### Vosk Development Setup

Source: https://github.com/alphacep/vosk-api/blob/master/ruby/README.md

Install dependencies and run tests for Vosk development using Bundler.

```bash
bundle install
bundle exec rake spec

```

--------------------------------

### Run Vosk API Go Example on Linux

Source: https://github.com/alphacep/vosk-api/blob/master/go/example/README.md

This snippet outlines the steps to clone the repository, download the Linux-specific Vosk library and a small English model, and then run the Go example with a test audio file. Ensure LD_LIBRARY_PATH and CGO flags are correctly set.

```bash
git clone https://github.com/alphacep/vosk-api
cd vosk-api/go/example
wget https://github.com/alphacep/vosk-api/releases/download/v0.3.45/vosk-linux-x86_64-0.3.45.zip
unzip vosk-linux-x86_64-0.3.45.zip
wget https://alphacephei.com/vosk/models/vosk-model-small-en-us-0.15.zip
unzip vosk-model-small-en-us-0.15.zip
mv vosk-model-small-en-us-0.15 model
cp ../../python/example/test.wav .
VOSK_PATH=`pwd`/vosk-linux-x86_64-0.3.45 LD_LIBRARY_PATH=$VOSK_PATH CGO_CPPFLAGS="-I $VOSK_PATH" CGO_LDFLAGS="-L $VOSK_PATH" go run . -f test.wav
```

--------------------------------

### Run Vosk API Go Example on Windows

Source: https://github.com/alphacep/vosk-api/blob/master/go/example/README.md

This snippet details the process for running the Vosk API Go example on Windows. It involves cloning the repository, downloading the Linux library (and extracting DLLs/headers), obtaining a small English model, and executing the Go program with appropriate linker flags.

```bash
git clone https://github.com/alphacep/vosk-api
cd vosk-api/go/example
wget https://github.com/alphacep/vosk-api/releases/download/v0.3.45/vosk-linux-x86_64-0.3.45.zip
unzip vosk-linux-x86_64-0.3.45.zip
cp vosk-linux-x86_64-0.3.45/*.dll .
cp vosk-linux-x86_64-0.3.45/*.h .
wget https://alphacephei.com/vosk/models/vosk-model-small-en-us-0.15.zip
unzip vosk-model-small-en-us-0.15.zip
mv vosk-model-small-en-us-0.15 model
cp ../../python/example/test.wav .
VOSK_PATH=`pwd` LD_LIBRARY_PATH=$VOSK_PATH CGO_CPPFLAGS="-I $VOSK_PATH" CGO_LDFLAGS="-L $VOSK_PATH -lvosk -lpthread -dl" go run . -f test.wav
```

--------------------------------

### Install Vosk Module

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/vosk.ipynb

Install the vosk module using pip. This is the first step before using any Vosk functionalities.

```python
!pip3 install vosk
```

--------------------------------

### Download Example Audio File

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/vosk.ipynb

Downloads a sample audio file for testing speech recognition. Replace the URL with your own audio file.

```python
!wget -q -O /content/test.wav https://github.com/alphacep/vosk-api/raw/master/python/example/test.wav
```

--------------------------------

### Setup Directories and Initial Checks

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/vosk-training.ipynb

This script sets up necessary directories for training and performs initial file checks. It's crucial for ensuring all prerequisites are met before proceeding with training.

```bash
gmm_dir=exp/$gmm
ali_dir=exp/${gmm}_ali
tree_dir=exp/chain${suffix}/tree${tree_affix:+_$tree_affix}
lang=data/lang_chain${suffix}
lat_dir=exp/chain${suffix}/${gmm}_${train_set}_lats
dir=exp/chain${suffix}/tdnn${affix}
train_data_dir=data/${train_set}

for f in $gmm_dir/final.mdl $train_data_dir/feats.scp $ali_dir/ali.1.gz; do
  [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
done
```

--------------------------------

### Install FLAC Encoder

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/vosk-training.ipynb

Installs the FLAC audio encoder, which is required for audio processing during speech recognition training.

```bash
!apt install flac
```

--------------------------------

### Run Data Preparation Stage

Source: https://github.com/alphacep/vosk-api/blob/master/training/README.md

Execute the initial stage of the training pipeline to download and prepare the LibriSpeech dataset. Ensure Kaldi is installed and paths are set.

```bash
bash run.sh --stage 0 --stop_stage 0
```

--------------------------------

### Install Phonetisaurus

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/vosk-adaptation.ipynb

Installs the Phonetisaurus library using pip. This is a prerequisite for certain phonetic-based adaptation tasks.

```bash
+ pip3 install phonetisaurus
```

--------------------------------

### Install Kaldi Build Dependencies

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/kaldi-build.ipynb

Installs essential build tools and libraries required for compiling Kaldi. This includes g++, automake, and subversion.

```bash
!apt-get install -qq g++ automake autoconf libtool gfortran sox subversion
```

```bash
!git clone -b vosk --single-branch https://github.com/alphacep/kaldi.git
```

--------------------------------

### Install OpenGrm Language Toolkit

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/kaldi-build.ipynb

Executes a shell script to install the OpenGrm toolkit. The script attempts to download a tarball and may fall back to using wget if the tarball is not found locally. This is part of setting up language modeling capabilities.

```bash
!extras/install_opengrm.sh
```

--------------------------------

### Go Multi-threaded GPU Setup

Source: https://context7.com/alphacep/vosk-api/llms.txt

Shows how to initialize GPU acceleration for Vosk in a multi-threaded Go application. `vosk.GPUInit()` must be called once from the main thread, and `vosk.GPUThreadInit()` once per worker thread.

```go
// Go — multi-threaded GPU setup
import vosk "github.com/alphacep/vosk-api/go"

func main() {
    vosk.GPUInit() // call once from main thread

    // In each worker goroutine:
    go func() {
        vosk.GPUThreadInit() // call once per thread
        // ... create recognizer and process audio ...
    }()
}
```

--------------------------------

### Compile Kaldi Components

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/kaldi-build.ipynb

Navigates to the Kaldi tools directory and initiates the compilation of specific components like cub and openfst, followed by installing OpenBLAS.

```bash
%cd /content/kaldi/tools
!make -j 8 cub openfst
```

```bash
!extras/install_openblas.sh
```

--------------------------------

### Install Vosk Ruby Gem

Source: https://github.com/alphacep/vosk-api/blob/master/ruby/README.md

Add the vosk gem to your Gemfile or install it directly using gem install.

```ruby
gem "vosk"
```

```bash
gem install vosk
```

--------------------------------

### Speaker Identification Setup

Source: https://github.com/alphacep/vosk-api/blob/master/ruby/README.md

Integrate speaker identification by initializing a speaker model and assigning it to the recognizer.

```ruby
spk_model = Vosk::SpkModel.new("/path/to/spk-model")
rec = Vosk::KaldiRecognizer.new(model, sample_rate)
rec.spk_model = spk_model

```

--------------------------------

### Start Chain Model Trainer

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/vosk-training.ipynb

Initiates the training process for a chain-based acoustic model. This command includes numerous parameters to control various aspects of the training, such as GPU usage, regularization, and data augmentation.

```bash
steps/nnet3/chain/train.py --use-gpu false --stage -10 --cmd run.pl --feat.online-ivector-dir exp/chain/ivectors_train --feat.cmvn-opts --norm-means=false --norm-vars=false --chain.xent-regularize 0.1 --chain.leaky-hmm-coefficient 0.1 --chain.l2-regularize 0.0 --chain.apply-deriv-weights false --chain.lm-opts=--num-extra-lm-states=2000 --egs.cmd run.pl --egs.dir  --egs.stage -10 --egs.opts --frames-overlap-per-eg 0 --constrained false --egs.chunk-width 140,100,160 --trainer.dropout-schedule 0,0@0.20,0.5@0.50,0 --trainer.add-option=--optimization.memory-compression-level=2 --trainer.num-chunk-per-minibatch 64 --trainer.frames-per-iter 2500000 --trainer.num-epochs 20 --trainer.optimization.num-jobs-initial 1 --trainer.optimization.num-jobs-final 1 --trainer.optimization.initial-effective-lrate 0.001 --trainer.optimization.final-effective-lrate 0.0001 --trainer.max-param-change 2.0 --cleanup.remove-egs true --feat-dir data/train --tree-dir exp/chain/tree --lat-dir exp/chain/tri3_train_lats --dir exp/chain/tdnn
```

--------------------------------

### Shell Script Setup for Vosk Training

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/vosk-training.ipynb

This snippet sets up shell options for immediate error checking and defines variables for training configuration. It's used to prepare the environment before executing training commands.

```shell
set -euo pipefail

stage=-1
decode_nj=10
train_set=train
gmm=tri3
net3_affix=
suffix=

affix=   # affix for the TDNN directory name
tree_affix=
train_stage=-10
get_egs_stage=-10
decode_iter=

chunk_width=140,100,160
common_egs_dir=
xent_regularize=0.1
dropout_schedule='0,0@0.20,0.5@0.50,0'

srand=0
remove_egs=true

echo "$0 $@"  # Print the command line for logging

. ./cmd.sh
. ./path.sh
. ./utils/parse_options.sh

# Problem: We have removed the "train_" prefix of our training set in
```

--------------------------------

### List Available Models and Languages

Source: https://github.com/alphacep/vosk-api/blob/master/ruby/README.md

Query the Vosk library to get a list of all available model names and supported language codes.

```ruby
puts Vosk.models      # all model names
puts Vosk.languages   # all supported language codes

```

--------------------------------

### Example WER Results

Source: https://github.com/alphacep/vosk-api/blob/master/training/README.md

Sample output from RESULTS.txt showing Word Error Rate (WER) calculations, including insertions, deletions, and substitutions.

```plaintext
%WER 14.10 [ 2839 / 20138, 214 ins, 487 del, 2138 sub ] exp/chain/tdnn/decode_test/wer_11_0.0
%WER 12.67 [ 2552 / 20138, 215 ins, 406 del, 1931 sub ] exp/chain/tdnn/decode_test_rescore/wer_11_0.0
```

--------------------------------

### Microphone Streaming with Vosk (Node.js)

Source: https://context7.com/alphacep/vosk-api/llms.txt

Recognize speech directly from a live microphone using the 'mic' package. Ensure 'mic' is installed. The audio stream is processed in chunks.

```javascript
const vosk = require('vosk');
const mic  = require('mic');

const SAMPLE_RATE = 16000;
const model = new vosk.Model('./model-en-us');
const rec   = new vosk.Recognizer({ model, sampleRate: SAMPLE_RATE });

const micInstance = mic({
    rate: String(SAMPLE_RATE),
    channels: '1',
    debug: false,
    device: 'default',
});

const micStream = micInstance.getAudioStream();

micStream.on('data', data => {
    if (rec.acceptWaveform(data))
        console.log(rec.result());       // complete utterance
    else
        console.log(rec.partialResult()); // in-progress text
});

micStream.on('audioProcessExitComplete', () => {
    console.log('Final:', rec.finalResult());
    rec.free();
    model.free();
});

process.on('SIGINT', () => micInstance.stop());
micInstance.start();
```

--------------------------------

### Copy Training Data

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/vosk-training.ipynb

Removes the default training directory within Kaldi and copies the training data from the cloned vosk-api repository. This ensures the correct training setup is used.

```bash
!rm -rf /content/kaldi/egs/ac/training
!cp -r /content/vosk-api/training /content/kaldi/egs/ac
```

--------------------------------

### result, partialResult, finalResult

Source: https://context7.com/alphacep/vosk-api/llms.txt

Methods to retrieve the recognizer output as JSON. `result()` gets a complete utterance after silence, `partialResult()` gets in-progress text, and `finalResult()` flushes the pipeline and returns any remaining text.

```APIDOC
## result / partialResult / finalResult

### Description
Three methods retrieve the recognizer output as JSON. `result()` returns a complete utterance after silence. `partialResult()` returns in-progress text. `finalResult()` flushes the pipeline and returns whatever remains — always call it at stream end.

### Method Signatures (JavaScript)
`result(): object`
`partialResult(): object`
`finalResult(): object`

### Response Example (JavaScript - with `setWords(true)` and `setMaxAlternatives(3)`)
```javascript
// After acceptWaveform returns true:
const res = rec.result();
// {
//   "result": [
//     { "conf": 1.0, "start": 0.87, "end": 1.11, "word": "what" },
//     { "conf": 1.0, "start": 1.11, "end": 1.53, "word": "zero" }
//   ],
//   "text": "what zero"
// }

// With setMaxAlternatives > 0:
// {
//   "alternatives": [
//     { "text": "what zero zero one", "confidence": 0.97 },
//     { "text": "what zero zero won", "confidence": 0.02 }
//   ]
// }

const partial = rec.partialResult();
// { "partial": "what ze" }

const final = rec.finalResult();
// { "text": "what zero zero one" }
```

### Response Example (Go - parsing final result)
```go
// Go — parse final result into a map
import "encoding/json"

var result map[string]interface{}
json.Unmarshal([]byte(rec.FinalResult()), &result)
fmt.Println(result["text"])
```
```

--------------------------------

### Configure and Run Training Script

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/vosk-training.ipynb

Navigates to the training directory, removes the 'exp' directory, modifies the run.sh script to use '--nj 2' for parallel processing, and then executes the training script up to stage 3. This initiates the acoustic model training process.

```bash
%cd /content/kaldi/egs/ac/training
!rm -rf exp
!sed -i 's:--nj 10:--nj 2:g' run.sh
!cat run.sh
!bash run.sh --stop-stage 3
```

--------------------------------

### Prepare Data Directory and Files

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/vosk-adaptation.ipynb

Cleans up old data, creates a new dictionary directory, and copies essential configuration files for model adaptation.

```bash
+ rm -rf data
```

```bash
+ rm -rf exp/tdnn/lgraph
```

```bash
+ rm -rf exp/tdnn/lgraph_orig
```

```bash
+ mkdir -p data/dict
```

```bash
+ cp db/phone/extra_questions.txt db/phone/nonsilence_phones.txt db/phone/optional_silence.txt db/phone/silence_phones.txt data/dict
```

--------------------------------

### Retrieve Recognition Output (Node.js)

Source: https://context7.com/alphacep/vosk-api/llms.txt

Retrieves recognition output as JSON. `result()` gets a complete utterance, `partialResult()` gets in-progress text, and `finalResult()` flushes the pipeline. Use `setWords(true)` for per-word timestamps and `setMaxAlternatives()` for multiple hypotheses.

```javascript
// Node.js — result shapes with setWords(true) and setMaxAlternatives(3)
rec.setWords(true);
rec.setMaxAlternatives(3);

// After acceptWaveform returns true:
const res = rec.result();
// { 
//   "result": [ 
//     { "conf": 1.0, "start": 0.87, "end": 1.11, "word": "what" }, 
//     { "conf": 1.0, "start": 1.11, "end": 1.53, "word": "zero" } 
//   ],
//   "text": "what zero"
// }

// With setMaxAlternatives > 0:
// {
//   "alternatives": [
//     { "text": "what zero zero one", "confidence": 0.97 },
//     { "text": "what zero zero won", "confidence": 0.02 }
//   ]
// }

const partial = rec.partialResult();
// { "partial": "what ze" }

const final = rec.finalResult();
// { "text": "what zero zero one" }

```

--------------------------------

### Initialize Vosk Recognizer

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/vosk.ipynb

Initializes the wave file, Vosk model, and KaldiRecognizer. Ensure the model is downloaded and accessible.

```python
wf = wave.open('/content/test.wav', 'rb')
model = Model(lang="en-us")
rec = KaldiRecognizer(model, wf.getframerate())
rec.SetWords(True)
```

--------------------------------

### Configure Kaldi Build

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/kaldi-build.ipynb

Navigates to the Kaldi source directory and runs the configure script with specified options. Ensure the path '/content/kaldi/src/' is correct and the script exists.

```python
%cd /content/kaldi/src/
!./configure --shared --mathlib=OPENBLAS
```

--------------------------------

### Get Tree Information

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/vosk-training.ipynb

Retrieves information about the decision tree used in the acoustic model training.

```bash
tree-info exp/chain/tdnn/tree 
tree-info exp/chain/tdnn/tree
```

--------------------------------

### Run Dictionary Preparation Stage

Source: https://github.com/alphacep/vosk-api/blob/master/training/README.md

Prepare the pronunciation dictionary required for Kaldi's language modeling scripts. This step is crucial for accurate speech recognition.

```bash
bash run.sh --stage 1 --stop_stage 1
```

--------------------------------

### Vosk Language Model Preparation Utility

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/vosk-adaptation.ipynb

This utility prepares a language model directory by validating dictionary files and creating necessary intermediate files like lexiconp.txt.

```bash
utils/prepare_lang.sh data/dict '[unk]' data/lang_local data/lang
```

--------------------------------

### Initialize Neural Network Model

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/vosk-training.ipynb

Initializes a raw neural network model using configuration files. This is a prerequisite for training.

```bash
nnet3-init exp/chain/tdnn/configs//ref.config exp/chain/tdnn/configs//ref.raw 
LOG (nnet3-init[5.5.1046~1-76cd5]:main():nnet3-init.cc:80) Initialized raw neural net and wrote it to exp/chain/tdnn/configs//ref.raw
```

```bash
nnet3-init exp/chain/tdnn/configs//ref.config exp/chain/tdnn/configs//ref.raw 
nnet3-info exp/chain/tdnn/configs//ref.raw 
LOG (nnet3-init[5.5.1046~1-76cd5]:main():nnet3-init.cc:80) Initialized raw neural net and wrote it to exp/chain/tdnn/configs//ref.raw
```

--------------------------------

### Process Audio and Get Results

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/vosk.ipynb

Processes an audio file chunk by chunk and prints recognition results. It distinguishes between final results and partial results.

```python
while True:
    data = wf.readframes(4000)
    if len(data) == 0:
        break
    if rec.AcceptWaveform(data):
        print(rec.Result())
    else:
        jres = json.loads(rec.PartialResult())
        print(jres)
```

--------------------------------

### C# Recognize from WAV using Byte Buffers or Float Arrays

Source: https://context7.com/alphacep/vosk-api/llms.txt

Demonstrates recognizing speech from a WAV file using both byte buffer and float array approaches. Ensure the Vosk library is imported and a language model is loaded.

```csharp
// C# — recognize from WAV file using byte buffers or float arrays
using Vosk;
using System.IO;

Vosk.Vosk.SetLogLevel(0); // -1 = silent
Model model = new Model("model-en-us");

// Byte-buffer approach
VoskRecognizer rec = new VoskRecognizer(model, 16000.0f);
rec.SetWords(true);
rec.SetMaxAlternatives(0);

using (Stream src = File.OpenRead("audio.wav")) {
    byte[] buf = new byte[4096];
    int n;
    while ((n = src.Read(buf, 0, buf.Length)) > 0) {
        if (rec.AcceptWaveform(buf, n))
            Console.WriteLine(rec.Result());
        else
            Console.WriteLine(rec.PartialResult());
    }
}
Console.WriteLine(rec.FinalResult());

// Float-array approach (useful when audio is already decoded to floats)
VoskRecognizer recF = new VoskRecognizer(model, 16000.0f);
recF.SetEndpointerMode(EndpointerMode.LONG);

using (Stream src = File.OpenRead("audio.wav")) {
    byte[] buf = new byte[4096];
    int n;
    while ((n = src.Read(buf, 0, buf.Length)) > 0) {
        float[] fbuf = new float[n / 2];
        for (int i = 0, j = 0; i < fbuf.Length; i++, j += 2)
            fbuf[i] = BitConverter.ToInt16(buf, j);
        if (recF.AcceptWaveform(fbuf, fbuf.Length))
            Console.WriteLine(recF.Result());
    }
}
Console.WriteLine(recF.FinalResult());
```

--------------------------------

### C GPU Initialization

Source: https://context7.com/alphacep/vosk-api/llms.txt

Demonstrates the C API for initializing GPU acceleration. `vosk_gpu_init()` is called once from the main thread, and `vosk_gpu_thread_init()` is called within each thread that will perform recognition.

```c
// C — GPU init
#include <vosk_api.h>

int main() {
    vosk_gpu_init();        // main thread, once
    // spawn threads ...
    // in each thread:
    vosk_gpu_thread_init(); // per thread
}
```

--------------------------------

### Process Audio and Get Results

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/vosk.ipynb

Iterates through audio data, processing it with the recognizer and printing partial or final results. Results are parsed from JSON strings.

```python
while True:
    data = wf.readframes(4000)
    if len(data) == 0:
        break
    if rec.AcceptWaveform(data):
        print(json.loads(rec.Result()))
    else:
        print(json.loads(rec.PartialResult()))

print(json.loads(rec.FinalResult()))
```

--------------------------------

### FFmpeg Integration for Media Transcription (Node.js)

Source: https://context7.com/alphacep/vosk-api/llms.txt

Transcribe any media format by piping audio through FFmpeg to Vosk. Ensure FFmpeg is installed and in your PATH. The output is raw PCM.

```javascript
// Node.js — transcribe any media file via ffmpeg pipe
const vosk  = require('vosk');
const { spawn } = require('child_process');

const SAMPLE_RATE = 16000;
const model = new vosk.Model('./model-en-us');
const rec   = new vosk.Recognizer({ model, sampleRate: SAMPLE_RATE });

const ffmpeg = spawn('ffmpeg', [
    '-loglevel', 'quiet',
    '-i', 'movie.mp4',          // any format ffmpeg supports
    '-ar', String(SAMPLE_RATE),
    '-ac', '1',                  // mono
    '-f', 's16le',               // raw PCM 16-bit LE
    '-bufsize', '4000',
    '-'                          // output to stdout
]);

ffmpeg.stdout.on('data', chunk => {
    if (rec.acceptWaveform(chunk))
        console.log(rec.result());
    else
        console.log(rec.partialResult());
});

ffmpeg.on('exit', () => {
    console.log(rec.finalResult());
    rec.free();
    model.free();
});
```

--------------------------------

### Load Language Model (Go)

Source: https://context7.com/alphacep/vosk-api/llms.txt

Loads a language model and optionally initializes GPU support. The model is reference-counted and thread-safe. Use defer model.Free() to ensure cleanup.

```go
import vosk "github.com/alphacep/vosk-api/go"

vosk.GPUInit() // no-op unless compiled with CUDA support

model, err := vosk.NewModel("model-en-us")
if err != nil {
    log.Fatal(err)
}
def model.Free()

// Check if a word is in the vocabulary (-1 = absent)
sym := model.FindWord("hello") // returns int symbol or -1
```

--------------------------------

### Set up CUDA Build Environment Variables

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/kaldi-build.ipynb

These lines define environment variables used by the CUDA build system, specifying paths to CUDA libraries, NVVM, and target directories.

```bash
#$ _NVVM_BRANCH_=nvvm
#$ _SPACE_= 
#$ _CUDART_=cudart
#$ _HERE_=/usr/local/cuda/bin
#$ _THERE_=/usr/local/cuda/bin
#$ _TARGET_SIZE_=
#$ _TARGET_DIR_=
#$ _TARGET_DIR_=targets/x86_64-linux
#$ TOP=/usr/local/cuda/bin/..
#$ NVVMIR_LIBRARY_DIR=/usr/local/cuda/bin/../nvvm/libdevice
#$ LD_LIBRARY_PATH=/usr/local/cuda/bin/../lib:/usr/lib64-nvidia
#$ PATH=/usr/local/cuda/bin/../nvvm/bin:/usr/local/cuda/bin:/opt/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/tools/node/bin:/tools/google-cloud-sdk/bin
#$ INCLUDES="-I/usr/local/cuda/bin/../targets/x86_64-linux/include"  
#$ LIBRARIES=  "-L/usr/local/cuda/bin/../targets/x86_64-linux/lib/stubs" "-L/usr/local/cuda/bin/../targets/x86_64-linux/lib"
#$ CUDAFE_FLAGS=
#$ PTXAS_FLAGS=
```

--------------------------------

### Clean Up Kaldi Build Artifacts

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/kaldi-build.ipynb

Removes object files (.o), static libraries (.a), the 'egs' directory, and the '.git' directory from the Kaldi installation to clean up build artifacts.

```bash
!find /content/kaldi -name "*.o" -exec rm {} \;
!find /content/kaldi -name "*.a" -exec rm {} \;
!rm -rf /content/kaldi/egs
!rm -rf /content/kaldi/.git
```

--------------------------------

### Initialize Recognizer with Grammar

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/vosk.ipynb

Initializes the KaldiRecognizer with a specific grammar to improve recognition accuracy for expected phrases. Ensure the audio file is opened in binary read mode.

```python
wf = wave.open('/content/test.wav', "rb")
rec = KaldiRecognizer(model, wf.getframerate(), '["one zero zero zero one", "nine oh two one oh", "zero one eight zero three", "[unk]"]')
```

--------------------------------

### Prepare Language Model and Decode

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/vosk-training.ipynb

This section prepares the language model and performs decoding for test sets. It involves formatting the language model, building a graph, and then executing decoding steps with specific parameters for acoustic modeling and lattice generation.

```bash
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then

  utils/format_lm.sh data/lang data/local/lm/lm_tgsmall.arpa.gz data/local/dict/lexicon.txt data/lang_test
  utils/mkgraph.sh --self-loop-scale 1.0 data/lang_test exp/chain/tdnn exp/chain/tdnn/graph
  utils/build_const_arpa_lm.sh data/local/lm/lm_tgmed.arpa.gz \
    data/lang data/lang_test_rescore

  for task in test; do

    steps/make_mfcc.sh --cmd "$train_cmd" --nj 2 data/$task exp/make_mfcc/$task $mfcc
    steps/compute_cmvn_stats.sh data/$task exp/make_mfcc/$task $mfcc

    steps/online/nnet2/extract_ivectors_online.sh --nj 2 \
        data/${task} exp/chain/extractor \
        exp/chain/ivectors_${task}

    steps/nnet3/decode.sh --cmd $decode_cmd --num-threads 10 --nj 1 \
         --beam 13.0 --max-active 7000 --lattice-beam 4.0 \
         --online-ivector-dir exp/chain/ivectors_${task} \
         --acwt 1.0 --post-decode-acwt 10.0 \
         exp/chain/tdnn/graph data/${task} exp/chain/tdnn/decode_${task}

    steps/lmrescore_const_arpa.sh data/lang_test data/lang_test_rescore \
        data/${task} exp/chain/tdnn/decode_${task} exp/chain/tdnn/decode_${task}_rescore
  done

  bash RESULTS
fi
```

--------------------------------

### Recognizer Constructors (Java/Android)

Source: https://context7.com/alphacep/vosk-api/llms.txt

Shows how to instantiate a Vosk Recognizer in Java/Android for standard, grammar-constrained, and speaker-identification tasks. Ensure the correct parameters are provided for each type.

```java
// Java / Android
import org.vosk.*;

Model model = new Model("model-en-us");

// Standard
Recognizer rec = new Recognizer(model, 16000f);

// Grammar-constrained
Recognizer recGrm = new Recognizer(model, 16000f,
    "[\"turn on the light\", \"turn off the light\", \"[unk]\"]");

// Speaker identification
SpeakerModel spkModel = new SpeakerModel("model-spk");
Recognizer recSpk = new Recognizer(model, 16000f, spkModel);

recSpk.close(); recGrm.close(); rec.close();
```

--------------------------------

### Language Model Preparation Script

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/vosk-adaptation.ipynb

This script prepares a language model by merging, renormalizing, and printing it in ARPA format. It requires specific symbol files and input models.

```bash
ngramshrink --method=count_prune --count_pattern=3+:3
ngramread
ngramprint --integers
grep -v '<unk>'
ngramread --renormalize_arpa --ARPA --symbols=data/mix.syms - data/en-us.mod
ngrammerge --method=bayes_model_merge --normalize --alpha=0.95 --beta=0.05 data/en-us.mod data/extra.mod data/en-us-mix.mod
gramprint --ARPA data/en-us-mix.mod
gzip -c
```

--------------------------------

### Play Audio File

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/vosk.ipynb

Plays the downloaded audio file using IPython.display.Audio. Ensure the audio file path is correct.

```python
import IPython
IPython.display.Audio("/content/test.wav")
```

--------------------------------

### Create Language Directory with Chain Topology

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/vosk-training.ipynb

Generates a language directory with a chain-type topology. This involves copying an existing language directory and creating a new topology file.

```bash
if [ $stage -le 10 ]; then
  echo "$0: creating lang directory $lang with chain-type topology"
  rm -rf $lang
  cp -r data/lang $lang
  silphonelist=$(cat $lang/phones/silence.csl) 
  nonsilphonelist=$(cat $lang/phones/nonsilence.csl) 
  steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >$lang/topo
fi
```

--------------------------------

### Import Vosk Modules

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/vosk.ipynb

Import the necessary modules from the vosk library, along with the wave and json modules for audio file handling and data parsing.

```python
from vosk import Model, KaldiRecognizer
import wave
import json
```

--------------------------------

### Kaldi Build Dependency Generation (fstbin)

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/kaldi-build.ipynb

Initiates dependency generation for Kaldi's fstbin module. This command typically precedes the actual compilation of fstbin related files.

```bash
make -C fstbin/ depend
make[2]: Entering directory '/content/kaldi/src/fstbin'
rm -f .depend.mk
```

--------------------------------

### Configure Vosk Recognizer Settings

Source: https://context7.com/alphacep/vosk-api/llms.txt

Fine-tune recognizer output and behavior before processing audio. Ensure the model is loaded before configuring.

```javascript
// Node.js — full configuration example
const rec = new vosk.Recognizer({ model, sampleRate: 16000 });

rec.setWords(true);          // include timestamps+confidence per word in result
rec.setPartialWords(true);   // include per-word data in partial results too
rec.setMaxAlternatives(5);   // return n-best list instead of single best

// Recognizer reset — restart from scratch without re-creating object
rec.reset();
```

```go
// Go — endpointer timing control
rec.SetWords(1)
rec.SetPartialWords(1)
rec.SetMaxAlternatives(3)
// startMax: initial silence timeout (s), end: post-speech timeout (s), max: force-end timeout (s)
rec.SetEndpointerDelays(5.0, 1.0, 20.0)
```

```java
// Java / Android
rec.setWords(true);
rec.setPartialWords(true);
rec.setMaxAlternatives(3);
rec.setEndpointerMode(Recognizer.EndpointerMode.LONG); // DEFAULT, SHORT, LONG, VERY_LONG
rec.setEndpointerDelays(5.0f, 1.0f, 20.0f);
rec.reset();
```

--------------------------------

### Open Audio File

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/vosk.ipynb

Opens a local audio file in read-byte mode as a wave object. This is a prerequisite for initializing the recognizer.

```python
wf = wave.open('/content/test.wav', 'rb')
```

--------------------------------

### Use Vosk Transcriber CLI

Source: https://github.com/alphacep/vosk-api/blob/master/ruby/README.md

Transcribe audio files directly from the command line using the included `vosk-transcriber` executable.

```bash
vosk-transcriber audio.wav

```

--------------------------------

### Model - Load a language model from disk

Source: https://context7.com/alphacep/vosk-api/llms.txt

Demonstrates how to load a Vosk language model. The `Model` object is reference-counted and thread-safe, allowing it to be shared across multiple recognizer threads. Remember to free the model when it's no longer needed.

```APIDOC
## Model

### Description
Loads a language model from disk. This object holds the static neural-network and language-model data. It is reference-counted and thread-safe.

### Usage

#### Node.js
```javascript
const vosk = require('vosk');

vosk.setLogLevel(0); // 0 = info+errors, -1 = silent, >0 = verbose

// Load model once; reuse across recognizers
const model = new vosk.Model('./model-en-us');

// Always free when no longer needed
// model.free();
```

#### Go
```go
import vosk "github.com/alphacep/vosk-api/go"

vosk.GPUInit() // no-op unless compiled with CUDA support

model, err := vosk.NewModel("model-en-us")
if err != nil {
    log.Fatal(err)
}
defer model.Free()

// Check if a word is in the vocabulary (-1 = absent)
sym := model.FindWord("hello") // returns int symbol or -1
```

#### C
```c
#include <vosk_api.h>

VoskModel *model = vosk_model_new("model-en-us");
int sym = vosk_model_find_word(model, "hello"); // -1 if not found
// ...
vosk_model_free(model);
```
```

--------------------------------

### Generate Neural Network Configurations

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/vosk-training.ipynb

Creates neural network configurations using the xconfig parser. This involves defining layers, input dimensions, and various training options.

```bash
if [ $stage -le 13 ]; then
  echo "$0: creating neural net configs using the xconfig parser";

  num_targets=$(tree-info $tree_dir/tree | grep num-pdfs | awk '{print $2}')
  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)

  affine_opts="l2-regularize=0.008 dropout-proportion=0.0 dropout-per-dim=true dropout-per-dim-continuous=true"
  tdnnf_opts="l2-regularize=0.008 dropout-proportion=0.0 bypass-scale=0.75"
  linear_opts="l2-regularize=0.008 orthonormal-constraint=-1.0"
  prefinal_opts="l2-regularize=0.008"
  output_opts="l2-regularize=0.002"

  mkdir -p $dir/configs
  cat <<EOF > $dir/configs/network.xconfig

  input dim=40 name=ivector
  input dim=40 name=input

  idct-layer name=idct input=input dim=40 cepstral-lifter=22 affine-transform-file=$dir/configs/idct.mat
  batchnorm-component name=batchnorm0 input=idct
  spec-augment-layer name=spec-augment freq-max-proportion=0.5 time-zeroed-proportion=0.2 time-mask-max-frames=20
  delta-layer name=delta input=spec-augment

  no-op-component name=input2 input=Append(delta, ReplaceIndex(ivector, t, 0))

  # the first splicing is moved before the lda layer, so no splicing here
  relu-batchnorm-dropout-layer name=tdnn1 $affine_opts dim=512 input=input2
  tdnnf-layer name=tdnnf2 $tdnnf_opts dim=512 bottleneck-dim=96 time-stride=1
  tdnnf-layer name=tdnnf3 $tdnnf_opts dim=512 bottleneck-dim=96 time-stride=1
  tdnnf-layer name=tdnnf4 $tdnnf_opts dim=512 bottleneck-dim=96 time-stride=1
  tdnnf-layer name=tdnnf5 $tdnnf_opts dim=512 bottleneck-dim=96 time-stride=0
  tdnnf-layer name=tdnnf6 $tdnnf_opts dim=512 bottleneck-dim=96 time-stride=3
  tdnnf-layer name=tdnnf7 $tdnnf_opts dim=512 bottleneck-dim=96 time-stride=3
  tdnnf-layer name=tdnnf8 $tdnnf_opts dim=512 bottleneck-dim=96 time-stride=3
  tdnnf-layer name=tdnnf9 $tdnnf_opts dim=512 bottleneck-dim=96 time-stride=3
  tdnnf-layer name=tdnnf10 $tdnnf_opts dim=512 bottleneck-dim=96 time-stride=3
  tdnnf-layer name=tdnnf11 $tdnnf_opts dim=512 bottleneck-dim=96 time-stride=3
  tdnnf-layer name=tdnnf12 $tdnnf_opts dim=512 bottleneck-dim=96 time-stride=3
  linear-component name=prefinal-l dim=192 $linear_opts

  ## adding the layers for chain branch
  prefinal-layer name=prefinal-chain input=prefinal-l $prefinal_opts small-dim=192 big-dim=512
  output-layer name=output include-log-softmax=false dim=$num_targets $output_opts

  # adding the layers for xent branch
  prefinal-layer name=prefinal-xent input=prefinal-l $prefinal_opts small-dim=192 big-dim=512
  output-layer name=output-xent dim=$num_targets learning-rate-factor=$learning_rate_factor $output_opts

EOF
  steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
fi
```

--------------------------------

### Generate Dependencies for nnet3bin

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/kaldi-build.ipynb

Generates dependency files for the nnet3bin module. This command is part of the Kaldi build system.

```bash
make -C transform/ depend
make[2]: Entering directory '/content/kaldi/src/transform'
rm -f .depend.mk
c++ -M -std=c++17 -I.. -isystem /content/kaldi/tools/openfst-1.8.0/include -O1 -Wno-sign-compare -Wall -Wno-sign-compare -Wno-unused-local-typedefs -Wno-deprecated-declarations -Winit-self -DKALDI_DOUBLEPRECISION=0 -DHAVE_EXECINFO_H=1 -DHAVE_CXXABI_H -DHAVE_OPENBLAS -I/content/kaldi/tools/OpenBLAS/install/include -msse -msse2 -pthread -g -fPIC -DHAVE_CUDA -I/usr/local/cuda/include -fPIC -pthread -isystem /content/kaldi/tools/openfst-1.8.0/include rnnlm-get-egs.cc rnnlm-train.cc rnnlm-get-sampling-lm.cc rnnlm-compute-prob.cc rnnlm-sentence-probs.cc rnnlm-get-word-embedding.cc >> .depend.mk
rm -f .depend.mk
c++ -M -std=c++17 -I.. -isystem /content/kaldi/tools/openfst-1.8.0/include -O1  -Wall -Wno-sign-compare -Wno-unused-local-typedefs -Wno-deprecated-declarations -Winit-self -DKALDI_DOUBLEPRECISION=0 -DHAVE_EXECINFO_H=1 -DHAVE_CXXABI_H -DHAVE_OPENBLAS -I/content/kaldi/tools/OpenBLAS/install/include -msse -msse2 -pthread -g -fPIC -DHAVE_CUDA -I/usr/local/cuda/include -fPIC -pthread -isystem /content/kaldi/tools/openfst-1.8.0/include lda-estimate-test.cc transform-common.cc regression-tree-test.cc compressed-transform-stats.cc decodable-am-diag-gmm-regtree.cc fmllr-raw-test.cc lda-estimate.cc lvtln.cc regression-tree.cc regtree-mllr-diag-gmm-test.cc fmllr-diag-gmm.cc regtree-fmllr-diag-gmm.cc cmvn.cc fmllr-raw.cc fmpe.cc mllt.cc fmllr-diag-gmm-test.cc regtree-mllr-diag-gmm.cc basis-fmllr-diag-gmm.cc regtree-fmllr-diag-gmm-test.cc fmpe-test.cc >> .depend.mk
make[2]: Leaving directory '/content/kaldi/src/nnet3bin'
```

--------------------------------

### Build Acoustic Model Tree

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/vosk-training.ipynb

Constructs the acoustic model tree using the build_tree.sh script. This script requires frame subsampling and context options to be specified.

```bash
if [ $stage -le 12 ]; then
  steps/nnet3/chain/build_tree.sh \
    --frame-subsampling-factor 3 \
    --context-opts "--context-width=2 --central-position=1" \
    --cmd "$train_cmd" 2500 ${train_data_dir} \
    $lang $ali_dir $tree_dir
fi
```

--------------------------------

### Kaldi Build Dependency Generation (fgmmbin)

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/kaldi-build.ipynb

Generates dependency files for Kaldi's fgmmbin module using C++17 standard and specific include paths. Excludes certain compiler warnings.

```bash
make -C fgmmbin/ depend
make[2]: Entering directory '/content/kaldi/src/fgmmbin'
rm -f .depend.mk
c++ -M -std=c++17 -I.. -isystem /content/kaldi/tools/openfst-1.8.0/include -O1 -Wno-sign-compare -Wall -Wno-sign-compare -Wno-unused-local-typedefs -Wno-deprecated-declarations -Winit-self -DKALDI_DOUBLEPRECISION=0 -DHAVE_EXECINFO_H=1 -DHAVE_CXXABI_H -DHAVE_OPENBLAS -I/content/kaldi/tools/OpenBLAS/install/include -msse -msse2 -pthread -g -fPIC -DHAVE_CUDA -I/usr/local/cuda/include -fPIC -pthread -isystem /content/kaldi/tools/openfst-1.8.0/include fgmm-global-merge.cc fgmm-global-info.cc fgmm-global-acc-stats-post.cc fgmm-global-init-from-accs.cc fgmm-global-est.cc fgmm-gselect.cc fgmm-global-sum-accs.cc fgmm-global-copy.cc fgmm-global-to-gmm.cc fgmm-global-acc-stats.cc fgmm-global-get-frame-likes.cc fgmm-global-gselect-to-post.cc >> .depend.mk
make[2]: Leaving directory '/content/kaldi/src/fgmmbin'
```

--------------------------------

### Compile Kaldi with Make

Source: https://github.com/alphacep/vosk-api/blob/master/python/example/colab/kaldi-build.ipynb

Compiles the Kaldi toolkit using the make command with parallel job execution. This command is typically run after the configure script has successfully completed.

```bash
!make -j 4
```

--------------------------------

### Asynchronous Audio Processing with acceptWaveformAsync (Node.js)

Source: https://context7.com/alphacep/vosk-api/llms.txt

Non-blocking version of acceptWaveform that returns a Promise, enabling parallel processing of multiple audio streams with a single shared model. Useful for handling multiple files concurrently.

```javascript
// Node.js — process 10 files concurrently sharing one model
const vosk  = require('vosk');
const async = require('async');
const fs    = require('fs');
const { Readable } = require('stream');
const wav   = require('wav');

const model = new vosk.Model('./model-en-us');
const files = Array(10).fill('audio.wav');

async.filter(files, function(filePath, callback) {
    const wfReader   = new wav.Reader();
    const wfReadable = new Readable().wrap(wfReader);

    wfReader.on('format', async ({ sampleRate }) => {
        const rec = new vosk.Recognizer({ model, sampleRate });

        for await (const data of wfReadable) {
            const endOfSpeech = await rec.acceptWaveformAsync(data);
            if (endOfSpeech) console.log(rec.result());
        }
        console.log(rec.finalResult());
        rec.free();
        callback(null, true);
    });

    fs.createReadStream(filePath, { highWaterMark: 4096 }).pipe(wfReader);

}, (err) => {
    model.free();
    console.log('All done');
});

```

--------------------------------

### Recognizer Constructors (Node.js)

Source: https://context7.com/alphacep/vosk-api/llms.txt

Illustrates the three constructor variants for the Vosk Recognizer in Node.js: standard large-vocabulary, grammar-constrained, and speaker-identification.

```javascript
// Node.js — three constructor forms

// 1. Standard large-vocabulary recognizer
const rec1 = new vosk.Recognizer({ model, sampleRate: 16000 });

// 2. Grammar-constrained recognizer (only recognizes listed phrases)
const rec2 = new vosk.Recognizer({
    model,
    sampleRate: 16000,
    grammar: ["turn on the light", "turn off the light", "what time is it", "[unk]"]
});

// 3. Speaker-identification recognizer
const rec3 = new vosk.Recognizer({
    model,
    sampleRate: 16000,
    speakerModel: speakerModel
});

rec1.free(); rec2.free(); rec3.free();
```