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Update decoder hyperparameters

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This commit is contained in:
Reuben Morais 2018-12-28 16:12:09 -02:00
parent e549f8978e
commit fa7cb1a983
12 changed files with 44 additions and 49 deletions
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9
examples/ffmpeg_vad_streaming/index.js
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@ -11,11 +11,10 @@ const util = require('util');
const BEAM_WIDTH = 1024;
// The alpha hyperparameter of the CTC decoder. Language Model weight
const LM_WEIGHT = 1.50;
const LM_ALPHA = 0.75;
// Valid word insertion weight. This is used to lessen the word insertion penalty
// when the inserted word is part of the vocabulary
const VALID_WORD_COUNT_WEIGHT = 2.25;
// 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
@ -63,7 +62,7 @@ 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_WEIGHT, VALID_WORD_COUNT_WEIGHT);
LM_ALPHA, LM_BETA);
const lm_load_end = process.hrtime(lm_load_start);
console.error('Loaded language model in %ds.', totalTime(lm_load_end));
}

13
examples/mic_vad_streaming/README.md
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@ -26,7 +26,7 @@ brew install portaudio
usage: mic_vad_streaming.py [-h] [-v VAD_AGGRESSIVENESS] [--nospinner]
[-w SAVEWAV] -m MODEL [-a ALPHABET] [-l LM]
[-t TRIE] [-nf N_FEATURES] [-nc N_CONTEXT]
[-lw LM_WEIGHT] [-vwcw VALID_WORD_COUNT_WEIGHT]
[-la LM_ALPHA] [-lb LM_BETA]
[-bw BEAM_WIDTH]
Stream from microphone to DeepSpeech using VAD
@ -56,13 +56,12 @@ optional arguments:
-nc N_CONTEXT, --n_context N_CONTEXT
Size of the context window used for producing
timesteps in the input vector. Default: 9
-lw LM_WEIGHT, --lm_weight LM_WEIGHT
-la LM_ALPHA, --lm_alpha LM_ALPHA
The alpha hyperparameter of the CTC decoder. Language
Model weight. Default: 1.5
-vwcw VALID_WORD_COUNT_WEIGHT, --valid_word_count_weight VALID_WORD_COUNT_WEIGHT
Valid word insertion weight. This is used to lessen
the word insertion penalty when the inserted word is
part of the vocabulary. Default: 2.1
Model weight. Default: 0.75
-lb LM_BETA, --lm_beta LM_BETA
The beta hyperparameter of the CTC decoder. Word insertion
bonus. Default: 1.85
-bw BEAM_WIDTH, --beam_width BEAM_WIDTH
Beam width used in the CTC decoder when building
candidate transcriptions. Default: 500

14
examples/mic_vad_streaming/mic_vad_streaming.py
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@ -118,7 +118,7 @@ def main(ARGS):
if ARGS.lm and ARGS.trie:
logging.info("ARGS.lm: %s", ARGS.lm)
logging.info("ARGS.trie: %s", ARGS.trie)
model.enableDecoderWithLM(ARGS.alphabet, ARGS.lm, ARGS.trie, ARGS.lm_weight, ARGS.valid_word_count_weight)
model.enableDecoderWithLM(ARGS.alphabet, ARGS.lm, ARGS.trie, ARGS.lm_alpha, ARGS.lm_beta)
# Start audio with VAD
vad_audio = VADAudio(aggressiveness=ARGS.vad_aggressiveness)
@ -148,8 +148,8 @@ def main(ARGS):
if __name__ == '__main__':
BEAM_WIDTH = 500
LM_WEIGHT = 1.50
VALID_WORD_COUNT_WEIGHT = 2.10
LM_ALPHA = 0.75
LM_BETA = 1.85
N_FEATURES = 26
N_CONTEXT = 9
@ -175,10 +175,10 @@ if __name__ == '__main__':
help=f"Number of MFCC features to use. Default: {N_FEATURES}")
parser.add_argument('-nc', '--n_context', type=int, default=N_CONTEXT,
help=f"Size of the context window used for producing timesteps in the input vector. Default: {N_CONTEXT}")
parser.add_argument('-lw', '--lm_weight', type=float, default=LM_WEIGHT,
help=f"The alpha hyperparameter of the CTC decoder. Language Model weight. Default: {LM_WEIGHT}")
parser.add_argument('-vwcw', '--valid_word_count_weight', type=float, default=VALID_WORD_COUNT_WEIGHT,
help=f"Valid word insertion weight. This is used to lessen the word insertion penalty when the inserted word is part of the vocabulary. Default: {VALID_WORD_COUNT_WEIGHT}")
parser.add_argument('-la', '--lm_alpha', type=float, default=LM_ALPHA,
help=f"The alpha hyperparameter of the CTC decoder. Language Model weight. Default: {LM_ALPHA}")
parser.add_argument('-lb', '--lm_beta', type=float, default=LM_BETA,
help=f"The beta hyperparameter of the CTC decoder. Word insertion bonus. Default: {LM_BETA}")
parser.add_argument('-bw', '--beam_width', type=int, default=BEAM_WIDTH,
help=f"Beam width used in the CTC decoder when building candidate transcriptions. Default: {BEAM_WIDTH}")

6
examples/net_framework/CSharpExamples/DeepSpeechConsole/Program.cs
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@ -38,8 +38,8 @@ namespace CSharpExamples
const uint N_CEP = 26;
const uint N_CONTEXT = 9;
const uint BEAM_WIDTH = 200;
const float LM_WEIGHT = 1.50f;
const float VALID_WORD_COUNT_WEIGHT = 2.10f;
const float LM_ALPHA = 0.75f;
const float LM_BETA = 1.85f;
Stopwatch stopwatch = new Stopwatch();
@ -76,7 +76,7 @@ namespace CSharpExamples
alphabet ?? "alphabet.txt",
lm ?? "lm.binary",
trie ?? "trie",
LM_WEIGHT, VALID_WORD_COUNT_WEIGHT);
LM_ALPHA, LM_BETA);
}
catch (IOException ex)
{

6
examples/net_framework/CSharpExamples/DeepSpeechWPF/MainWindow.xaml.cs
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@ -24,8 +24,8 @@ namespace DeepSpeechWPF
private const uint N_CEP = 26;
private const uint N_CONTEXT = 9;
private const uint BEAM_WIDTH = 500;
private const float LM_WEIGHT = 1.50f;
private const float VALID_WORD_COUNT_WEIGHT = 2.10f;
private const float LM_ALPHA = 0.75f;
private const float LM_BETA = 1.85f;
@ -160,7 +160,7 @@ namespace DeepSpeechWPF
{
try
{
if (_sttClient.EnableDecoderWithLM("alphabet.txt", "lm.binary", "trie", LM_WEIGHT, VALID_WORD_COUNT_WEIGHT) != 0)
if (_sttClient.EnableDecoderWithLM("alphabet.txt", "lm.binary", "trie", LM_ALPHA, LM_BETA) != 0)
{
MessageBox.Show("Error loading LM.");
Dispatcher.Invoke(() => btnEnableLM.IsEnabled = true);

6
examples/vad_transcriber/wavTranscriber.py
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@ -19,8 +19,8 @@ def load_model(models, alphabet, lm, trie):
N_FEATURES = 26
N_CONTEXT = 9
BEAM_WIDTH = 500
LM_WEIGHT = 1.50
VALID_WORD_COUNT_WEIGHT = 2.10
LM_ALPHA = 0.75
LM_BETA = 1.85
model_load_start = timer()
ds = Model(models, N_FEATURES, N_CONTEXT, alphabet, BEAM_WIDTH)
@ -28,7 +28,7 @@ def load_model(models, alphabet, lm, trie):
logging.debug("Loaded model in %0.3fs." % (model_load_end))
lm_load_start = timer()
ds.enableDecoderWithLM(alphabet, lm, trie, LM_WEIGHT, VALID_WORD_COUNT_WEIGHT)
ds.enableDecoderWithLM(alphabet, lm, trie, LM_ALPHA, LM_BETA)
lm_load_end = timer() - lm_load_start
logging.debug('Loaded language model in %0.3fs.' % (lm_load_end))

8
native_client/client.cc
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@ -24,8 +24,8 @@
#define N_CEP 26
#define N_CONTEXT 9
#define BEAM_WIDTH 500
#define LM_WEIGHT 1.50f
#define VALID_WORD_COUNT_WEIGHT 2.10f
#define LM_ALPHA 0.75f
#define LM_BETA 1.85f
typedef struct {
const char* string;
@ -253,8 +253,8 @@ main(int argc, char **argv)
alphabet,
lm,
trie,
LM_WEIGHT,
VALID_WORD_COUNT_WEIGHT);
LM_ALPHA,
LM_BETA);
if (status != 0) {
fprintf(stderr, "Could not enable CTC decoder with LM.\n");
return 1;

4
native_client/java/app/src/main/java/deepspeech/mozilla/org/deepspeech/DeepSpeechActivity.java
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@ -38,8 +38,8 @@ public class DeepSpeechActivity extends AppCompatActivity {
final int N_CEP = 26;
final int N_CONTEXT = 9;
final int BEAM_WIDTH = 50;
final float LM_WEIGHT = 1.50f;
final float VALID_WORD_COUNT_WEIGHT = 2.10f;
final float LM_ALPHA = 0.75f;
final float LM_BETA = 1.85f;
private char readLEChar(RandomAccessFile f) throws IOException {
byte b1 = f.readByte();

4
native_client/java/app/src/main/java/deepspeech/mozilla/org/deepspeech/Model.java
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@ -16,8 +16,8 @@ public class Model {
impl.DestroyModel(this._msp);
}
public void enableDecoderWihLM(String alphabet, String lm, String trie, float lm_weight, float valid_word_count_weight) {
impl.EnableDecoderWithLM(this._msp, alphabet, lm, trie, lm_weight, valid_word_count_weight);
public void enableDecoderWihLM(String alphabet, String lm, String trie, float lm_alpha, float lm_beta) {
impl.EnableDecoderWithLM(this._msp, alphabet, lm, trie, lm_alpha, lm_beta);
}
public String stt(short[] buffer, int buffer_size, int sample_rate) {

9
native_client/javascript/client.js
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@ -15,11 +15,10 @@ const util = require('util');
const BEAM_WIDTH = 500;
// The alpha hyperparameter of the CTC decoder. Language Model weight
const LM_WEIGHT = 1.50;
const LM_ALPHA = 0.75;
// Valid word insertion weight. This is used to lessen the word insertion penalty
// when the inserted word is part of the vocabulary
const VALID_WORD_COUNT_WEIGHT = 2.10;
// 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
@ -102,7 +101,7 @@ audioStream.on('finish', () => {
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_WEIGHT, VALID_WORD_COUNT_WEIGHT);
LM_ALPHA, LM_BETA);
const lm_load_end = process.hrtime(lm_load_start);
console.error('Loaded language model in %ds.', totalTime(lm_load_end));
}

10
native_client/python/client.py
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@ -23,11 +23,10 @@ except ImportError:
BEAM_WIDTH = 500
# The alpha hyperparameter of the CTC decoder. Language Model weight
LM_WEIGHT = 1.50
LM_ALPHA = 0.75
# Valid word insertion weight. This is used to lessen the word insertion penalty
# when the inserted word is part of the vocabulary
VALID_WORD_COUNT_WEIGHT = 2.10
# The beta hyperparameter of the CTC decoder. Word insertion bonus.
LM_BETA = 1.85
# These constants are tied to the shape of the graph used (changing them changes
@ -85,8 +84,7 @@ def main():
if args.lm and args.trie:
print('Loading language model from files {} {}'.format(args.lm, args.trie), file=sys.stderr)
lm_load_start = timer()
ds.enableDecoderWithLM(args.alphabet, args.lm, args.trie, LM_WEIGHT,
VALID_WORD_COUNT_WEIGHT)
ds.enableDecoderWithLM(args.alphabet, args.lm, args.trie, LM_ALPHA, LM_BETA)
lm_load_end = timer() - lm_load_start
print('Loaded language model in {:.3}s.'.format(lm_load_end), file=sys.stderr)

4
util/flags.py
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@ -131,8 +131,8 @@ def create_flags():
tf.app.flags.DEFINE_string ('lm_binary_path', 'data/lm/lm.binary', 'path to the language model binary file created with KenLM')
tf.app.flags.DEFINE_string ('lm_trie_path', 'data/lm/trie', 'path to the language model trie file created with native_client/generate_trie')
tf.app.flags.DEFINE_integer ('beam_width', 1024, 'beam width used in the CTC decoder when building candidate transcriptions')
tf.app.flags.DEFINE_float ('lm_alpha', 1.50, 'the alpha hyperparameter of the CTC decoder. Language Model weight.')
tf.app.flags.DEFINE_float ('lm_beta', 2.10, 'the beta hyperparameter of the CTC decoder. Word insertion weight.')
tf.app.flags.DEFINE_float ('lm_alpha', 0.75, 'the alpha hyperparameter of the CTC decoder. Language Model weight.')
tf.app.flags.DEFINE_float ('lm_beta', 1.85, 'the beta hyperparameter of the CTC decoder. Word insertion weight.')
# Inference mode