speaker.js

@@ -1,47 +1,90 @@
-#!/usr/bin/env node
+const wav = require('wav')
+const fs = require('fs')
 
-const fs = require("fs");
-const { Readable } = require("stream");
-const wav = require("wav");
-
-const { Model, KaldiRecognizer, SpkModel } = require("..");
+const {Readable} = require('stream')
+const {Model, KaldiRecognizer, SpkModel} = require('..')
 
 try {
-    fs.accessSync("model", fs.constants.R_OK);
-} catch(err) {
-    console.error("Please download the model from https://github.com/alphacep/kaldi-android-demo/releases and unpack as 'model' in the current folder.");
-    process.exit(1);
+    fs.accessSync('model', fs.constants.R_OK)
+} catch (err) {
+    console.error("Please download the model from https://github.com/alphacep/kaldi-android-demo/releases and unpack as 'model' in the current folder.")
+    process.exit(1)
 }
 
 try {
-    fs.accessSync("model-spk", fs.constants.R_OK);
-} catch(err) {
-    console.error("Please download the speaker model from https://github.com/alphacep/kaldi-android-demo/releases and unpack as 'model-spk' in the current folder.");
-    process.exit(1);
+    fs.accessSync('model-spk', fs.constants.R_OK)
+} catch (err) {
+    console.error("Please download the speaker model from https://github.com/alphacep/kaldi-android-demo/releases and unpack as 'model-spk' in the current folder.")
+    process.exit(1)
 }
 
+const wfStream = fs.createReadStream('test.wav', { highWaterMark: 4096 })
+const wfReader = new wav.Reader()
 
-const wfStream = fs.createReadStream("test.wav", {'highWaterMark': 4096});
-const wfReader = new wav.Reader();
+const model = new Model('model')
+const spkModel = new SpkModel('model-spk')
 
-const model = new Model("model");
-const spkModel = new SpkModel("model-spk");
+const spk_sig = [4.658117, 1.277387, 3.346158, -1.473036, -2.15727,
+2.461757, 3.76756, -1.241252, 2.333765, 0.642588, -2.848165, 1.229534,
+3.907015, 1.726496, -1.188692, 1.16322, -0.668811, -0.623309, 4.628018,
+0.407197, 0.089955, 0.920438, 1.47237, -0.311365, -0.437051, -0.531738,
+-1.591781, 3.095415, 0.439524, -0.274787, 4.03165, 2.665864, 4.815553,
+1.581063, 1.078242, 5.017717, -0.089395, -3.123428, 5.34038, 0.456982,
+2.465727, 2.131833, 4.056272, 1.178392, -2.075712, -1.568503, 0.847139,
+0.409214, 1.84727, 0.986758, 4.222116, 2.235512, 1.369377, 4.283126,
+2.278125, -1.467577, -0.999971, 3.070041, 1.462214, 0.423204, 2.143578,
+0.567174, -2.294655, 1.864723, 4.307356, 2.610872, -1.238721, 0.551861,
+2.861954, 0.59613, -0.715396, -1.395357, 2.706177, -2.004444, 2.055255,
+0.458283, 1.231968, 3.48234, 2.993858, 0.402819, 0.940885, 0.360162,
+-2.173674, -2.504609, 0.329541, 3.653913, 3.638025, -1.406409, 2.14059,
+1.662765, -0.991323, 0.770921, 0.010094, 3.775469, 1.847511, 2.074432,
+-1.928593, 0.807414, 2.964505, 0.128597, 1.297962, 2.645227, 0.136405,
+-2.543087, 0.932246, 2.405783, -2.122267, 3.044013, 0.486728, 4.395338,
+0.474267, 0.781297, 1.694144, -0.831078, -0.462362, -0.964715, 3.187863,
+6.008708, 1.725954, 3.667886, -1.467623, 3.370667, 2.72555, -0.796541,
+2.416543, 0.675401, -0.737634, -1.709676]
+
+function dotp(x, y) {
+    function dotp_sum(a, b) {
+        return a + b
+    }
+    function dotp_times(a, i) {
+        return x[i] * y[i]
+    }
+    return x.map(dotp_times).reduce(dotp_sum, 0)
+}
+
+function cosineSimilarity(A, B) {
+    var similarity =
+        dotp(A, B) / (Math.sqrt(dotp(A, A)) * Math.sqrt(dotp(B, B)))
+        return similarity
+}
+
+function cosine_dist(x, y) {
+    return 1 - cosineSimilarity(x, y)
+}
 
 wfReader.on('format', async ({ audioFormat, sampleRate, channels }) => {
     if (audioFormat != 1 || channels != 1) {
-        console.error("Audio file must be WAV format mono PCM.");
-        process.exit(1);
+        console.error('Audio file must be WAV format mono PCM.')
+        process.exit(1)
     }
-    const rec = new KaldiRecognizer(model, spkModel, sampleRate);
+    const rec = new KaldiRecognizer(model, spkModel, sampleRate)
     for await (const data of new Readable().wrap(wfReader)) {
-        const result = await rec.AcceptWaveform(data);
-        if (result != 0) {
-           console.log(await rec.Result());
+        const endOfSpeech = await rec.AcceptWaveform(data)
+        if (endOfSpeech) {
+            res = await JSON.parse(rec.Result());
+            console.log(res)
+            console.log('X-vector:', JSON.stringify(res['spk']))
+            console.log('Speaker distance:', cosine_dist(spk_sig, res['spk']))
         } else {
-           console.log(await rec.PartialResult());
+            console.log(await rec.PartialResult())
         }
     }
-    console.log(await rec.FinalResult());
-});
+    res = await JSON.parse(rec.FinalResult());
+    console.log(res)
+    console.log('X-vector:', JSON.stringify(res['spk']))
+    console.log('Speaker distance:', cosine_dist(spk_sig, res['spk']))
+})
 
-wfStream.pipe(wfReader);
+wfStream.pipe(wfReader)

python/setup.py

@@ -73,7 +73,7 @@ vosk_ext = Extension('vosk._vosk',
 
 setuptools.setup(
     name="vosk", # Replace with your own username
-    version="0.3.9",
+    version="0.3.10",
     author="Alpha Cephei Inc",
     author_email="contact@alphacephei.com",
     description="API for Kaldi and Vosk",

src/kaldi_recognizer.cc

@@ -125,9 +125,9 @@ KaldiRecognizer::KaldiRecognizer(Model *model, SpkModel *spk_model, float sample
 }
 
 KaldiRecognizer::~KaldiRecognizer() {
+    delete decoder_;
     delete feature_pipeline_;
     delete silence_weighting_;
-    delete decoder_;
     delete g_fst_;
     delete decode_fst_;
     delete spk_feature_;
@@ -164,12 +164,13 @@ void KaldiRecognizer::CleanUp()
     delete silence_weighting_;
     silence_weighting_ = new kaldi::OnlineSilenceWeighting(*model_->trans_model_, model_->feature_info_.silence_weighting_config, 3);
 
-    if (spk_feature_) {
+    if (spk_model_) {
         delete spk_feature_;
         spk_feature_ = new OnlineMfcc(spk_model_->spkvector_mfcc_opts);
     }
 
-    frame_offset_ += decoder_->NumFramesDecoded();
+    if (decoder_)
+       frame_offset_ += decoder_->NumFramesDecoded();
 
     // Each 10 minutes we drop the pipeline to save frontend memory in continuous processing
     // here we drop few frames remaining in the feature pipeline but hope it will not
@@ -177,8 +178,9 @@ void KaldiRecognizer::CleanUp()
 
     // Also restart if we retrieved final result already
 
-    if (frame_offset_ > 20000 || state_ == RECOGNIZER_FINALIZED) {
+    if (decoder_ == NULL || state_ == RECOGNIZER_FINALIZED || frame_offset_ > 20000) {
         samples_round_start_ += samples_processed_;
+        samples_processed_ = 0;
         frame_offset_ = 0;
 
         delete decoder_;
@@ -454,7 +456,21 @@ const char* KaldiRecognizer::FinalResult()
     decoder_->AdvanceDecoding();
     decoder_->FinalizeDecoding();
     state_ = RECOGNIZER_FINALIZED;
-    return GetResult();
+    GetResult();
+
+    // Free some memory while we are finalized, next
+    // iteration will reinitialize them anyway
+    delete decoder_;
+    delete feature_pipeline_;
+    delete silence_weighting_;
+    delete spk_feature_;
+
+    feature_pipeline_ = NULL;
+    silence_weighting_ = NULL;
+    decoder_ = NULL;
+    spk_feature_ = NULL;
+
+    return last_result_.c_str();
 }
 
 // Store result in recognizer and return as const string