optimize the 1xt2x

deepspeech/frontend/featurizer/text_featurizer.py

@@ -84,8 +84,6 @@ class TextFeaturizer():
         tokens = self.tokenize(text)
         ids = []
         for token in tokens:
-            if '' in self.vocab_dict and token == ' ':
-                token = ''
             token = token if token in self.vocab_dict else self.unk
             ids.append(self.vocab_dict[token])
         return ids
@@ -201,7 +199,6 @@ class TextFeaturizer():
         """Load vocabulary from file."""
         vocab_list = load_dict(vocab_filepath, maskctc)
         assert vocab_list is not None
-        assert SPACE in vocab_list
 
         id2token = dict(
             [(idx, token) for (idx, token) in enumerate(vocab_list)])

examples/1xt2x/aishell/local/download_model.sh

@@ -10,7 +10,7 @@ ckpt_dir=$1
 . ${MAIN_ROOT}/utils/utility.sh
 
 URL='https://deepspeech.bj.bcebos.com/mandarin_models/aishell_model_v1.8_to_v2.x.tar.gz'
-MD5=4ade113c69ea291b8ce5ec6a03296659
+MD5=87e7577d4bea737dbf3e8daab37aa808
 TARGET=${ckpt_dir}/aishell_model_v1.8_to_v2.x.tar.gz
 
 

examples/1xt2x/aishell/run.sh

@@ -7,6 +7,7 @@ stop_stage=100
 conf_path=conf/deepspeech2.yaml
 avg_num=1
 model_type=offline
+gpus=2
 
 source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
 
@@ -22,6 +23,6 @@ fi
 
 if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
     # test ckpt avg_n
-    CUDA_VISIBLE_DEVICES=2 ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${v18_ckpt} ${model_type}|| exit -1
+    CUDA_VISIBLE_DEVICES=${gpus} ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${v18_ckpt} ${model_type}|| exit -1
 fi
 

examples/1xt2x/baidu_en8k/local/download_model.sh

@@ -10,7 +10,7 @@ ckpt_dir=$1
 . ${MAIN_ROOT}/utils/utility.sh
 
 URL='https://deepspeech.bj.bcebos.com/eng_models/baidu_en8k_v1.8_to_v2.x.tar.gz'
-MD5=fdabeb6c96963ac85d9188f0275c6a1b
+MD5=c1676be8505cee436e6f312823e9008c
 TARGET=${ckpt_dir}/baidu_en8k_v1.8_to_v2.x.tar.gz
 
 

examples/1xt2x/baidu_en8k/run.sh

@@ -7,6 +7,7 @@ stop_stage=100
 conf_path=conf/deepspeech2.yaml
 avg_num=1
 model_type=offline
+gpus=0
 
 source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
 
@@ -22,6 +23,6 @@ fi
 
 if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
     # test ckpt avg_n
-    CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${v18_ckpt} ${model_type}|| exit -1
+    CUDA_VISIBLE_DEVICES=${gpus} ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${v18_ckpt} ${model_type}|| exit -1
 fi
 

examples/1xt2x/librispeech/local/download_model.sh

@@ -10,7 +10,7 @@ ckpt_dir=$1
 . ${MAIN_ROOT}/utils/utility.sh
 
 URL='https://deepspeech.bj.bcebos.com/eng_models/librispeech_v1.8_to_v2.x.tar.gz'
-MD5=7b0f582fe2f5a840b840e7ee52246bc5
+MD5=a06d9aadb560ea113984dc98d67232c8
 TARGET=${ckpt_dir}/librispeech_v1.8_to_v2.x.tar.gz
 
 

examples/1xt2x/librispeech/run.sh

@@ -7,6 +7,7 @@ stop_stage=100
 conf_path=conf/deepspeech2.yaml
 avg_num=1
 model_type=offline
+gpus=1
 
 source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
 
@@ -22,5 +23,5 @@ fi
 
 if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
     # test ckpt avg_n
-    CUDA_VISIBLE_DEVICES=1 ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${v18_ckpt} ${model_type}|| exit -1
+    CUDA_VISIBLE_DEVICES=${gpus} ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${v18_ckpt} ${model_type}|| exit -1
 fi

examples/1xt2x/src_deepspeech2x/test_model.py

@@ -26,6 +26,7 @@ from src_deepspeech2x.models.ds2 import DeepSpeech2InferModel
 from src_deepspeech2x.models.ds2 import DeepSpeech2Model
 from yacs.config import CfgNode
 
+from deepspeech.frontend.featurizer.text_featurizer import TextFeaturizer
 from deepspeech.io.collator import SpeechCollator
 from deepspeech.io.dataset import ManifestDataset
 from deepspeech.io.sampler import SortagradBatchSampler
@@ -38,7 +39,6 @@ from deepspeech.utils import error_rate
 from deepspeech.utils import layer_tools
 from deepspeech.utils import mp_tools
 from deepspeech.utils.log import Log
-#from deepspeech.utils.log import Autolog
 
 logger = Log(__name__).getlog()
 
@@ -272,6 +272,9 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
         return default
 
     def __init__(self, config, args):
+
+        self._text_featurizer = TextFeaturizer(
+            unit_type=config.collator.unit_type, vocab_filepath=None)
         super().__init__(config, args)
 
     def ordid2token(self, texts, texts_len):
@@ -296,9 +299,6 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
         error_rate_func = error_rate.cer if cfg.error_rate_type == 'cer' else error_rate.wer
 
         vocab_list = self.test_loader.collate_fn.vocab_list
-        if "" in vocab_list:
-            space_id = vocab_list.index("")
-            vocab_list[space_id] = " "
 
         target_transcripts = self.ordid2token(texts, texts_len)
 
@@ -337,6 +337,10 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
             cutoff_prob=cfg.cutoff_prob,
             cutoff_top_n=cfg.cutoff_top_n,
             num_processes=cfg.num_proc_bsearch)
+        result_transcripts = [
+            self._text_featurizer.detokenize(item)
+            for item in result_transcripts
+        ]
         return result_transcripts
 
     @mp_tools.rank_zero_only
@@ -367,8 +371,6 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
             error_rate_type, num_ins, num_ins, errors_sum / len_refs)
         logger.info(msg)
 
-    # self.autolog.report()
-
     def run_test(self):
         self.resume_or_scratch()
         try: