add ds2 model multi session, test=doc

paddlespeech/server/conf/ws_application.yaml

@@ -18,44 +18,10 @@ engine_list: ['asr_online']
 #                                ENGINE CONFIG                                  #
 #################################################################################
 
-# ################################### ASR #########################################
-# ################### speech task: asr; engine_type: online #######################
-# asr_online:
-#     model_type: 'deepspeech2online_aishell'
-#     am_model: # the pdmodel file of am static model [optional]
-#     am_params:  # the pdiparams file of am static model [optional]
-#     lang: 'zh'
-#     sample_rate: 16000
-#     cfg_path: 
-#     decode_method: 
-#     force_yes: True
-
-#     am_predictor_conf:
-#         device:  # set 'gpu:id' or 'cpu'
-#         switch_ir_optim: True
-#         glog_info: False  # True -> print glog
-#         summary: True  # False -> do not show predictor config
-
-#     chunk_buffer_conf:
-#         frame_duration_ms: 80
-#         shift_ms: 40
-#         sample_rate: 16000
-#         sample_width: 2
-
-#     vad_conf:
-#         aggressiveness: 2
-#         sample_rate: 16000
-#         frame_duration_ms: 20
-#         sample_width: 2
-#         padding_ms: 200
-#         padding_ratio: 0.9
-
-
-
 ################################### ASR #########################################
 ################### speech task: asr; engine_type: online #######################
 asr_online:
-    model_type: 'conformer2online_aishell'
+    model_type: 'deepspeech2online_aishell'
     am_model: # the pdmodel file of am static model [optional]
     am_params:  # the pdiparams file of am static model [optional]
     lang: 'zh'
@@ -71,9 +37,19 @@ asr_online:
         summary: True  # False -> do not show predictor config
 
     chunk_buffer_conf:
+        frame_duration_ms: 80
+        shift_ms: 40
+        sample_rate: 16000
+        sample_width: 2
         window_n: 7     # frame
         shift_n: 4      # frame
-        window_ms: 25   # ms
+        window_ms: 20   # ms
         shift_ms: 10    # ms
+
+    vad_conf:
+        aggressiveness: 2
         sample_rate: 16000
-        sample_width: 2
+        frame_duration_ms: 20
\ No newline at end of file
+        sample_width: 2
+        padding_ms: 200
+        padding_ratio: 0.9
\ No newline at end of file

paddlespeech/server/conf/ws_conformer_application.yaml

+# This is the parameter configuration file for PaddleSpeech Serving.
+
+#################################################################################
+#                             SERVER SETTING                                    #
+#################################################################################
+host: 0.0.0.0
+port: 8090
+
+# The task format in the engin_list is: <speech task>_<engine type>
+# task choices = ['asr_online', 'tts_online']
+# protocol = ['websocket', 'http'] (only one can be selected).
+# websocket only support online engine type.
+protocol: 'websocket'
+engine_list: ['asr_online']
+
+
+#################################################################################
+#                                ENGINE CONFIG                                  #
+#################################################################################
+
+################################### ASR #########################################
+################### speech task: asr; engine_type: online #######################
+asr_online:
+    model_type: 'conformer2online_aishell'
+    am_model: # the pdmodel file of am static model [optional]
+    am_params:  # the pdiparams file of am static model [optional]
+    lang: 'zh'
+    sample_rate: 16000
+    cfg_path: 
+    decode_method: 
+    force_yes: True
+
+    am_predictor_conf:
+        device:  # set 'gpu:id' or 'cpu'
+        switch_ir_optim: True
+        glog_info: False  # True -> print glog
+        summary: True  # False -> do not show predictor config
+
+    chunk_buffer_conf:
+        window_n: 7     # frame
+        shift_n: 4      # frame
+        window_ms: 25   # ms
+        shift_ms: 10    # ms
+        sample_rate: 16000
+        sample_width: 2
\ No newline at end of file

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

@@ -13,7 +13,7 @@
 # limitations under the License.
 import os
 from typing import Optional
-
+import copy
 import numpy as np
 import paddle
 from numpy import float32
@@ -93,7 +93,7 @@ class PaddleASRConnectionHanddler:
         )
         self.config = asr_engine.config
         self.model_config = asr_engine.executor.config
-        self.model = asr_engine.executor.model
+        # self.model = asr_engine.executor.model
         self.asr_engine = asr_engine
 
         self.init()
@@ -102,7 +102,32 @@ class PaddleASRConnectionHanddler:
     def init(self):
         self.model_type = self.asr_engine.executor.model_type
         if "deepspeech2online" in self.model_type or "deepspeech2offline" in self.model_type:
-            pass
+            from paddlespeech.s2t.io.collator import SpeechCollator
+            self.sample_rate = self.asr_engine.executor.sample_rate
+            self.am_predictor = self.asr_engine.executor.am_predictor
+            self.text_feature = self.asr_engine.executor.text_feature
+            self.collate_fn_test = SpeechCollator.from_config(self.model_config)
+            self.decoder = CTCDecoder(
+                odim=self.model_config.output_dim,  # <blank> is in  vocab
+                enc_n_units=self.model_config.rnn_layer_size * 2,
+                blank_id=self.model_config.blank_id,
+                dropout_rate=0.0,
+                reduction=True,  # sum
+                batch_average=True,  # sum / batch_size
+                grad_norm_type=self.model_config.get('ctc_grad_norm_type', None)) 
+
+            cfg = self.model_config.decode
+            decode_batch_size = 1  # for online
+            self.decoder.init_decoder(
+                decode_batch_size, self.text_feature.vocab_list,
+                cfg.decoding_method, cfg.lang_model_path, cfg.alpha, cfg.beta,
+                cfg.beam_size, cfg.cutoff_prob, cfg.cutoff_top_n,
+                cfg.num_proc_bsearch)
+                # frame window samples length and frame shift samples length
+                
+            self.win_length = int(self.model_config.window_ms * self.sample_rate)
+            self.n_shift = int(self.model_config.stride_ms * self.sample_rate)
+
         elif "conformer" in self.model_type or "transformer" in self.model_type or "wenetspeech" in self.model_type:
             self.sample_rate = self.asr_engine.executor.sample_rate
 
@@ -127,7 +152,65 @@ class PaddleASRConnectionHanddler:
 
     def extract_feat(self, samples):
         if "deepspeech2online" in self.model_type:
-            pass
+            # 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}"
+            )
+
+            # pcm16 -> pcm 32
+            samples = pcm2float(self.remained_wav)
+            # read audio
+            speech_segment = SpeechSegment.from_pcm(
+                samples, self.sample_rate, transcript=" ")
+            # audio augment
+            self.collate_fn_test.augmentation.transform_audio(speech_segment)
+
+            # extract speech feature
+            spectrum, transcript_part = self.collate_fn_test._speech_featurizer.featurize(
+                speech_segment, self.collate_fn_test.keep_transcription_text)
+            # CMVN spectrum
+            if self.collate_fn_test._normalizer:
+                spectrum = self.collate_fn_test._normalizer.apply(spectrum)
+
+            # spectrum augment
+            audio = self.collate_fn_test.augmentation.transform_feature(
+                spectrum)
+
+            audio_len = audio.shape[0]
+            audio = paddle.to_tensor(audio, dtype='float32')
+            # audio_len = paddle.to_tensor(audio_len)
+            audio = paddle.unsqueeze(audio, axis=0)
+            
+            if self.cached_feat is None:
+                self.cached_feat = audio
+            else:
+                assert (len(audio.shape) == 3)
+                assert (len(self.cached_feat.shape) == 3)
+                self.cached_feat = paddle.concat(
+                    [self.cached_feat, audio], axis=1)
+
+                # set the feat device
+            if self.device is None:
+                self.device = self.cached_feat.place
+
+            self.num_frames += audio_len
+            self.remained_wav = self.remained_wav[self.n_shift * audio_len:]
+
+            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 "conformer2online" in self.model_type:
             logger.info("Online ASR extract the feat")
             samples = np.frombuffer(samples, dtype=np.int16)
@@ -179,24 +262,81 @@ class PaddleASRConnectionHanddler:
             # logger.info(f"accumulate samples: {self.num_samples}")       
 
     def reset(self):
-        self.subsampling_cache = None
-        self.elayers_output_cache = None
-        self.conformer_cnn_cache = None
-        self.encoder_out = None
-        self.cached_feat = None
-        self.remained_wav = None
-        self.offset = 0
-        self.num_samples = 0
-        self.device = None
-        self.hyps = []
-        self.num_frames = 0
-        self.chunk_num = 0
-        self.global_frame_offset = 0
-        self.result_transcripts = ['']
+        if "deepspeech2online" in self.model_type or "deepspeech2offline" in self.model_type:
+            # for deepspeech2 
+            self.chunk_state_h_box = copy.deepcopy(self.asr_engine.executor.chunk_state_h_box)
+            self.chunk_state_c_box = copy.deepcopy(self.asr_engine.executor.chunk_state_c_box)
+            self.decoder.reset_decoder(batch_size=1)
+        elif "conformer" in self.model_type or "transformer" in self.model_type or "wenetspeech" in self.model_type:
+            # for conformer online
+            self.subsampling_cache = None
+            self.elayers_output_cache = None
+            self.conformer_cnn_cache = None
+            self.encoder_out = None
+            self.cached_feat = None
+            self.remained_wav = None
+            self.offset = 0
+            self.num_samples = 0
+            self.device = None
+            self.hyps = []
+            self.num_frames = 0
+            self.chunk_num = 0
+            self.global_frame_offset = 0
+            self.result_transcripts = ['']
 
     def decode(self, is_finished=False):
         if "deepspeech2online" in self.model_type:
-            pass
+            # x_chunk 是特征数据
+            decoding_chunk_size = 1 # decoding_chunk_size=1 in deepspeech2 model
+            context = 7             # context=7 in deepspeech2 model
+            subsampling = 4         # subsampling=4 in deepspeech2 model
+            stride = subsampling * decoding_chunk_size
+            cached_feature_num = context - subsampling
+            # decoding window for model
+            decoding_window = (decoding_chunk_size - 1) * subsampling + context        
+            
+            if self.cached_feat is None:
+                logger.info("no audio feat, please input more pcm data")
+                return            
+            
+            num_frames = self.cached_feat.shape[1]
+            logger.info(
+                f"Required decoding window {decoding_window} frames, and the connection has {num_frames} frames"
+            )
+            # the cached feat must be larger decoding_window
+            if num_frames < decoding_window and not is_finished:
+                logger.info(
+                f"frame feat num is less than {decoding_window}, please input more pcm data"
+                )
+                return None, None
+
+            # if is_finished=True, we need at least context frames
+            if num_frames < context:
+                logger.info(
+                "flast {num_frames} is less than context {context} frames, and we cannot do model forward"
+                )
+                return None, None
+            logger.info("start to do model forward")
+            # num_frames - context + 1 ensure that current frame can get context window
+            if is_finished:
+                # if get the finished chunk, we need process the last context
+                left_frames = context
+            else:
+                # we only process decoding_window frames for one chunk
+                left_frames = decoding_window
+
+            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]
+
+            self.cached_feat = self.cached_feat[:, end -
+                                            cached_feature_num:, :]
+            # return trans_best[0]            
         elif "conformer" in self.model_type or "transformer" in self.model_type:
             try:
                 logger.info(
@@ -210,6 +350,48 @@ class PaddleASRConnectionHanddler:
         else:
             raise Exception("invalid model name")
 
+    def decode_one_chunk(self, x_chunk, x_chunk_lens):
+        logger.info("start to decoce one chunk with deepspeech2 model")
+        input_names = self.am_predictor.get_input_names()
+        audio_handle = self.am_predictor.get_input_handle(input_names[0])
+        audio_len_handle = self.am_predictor.get_input_handle(
+                input_names[1])
+        h_box_handle = self.am_predictor.get_input_handle(input_names[2])
+        c_box_handle = self.am_predictor.get_input_handle(input_names[3])
+
+        audio_handle.reshape(x_chunk.shape)
+        audio_handle.copy_from_cpu(x_chunk)
+
+        audio_len_handle.reshape(x_chunk_lens.shape)
+        audio_len_handle.copy_from_cpu(x_chunk_lens)
+
+        h_box_handle.reshape(self.chunk_state_h_box.shape)
+        h_box_handle.copy_from_cpu(self.chunk_state_h_box)
+
+        c_box_handle.reshape(self.chunk_state_c_box.shape)
+        c_box_handle.copy_from_cpu(self.chunk_state_c_box)
+
+        output_names = self.am_predictor.get_output_names()
+        output_handle = self.am_predictor.get_output_handle(output_names[0])
+        output_lens_handle = self.am_predictor.get_output_handle(
+                output_names[1])
+        output_state_h_handle = self.am_predictor.get_output_handle(
+                output_names[2])
+        output_state_c_handle = self.am_predictor.get_output_handle(
+                output_names[3])
+
+        self.am_predictor.run()
+
+        output_chunk_probs = output_handle.copy_to_cpu()
+        output_chunk_lens = output_lens_handle.copy_to_cpu()
+        self.chunk_state_h_box = output_state_h_handle.copy_to_cpu()
+        self.chunk_state_c_box = output_state_c_handle.copy_to_cpu()
+
+        self.decoder.next(output_chunk_probs, output_chunk_lens)
+        trans_best, trans_beam = self.decoder.decode()
+        logger.info(f"decode one one best result: {trans_best[0]}")
+        return trans_best[0]
+
     def advance_decoding(self, is_finished=False):
         logger.info("start to decode with advanced_decoding method")
         cfg = self.ctc_decode_config
@@ -240,6 +422,7 @@ class PaddleASRConnectionHanddler:
             )
             return None, None
 
+        # if is_finished=True, we need at least context frames
         if num_frames < context:
             logger.info(
                 "flast {num_frames} is less than context {context} frames, and we cannot do model forward"
@@ -315,6 +498,9 @@ class PaddleASRConnectionHanddler:
             return ''
 
     def rescoring(self):
+        if "deepspeech2online" in self.model_type or "deepspeech2offline" in self.model_type:
+            return
+        
         logger.info("rescoring the final result")
         if "attention_rescoring" != self.ctc_decode_config.decoding_method:
             return