refactor asr online server

.pre-commit-config.yaml

@@ -51,12 +51,12 @@ repos:
         language: system
         files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|cuh|proto)$
         exclude: (?=speechx/speechx/kaldi|speechx/patch|speechx/tools/fstbin|speechx/tools/lmbin).*(\.cpp|\.cc|\.h|\.py)$
-    -   id: copyright_checker
-        name: copyright_checker
-        entry: python .pre-commit-hooks/copyright-check.hook
-        language: system
-        files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|proto|py)$
-        exclude: (?=third_party|pypinyin|speechx/speechx/kaldi|speechx/patch|speechx/tools/fstbin|speechx/tools/lmbin).*(\.cpp|\.cc|\.h|\.py)$
+    #-   id: copyright_checker
+    #    name: copyright_checker
+    #    entry: python .pre-commit-hooks/copyright-check.hook
+    #    language: system
+    #    files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|proto|py)$
+    #    exclude: (?=third_party|pypinyin|speechx/speechx/kaldi|speechx/patch|speechx/tools/fstbin|speechx/tools/lmbin).*(\.cpp|\.cc|\.h|\.py)$
 -   repo: https://github.com/asottile/reorder_python_imports
     rev: v2.4.0
     hooks:

demos/streaming_asr_server/server.sh

@@ -5,4 +5,5 @@ export CUDA_VISIBLE_DEVICE=0,1,2,3
 paddlespeech_server start --config_file conf/punc_application.yaml &> punc.log &
 
 # nohup python3 streaming_asr_server.py --config_file conf/ws_conformer_application.yaml > streaming_asr.log 2>&1 &
-paddlespeech_server start --config_file conf/ws_conformer_application.yaml &> streaming_asr.log  &
+paddlespeech_server start --config_file conf/ws_conformer_application.yaml &> streaming_asr.log  &
+

demos/streaming_asr_server/test.sh

@@ -9,4 +9,5 @@ paddlespeech_client asr_online --server_ip 127.0.0.1 --port 8290 --input ./zh.wa
 # read the wav and call streaming and punc service
 # If `127.0.0.1` is not accessible, you need to use the actual service IP address.
 # python3 websocket_client.py --server_ip 127.0.0.1 --port 8290 --punc.server_ip 127.0.0.1 --punc.port 8190 --wavfile ./zh.wav
-paddlespeech_client asr_online --server_ip 127.0.0.1 --port 8290 --punc.server_ip 127.0.0.1 --punc.port 8190 --input ./zh.wav
+paddlespeech_client asr_online --server_ip 127.0.0.1 --port 8290 --punc.server_ip 127.0.0.1 --punc.port 8190 --input ./zh.wav
+

paddlespeech/__init__.py

@@ -14,3 +14,7 @@
 import _locale
 
 _locale._getdefaultlocale = (lambda *args: ['en_US', 'utf8'])
+
+
+
+

paddlespeech/server/engine/asr/online/asr_engine.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 import os
 import sys
+from typing import ByteString
 from typing import Optional
 
 import numpy as np
@@ -30,9 +31,10 @@ from paddlespeech.s2t.transform.transformation import Transformation
 from paddlespeech.s2t.utils.tensor_utils import add_sos_eos
 from paddlespeech.s2t.utils.tensor_utils import pad_sequence
 from paddlespeech.s2t.utils.utility import UpdateConfig
+from paddlespeech.server.engine.asr.online.ctc_endpoint import OnlineCTCEndpoingOpt
+from paddlespeech.server.engine.asr.online.ctc_endpoint import OnlineCTCEndpoint
 from paddlespeech.server.engine.asr.online.ctc_search import CTCPrefixBeamSearch
 from paddlespeech.server.engine.base_engine import BaseEngine
-from paddlespeech.server.utils.audio_process import pcm2float
 from paddlespeech.server.utils.paddle_predictor import init_predictor
 
 __all__ = ['PaddleASRConnectionHanddler', 'ASRServerExecutor', 'ASREngine']
@@ -54,24 +56,33 @@ class PaddleASRConnectionHanddler:
         self.model_config = asr_engine.executor.config
         self.asr_engine = asr_engine
 
-        self.init()
-        self.reset()
-
-    def init(self):
         # model_type, sample_rate and text_feature is shared for deepspeech2 and conformer
         self.model_type = self.asr_engine.executor.model_type
         self.sample_rate = self.asr_engine.executor.sample_rate
         # tokens to text
         self.text_feature = self.asr_engine.executor.text_feature
 
+        # extract feat, new only fbank in conformer model
+        self.preprocess_conf = self.model_config.preprocess_config
+        self.preprocess_args = {"train": False}
+        self.preprocessing = Transformation(self.preprocess_conf)
+
+        # frame window and frame shift, in samples unit
+        self.win_length = self.preprocess_conf.process[0]['win_length']
+        self.n_shift = self.preprocess_conf.process[0]['n_shift']
+
+        assert self.preprocess_conf.process[0]['fs'] == self.sample_rate, (
+            self.sample_rate, self.preprocess_conf.process[0]['fs'])
+        self.frame_shift_in_ms = int(
+            self.n_shift / self.preprocess_conf.process[0]['fs'] * 1000)
+
+        self.init_decoder()
+        self.reset()
+
+    def init_decoder(self):
         if "deepspeech2" in self.model_type:
             self.am_predictor = self.asr_engine.executor.am_predictor
 
-            # extract feat, new only fbank in conformer model
-            self.preprocess_conf = self.model_config.preprocess_config
-            self.preprocess_args = {"train": False}
-            self.preprocessing = Transformation(self.preprocess_conf)
-
             self.decoder = CTCDecoder(
                 odim=self.model_config.output_dim,  # <blank> is in  vocab
                 enc_n_units=self.model_config.rnn_layer_size * 2,
@@ -90,10 +101,6 @@ class PaddleASRConnectionHanddler:
                 cfg.beam_size, cfg.cutoff_prob, cfg.cutoff_top_n,
                 cfg.num_proc_bsearch)
 
-            # frame window and frame shift, in samples unit
-            self.win_length = self.preprocess_conf.process[0]['win_length']
-            self.n_shift = self.preprocess_conf.process[0]['n_shift']
-
         elif "conformer" in self.model_type or "transformer" in self.model_type:
             # acoustic model
             self.model = self.asr_engine.executor.model
@@ -102,130 +109,40 @@ class PaddleASRConnectionHanddler:
             self.ctc_decode_config = self.asr_engine.executor.config.decode
             self.searcher = CTCPrefixBeamSearch(self.ctc_decode_config)
 
-            # extract feat, new only fbank in conformer model
-            self.preprocess_conf = self.model_config.preprocess_config
-            self.preprocess_args = {"train": False}
-            self.preprocessing = Transformation(self.preprocess_conf)
-
-            # frame window and frame shift, in samples unit
-            self.win_length = self.preprocess_conf.process[0]['win_length']
-            self.n_shift = self.preprocess_conf.process[0]['n_shift']
+            # ctc endpoint
+            self.endpoint_opt = OnlineCTCEndpoingOpt(
+                frame_shift_in_ms=self.frame_shift_in_ms, blank=0)
+            self.endpointer = OnlineCTCEndpoint(self.endpoint_opt)
         else:
             raise ValueError(f"Not supported: {self.model_type}")
 
-    def extract_feat(self, samples):
-        # we compute the elapsed time of first char occuring
-        # and we record the start time at the first pcm sample arraving
-
-        if "deepspeech2online" in self.model_type:
-            # self.reamined_wav stores all the samples,
-            # include the original remained_wav and this package samples
-            samples = np.frombuffer(samples, dtype=np.int16)
-            assert samples.ndim == 1
-
-            if self.remained_wav is None:
-                self.remained_wav = samples
-            else:
-                assert self.remained_wav.ndim == 1
-                self.remained_wav = np.concatenate([self.remained_wav, samples])
-            logger.info(
-                f"The connection remain the audio samples: {self.remained_wav.shape}"
-            )
-
-            # fbank
-            feat = self.preprocessing(self.remained_wav,
-                                         **self.preprocess_args)
-            feat = paddle.to_tensor(
-                feat, dtype="float32").unsqueeze(axis=0)
-
-            if self.cached_feat is None:
-                self.cached_feat = feat
-            else:
-                assert (len(feat.shape) == 3)
-                assert (len(self.cached_feat.shape) == 3)
-                self.cached_feat = paddle.concat(
-                    [self.cached_feat, feat], axis=1)
-
-                # set the feat device
-            if self.device is None:
-                self.device = self.cached_feat.place
-
-            # cur frame step
-            num_frames = feat.shape[1]
-
-            self.num_frames += num_frames
-            self.remained_wav = self.remained_wav[self.n_shift * num_frames:]
-
-            logger.info(
-                f"process the audio feature success, the connection feat shape: {self.cached_feat.shape}"
-            )
-            logger.info(
-                f"After extract feat, the connection remain the audio samples: {self.remained_wav.shape}"
-            )
-
-        elif "conformer_online" in self.model_type:
-            logger.info("Online ASR extract the feat")
-            samples = np.frombuffer(samples, dtype=np.int16)
-            assert samples.ndim == 1
-
-            self.num_samples += samples.shape[0]
-            logger.info(
-                f"This package receive {samples.shape[0]} pcm data. Global samples:{self.num_samples}"
-            )
-
-            # self.reamined_wav stores all the samples,
-            # include the original remained_wav and this package samples
-            if self.remained_wav is None:
-                self.remained_wav = samples
-            else:
-                assert self.remained_wav.ndim == 1  # (T,)
-                self.remained_wav = np.concatenate([self.remained_wav, samples])
-            logger.info(
-                f"The concatenation of remain and now audio samples length is: {self.remained_wav.shape}"
-            )
-
-            if len(self.remained_wav) < self.win_length:
-                # samples not enough for feature window
-                return 0
-
-            # fbank
-            x_chunk = self.preprocessing(self.remained_wav,
-                                         **self.preprocess_args)
-            x_chunk = paddle.to_tensor(
-                x_chunk, dtype="float32").unsqueeze(axis=0)
-
-            # feature cache
-            if self.cached_feat is None:
-                self.cached_feat = x_chunk
-            else:
-                assert (len(x_chunk.shape) == 3)  # (B,T,D)
-                assert (len(self.cached_feat.shape) == 3)  # (B,T,D)
-                self.cached_feat = paddle.concat(
-                    [self.cached_feat, x_chunk], axis=1)
-
-            # set the feat device
-            if self.device is None:
-                self.device = self.cached_feat.place
+    def model_reset(self):
+        if "deepspeech2" in self.model_type:
+            return
 
-            # cur frame step
-            num_frames = x_chunk.shape[1]
+        # feature cache
+        self.cached_feat = None
 
-            # global frame step
-            self.num_frames += num_frames
+        ## conformer
+        # cache for conformer online
+        self.subsampling_cache = None
+        self.elayers_output_cache = None
+        self.conformer_cnn_cache = None
+        self.encoder_out = None
+        # conformer decoding state
+        self.offset = 0  # global offset in decoding frame unit
 
-            # update remained wav
-            self.remained_wav = self.remained_wav[self.n_shift * num_frames:]
+        ## just for record info
+        self.chunk_num = 0  # global decoding chunk num, not used
 
-            logger.info(
-                f"process the audio feature success, the cached feat shape: {self.cached_feat.shape}"
-            )
-            logger.info(
-                f"After extract feat, the cached remain the audio samples: {self.remained_wav.shape}"
-            )
-            logger.info(f"global samples: {self.num_samples}")
-            logger.info(f"global frames: {self.num_frames}")
-        else:
-            raise ValueError(f"not supported: {self.model_type}")
+    def reset_continuous_decoding(self):
+        """
+        when in continous decoding, reset for next utterance.
+        """
+        self.global_frame_offset = self.num_frames
+        self.model_reset()
+        self.searcher.reset()
+        self.endpointer.reset()
 
     def reset(self):
         if "deepspeech2" in self.model_type:
@@ -241,53 +158,110 @@ class PaddleASRConnectionHanddler:
                 dtype=float32)
             self.decoder.reset_decoder(batch_size=1)
 
+        if "conformer" in self.model_type or "transformer" in self.model_type:
+            self.searcher.reset()
+            self.endpointer.reset()
+
         self.device = None
 
         ## common
-
         # global sample and frame step
         self.num_samples = 0
+        self.global_frame_offset = 0
+        # frame step of cur utterance
         self.num_frames = 0
 
         # cache for audio and feat
         self.remained_wav = None
         self.cached_feat = None
 
-        # partial/ending decoding results
-        self.result_transcripts = ['']
-
         ## conformer
+        self.model_reset()
 
-        # cache for conformer online
-        self.subsampling_cache = None
-        self.elayers_output_cache = None
-        self.conformer_cnn_cache = None
-        self.encoder_out = None
-        # conformer decoding state
-        self.chunk_num = 0  # globa decoding chunk num
-        self.offset = 0  # global offset in decoding frame unit
-        self.hyps = []
-
+        ## outputs
+        # partial/ending decoding results
+        self.result_transcripts = ['']
         # token timestamp result
         self.word_time_stamp = []
 
+        ## just for record
+        self.hyps = []
+
         # one best timestamp viterbi prob is large.
         self.time_stamp = []
 
+    def extract_feat(self, samples: ByteString):
+        logger.info("Online ASR extract the feat")
+        samples = np.frombuffer(samples, dtype=np.int16)
+        assert samples.ndim == 1
+
+        self.num_samples += samples.shape[0]
+        logger.info(
+            f"This package receive {samples.shape[0]} pcm data. Global samples:{self.num_samples}"
+        )
+
+        # self.reamined_wav stores all the samples,
+        # include the original remained_wav and this package samples
+        if self.remained_wav is None:
+            self.remained_wav = samples
+        else:
+            assert self.remained_wav.ndim == 1  # (T,)
+            self.remained_wav = np.concatenate([self.remained_wav, samples])
+        logger.info(
+            f"The concatenation of remain and now audio samples length is: {self.remained_wav.shape}"
+        )
+
+        if len(self.remained_wav) < self.win_length:
+            # samples not enough for feature window
+            return 0
+
+        # fbank
+        x_chunk = self.preprocessing(self.remained_wav, **self.preprocess_args)
+        x_chunk = paddle.to_tensor(x_chunk, dtype="float32").unsqueeze(axis=0)
+
+        # feature cache
+        if self.cached_feat is None:
+            self.cached_feat = x_chunk
+        else:
+            assert (len(x_chunk.shape) == 3)  # (B,T,D)
+            assert (len(self.cached_feat.shape) == 3)  # (B,T,D)
+            self.cached_feat = paddle.concat(
+                [self.cached_feat, x_chunk], axis=1)
+
+        # set the feat device
+        if self.device is None:
+            self.device = self.cached_feat.place
+
+        # cur frame step
+        num_frames = x_chunk.shape[1]
+
+        # global frame step
+        self.num_frames += num_frames
+
+        # update remained wav
+        self.remained_wav = self.remained_wav[self.n_shift * num_frames:]
+
+        logger.info(
+            f"process the audio feature success, the cached feat shape: {self.cached_feat.shape}"
+        )
+        logger.info(
+            f"After extract feat, the cached remain the audio samples: {self.remained_wav.shape}"
+        )
+        logger.info(f"global samples: {self.num_samples}")
+        logger.info(f"global frames: {self.num_frames}")
+
     def decode(self, is_finished=False):
         """advance decoding
 
         Args:
             is_finished (bool, optional): Is last frame or not. Defaults to False.
 
-        Raises:
-            Exception: when not support model.
-
         Returns:
-            None: nothing
+            None: 
         """
-        if "deepspeech2online" in self.model_type:
+        if "deepspeech2" in self.model_type:
             decoding_chunk_size = 1  # decoding chunk size = 1. int decoding frame unit
+
             context = 7  # context=7, in audio frame unit
             subsampling = 4  # subsampling=4, in audio frame unit
 
@@ -332,9 +306,11 @@ class PaddleASRConnectionHanddler:
             end = None
             for cur in range(0, num_frames - left_frames + 1, stride):
                 end = min(cur + decoding_window, num_frames)
+
                 # extract the audio
                 x_chunk = self.cached_feat[:, cur:end, :].numpy()
                 x_chunk_lens = np.array([x_chunk.shape[1]])
+
                 trans_best = self.decode_one_chunk(x_chunk, x_chunk_lens)
 
             self.result_transcripts = [trans_best]
@@ -409,31 +385,38 @@ class PaddleASRConnectionHanddler:
 
     @paddle.no_grad()
     def advance_decoding(self, is_finished=False):
+        if "deepspeech" in self.model_type:
+            return
+
         logger.info(
             "Conformer/Transformer: start to decode with advanced_decoding method"
         )
         cfg = self.ctc_decode_config
 
-        # cur chunk size, in decoding frame unit
+        # cur chunk size, in decoding frame unit, e.g. 16
         decoding_chunk_size = cfg.decoding_chunk_size
-        # using num of history chunks
+        # using num of history chunks, e.g -1
         num_decoding_left_chunks = cfg.num_decoding_left_chunks
         assert decoding_chunk_size > 0
 
+        # e.g. 4
         subsampling = self.model.encoder.embed.subsampling_rate
+        # e.g. 7
         context = self.model.encoder.embed.right_context + 1
 
-        # processed chunk feature cached for next chunk
+        # processed chunk feature cached for next chunk, e.g. 3
         cached_feature_num = context - subsampling
-        # decoding stride, in audio frame unit
-        stride = subsampling * decoding_chunk_size
+
         # decoding window, in audio frame unit
         decoding_window = (decoding_chunk_size - 1) * subsampling + context
+        # decoding stride, in audio frame unit
+        stride = subsampling * decoding_chunk_size
 
         if self.cached_feat is None:
             logger.info("no audio feat, please input more pcm data")
             return
 
+        # (B=1,T,D)
         num_frames = self.cached_feat.shape[1]
         logger.info(
             f"Required decoding window {decoding_window} frames, and the connection has {num_frames} frames"
@@ -454,9 +437,6 @@ class PaddleASRConnectionHanddler:
             return None, None
 
         logger.info("start to do model forward")
-        # hist of chunks, in deocding frame unit
-        required_cache_size = decoding_chunk_size * num_decoding_left_chunks
-        outputs = []
 
         # num_frames - context + 1 ensure that current frame can get context window
         if is_finished:
@@ -466,7 +446,11 @@ class PaddleASRConnectionHanddler:
             # we only process decoding_window frames for one chunk
             left_frames = decoding_window
 
+        # hist of chunks, in deocding frame unit
+        required_cache_size = decoding_chunk_size * num_decoding_left_chunks
+
         # record the end for removing the processed feat
+        outputs = []
         end = None
         for cur in range(0, num_frames - left_frames + 1, stride):
             end = min(cur + decoding_window, num_frames)
@@ -491,30 +475,28 @@ class PaddleASRConnectionHanddler:
             self.encoder_out = ys
         else:
             self.encoder_out = paddle.concat([self.encoder_out, ys], axis=1)
+        logger.info(
+            f"This connection handler encoder out shape: {self.encoder_out.shape}"
+        )
 
         # get the ctc probs
         ctc_probs = self.model.ctc.log_softmax(ys)  # (1, maxlen, vocab_size)
         ctc_probs = ctc_probs.squeeze(0)
 
+        ## decoding
         # advance decoding
         self.searcher.search(ctc_probs, self.cached_feat.place)
         # get one best hyps
         self.hyps = self.searcher.get_one_best_hyps()
 
-        assert self.cached_feat.shape[0] == 1
-        assert end >= cached_feature_num
-
         # advance cache of feat
-        self.cached_feat = self.cached_feat[0, end -
-                                            cached_feature_num:, :].unsqueeze(0)
+        assert self.cached_feat.shape[0] == 1  #(B=1,T,D)
+        assert end >= cached_feature_num
+        self.cached_feat = self.cached_feat[:, end - cached_feature_num:, :]
         assert len(
             self.cached_feat.shape
         ) == 3, f"current cache feat shape is: {self.cached_feat.shape}"
 
-        logger.info(
-            f"This connection handler encoder out shape: {self.encoder_out.shape}"
-        )
-
     def update_result(self):
         """Conformer/Transformer hyps to result.
         """
@@ -654,24 +636,28 @@ class PaddleASRConnectionHanddler:
 
         # update each word start and end time stamp
         # decoding frame to audio frame
-        frame_shift = self.model.encoder.embed.subsampling_rate
-        frame_shift_in_sec = frame_shift * (self.n_shift / self.sample_rate)
-        logger.info(f"frame shift sec: {frame_shift_in_sec}")
+        decode_frame_shift = self.model.encoder.embed.subsampling_rate
+        decode_frame_shift_in_sec = decode_frame_shift * (self.n_shift /
+                                                          self.sample_rate)
+        logger.info(f"decode frame shift in sec: {decode_frame_shift_in_sec}")
+
+        global_offset_in_sec = self.global_frame_offset * self.frame_shift_in_ms / 1000.0
+        logger.info(f"global offset: {global_offset_in_sec} sec.")
 
         word_time_stamp = []
         for idx, _ in enumerate(self.time_stamp):
             start = (self.time_stamp[idx - 1] + self.time_stamp[idx]
                      ) / 2.0 if idx > 0 else 0
-            start = start * frame_shift_in_sec
+            start = start * decode_frame_shift_in_sec
 
             end = (self.time_stamp[idx] + self.time_stamp[idx + 1]
                    ) / 2.0 if idx < len(self.time_stamp) - 1 else self.offset
 
-            end = end * frame_shift_in_sec
+            end = end * decode_frame_shift_in_sec
             word_time_stamp.append({
                 "w": self.result_transcripts[0][idx],
-                "bg": start,
-                "ed": end
+                "bg": global_offset_in_sec + start,
+                "ed": global_offset_in_sec + end
             })
             # logger.info(f"{word_time_stamp[-1]}")
 
@@ -705,13 +691,14 @@ class ASRServerExecutor(ASRExecutor):
 
         self.model_type = model_type
         self.sample_rate = sample_rate
+        logger.info(f"model_type: {self.model_type}")
+
         sample_rate_str = '16k' if sample_rate == 16000 else '8k'
         tag = model_type + '-' + lang + '-' + sample_rate_str
         self.task_resource.set_task_model(model_tag=tag)
+
         if cfg_path is None or am_model is None or am_params is None:
-            logger.info(f"Load the pretrained model, tag = {tag}")
             self.res_path = self.task_resource.res_dir
-
             self.cfg_path = os.path.join(
                 self.res_path, self.task_resource.res_dict['cfg_path'])
 
@@ -719,7 +706,6 @@ class ASRServerExecutor(ASRExecutor):
                                          self.task_resource.res_dict['model'])
             self.am_params = os.path.join(self.res_path,
                                           self.task_resource.res_dict['params'])
-            logger.info(self.res_path)
         else:
             self.cfg_path = os.path.abspath(cfg_path)
             self.am_model = os.path.abspath(am_model)
@@ -727,9 +713,12 @@ class ASRServerExecutor(ASRExecutor):
             self.res_path = os.path.dirname(
                 os.path.dirname(os.path.abspath(self.cfg_path)))
 
-        logger.info(self.cfg_path)
-        logger.info(self.am_model)
-        logger.info(self.am_params)
+        logger.info("Load the pretrained model:")
+        logger.info(f"  tag = {tag}")
+        logger.info(f"  res_path: {self.res_path}")
+        logger.info(f"  cfg path: {self.cfg_path}")
+        logger.info(f"  am_model path: {self.am_model}")
+        logger.info(f"  am_params path: {self.am_params}")
 
         #Init body.
         self.config = CfgNode(new_allowed=True)
@@ -738,25 +727,39 @@ class ASRServerExecutor(ASRExecutor):
         if self.config.spm_model_prefix:
             self.config.spm_model_prefix = os.path.join(
                 self.res_path, self.config.spm_model_prefix)
+            logger.info(f"spm model path: {self.config.spm_model_prefix}")
+
+        self.vocab = self.config.vocab_filepath
+
         self.text_feature = TextFeaturizer(
             unit_type=self.config.unit_type,
             vocab=self.config.vocab_filepath,
             spm_model_prefix=self.config.spm_model_prefix)
-        self.vocab = self.config.vocab_filepath
-        with UpdateConfig(self.config):
-            if "deepspeech2" in model_type:
+
+        if "deepspeech2" in model_type:
+            with UpdateConfig(self.config):
+                # download lm
                 self.config.decode.lang_model_path = os.path.join(
                     MODEL_HOME, 'language_model',
                     self.config.decode.lang_model_path)
 
-                lm_url = self.task_resource.res_dict['lm_url']
-                lm_md5 = self.task_resource.res_dict['lm_md5']
-                logger.info(f"Start to load language model {lm_url}")
-                self.download_lm(
-                    lm_url,
-                    os.path.dirname(self.config.decode.lang_model_path), lm_md5)
+            lm_url = self.task_resource.res_dict['lm_url']
+            lm_md5 = self.task_resource.res_dict['lm_md5']
+            logger.info(f"Start to load language model {lm_url}")
+            self.download_lm(
+                lm_url,
+                os.path.dirname(self.config.decode.lang_model_path), lm_md5)
+
+            # AM predictor
+            logger.info("ASR engine start to init the am predictor")
+            self.am_predictor_conf = am_predictor_conf
+            self.am_predictor = init_predictor(
+                model_file=self.am_model,
+                params_file=self.am_params,
+                predictor_conf=self.am_predictor_conf)
 
-            elif "conformer" in model_type or "transformer" in model_type:
+        elif "conformer" in model_type or "transformer" in model_type:
+            with UpdateConfig(self.config):
                 logger.info("start to create the stream conformer asr engine")
                 # update the decoding method
                 if decode_method:
@@ -770,37 +773,24 @@ class ASRServerExecutor(ASRExecutor):
                     logger.info(
                         "we set the decoding_method to attention_rescoring")
                     self.config.decode.decoding_method = "attention_rescoring"
+
                 assert self.config.decode.decoding_method in [
                     "ctc_prefix_beam_search", "attention_rescoring"
                 ], f"we only support ctc_prefix_beam_search and attention_rescoring dedoding method, current decoding method is {self.config.decode.decoding_method}"
-            else:
-                raise Exception("wrong type")
 
-        if "deepspeech2" in model_type:
-            # AM predictor
-            logger.info("ASR engine start to init the am predictor")
-            self.am_predictor_conf = am_predictor_conf
-            self.am_predictor = init_predictor(
-                model_file=self.am_model,
-                params_file=self.am_params,
-                predictor_conf=self.am_predictor_conf)
-        elif "conformer" in model_type or "transformer" in model_type:
+            # load model
             model_name = model_type[:model_type.rindex(
                 '_')]  # model_type: {model_name}_{dataset}
             logger.info(f"model name: {model_name}")
             model_class = self.task_resource.get_model_class(model_name)
-            model_conf = self.config
-            model = model_class.from_config(model_conf)
+            model = model_class.from_config(self.config)
             self.model = model
+            self.model.set_state_dict(paddle.load(self.am_model))
             self.model.eval()
-
-            # load model
-            model_dict = paddle.load(self.am_model)
-            self.model.set_state_dict(model_dict)
-            logger.info("create the transformer like model success")
         else:
-            raise ValueError(f"Not support: {model_type}")
+            raise Exception(f"not support: {model_type}")
 
+        logger.info(f"create the {model_type} model success")
         return True
 
 

paddlespeech/server/engine/asr/online/ctc_endpoint.py

@@ -0,0 +1,108 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from dataclasses import dataclass
+from typing import List
+
+from paddlespeech.cli.log import logger
+
+
+@dataclass
+class OnlineCTCEndpointRule:
+    must_contain_nonsilence: bool = True
+    min_trailing_silence: int = 1000
+    min_utterance_length: int = 0
+
+
+@dataclass
+class OnlineCTCEndpoingOpt:
+    frame_shift_in_ms: int = 10
+
+    blank: int = 0  # blank id, that we consider as silence for purposes of endpointing.
+    blank_threshold: float = 0.8  # above blank threshold is silence
+
+    # We support three rules.  We terminate decoding if ANY of these rules
+    # evaluates to "true". If you want to add more rules, do it by changing this
+    # code.  If you want to disable a rule, you can set the silence-timeout for
+    # that rule to a very large number.
+
+    # rule1 times out after 5 seconds of silence, even if we decoded nothing.
+    rule1: OnlineCTCEndpointRule = OnlineCTCEndpointRule(False, 5000, 0)
+    # rule4 times out after 1.0 seconds of silence after decoding something,
+    # even if we did not reach a final-state at all.
+    rule2: OnlineCTCEndpointRule = OnlineCTCEndpointRule(True, 1000, 0)
+    # rule5 times out after the utterance is 20 seconds long, regardless of
+    # anything else.
+    rule3: OnlineCTCEndpointRule = OnlineCTCEndpointRule(False, 0, 20000)
+
+
+class OnlineCTCEndpoint:
+    """
+    [END-TO-END AUTOMATIC SPEECH RECOGNITION INTEGRATED WITH CTC-BASED VOICE ACTIVITY DETECTION](https://arxiv.org/pdf/2002.00551.pdf)
+    """
+
+    def __init__(self, opts: OnlineCTCEndpoingOpt):
+        self.opts = opts
+        logger.info(f"Endpont Opts: {opts}")
+        self.frame_shift_in_ms = opts.frame_shift_in_ms
+
+        self.num_frames_decoded = 0
+        self.trailing_silence_frames = 0
+
+        self.reset()
+
+    def reset(self):
+        self.num_frames_decoded = 0
+        self.trailing_silence_frames = 0
+
+    def rule_activated(self,
+                       rule: OnlineCTCEndpointRule,
+                       rule_name: str,
+                       decoding_something: bool,
+                       trailine_silence: int,
+                       utterance_length: int) -> bool:
+        ans = (
+            decoding_something or (not rule.must_contain_nonsilence)
+        ) and trailine_silence >= rule.min_trailing_silence and utterance_length >= rule.min_utterance_length
+        if (ans):
+            logger.info(
+                f"Endpoint Rule: {rule_name} activated: {decoding_something}, {trailine_silence}, {utterance_length}"
+            )
+        return ans
+
+    def endpoint_detected(ctc_log_probs: List[List[float]],
+                          decoding_something: bool) -> bool:
+        for logprob in ctc_log_probs:
+            blank_prob = exp(logprob[self.opts.blank_id])
+
+            self.num_frames_decoded += 1
+            if blank_prob > self.opts.blank_threshold:
+                self.trailing_silence_frames += 1
+            else:
+                self.trailing_silence_frames = 0
+
+        assert self.num_frames_decoded >= self.trailing_silence_frames
+        assert self.frame_shift_in_ms > 0
+
+        utterance_length = self.num_frames_decoded * self.frame_shift_in_ms
+        trailing_silence = self.trailing_silence_frames * self.frame_shift_in_ms
+        if self.rule_activated(self.opts.rule1, 'rule1', decoding_something,
+                               trailing_silence, utterance_length):
+            return True
+        if self.rule_activated(self.opts.rule2, 'rule2', decoding_something,
+                               trailing_silence, utterance_length):
+            return True
+        if self.rule_activated(self.opts.rule3, 'rule3', decoding_something,
+                               trailing_silence, utterance_length):
+            return True
+        return False

paddlespeech/server/engine/asr/online/ctc_search.py

@@ -30,8 +30,29 @@ class CTCPrefixBeamSearch:
             config (yacs.config.CfgNode): the ctc prefix beam search configuration
         """
         self.config = config
+
+        # beam size
+        self.first_beam_size = self.config.beam_size
+        # TODO(support second beam size)
+        self.second_beam_size = int(self.first_beam_size * 1.0)
+        logger.info(
+            f"first and second beam size: {self.first_beam_size}, {self.second_beam_size}"
+        )
+
+        # state
+        self.cur_hyps = None
+        self.hyps = None
+        self.abs_time_step = 0
+
         self.reset()
 
+    def reset(self):
+        """Rest the search cache value
+        """
+        self.cur_hyps = None
+        self.hyps = None
+        self.abs_time_step = 0
+
     @paddle.no_grad()
     def search(self, ctc_probs, device, blank_id=0):
         """ctc prefix beam search method decode a chunk feature
@@ -47,12 +68,17 @@ class CTCPrefixBeamSearch:
         """
         # decode 
         logger.info("start to ctc prefix search")
-
+        assert len(ctc_probs.shape) == 2
         batch_size = 1
-        beam_size = self.config.beam_size
-        maxlen = ctc_probs.shape[0]
 
-        assert len(ctc_probs.shape) == 2
+        vocab_size = ctc_probs.shape[1]
+        first_beam_size = min(self.first_beam_size, vocab_size)
+        second_beam_size = min(self.second_beam_size, vocab_size)
+        logger.info(
+            f"effect first and second beam size: {self.first_beam_size}, {self.second_beam_size}"
+        )
+
+        maxlen = ctc_probs.shape[0]
 
         # cur_hyps: (prefix, (blank_ending_score, none_blank_ending_score))
         # 0. blank_ending_score,
@@ -75,7 +101,8 @@ class CTCPrefixBeamSearch:
 
             # 2.1 First beam prune: select topk best
             #     do token passing process
-            top_k_logp, top_k_index = logp.topk(beam_size)  # (beam_size,)
+            top_k_logp, top_k_index = logp.topk(
+                first_beam_size)  # (first_beam_size,)
             for s in top_k_index:
                 s = s.item()
                 ps = logp[s].item()
@@ -148,7 +175,7 @@ class CTCPrefixBeamSearch:
                 next_hyps.items(),
                 key=lambda x: log_add([x[1][0], x[1][1]]),
                 reverse=True)
-            self.cur_hyps = next_hyps[:beam_size]
+            self.cur_hyps = next_hyps[:second_beam_size]
 
             # 2.3 update the absolute time step
             self.abs_time_step += 1
@@ -163,7 +190,7 @@ class CTCPrefixBeamSearch:
         """Return the one best result
 
         Returns:
-            list: the one best result
+            list: the one best result, List[str]
         """
         return [self.hyps[0][0]]
 
@@ -171,17 +198,10 @@ class CTCPrefixBeamSearch:
         """Return the search hyps
 
         Returns:
-            list: return the search hyps
+            list: return the search hyps, List[Tuple[str, float, ...]]
         """
         return self.hyps
 
-    def reset(self):
-        """Rest the search cache value
-        """
-        self.cur_hyps = None
-        self.hyps = None
-        self.abs_time_step = 0
-
     def finalize_search(self):
         """do nothing in ctc_prefix_beam_search
         """