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531 lines
19 KiB
531 lines
19 KiB
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import sys
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from typing import ByteString
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from typing import Optional
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import numpy as np
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import paddle
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from numpy import float32
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from yacs.config import CfgNode
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from paddlespeech.cli.asr.infer import ASRExecutor
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from paddlespeech.cli.log import logger
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from paddlespeech.resource import CommonTaskResource
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from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
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from paddlespeech.s2t.modules.ctc import CTCDecoder
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from paddlespeech.audio.transform.transformation import Transformation
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from paddlespeech.s2t.utils.utility import UpdateConfig
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from paddlespeech.server.engine.base_engine import BaseEngine
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from paddlespeech.server.utils import onnx_infer
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from paddlespeech.utils.env import MODEL_HOME
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__all__ = ['PaddleASRConnectionHanddler', 'ASRServerExecutor', 'ASREngine']
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# ASR server connection process class
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class PaddleASRConnectionHanddler:
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def __init__(self, asr_engine):
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"""Init a Paddle ASR Connection Handler instance
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Args:
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asr_engine (ASREngine): the global asr engine
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"""
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super().__init__()
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logger.debug(
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"create an paddle asr connection handler to process the websocket connection"
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)
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self.config = asr_engine.config # server config
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self.model_config = asr_engine.executor.config
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self.asr_engine = asr_engine
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# model_type, sample_rate and text_feature is shared for deepspeech2 and conformer
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self.model_type = self.asr_engine.executor.model_type
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self.sample_rate = self.asr_engine.executor.sample_rate
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# tokens to text
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self.text_feature = self.asr_engine.executor.text_feature
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# extract feat, new only fbank in conformer model
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self.preprocess_conf = self.model_config.preprocess_config
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self.preprocess_args = {"train": False}
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self.preprocessing = Transformation(self.preprocess_conf)
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# frame window and frame shift, in samples unit
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self.win_length = self.preprocess_conf.process[0]['win_length']
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self.n_shift = self.preprocess_conf.process[0]['n_shift']
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assert self.preprocess_conf.process[0]['fs'] == self.sample_rate, (
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self.sample_rate, self.preprocess_conf.process[0]['fs'])
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self.frame_shift_in_ms = int(
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self.n_shift / self.preprocess_conf.process[0]['fs'] * 1000)
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self.continuous_decoding = self.config.get("continuous_decoding", False)
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self.init_decoder()
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self.reset()
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def init_decoder(self):
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if "deepspeech2" in self.model_type:
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assert self.continuous_decoding is False, "ds2 model not support endpoint"
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self.am_predictor = self.asr_engine.executor.am_predictor
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self.decoder = CTCDecoder(
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odim=self.model_config.output_dim, # <blank> is in vocab
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enc_n_units=self.model_config.rnn_layer_size * 2,
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blank_id=self.model_config.blank_id,
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dropout_rate=0.0,
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reduction=True, # sum
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batch_average=True, # sum / batch_size
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grad_norm_type=self.model_config.get('ctc_grad_norm_type',
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None))
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cfg = self.model_config.decode
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decode_batch_size = 1 # for online
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self.decoder.init_decoder(
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decode_batch_size, self.text_feature.vocab_list,
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cfg.decoding_method, cfg.lang_model_path, cfg.alpha, cfg.beta,
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cfg.beam_size, cfg.cutoff_prob, cfg.cutoff_top_n,
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cfg.num_proc_bsearch)
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else:
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raise ValueError(f"Not supported: {self.model_type}")
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def model_reset(self):
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# cache for audio and feat
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self.remained_wav = None
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self.cached_feat = None
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def output_reset(self):
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## outputs
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# partial/ending decoding results
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self.result_transcripts = ['']
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def reset_continuous_decoding(self):
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"""
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when in continous decoding, reset for next utterance.
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"""
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self.global_frame_offset = self.num_frames
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self.model_reset()
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def reset(self):
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if "deepspeech2" in self.model_type:
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# for deepspeech2
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# init state
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self.chunk_state_h_box = np.zeros(
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(self.model_config.num_rnn_layers, 1,
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self.model_config.rnn_layer_size),
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dtype=float32)
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self.chunk_state_c_box = np.zeros(
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(self.model_config.num_rnn_layers, 1,
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self.model_config.rnn_layer_size),
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dtype=float32)
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self.decoder.reset_decoder(batch_size=1)
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else:
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raise NotImplementedError(f"{self.model_type} not support.")
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self.device = None
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## common
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# global sample and frame step
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self.num_samples = 0
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self.global_frame_offset = 0
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# frame step of cur utterance
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self.num_frames = 0
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## endpoint
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self.endpoint_state = False # True for detect endpoint
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## conformer
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self.model_reset()
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## outputs
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self.output_reset()
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def extract_feat(self, samples: ByteString):
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logger.debug("Online ASR extract the feat")
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samples = np.frombuffer(samples, dtype=np.int16)
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assert samples.ndim == 1
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self.num_samples += samples.shape[0]
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logger.debug(
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f"This package receive {samples.shape[0]} pcm data. Global samples:{self.num_samples}"
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)
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# self.reamined_wav stores all the samples,
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# include the original remained_wav and this package samples
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if self.remained_wav is None:
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self.remained_wav = samples
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else:
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assert self.remained_wav.ndim == 1 # (T,)
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self.remained_wav = np.concatenate([self.remained_wav, samples])
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logger.debug(
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f"The concatenation of remain and now audio samples length is: {self.remained_wav.shape}"
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)
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if len(self.remained_wav) < self.win_length:
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# samples not enough for feature window
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return 0
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# fbank
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x_chunk = self.preprocessing(self.remained_wav, **self.preprocess_args)
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x_chunk = paddle.to_tensor(x_chunk, dtype="float32").unsqueeze(axis=0)
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# feature cache
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if self.cached_feat is None:
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self.cached_feat = x_chunk
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else:
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assert (len(x_chunk.shape) == 3) # (B,T,D)
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assert (len(self.cached_feat.shape) == 3) # (B,T,D)
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self.cached_feat = paddle.concat(
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[self.cached_feat, x_chunk], axis=1)
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# set the feat device
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if self.device is None:
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self.device = self.cached_feat.place
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# cur frame step
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num_frames = x_chunk.shape[1]
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# global frame step
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self.num_frames += num_frames
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# update remained wav
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self.remained_wav = self.remained_wav[self.n_shift * num_frames:]
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logger.debug(
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f"process the audio feature success, the cached feat shape: {self.cached_feat.shape}"
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)
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logger.debug(
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f"After extract feat, the cached remain the audio samples: {self.remained_wav.shape}"
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)
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logger.debug(f"global samples: {self.num_samples}")
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logger.debug(f"global frames: {self.num_frames}")
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def decode(self, is_finished=False):
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"""advance decoding
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Args:
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is_finished (bool, optional): Is last frame or not. Defaults to False.
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Returns:
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None:
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"""
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if "deepspeech2" in self.model_type:
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decoding_chunk_size = 1 # decoding chunk size = 1. int decoding frame unit
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context = 7 # context=7, in audio frame unit
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subsampling = 4 # subsampling=4, in audio frame unit
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cached_feature_num = context - subsampling
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# decoding window for model, in audio frame unit
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decoding_window = (decoding_chunk_size - 1) * subsampling + context
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# decoding stride for model, in audio frame unit
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stride = subsampling * decoding_chunk_size
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if self.cached_feat is None:
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logger.info("no audio feat, please input more pcm data")
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return
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num_frames = self.cached_feat.shape[1]
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logger.debug(
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f"Required decoding window {decoding_window} frames, and the connection has {num_frames} frames"
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)
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# the cached feat must be larger decoding_window
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if num_frames < decoding_window and not is_finished:
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logger.info(
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f"frame feat num is less than {decoding_window}, please input more pcm data"
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)
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return None, None
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# if is_finished=True, we need at least context frames
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if num_frames < context:
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logger.info(
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"flast {num_frames} is less than context {context} frames, and we cannot do model forward"
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)
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return None, None
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logger.info("start to do model forward")
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# num_frames - context + 1 ensure that current frame can get context window
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if is_finished:
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# if get the finished chunk, we need process the last context
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left_frames = context
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else:
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# we only process decoding_window frames for one chunk
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left_frames = decoding_window
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end = None
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for cur in range(0, num_frames - left_frames + 1, stride):
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end = min(cur + decoding_window, num_frames)
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# extract the audio
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x_chunk = self.cached_feat[:, cur:end, :].numpy()
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x_chunk_lens = np.array([x_chunk.shape[1]])
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trans_best = self.decode_one_chunk(x_chunk, x_chunk_lens)
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self.result_transcripts = [trans_best]
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# update feat cache
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self.cached_feat = self.cached_feat[:, end - cached_feature_num:, :]
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# return trans_best[0]
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else:
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raise Exception(f"{self.model_type} not support paddleinference.")
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@paddle.no_grad()
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def decode_one_chunk(self, x_chunk, x_chunk_lens):
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"""forward one chunk frames
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Args:
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x_chunk (np.ndarray): (B,T,D), audio frames.
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x_chunk_lens ([type]): (B,), audio frame lens
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Returns:
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logprob: poster probability.
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"""
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logger.info("start to decoce one chunk for deepspeech2")
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# state_c, state_h, audio_lens, audio
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# 'chunk_state_c_box', 'chunk_state_h_box', 'audio_chunk_lens', 'audio_chunk'
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input_names = [n.name for n in self.am_predictor.get_inputs()]
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logger.info(f"ort inputs: {input_names}")
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# 'softmax_0.tmp_0', 'tmp_5', 'concat_0.tmp_0', 'concat_1.tmp_0'
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# audio, audio_lens, state_h, state_c
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output_names = [n.name for n in self.am_predictor.get_outputs()]
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logger.info(f"ort outpus: {output_names}")
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assert (len(input_names) == len(output_names))
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assert isinstance(input_names[0], str)
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input_datas = [
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self.chunk_state_c_box, self.chunk_state_h_box, x_chunk_lens,
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x_chunk
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]
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feeds = dict(zip(input_names, input_datas))
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outputs = self.am_predictor.run([*output_names], {**feeds})
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output_chunk_probs, output_chunk_lens, self.chunk_state_h_box, self.chunk_state_c_box = outputs
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self.decoder.next(output_chunk_probs, output_chunk_lens)
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trans_best, trans_beam = self.decoder.decode()
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logger.info(f"decode one best result for deepspeech2: {trans_best[0]}")
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return trans_best[0]
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def get_result(self):
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"""return partial/ending asr result.
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Returns:
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str: one best result of partial/ending.
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"""
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if len(self.result_transcripts) > 0:
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return self.result_transcripts[0]
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else:
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return ''
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def get_word_time_stamp(self):
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return []
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@paddle.no_grad()
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def rescoring(self):
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...
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class ASRServerExecutor(ASRExecutor):
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def __init__(self):
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super().__init__()
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self.task_resource = CommonTaskResource(
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task='asr', model_format='onnx', inference_mode='online')
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def update_config(self) -> None:
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if "deepspeech2" in self.model_type:
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with UpdateConfig(self.config):
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# download lm
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self.config.decode.lang_model_path = os.path.join(
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MODEL_HOME, 'language_model',
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self.config.decode.lang_model_path)
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lm_url = self.task_resource.res_dict['lm_url']
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lm_md5 = self.task_resource.res_dict['lm_md5']
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logger.debug(f"Start to load language model {lm_url}")
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self.download_lm(
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lm_url,
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os.path.dirname(self.config.decode.lang_model_path), lm_md5)
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else:
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raise NotImplementedError(
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f"{self.model_type} not support paddleinference.")
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def init_model(self) -> None:
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if "deepspeech2" in self.model_type:
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# AM predictor
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logger.debug("ASR engine start to init the am predictor")
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self.am_predictor = onnx_infer.get_sess(
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model_path=self.am_model, sess_conf=self.am_predictor_conf)
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else:
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raise NotImplementedError(
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f"{self.model_type} not support paddleinference.")
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def _init_from_path(self,
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model_type: str=None,
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am_model: Optional[os.PathLike]=None,
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am_params: Optional[os.PathLike]=None,
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lang: str='zh',
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sample_rate: int=16000,
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cfg_path: Optional[os.PathLike]=None,
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decode_method: str='attention_rescoring',
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num_decoding_left_chunks: int=-1,
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am_predictor_conf: dict=None):
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"""
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Init model and other resources from a specific path.
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"""
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if not model_type or not lang or not sample_rate:
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logger.error(
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"The model type or lang or sample rate is None, please input an valid server parameter yaml"
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)
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return False
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assert am_params is None, "am_params not used in onnx engine"
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self.model_type = model_type
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self.sample_rate = sample_rate
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self.decode_method = decode_method
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self.num_decoding_left_chunks = num_decoding_left_chunks
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# conf for paddleinference predictor or onnx
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self.am_predictor_conf = am_predictor_conf
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logger.debug(f"model_type: {self.model_type}")
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sample_rate_str = '16k' if sample_rate == 16000 else '8k'
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tag = model_type + '-' + lang + '-' + sample_rate_str
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self.task_resource.set_task_model(model_tag=tag)
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if cfg_path is None:
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self.res_path = self.task_resource.res_dir
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self.cfg_path = os.path.join(
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self.res_path, self.task_resource.res_dict['cfg_path'])
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else:
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self.cfg_path = os.path.abspath(cfg_path)
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self.res_path = os.path.dirname(
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os.path.dirname(os.path.abspath(self.cfg_path)))
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self.am_model = os.path.join(self.res_path, self.task_resource.res_dict[
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'onnx_model']) if am_model is None else os.path.abspath(am_model)
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# self.am_params = os.path.join(
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# self.res_path, self.task_resource.res_dict[
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# 'params']) if am_params is None else os.path.abspath(am_params)
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logger.debug("Load the pretrained model:")
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logger.debug(f" tag = {tag}")
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logger.debug(f" res_path: {self.res_path}")
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logger.debug(f" cfg path: {self.cfg_path}")
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logger.debug(f" am_model path: {self.am_model}")
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#Init body.
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self.config = CfgNode(new_allowed=True)
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self.config.merge_from_file(self.cfg_path)
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if self.config.spm_model_prefix:
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self.config.spm_model_prefix = os.path.join(
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self.res_path, self.config.spm_model_prefix)
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logger.debug(f"spm model path: {self.config.spm_model_prefix}")
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self.vocab = self.config.vocab_filepath
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self.text_feature = TextFeaturizer(
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unit_type=self.config.unit_type,
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vocab=self.config.vocab_filepath,
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spm_model_prefix=self.config.spm_model_prefix)
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self.update_config()
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# AM predictor
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self.init_model()
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logger.debug(f"create the {model_type} model success")
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return True
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class ASREngine(BaseEngine):
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"""ASR model resource
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Args:
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metaclass: Defaults to Singleton.
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"""
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def __init__(self):
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super(ASREngine, self).__init__()
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def init_model(self) -> bool:
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if not self.executor._init_from_path(
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model_type=self.config.model_type,
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am_model=self.config.am_model,
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am_params=self.config.am_params,
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lang=self.config.lang,
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sample_rate=self.config.sample_rate,
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cfg_path=self.config.cfg_path,
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decode_method=self.config.decode_method,
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num_decoding_left_chunks=self.config.num_decoding_left_chunks,
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am_predictor_conf=self.config.am_predictor_conf):
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return False
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return True
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def init(self, config: dict) -> bool:
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"""init engine resource
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Args:
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config_file (str): config file
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Returns:
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bool: init failed or success
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"""
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self.config = config
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self.executor = ASRServerExecutor()
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try:
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self.device = self.config.get("device", paddle.get_device())
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paddle.set_device(self.device)
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except BaseException as e:
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logger.error(
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f"Set device failed, please check if device '{self.device}' is already used and the parameter 'device' in the yaml file"
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)
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logger.error(
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"If all GPU or XPU is used, you can set the server to 'cpu'")
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sys.exit(-1)
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logger.debug(f"paddlespeech_server set the device: {self.device}")
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if not self.init_model():
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logger.error(
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"Init the ASR server occurs error, please check the server configuration yaml"
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)
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return False
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logger.info("Initialize ASR server engine successfully on device: %s." %
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(self.device))
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return True
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def new_handler(self):
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"""New handler from model.
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Returns:
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PaddleASRConnectionHanddler: asr handler instance
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"""
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return PaddleASRConnectionHanddler(self)
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def preprocess(self, *args, **kwargs):
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raise NotImplementedError("Online not using this.")
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def run(self, *args, **kwargs):
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raise NotImplementedError("Online not using this.")
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def postprocess(self):
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raise NotImplementedError("Online not using this.")
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