[Config]clear the u2 decode config for asr (#1107)

* clear the u2 decode config

* rename the vocab_filepath and cmvn_path

paddlespeech/cli/asr/infer.py

@@ -179,7 +179,7 @@ class ASRExecutor(BaseExecutor):
                 self.collate_fn_test = SpeechCollator.from_config(self.config)
                 text_feature = TextFeaturizer(
                     unit_type=self.config.collator.unit_type,
-                    vocab_filepath=self.config.collator.vocab_filepath,
+                    vocab=self.config.collator.vocab_filepath,
                     spm_model_prefix=self.config.collator.spm_model_prefix)
                 self.config.model.input_dim = self.collate_fn_test.feature_size
                 self.config.model.output_dim = text_feature.vocab_size
@@ -192,7 +192,7 @@ class ASRExecutor(BaseExecutor):
                     res_path, self.config.collator.spm_model_prefix)
                 text_feature = TextFeaturizer(
                     unit_type=self.config.collator.unit_type,
-                    vocab_filepath=self.config.collator.vocab_filepath,
+                    vocab=self.config.collator.vocab_filepath,
                     spm_model_prefix=self.config.collator.spm_model_prefix)
                 self.config.model.input_dim = self.config.collator.feat_dim
                 self.config.model.output_dim = text_feature.vocab_size
@@ -279,7 +279,7 @@ class ASRExecutor(BaseExecutor):
             audio = paddle.to_tensor(audio, dtype='float32').unsqueeze(axis=0)
             text_feature = TextFeaturizer(
                 unit_type=self.config.collator.unit_type,
-                vocab_filepath=self.config.collator.vocab_filepath,
+                vocab=self.config.collator.vocab_filepath,
                 spm_model_prefix=self.config.collator.spm_model_prefix)
             self._inputs["audio"] = audio
             self._inputs["audio_len"] = audio_len
@@ -295,7 +295,7 @@ class ASRExecutor(BaseExecutor):
         """
         text_feature = TextFeaturizer(
             unit_type=self.config.collator.unit_type,
-            vocab_filepath=self.config.collator.vocab_filepath,
+            vocab=self.config.collator.vocab_filepath,
             spm_model_prefix=self.config.collator.spm_model_prefix)
         cfg = self.config.decoding
         audio = self._inputs["audio"]
@@ -321,13 +321,7 @@ class ASRExecutor(BaseExecutor):
                 audio_len,
                 text_feature=text_feature,
                 decoding_method=cfg.decoding_method,
-                lang_model_path=cfg.lang_model_path,
-                beam_alpha=cfg.alpha,
-                beam_beta=cfg.beta,
                 beam_size=cfg.beam_size,
-                cutoff_prob=cfg.cutoff_prob,
-                cutoff_top_n=cfg.cutoff_top_n,
-                num_processes=cfg.num_proc_bsearch,
                 ctc_weight=cfg.ctc_weight,
                 decoding_chunk_size=cfg.decoding_chunk_size,
                 num_decoding_left_chunks=cfg.num_decoding_left_chunks,

paddlespeech/cli/st/infer.py

@@ -180,7 +180,7 @@ class STExecutor(BaseExecutor):
                 res_path, self.config.collator.spm_model_prefix)
             self.text_feature = TextFeaturizer(
                 unit_type=self.config.collator.unit_type,
-                vocab_filepath=self.config.collator.vocab_filepath,
+                vocab=self.config.collator.vocab_filepath,
                 spm_model_prefix=self.config.collator.spm_model_prefix)
             self.config.model.input_dim = self.config.collator.feat_dim
             self.config.model.output_dim = self.text_feature.vocab_size
@@ -292,14 +292,7 @@ class STExecutor(BaseExecutor):
                 audio_len,
                 text_feature=self.text_feature,
                 decoding_method=cfg.decoding_method,
-                lang_model_path=None,
-                beam_alpha=cfg.alpha,
-                beam_beta=cfg.beta,
                 beam_size=cfg.beam_size,
-                cutoff_prob=cfg.cutoff_prob,
-                cutoff_top_n=cfg.cutoff_top_n,
-                num_processes=cfg.num_proc_bsearch,
-                ctc_weight=cfg.ctc_weight,
                 word_reward=cfg.word_reward,
                 decoding_chunk_size=cfg.decoding_chunk_size,
                 num_decoding_left_chunks=cfg.num_decoding_left_chunks,

paddlespeech/s2t/exps/deepspeech2/bin/test_hub.py

@@ -41,7 +41,7 @@ class DeepSpeech2Tester_hub():
         self.audio_file = args.audio_file
         self.collate_fn_test = SpeechCollator.from_config(config)
         self._text_featurizer = TextFeaturizer(
-            unit_type=config.collator.unit_type, vocab_filepath=None)
+            unit_type=config.collator.unit_type, vocab=None)
 
     def compute_result_transcripts(self, audio, audio_len, vocab_list, cfg):
         result_transcripts = self.model.decode(

paddlespeech/s2t/exps/deepspeech2/model.py

@@ -286,7 +286,7 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
     def __init__(self, config, args):
         super().__init__(config, args)
         self._text_featurizer = TextFeaturizer(
-            unit_type=config.collator.unit_type, vocab_filepath=None)
+            unit_type=config.collator.unit_type, vocab=None)
 
     def ordid2token(self, texts, texts_len):
         """ ord() id to chr() chr """

paddlespeech/s2t/exps/u2/bin/test_wav.py

@@ -44,7 +44,7 @@ class U2Infer():
 
         self.text_feature = TextFeaturizer(
             unit_type=config.collator.unit_type,
-            vocab_filepath=config.collator.vocab_filepath,
+            vocab=config.collator.vocab_filepath,
             spm_model_prefix=config.collator.spm_model_prefix)
 
         paddle.set_device('gpu' if self.args.ngpu > 0 else 'cpu')
@@ -91,13 +91,7 @@ class U2Infer():
                 ilen,
                 text_feature=self.text_feature,
                 decoding_method=cfg.decoding_method,
-                lang_model_path=cfg.lang_model_path,
-                beam_alpha=cfg.alpha,
-                beam_beta=cfg.beta,
                 beam_size=cfg.beam_size,
-                cutoff_prob=cfg.cutoff_prob,
-                cutoff_top_n=cfg.cutoff_top_n,
-                num_processes=cfg.num_proc_bsearch,
                 ctc_weight=cfg.ctc_weight,
                 decoding_chunk_size=cfg.decoding_chunk_size,
                 num_decoding_left_chunks=cfg.num_decoding_left_chunks,

paddlespeech/s2t/exps/u2/model.py

@@ -437,7 +437,7 @@ class U2Tester(U2Trainer):
         super().__init__(config, args)
         self.text_feature = TextFeaturizer(
             unit_type=self.config.collator.unit_type,
-            vocab_filepath=self.config.collator.vocab_filepath,
+            vocab=self.config.collator.vocab_filepath,
             spm_model_prefix=self.config.collator.spm_model_prefix)
         self.vocab_list = self.text_feature.vocab_list
 
@@ -469,13 +469,7 @@ class U2Tester(U2Trainer):
             audio_len,
             text_feature=self.text_feature,
             decoding_method=cfg.decoding_method,
-            lang_model_path=cfg.lang_model_path,
-            beam_alpha=cfg.alpha,
-            beam_beta=cfg.beta,
             beam_size=cfg.beam_size,
-            cutoff_prob=cfg.cutoff_prob,
-            cutoff_top_n=cfg.cutoff_top_n,
-            num_processes=cfg.num_proc_bsearch,
             ctc_weight=cfg.ctc_weight,
             decoding_chunk_size=cfg.decoding_chunk_size,
             num_decoding_left_chunks=cfg.num_decoding_left_chunks,

paddlespeech/s2t/exps/u2_kaldi/model.py

@@ -393,7 +393,7 @@ class U2Tester(U2Trainer):
         super().__init__(config, args)
         self.text_feature = TextFeaturizer(
             unit_type=self.config.collator.unit_type,
-            vocab_filepath=self.config.collator.vocab_filepath,
+            vocab=self.config.collator.vocab_filepath,
             spm_model_prefix=self.config.collator.spm_model_prefix)
         self.vocab_list = self.text_feature.vocab_list
 
@@ -425,13 +425,7 @@ class U2Tester(U2Trainer):
             audio_len,
             text_feature=self.text_feature,
             decoding_method=cfg.decoding_method,
-            lang_model_path=cfg.lang_model_path,
-            beam_alpha=cfg.alpha,
-            beam_beta=cfg.beta,
             beam_size=cfg.beam_size,
-            cutoff_prob=cfg.cutoff_prob,
-            cutoff_top_n=cfg.cutoff_top_n,
-            num_processes=cfg.num_proc_bsearch,
             ctc_weight=cfg.ctc_weight,
             decoding_chunk_size=cfg.decoding_chunk_size,
             num_decoding_left_chunks=cfg.num_decoding_left_chunks,

paddlespeech/s2t/exps/u2_st/model.py

@@ -437,14 +437,7 @@ class U2STTester(U2STTrainer):
             audio_len,
             text_feature=text_feature,
             decoding_method=cfg.decoding_method,
-            lang_model_path=cfg.lang_model_path,
-            beam_alpha=cfg.alpha,
-            beam_beta=cfg.beta,
             beam_size=cfg.beam_size,
-            cutoff_prob=cfg.cutoff_prob,
-            cutoff_top_n=cfg.cutoff_top_n,
-            num_processes=cfg.num_proc_bsearch,
-            ctc_weight=cfg.ctc_weight,
             word_reward=cfg.word_reward,
             decoding_chunk_size=cfg.decoding_chunk_size,
             num_decoding_left_chunks=cfg.num_decoding_left_chunks,
@@ -475,14 +468,7 @@ class U2STTester(U2STTrainer):
             audio_len,
             text_feature=text_feature,
             decoding_method=cfg.decoding_method,
-            lang_model_path=cfg.lang_model_path,
-            beam_alpha=cfg.alpha,
-            beam_beta=cfg.beta,
             beam_size=cfg.beam_size,
-            cutoff_prob=cfg.cutoff_prob,
-            cutoff_top_n=cfg.cutoff_top_n,
-            num_processes=cfg.num_proc_bsearch,
-            ctc_weight=cfg.ctc_weight,
             word_reward=cfg.word_reward,
             decoding_chunk_size=cfg.decoding_chunk_size,
             num_decoding_left_chunks=cfg.num_decoding_left_chunks,

paddlespeech/s2t/frontend/featurizer/speech_featurizer.py

@@ -55,7 +55,7 @@ class SpeechFeaturizer():
 
         self.text_feature = TextFeaturizer(
             unit_type=unit_type,
-            vocab_filepath=vocab_filepath,
+            vocab=vocab_filepath,
             spm_model_prefix=spm_model_prefix,
             maskctc=maskctc)
         self.vocab_size = self.text_feature.vocab_size

paddlespeech/s2t/frontend/featurizer/text_featurizer.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 """Contains the text featurizer class."""
 from pprint import pformat
+from typing import Union
 
 import sentencepiece as spm
 
@@ -31,11 +32,7 @@ __all__ = ["TextFeaturizer"]
 
 
 class TextFeaturizer():
-    def __init__(self,
-                 unit_type,
-                 vocab_filepath,
-                 spm_model_prefix=None,
-                 maskctc=False):
+    def __init__(self, unit_type, vocab, spm_model_prefix=None, maskctc=False):
         """Text featurizer, for processing or extracting features from text.
 
         Currently, it supports char/word/sentence-piece level tokenizing and conversion into
@@ -44,7 +41,7 @@ class TextFeaturizer():
 
         Args:
             unit_type (str): unit type, e.g. char, word, spm
-            vocab_filepath (str): Filepath to load vocabulary for token indices conversion.
+            vocab Option[str, list]: Filepath to load vocabulary for token indices conversion, or vocab list.
             spm_model_prefix (str, optional): spm model prefix. Defaults to None.
         """
         assert unit_type in ('char', 'spm', 'word')
@@ -52,12 +49,12 @@ class TextFeaturizer():
         self.unk = UNK
         self.maskctc = maskctc
 
-        if vocab_filepath:
+        if vocab:
             self.vocab_dict, self._id2token, self.vocab_list, self.unk_id, self.eos_id, self.blank_id = self._load_vocabulary_from_file(
-                vocab_filepath, maskctc)
+                vocab, maskctc)
             self.vocab_size = len(self.vocab_list)
         else:
-            logger.warning("TextFeaturizer: not have vocab file.")
+            logger.warning("TextFeaturizer: not have vocab file or vocab list.")
 
         if unit_type == 'spm':
             spm_model = spm_model_prefix + '.model'
@@ -207,9 +204,13 @@ class TextFeaturizer():
 
         return decode(tokens)
 
-    def _load_vocabulary_from_file(self, vocab_filepath: str, maskctc: bool):
+    def _load_vocabulary_from_file(self, vocab: Union[str, list],
+                                   maskctc: bool):
         """Load vocabulary from file."""
-        vocab_list = load_dict(vocab_filepath, maskctc)
+        if isinstance(vocab, list):
+            vocab_list = vocab
+        else:
+            vocab_list = load_dict(vocab, maskctc)
         assert vocab_list is not None
         logger.debug(f"Vocab: {pformat(vocab_list)}")
 

paddlespeech/s2t/models/lm/dataset.py

@@ -42,7 +42,7 @@ class TextCollatorSpm():
         assert (vocab_filepath is not None)
         self.text_featurizer = TextFeaturizer(
             unit_type=unit_type,
-            vocab_filepath=vocab_filepath,
+            vocab=vocab_filepath,
             spm_model_prefix=spm_model_prefix)
         self.eos_id = self.text_featurizer.eos_id
         self.blank_id = self.text_featurizer.blank_id

paddlespeech/s2t/models/u2/u2.py

@@ -717,13 +717,7 @@ class U2BaseModel(ASRInterface, nn.Layer):
                feats_lengths: paddle.Tensor,
                text_feature: Dict[str, int],
                decoding_method: str,
-               lang_model_path: str,
-               beam_alpha: float,
-               beam_beta: float,
                beam_size: int,
-               cutoff_prob: float,
-               cutoff_top_n: int,
-               num_processes: int,
                ctc_weight: float=0.0,
                decoding_chunk_size: int=-1,
                num_decoding_left_chunks: int=-1,
@@ -737,13 +731,7 @@ class U2BaseModel(ASRInterface, nn.Layer):
             decoding_method (str): decoding mode, e.g.
                     'attention', 'ctc_greedy_search',
                     'ctc_prefix_beam_search', 'attention_rescoring'
-            lang_model_path (str): lm path.
-            beam_alpha (float): lm weight.
-            beam_beta (float): length penalty.
             beam_size (int): beam size for search
-            cutoff_prob (float): for prune.
-            cutoff_top_n (int): for prune.
-            num_processes (int):
             ctc_weight (float, optional): ctc weight for attention rescoring decode mode. Defaults to 0.0.
             decoding_chunk_size (int, optional): decoding chunk size. Defaults to -1.
                     <0: for decoding, use full chunk.
@@ -839,12 +827,13 @@ class U2Model(U2DecodeModel):
     def __init__(self, configs: dict):
         vocab_size, encoder, decoder, ctc = U2Model._init_from_config(configs)
 
+        model_conf = configs.get('model_conf', dict())
         super().__init__(
             vocab_size=vocab_size,
             encoder=encoder,
             decoder=decoder,
             ctc=ctc,
-            **configs['model_conf'])
+            **model_conf)
 
     @classmethod
     def _init_from_config(cls, configs: dict):
@@ -893,7 +882,7 @@ class U2Model(U2DecodeModel):
                                      **configs['decoder_conf'])
 
         # ctc decoder and ctc loss
-        model_conf = configs['model_conf']
+        model_conf = configs.get('model_conf', dict())
         dropout_rate = model_conf.get('ctc_dropout_rate', 0.0)
         grad_norm_type = model_conf.get('ctc_grad_norm_type', None)
         ctc = CTCDecoder(

paddlespeech/s2t/models/u2_st/u2_st.py

@@ -522,14 +522,7 @@ class U2STBaseModel(nn.Layer):
                feats_lengths: paddle.Tensor,
                text_feature: Dict[str, int],
                decoding_method: str,
-               lang_model_path: str,
-               beam_alpha: float,
-               beam_beta: float,
                beam_size: int,
-               cutoff_prob: float,
-               cutoff_top_n: int,
-               num_processes: int,
-               ctc_weight: float=0.0,
                word_reward: float=0.0,
                decoding_chunk_size: int=-1,
                num_decoding_left_chunks: int=-1,
@@ -543,14 +536,7 @@ class U2STBaseModel(nn.Layer):
             decoding_method (str): decoding mode, e.g. 
                     'fullsentence', 
                     'simultaneous'
-            lang_model_path (str): lm path.
-            beam_alpha (float): lm weight.
-            beam_beta (float): length penalty.
             beam_size (int): beam size for search
-            cutoff_prob (float): for prune.
-            cutoff_top_n (int): for prune.
-            num_processes (int): 
-            ctc_weight (float, optional): ctc weight for attention rescoring decode mode. Defaults to 0.0.
             decoding_chunk_size (int, optional): decoding chunk size. Defaults to -1.
                     <0: for decoding, use full chunk.
                     >0: for decoding, use fixed chunk size as set.

paddlespeech/s2t/transform/cmvn.py

@@ -168,13 +168,17 @@ class GlobalCMVN():
                  norm_means=True,
                  norm_vars=True,
                  std_floor=1.0e-20):
-        self.cmvn_path = cmvn_path
+        # cmvn_path: Option[str, dict]
+        cmvn = cmvn_path
+        self.cmvn = cmvn
         self.norm_means = norm_means
         self.norm_vars = norm_vars
         self.std_floor = std_floor
-
-        with open(cmvn_path) as f:
-            cmvn_stats = json.load(f)
+        if isinstance(cmvn, dict):
+            cmvn_stats = cmvn
+        else:
+            with open(cmvn) as f:
+                cmvn_stats = json.load(f)
         self.count = cmvn_stats['frame_num']
         self.mean = np.array(cmvn_stats['mean_stat']) / self.count
         self.square_sums = np.array(cmvn_stats['var_stat'])
@@ -183,8 +187,8 @@ class GlobalCMVN():
 
     def __repr__(self):
         return f"""{self.__class__.__name__}(
-            cmvn_path={self.cmvn_path}, 
-            norm_means={self.norm_means}, 
+            cmvn_path={self.cmvn},
+            norm_means={self.norm_means},
             norm_vars={self.norm_vars},)"""
 
     def __call__(self, x, uttid=None):