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Merge pull request #1639 from mozilla/issue1638
Fixed #1638 (Update Hyperparameters for 0.3.0)
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lissyx
GitHub
commit
9fa29935cf
@ -162,7 +162,7 @@ def create_flags():
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tf.app.flags.DEFINE_string ('lm_trie_path', 'data/lm/trie', 'path to the language model trie file created with native_client/generate_trie')
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tf.app.flags.DEFINE_integer ('beam_width', 1024, 'beam width used in the CTC decoder when building candidate transcriptions')
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tf.app.flags.DEFINE_float ('lm_weight', 1.50, 'the alpha hyperparameter of the CTC decoder. Language Model weight.')
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tf.app.flags.DEFINE_float ('valid_word_count_weight', 2.25, 'valid word insertion weight. This is used to lessen the word insertion penalty when the inserted word is part of the vocabulary.')
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tf.app.flags.DEFINE_float ('valid_word_count_weight', 2.10, 'valid word insertion weight. This is used to lessen the word insertion penalty when the inserted word is part of the vocabulary.')
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# Inference mode
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@ -14,7 +14,7 @@ python -u DeepSpeech.py \
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--train_files ${ldc93s1_csv} --train_batch_size 1 \
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--dev_files ${ldc93s1_csv} --dev_batch_size 1 \
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--test_files ${ldc93s1_csv} --test_batch_size 1 \
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--n_hidden 494 --epoch 85 --random_seed 4567 --default_stddev 0.046875 \
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--n_hidden 494 --epoch 105 --random_seed 4567 --default_stddev 0.046875 \
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--max_to_keep 1 --checkpoint_dir '/tmp/ckpt' --checkpoint_secs 0 \
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--learning_rate 0.001 --dropout_rate 0.05 --export_dir '/tmp/train' \
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--decoder_library_path '/tmp/ds/libctc_decoder_with_kenlm.so' \
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@ -19,8 +19,8 @@ def load_model(models, alphabet, lm, trie):
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N_FEATURES = 26
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N_CONTEXT = 9
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BEAM_WIDTH = 500
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LM_WEIGHT = 1.75
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VALID_WORD_COUNT_WEIGHT = 1.00
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LM_WEIGHT = 1.50
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VALID_WORD_COUNT_WEIGHT = 2.10
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model_load_start = timer()
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ds = Model(models, N_FEATURES, N_CONTEXT, alphabet, BEAM_WIDTH)
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@ -23,7 +23,7 @@
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#define N_CONTEXT 9
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#define BEAM_WIDTH 500
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#define LM_WEIGHT 1.50f
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#define VALID_WORD_COUNT_WEIGHT 2.25f
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#define VALID_WORD_COUNT_WEIGHT 2.10f
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typedef struct {
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const char* string;
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@ -19,7 +19,7 @@ const LM_WEIGHT = 1.50;
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// Valid word insertion weight. This is used to lessen the word insertion penalty
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// when the inserted word is part of the vocabulary
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const VALID_WORD_COUNT_WEIGHT = 2.25;
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const VALID_WORD_COUNT_WEIGHT = 2.10;
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// These constants are tied to the shape of the graph used (changing them changes
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@ -27,7 +27,7 @@ LM_WEIGHT = 1.50
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# Valid word insertion weight. This is used to lessen the word insertion penalty
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# when the inserted word is part of the vocabulary
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VALID_WORD_COUNT_WEIGHT = 2.25
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VALID_WORD_COUNT_WEIGHT = 2.10
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# These constants are tied to the shape of the graph used (changing them changes
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@ -135,26 +135,30 @@ assert_shows_something()
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assert_correct_ldc93s1()
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{
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# FIXME: RE-ENABLE AFTER FIXING LM HYPERPARAMETERS
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# assert_correct_inference "$1" "she had your dark suit in greasy wash water all year"
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assert_working_inference "$1" "she had"
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assert_correct_inference "$1" "she had your dark suit in greasy wash water all year"
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}
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assert_correct_ldc93s1_lm()
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{
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assert_correct_inference "$1" "she had your dark suit in greasy wash water all year"
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}
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assert_correct_multi_ldc93s1()
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{
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# FIXME: RE-ENABLE AFTER FIXING LM HYPERPARAMETERS
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#assert_shows_something "$1" "/LDC93S1.wav%she had your dark suit in greasy wash water all year%"
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#assert_shows_something "$1" "/LDC93S1_pcms16le_2_44100.wav%she had your dark suit in greasy wash water all year%"
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return 0
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assert_shows_something "$1" "/LDC93S1.wav%she had your dark suit in greasy wash water all year%"
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assert_shows_something "$1" "/LDC93S1_pcms16le_2_44100.wav%she had your dark suit in greasy wash water all year%"
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## 8k will output garbage anyway ...
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# assert_shows_something "$1" "/LDC93S1_pcms16le_1_8000.wav%she hayorasryrtl lyreasy asr watal w water all year%"
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}
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assert_correct_ldc93s1_prodmodel()
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{
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# FIXME: RE-ENABLE AFTER FIXING LM HYPERPARAMETERS
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#assert_correct_inference "$1" "she had tired or so and greasy wash war or year"
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assert_working_inference "$1" "she had"
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assert_correct_inference "$1" "she hadered or so and greasy wash war or year"
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}
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assert_correct_ldc93s1_prodmodel_stereo_44k()
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{
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assert_correct_inference "$1" "she had tered or so and greasy wash war or year"
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}
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assert_correct_warning_upsampling()
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@ -185,13 +189,13 @@ run_all_inference_tests()
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assert_correct_ldc93s1 "${phrase_pbmodel_nolm}"
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phrase_pbmodel_withlm=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --alphabet ${TASKCLUSTER_TMP_DIR}/alphabet.txt --lm ${TASKCLUSTER_TMP_DIR}/lm.binary --trie ${TASKCLUSTER_TMP_DIR}/trie --audio ${TASKCLUSTER_TMP_DIR}/LDC93S1.wav)
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assert_correct_ldc93s1 "${phrase_pbmodel_withlm}"
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assert_correct_ldc93s1_lm "${phrase_pbmodel_withlm}"
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phrase_pbmodel_nolm_stereo_44k=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --alphabet ${TASKCLUSTER_TMP_DIR}/alphabet.txt --audio ${TASKCLUSTER_TMP_DIR}/LDC93S1_pcms16le_2_44100.wav)
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assert_correct_ldc93s1 "${phrase_pbmodel_nolm_stereo_44k}"
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phrase_pbmodel_withlm_stereo_44k=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --alphabet ${TASKCLUSTER_TMP_DIR}/alphabet.txt --lm ${TASKCLUSTER_TMP_DIR}/lm.binary --trie ${TASKCLUSTER_TMP_DIR}/trie --audio ${TASKCLUSTER_TMP_DIR}/LDC93S1_pcms16le_2_44100.wav)
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assert_correct_ldc93s1 "${phrase_pbmodel_withlm_stereo_44k}"
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assert_correct_ldc93s1_lm "${phrase_pbmodel_withlm_stereo_44k}"
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phrase_pbmodel_nolm_mono_8k=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --alphabet ${TASKCLUSTER_TMP_DIR}/alphabet.txt --audio ${TASKCLUSTER_TMP_DIR}/LDC93S1_pcms16le_1_8000.wav 2>&1 1>/dev/null)
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assert_correct_warning_upsampling "${phrase_pbmodel_nolm_mono_8k}"
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@ -209,7 +213,7 @@ run_prod_inference_tests()
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assert_correct_ldc93s1_prodmodel "${phrase_pbmodel_withlm}"
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phrase_pbmodel_withlm_stereo_44k=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --alphabet ${TASKCLUSTER_TMP_DIR}/alphabet.txt --lm ${TASKCLUSTER_TMP_DIR}/lm.binary --trie ${TASKCLUSTER_TMP_DIR}/trie --audio ${TASKCLUSTER_TMP_DIR}/LDC93S1_pcms16le_2_44100.wav)
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assert_correct_ldc93s1_prodmodel "${phrase_pbmodel_withlm_stereo_44k}"
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assert_correct_ldc93s1_prodmodel_stereo_44k "${phrase_pbmodel_withlm_stereo_44k}"
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phrase_pbmodel_withlm_mono_8k=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --alphabet ${TASKCLUSTER_TMP_DIR}/alphabet.txt --lm ${TASKCLUSTER_TMP_DIR}/lm.binary --trie ${TASKCLUSTER_TMP_DIR}/trie --audio ${TASKCLUSTER_TMP_DIR}/LDC93S1_pcms16le_1_8000.wav 2>&1 1>/dev/null)
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assert_correct_warning_upsampling "${phrase_pbmodel_withlm_mono_8k}"
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