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

# 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.
import copy
import os
from typing import Optional

import numpy as np
import paddle
from numpy import float32
from yacs.config import CfgNode

from paddlespeech.cli.asr.infer import ASRExecutor
from paddlespeech.cli.asr.infer import model_alias
from paddlespeech.cli.log import logger
from paddlespeech.cli.utils import download_and_decompress
from paddlespeech.cli.utils import MODEL_HOME
from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
from paddlespeech.s2t.frontend.speech import SpeechSegment
from paddlespeech.s2t.modules.ctc import CTCDecoder
from paddlespeech.s2t.transform.transformation import Transformation
from paddlespeech.s2t.utils.dynamic_import import dynamic_import
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_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__ = ['ASREngine']

pretrained_models = {
    "deepspeech2online_aishell-zh-16k": {
        'url':
        'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_fbank161_ckpt_0.2.1.model.tar.gz',
        'md5':
        '98b87b171b7240b7cae6e07d8d0bc9be',
        'cfg_path':
        'model.yaml',
        'ckpt_path':
        'exp/deepspeech2_online/checkpoints/avg_1',
        'model':
        'exp/deepspeech2_online/checkpoints/avg_1.jit.pdmodel',
        'params':
        'exp/deepspeech2_online/checkpoints/avg_1.jit.pdiparams',
        'lm_url':
        'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
        'lm_md5':
        '29e02312deb2e59b3c8686c7966d4fe3'
    },
    "conformer_online_multicn-zh-16k": {
        'url':
        'https://paddlespeech.bj.bcebos.com/s2t/multi_cn/asr1/asr1_chunk_conformer_multi_cn_ckpt_0.2.3.model.tar.gz',
        'md5':
        '0ac93d390552336f2a906aec9e33c5fa',
        'cfg_path':
        'model.yaml',
        'ckpt_path':
        'exp/chunk_conformer/checkpoints/multi_cn',
        'model':
        'exp/chunk_conformer/checkpoints/multi_cn.pdparams',
        'params':
        'exp/chunk_conformer/checkpoints/multi_cn.pdparams',
        'lm_url':
        'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
        'lm_md5':
        '29e02312deb2e59b3c8686c7966d4fe3'
    },
}


# ASR server connection process class
class PaddleASRConnectionHanddler:
    def __init__(self, asr_engine):
        """Init a Paddle ASR Connection Handler instance

        Args:
            asr_engine (ASREngine): the global asr engine
        """
        super().__init__()
        logger.info(
            "create an paddle asr connection handler to process the websocket connection"
        )
        self.config = asr_engine.config
        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

        if "deepspeech2online" in self.model_type or "deepspeech2offline" in self.model_type:
            from paddlespeech.s2t.io.collator import SpeechCollator
            self.am_predictor = self.asr_engine.executor.am_predictor

            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 / 1000 *
                                  self.sample_rate)
            self.n_shift = int(self.model_config.stride_ms / 1000 *
                               self.sample_rate)

        elif "conformer" in self.model_type or "transformer" in self.model_type:
            # acoustic model
            self.model = self.asr_engine.executor.model

            # ctc decoding config
            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 samples length and frame shift samples length
            self.win_length = self.preprocess_conf.process[0]['win_length']
            self.n_shift = self.preprocess_conf.process[0]['n_shift']

    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 self.first_char_occur_elapsed is not None:
        #     self.first_char_occur_elapsed = time.time()

        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

            # pcm16 -> pcm 32
            # pcm2float will change the orignal samples, 
            # so we shoule do pcm2float before concatenate
            samples = pcm2float(samples)

            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}"
            )

            # read audio
            speech_segment = SpeechSegment.from_pcm(
                self.remained_wav, 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 "conformer_online" in self.model_type:
            logger.info("Online ASR extract the feat")
            samples = np.frombuffer(samples, dtype=np.int16)
            assert samples.ndim == 1

            logger.info(f"This package receive {samples.shape[0]} pcm data")
            self.num_samples += samples.shape[0]

            # 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
                self.remained_wav = np.concatenate([self.remained_wav, samples])
            logger.info(
                f"The connection remain the audio samples: {self.remained_wav.shape}"
            )
            if len(self.remained_wav) < self.win_length:
                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)
            if self.cached_feat is None:
                self.cached_feat = x_chunk
            else:
                assert (len(x_chunk.shape) == 3)
                assert (len(self.cached_feat.shape) == 3)
                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

            num_frames = x_chunk.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}"
            )
            # logger.info(f"accumulate samples: {self.num_samples}")       

    def reset(self):
        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)

        # 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 = ['']
        self.word_time_stamp = []
        self.time_stamp = []
        self.first_char_occur_elapsed = None

    def decode(self, is_finished=False):
        if "deepspeech2online" in self.model_type:
            # 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(
                    f"we will use the transformer like model : {self.model_type}"
                )
                self.advance_decoding(is_finished)
                self.update_result()

            except Exception as e:
                logger.exception(e)
        else:
            raise Exception("invalid model name")

    @paddle.no_grad()
    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 best result: {trans_best[0]}")
        return trans_best[0]

    @paddle.no_grad()
    def advance_decoding(self, is_finished=False):
        logger.info("start to decode with advanced_decoding method")
        cfg = self.ctc_decode_config
        decoding_chunk_size = cfg.decoding_chunk_size
        num_decoding_left_chunks = cfg.num_decoding_left_chunks

        assert decoding_chunk_size > 0
        subsampling = self.model.encoder.embed.subsampling_rate
        context = self.model.encoder.embed.right_context + 1
        stride = subsampling * decoding_chunk_size
        cached_feature_num = context - subsampling  # processed chunk feature cached for next chunk

        # 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")
        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:
            # 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

        # record the end for removing the processed feat
        end = None
        for cur in range(0, num_frames - left_frames + 1, stride):
            end = min(cur + decoding_window, num_frames)

            self.chunk_num += 1
            chunk_xs = self.cached_feat[:, cur:end, :]
            (y, self.subsampling_cache, self.elayers_output_cache,
             self.conformer_cnn_cache) = self.model.encoder.forward_chunk(
                 chunk_xs, self.offset, required_cache_size,
                 self.subsampling_cache, self.elayers_output_cache,
                 self.conformer_cnn_cache)
            outputs.append(y)

            # update the offset
            self.offset += y.shape[1]

        ys = paddle.cat(outputs, 1)
        if self.encoder_out is None:
            self.encoder_out = ys
        else:
            self.encoder_out = paddle.concat([self.encoder_out, ys], axis=1)

        # get the ctc probs
        ctc_probs = self.model.ctc.log_softmax(ys)  # (1, maxlen, vocab_size)
        ctc_probs = ctc_probs.squeeze(0)

        self.searcher.search(ctc_probs, self.cached_feat.place)

        self.hyps = self.searcher.get_one_best_hyps()
        assert self.cached_feat.shape[0] == 1
        assert end >= cached_feature_num

        self.cached_feat = self.cached_feat[0, end -
                                            cached_feature_num:, :].unsqueeze(0)
        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):
        logger.info("update the final result")
        hyps = self.hyps
        self.result_transcripts = [
            self.text_feature.defeaturize(hyp) for hyp in hyps
        ]
        self.result_tokenids = [hyp for hyp in hyps]

    def get_result(self):
        if len(self.result_transcripts) > 0:
            return self.result_transcripts[0]
        else:
            return ''

    def get_word_time_stamp(self):
        return self.word_time_stamp

    @paddle.no_grad()
    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

        self.searcher.finalize_search()
        self.update_result()

        beam_size = self.ctc_decode_config.beam_size
        hyps = self.searcher.get_hyps()
        if hyps is None or len(hyps) == 0:
            return

        # assert len(hyps) == beam_size
        hyp_list = []
        for hyp in hyps:
            hyp_content = hyp[0]
            # Prevent the hyp is empty
            if len(hyp_content) == 0:
                hyp_content = (self.model.ctc.blank_id, )
            hyp_content = paddle.to_tensor(
                hyp_content, place=self.device, dtype=paddle.long)
            hyp_list.append(hyp_content)
        hyps_pad = pad_sequence(hyp_list, True, self.model.ignore_id)
        hyps_lens = paddle.to_tensor(
            [len(hyp[0]) for hyp in hyps], place=self.device,
            dtype=paddle.long)  # (beam_size,)
        hyps_pad, _ = add_sos_eos(hyps_pad, self.model.sos, self.model.eos,
                                  self.model.ignore_id)
        hyps_lens = hyps_lens + 1  # Add <sos> at begining

        encoder_out = self.encoder_out.repeat(beam_size, 1, 1)
        encoder_mask = paddle.ones(
            (beam_size, 1, encoder_out.shape[1]), dtype=paddle.bool)
        decoder_out, _ = self.model.decoder(
            encoder_out, encoder_mask, hyps_pad,
            hyps_lens)  # (beam_size, max_hyps_len, vocab_size)
        # ctc score in ln domain
        decoder_out = paddle.nn.functional.log_softmax(decoder_out, axis=-1)
        decoder_out = decoder_out.numpy()

        # Only use decoder score for rescoring
        best_score = -float('inf')
        best_index = 0
        # hyps is List[(Text=List[int], Score=float)], len(hyps)=beam_size
        for i, hyp in enumerate(hyps):
            score = 0.0
            for j, w in enumerate(hyp[0]):
                score += decoder_out[i][j][w]
            # last decoder output token is `eos`, for laste decoder input token.
            score += decoder_out[i][len(hyp[0])][self.model.eos]
            # add ctc score (which in ln domain)
            score += hyp[1] * self.ctc_decode_config.ctc_weight
            if score > best_score:
                best_score = score
                best_index = i

        # update the one best result
        # hyps stored the beam results and each fields is:

        logger.info(f"best index: {best_index}")
        # logger.info(f'best result: {hyps[best_index]}')
        # the field of the hyps is:
        # hyps[0][0]: the sentence word-id in the vocab with a tuple
        # hyps[0][1]: the sentence decoding probability with all paths
        # hyps[0][2]: viterbi_blank ending probability
        # hyps[0][3]: viterbi_non_blank probability
        # hyps[0][4]: current_token_prob,
        # hyps[0][5]: times_viterbi_blank, 
        # hyps[0][6]: times_titerbi_non_blank 
        self.hyps = [hyps[best_index][0]]

        # update the hyps time stamp
        self.time_stamp = hyps[best_index][5] if hyps[best_index][2] > hyps[
            best_index][3] else hyps[best_index][6]
        logger.info(f"time stamp: {self.time_stamp}")

        self.update_result()

        # update each word start and end time stamp
        frame_shift_in_ms = self.model.encoder.embed.subsampling_rate * self.n_shift / self.sample_rate
        logger.info(f"frame shift ms: {frame_shift_in_ms}")
        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_ms

            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_ms
            word_time_stamp.append({
                "w": self.result_transcripts[0][idx],
                "bg": start,
                "ed": end
            })
            # logger.info(f"{self.result_transcripts[0][idx]}, start: {start}, end: {end}")
        self.word_time_stamp = word_time_stamp
        logger.info(f"word time stamp: {self.word_time_stamp}")


class ASRServerExecutor(ASRExecutor):
    def __init__(self):
        super().__init__()
        pass

    def _get_pretrained_path(self, tag: str) -> os.PathLike:
        """
        Download and returns pretrained resources path of current task.
        """
        support_models = list(pretrained_models.keys())
        assert tag in pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format(
            tag, '\n\t\t'.join(support_models))

        res_path = os.path.join(MODEL_HOME, tag)
        decompressed_path = download_and_decompress(pretrained_models[tag],
                                                    res_path)
        decompressed_path = os.path.abspath(decompressed_path)
        logger.info(
            'Use pretrained model stored in: {}'.format(decompressed_path))

        return decompressed_path

    def _init_from_path(self,
                        model_type: str='deepspeech2online_aishell',
                        am_model: Optional[os.PathLike]=None,
                        am_params: Optional[os.PathLike]=None,
                        lang: str='zh',
                        sample_rate: int=16000,
                        cfg_path: Optional[os.PathLike]=None,
                        decode_method: str='attention_rescoring',
                        am_predictor_conf: dict=None):
        """
        Init model and other resources from a specific path.
        """
        self.model_type = model_type
        self.sample_rate = sample_rate
        if cfg_path is None or am_model is None or am_params is None:
            sample_rate_str = '16k' if sample_rate == 16000 else '8k'
            tag = model_type + '-' + lang + '-' + sample_rate_str
            logger.info(f"Load the pretrained model, tag = {tag}")
            res_path = self._get_pretrained_path(tag)  # wenetspeech_zh
            self.res_path = res_path

            self.cfg_path = os.path.join(res_path,
                                         pretrained_models[tag]['cfg_path'])

            self.am_model = os.path.join(res_path,
                                         pretrained_models[tag]['model'])
            self.am_params = os.path.join(res_path,
                                          pretrained_models[tag]['params'])
            logger.info(res_path)
        else:
            self.cfg_path = os.path.abspath(cfg_path)
            self.am_model = os.path.abspath(am_model)
            self.am_params = os.path.abspath(am_params)
            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)

        #Init body.
        self.config = CfgNode(new_allowed=True)
        self.config.merge_from_file(self.cfg_path)

        with UpdateConfig(self.config):
            if "deepspeech2online" in model_type or "deepspeech2offline" in model_type:
                from paddlespeech.s2t.io.collator import SpeechCollator
                self.vocab = self.config.vocab_filepath
                self.config.decode.lang_model_path = os.path.join(
                    MODEL_HOME, 'language_model',
                    self.config.decode.lang_model_path)
                self.collate_fn_test = SpeechCollator.from_config(self.config)
                self.text_feature = TextFeaturizer(
                    unit_type=self.config.unit_type, vocab=self.vocab)

                lm_url = pretrained_models[tag]['lm_url']
                lm_md5 = pretrained_models[tag]['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)
            elif "conformer" in model_type or "transformer" in model_type:
                logger.info("start to create the stream conformer asr engine")
                if self.config.spm_model_prefix:
                    self.config.spm_model_prefix = os.path.join(
                        self.res_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)
                # update the decoding method
                if decode_method:
                    self.config.decode.decoding_method = decode_method

                # we only support ctc_prefix_beam_search and attention_rescoring dedoding method
                # Generally we set the decoding_method to attention_rescoring
                if self.config.decode.decoding_method not in [
                        "ctc_prefix_beam_search", "attention_rescoring"
                ]:
                    logger.info(
                        "we set the decoding_method to attention_rescoring")
                    self.config.decode.decoding = "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 "deepspeech2online" in model_type or "deepspeech2offline" 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)

            # decoder
            logger.info("ASR engine start to create the ctc decoder instance")
            self.decoder = CTCDecoder(
                odim=self.config.output_dim,  # <blank> is in  vocab
                enc_n_units=self.config.rnn_layer_size * 2,
                blank_id=self.config.blank_id,
                dropout_rate=0.0,
                reduction=True,  # sum
                batch_average=True,  # sum / batch_size
                grad_norm_type=self.config.get('ctc_grad_norm_type', None))

            # init decoder
            logger.info("ASR engine start to init the ctc decoder")
            cfg = self.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)

            # init state box
            self.chunk_state_h_box = np.zeros(
                (self.config.num_rnn_layers, 1, self.config.rnn_layer_size),
                dtype=float32)
            self.chunk_state_c_box = np.zeros(
                (self.config.num_rnn_layers, 1, self.config.rnn_layer_size),
                dtype=float32)
        elif "conformer" in model_type or "transformer" in model_type:
            model_name = model_type[:model_type.rindex(
                '_')]  # model_type: {model_name}_{dataset}
            logger.info(f"model name: {model_name}")
            model_class = dynamic_import(model_name, model_alias)
            model_conf = self.config
            model = model_class.from_config(model_conf)
            self.model = 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")

            # update the ctc decoding
            self.searcher = CTCPrefixBeamSearch(self.config.decode)
            self.transformer_decode_reset()

    def reset_decoder_and_chunk(self):
        """reset decoder and chunk state for an new audio
        """
        if "deepspeech2online" in self.model_type or "deepspeech2offline" in self.model_type:
            self.decoder.reset_decoder(batch_size=1)
            # init state box, for new audio request
            self.chunk_state_h_box = np.zeros(
                (self.config.num_rnn_layers, 1, self.config.rnn_layer_size),
                dtype=float32)
            self.chunk_state_c_box = np.zeros(
                (self.config.num_rnn_layers, 1, self.config.rnn_layer_size),
                dtype=float32)
        elif "conformer" in self.model_type or "transformer" in self.model_type:
            self.transformer_decode_reset()

    def decode_one_chunk(self, x_chunk, x_chunk_lens, model_type: str):
        """decode one chunk

        Args:
            x_chunk (numpy.array): shape[B, T, D]
            x_chunk_lens (numpy.array): shape[B]
            model_type (str): online model type

        Returns:
            str: one best result
        """
        logger.info("start to decoce chunk by chunk")
        if "deepspeech2online" in model_type:
            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 best result: {trans_best[0]}")
            return trans_best[0]

        elif "conformer" in model_type or "transformer" in model_type:
            try:
                logger.info(
                    f"we will use the transformer like model : {self.model_type}"
                )
                self.advanced_decoding(x_chunk, x_chunk_lens)
                self.update_result()

                return self.result_transcripts[0]
            except Exception as e:
                logger.exception(e)
        else:
            raise Exception("invalid model name")

    def advanced_decoding(self, xs: paddle.Tensor, x_chunk_lens):
        logger.info("start to decode with advanced_decoding method")
        encoder_out, encoder_mask = self.encoder_forward(xs)
        ctc_probs = self.model.ctc.log_softmax(
            encoder_out)  # (1, maxlen, vocab_size)
        ctc_probs = ctc_probs.squeeze(0)
        self.searcher.search(ctc_probs, xs.place)
        # update the one best result
        self.hyps = self.searcher.get_one_best_hyps()

        # now we supprot ctc_prefix_beam_search and attention_rescoring
        if "attention_rescoring" in self.config.decode.decoding_method:
            self.rescoring(encoder_out, xs.place)

    def encoder_forward(self, xs):
        logger.info("get the model out from the feat")
        cfg = self.config.decode
        decoding_chunk_size = cfg.decoding_chunk_size
        num_decoding_left_chunks = cfg.num_decoding_left_chunks

        assert decoding_chunk_size > 0
        subsampling = self.model.encoder.embed.subsampling_rate
        context = self.model.encoder.embed.right_context + 1
        stride = subsampling * decoding_chunk_size

        # decoding window for model
        decoding_window = (decoding_chunk_size - 1) * subsampling + context
        num_frames = xs.shape[1]
        required_cache_size = decoding_chunk_size * num_decoding_left_chunks

        logger.info("start to do model forward")
        outputs = []

        # num_frames - context + 1 ensure that current frame can get context window
        for cur in range(0, num_frames - context + 1, stride):
            end = min(cur + decoding_window, num_frames)
            chunk_xs = xs[:, cur:end, :]
            (y, self.subsampling_cache, self.elayers_output_cache,
             self.conformer_cnn_cache) = self.model.encoder.forward_chunk(
                 chunk_xs, self.offset, required_cache_size,
                 self.subsampling_cache, self.elayers_output_cache,
                 self.conformer_cnn_cache)
            outputs.append(y)
            self.offset += y.shape[1]

        ys = paddle.cat(outputs, 1)
        masks = paddle.ones([1, ys.shape[1]], dtype=paddle.bool)
        masks = masks.unsqueeze(1)
        return ys, masks

    def rescoring(self, encoder_out, device):
        logger.info("start to rescoring the hyps")
        beam_size = self.config.decode.beam_size
        hyps = self.searcher.get_hyps()
        assert len(hyps) == beam_size

        hyp_list = []
        for hyp in hyps:
            hyp_content = hyp[0]
            # Prevent the hyp is empty
            if len(hyp_content) == 0:
                hyp_content = (self.model.ctc.blank_id, )
            hyp_content = paddle.to_tensor(
                hyp_content, place=device, dtype=paddle.long)
            hyp_list.append(hyp_content)
        hyps_pad = pad_sequence(hyp_list, True, self.model.ignore_id)
        hyps_lens = paddle.to_tensor(
            [len(hyp[0]) for hyp in hyps], place=device,
            dtype=paddle.long)  # (beam_size,)
        hyps_pad, _ = add_sos_eos(hyps_pad, self.model.sos, self.model.eos,
                                  self.model.ignore_id)
        hyps_lens = hyps_lens + 1  # Add <sos> at begining

        encoder_out = encoder_out.repeat(beam_size, 1, 1)
        encoder_mask = paddle.ones(
            (beam_size, 1, encoder_out.shape[1]), dtype=paddle.bool)
        decoder_out, _ = self.model.decoder(
            encoder_out, encoder_mask, hyps_pad,
            hyps_lens)  # (beam_size, max_hyps_len, vocab_size)
        # ctc score in ln domain
        decoder_out = paddle.nn.functional.log_softmax(decoder_out, axis=-1)
        decoder_out = decoder_out.numpy()

        # Only use decoder score for rescoring
        best_score = -float('inf')
        best_index = 0
        # hyps is List[(Text=List[int], Score=float)], len(hyps)=beam_size
        for i, hyp in enumerate(hyps):
            score = 0.0
            for j, w in enumerate(hyp[0]):
                score += decoder_out[i][j][w]
            # last decoder output token is `eos`, for laste decoder input token.
            score += decoder_out[i][len(hyp[0])][self.model.eos]
            # add ctc score (which in ln domain)
            score += hyp[1] * self.config.decode.ctc_weight
            if score > best_score:
                best_score = score
                best_index = i

        # update the one best result
        self.hyps = [hyps[best_index][0]]
        return hyps[best_index][0]

    def transformer_decode_reset(self):
        self.subsampling_cache = None
        self.elayers_output_cache = None
        self.conformer_cnn_cache = None
        self.offset = 0
        # decoding reset
        self.searcher.reset()

    def update_result(self):
        logger.info("update the final result")
        hyps = self.hyps
        self.result_transcripts = [
            self.text_feature.defeaturize(hyp) for hyp in hyps
        ]
        self.result_tokenids = [hyp for hyp in hyps]

    def extract_feat(self, samples, sample_rate):
        """extract feat

        Args:
            samples (numpy.array): numpy.float32
            sample_rate (int): sample rate

        Returns:
            x_chunk (numpy.array): shape[B, T, D]
            x_chunk_lens (numpy.array): shape[B]
        """

        if "deepspeech2online" in self.model_type:
            # pcm16 -> pcm 32
            samples = pcm2float(samples)
            # read audio
            speech_segment = SpeechSegment.from_pcm(
                samples, 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)

            x_chunk = audio.numpy()
            x_chunk_lens = np.array([audio_len])

            return x_chunk, x_chunk_lens
        elif "conformer_online" in self.model_type:

            if sample_rate != self.sample_rate:
                logger.info(f"audio sample rate {sample_rate} is not match,"
                            "the model sample_rate is {self.sample_rate}")
            logger.info(f"ASR Engine use the {self.model_type} to process")
            logger.info("Create the preprocess instance")
            preprocess_conf = self.config.preprocess_config
            preprocess_args = {"train": False}
            preprocessing = Transformation(preprocess_conf)

            logger.info("Read the audio file")
            logger.info(f"audio shape: {samples.shape}")
            # fbank
            x_chunk = preprocessing(samples, **preprocess_args)
            x_chunk_lens = paddle.to_tensor(x_chunk.shape[0])
            x_chunk = paddle.to_tensor(
                x_chunk, dtype="float32").unsqueeze(axis=0)
            logger.info(
                f"process the audio feature success, feat shape: {x_chunk.shape}"
            )
            return x_chunk, x_chunk_lens


class ASREngine(BaseEngine):
    """ASR server engine

    Args:
        metaclass: Defaults to Singleton.
    """

    def __init__(self):
        super(ASREngine, self).__init__()
        logger.info("create the online asr engine instance")

    def init(self, config: dict) -> bool:
        """init engine resource

        Args:
            config_file (str): config file

        Returns:
            bool: init failed or success
        """
        self.input = None
        self.output = ""
        self.executor = ASRServerExecutor()
        self.config = config
        try:
            if self.config.get("device", None):
                self.device = self.config.device
            else:
                self.device = paddle.get_device()
            logger.info(f"paddlespeech_server set the device: {self.device}")
            paddle.set_device(self.device)
        except BaseException:
            logger.error(
                "Set device failed, please check if device is already used and the parameter 'device' in the yaml file"
            )

        self.executor._init_from_path(
            model_type=self.config.model_type,
            am_model=self.config.am_model,
            am_params=self.config.am_params,
            lang=self.config.lang,
            sample_rate=self.config.sample_rate,
            cfg_path=self.config.cfg_path,
            decode_method=self.config.decode_method,
            am_predictor_conf=self.config.am_predictor_conf)

        logger.info("Initialize ASR server engine successfully.")
        return True

    def preprocess(self,
                   samples,
                   sample_rate,
                   model_type="deepspeech2online_aishell-zh-16k"):
        """preprocess

        Args:
            samples (numpy.array): numpy.float32
            sample_rate (int): sample rate

        Returns:
            x_chunk (numpy.array): shape[B, T, D]
            x_chunk_lens (numpy.array): shape[B]
        """
        # if "deepspeech" in model_type:
        x_chunk, x_chunk_lens = self.executor.extract_feat(samples, sample_rate)
        return x_chunk, x_chunk_lens

    def run(self, x_chunk, x_chunk_lens, decoder_chunk_size=1):
        """run online engine

        Args:
            x_chunk (numpy.array): shape[B, T, D]
            x_chunk_lens (numpy.array): shape[B]
            decoder_chunk_size(int)
        """
        self.output = self.executor.decode_one_chunk(x_chunk, x_chunk_lens,
                                                     self.config.model_type)

    def postprocess(self):
        """postprocess
        """
        return self.output

    def reset(self):
        """reset engine decoder and inference state
        """
        self.executor.reset_decoder_and_chunk()
        self.output = ""