DeepSpeech/native_client/javascript/client.js

#!/usr/bin/env node

'use strict';

const Fs = require('fs');
const Sox = require('sox-stream');
const Ds = require('./index.js');
const argparse = require('argparse');
const MemoryStream = require('memory-stream');
const Wav = require('node-wav');
const Duplex = require('stream').Duplex;
const util = require('util');

// These constants control the beam search decoder

// Beam width used in the CTC decoder when building candidate transcriptions
const BEAM_WIDTH = 500;

// The alpha hyperparameter of the CTC decoder. Language Model weight
const LM_ALPHA = 0.75;

// The beta hyperparameter of the CTC decoder. Word insertion bonus.
const LM_BETA = 1.85;


// These constants are tied to the shape of the graph used (changing them changes
// the geometry of the first layer), so make sure you use the same constants that
// were used during training

// Number of MFCC features to use
const N_FEATURES = 26;

// Size of the context window used for producing timesteps in the input vector
const N_CONTEXT = 9;

var VersionAction = function VersionAction(options) {
  options = options || {};
  options.nargs = 0;
  argparse.Action.call(this, options);
}
util.inherits(VersionAction, argparse.Action);

VersionAction.prototype.call = function(parser) {
  Ds.printVersions();
  let runtime = 'Node';
  if (process.versions.electron) {
    runtime = 'Electron';
  }
  console.error('Runtime: ' + runtime);
  process.exit(0);
}

var parser = new argparse.ArgumentParser({addHelp: true, description: 'Running DeepSpeech inference.'});
parser.addArgument(['--model'], {required: true, help: 'Path to the model (protocol buffer binary file)'});
parser.addArgument(['--alphabet'], {required: true, help: 'Path to the configuration file specifying the alphabet used by the network'});
parser.addArgument(['--lm'], {help: 'Path to the language model binary file', nargs: '?'});
parser.addArgument(['--trie'], {help: 'Path to the language model trie file created with native_client/generate_trie', nargs: '?'});
parser.addArgument(['--audio'], {required: true, help: 'Path to the audio file to run (WAV format)'});
parser.addArgument(['--version'], {action: VersionAction, help: 'Print version and exits'});
parser.addArgument(['--extended'], {action: 'storeTrue', help: 'Output string from extended metadata'});
var args = parser.parseArgs();

function totalTime(hrtimeValue) {
  return (hrtimeValue[0] + hrtimeValue[1] / 1000000000).toPrecision(4);
}

function metadataToString(metadata) {
  var retval = ""
  for (var i = 0; i < metadata.num_items; ++i) {
    retval += metadata.items[i].character;
  }
  return retval;
}

const buffer = Fs.readFileSync(args['audio']);
const result = Wav.decode(buffer);

if (result.sampleRate < 16000) {
  console.error('Warning: original sample rate (' + result.sampleRate + ') is lower than 16kHz. Up-sampling might produce erratic speech recognition.');
}

function bufferToStream(buffer) {
  var stream = new Duplex();
  stream.push(buffer);
  stream.push(null);
  return stream;
}

var audioStream = new MemoryStream();
bufferToStream(buffer).
  pipe(Sox({
    global: {
      'no-dither': true,
    },
    output: {
      bits: 16,
      rate: 16000,
      channels: 1,
      encoding: 'signed-integer',
      endian: 'little',
      compression: 0.0,
      type: 'raw'
    }
  })).
  pipe(audioStream);

audioStream.on('finish', () => {
  let audioBuffer = audioStream.toBuffer();

  console.error('Loading model from file %s', args['model']);
  const model_load_start = process.hrtime();
  var model = new Ds.Model(args['model'], N_FEATURES, N_CONTEXT, args['alphabet'], BEAM_WIDTH);
  const model_load_end = process.hrtime(model_load_start);
  console.error('Loaded model in %ds.', totalTime(model_load_end));

  if (args['lm'] && args['trie']) {
    console.error('Loading language model from files %s %s', args['lm'], args['trie']);
    const lm_load_start = process.hrtime();
    model.enableDecoderWithLM(args['alphabet'], args['lm'], args['trie'],
                              LM_ALPHA, LM_BETA);
    const lm_load_end = process.hrtime(lm_load_start);
    console.error('Loaded language model in %ds.', totalTime(lm_load_end));
  }

  const inference_start = process.hrtime();
  console.error('Running inference.');
  const audioLength = (audioBuffer.length / 2) * ( 1 / 16000);

  // We take half of the buffer_size because buffer is a char* while
  // LocalDsSTT() expected a short*
  if (args['extended']) {
    console.log(metadataToString(model.sttWithMetadata(audioBuffer.slice(0, audioBuffer.length / 2), 16000)));
  } else {
    console.log(model.stt(audioBuffer.slice(0, audioBuffer.length / 2), 16000));
  }
  const inference_stop = process.hrtime(inference_start);
  console.error('Inference took %ds for %ds audio file.', totalTime(inference_stop), audioLength.toPrecision(4));
  process.exit(0);
});