You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
531 lines
19 KiB
531 lines
19 KiB
# 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 os
|
|
import sys
|
|
from typing import ByteString
|
|
from typing import Optional
|
|
|
|
import numpy as np
|
|
import paddle
|
|
from numpy import float32
|
|
from yacs.config import CfgNode
|
|
|
|
from paddlespeech.audio.transform.transformation import Transformation
|
|
from paddlespeech.cli.asr.infer import ASRExecutor
|
|
from paddlespeech.cli.log import logger
|
|
from paddlespeech.resource import CommonTaskResource
|
|
from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
|
|
from paddlespeech.s2t.modules.ctc import CTCDecoder
|
|
from paddlespeech.s2t.utils.utility import UpdateConfig
|
|
from paddlespeech.server.engine.base_engine import BaseEngine
|
|
from paddlespeech.server.utils import onnx_infer
|
|
from paddlespeech.utils.env import MODEL_HOME
|
|
|
|
__all__ = ['PaddleASRConnectionHanddler', 'ASRServerExecutor', 'ASREngine']
|
|
|
|
|
|
# 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.debug(
|
|
"create an paddle asr connection handler to process the websocket connection"
|
|
)
|
|
self.config = asr_engine.config # server config
|
|
self.model_config = asr_engine.executor.config
|
|
self.asr_engine = asr_engine
|
|
|
|
# model_type, sample_rate and text_feature is shared for deepspeech2 and conformer
|
|
self.model_type = self.asr_engine.executor.model_type
|
|
self.sample_rate = self.asr_engine.executor.sample_rate
|
|
# tokens to text
|
|
self.text_feature = self.asr_engine.executor.text_feature
|
|
|
|
# extract feat, new only fbank in conformer model
|
|
self.preprocess_conf = self.model_config.preprocess_config
|
|
self.preprocess_args = {"train": False}
|
|
self.preprocessing = Transformation(self.preprocess_conf)
|
|
|
|
# frame window and frame shift, in samples unit
|
|
self.win_length = self.preprocess_conf.process[0]['win_length']
|
|
self.n_shift = self.preprocess_conf.process[0]['n_shift']
|
|
|
|
assert self.preprocess_conf.process[0]['fs'] == self.sample_rate, (
|
|
self.sample_rate, self.preprocess_conf.process[0]['fs'])
|
|
self.frame_shift_in_ms = int(
|
|
self.n_shift / self.preprocess_conf.process[0]['fs'] * 1000)
|
|
|
|
self.continuous_decoding = self.config.get("continuous_decoding", False)
|
|
self.init_decoder()
|
|
self.reset()
|
|
|
|
def init_decoder(self):
|
|
if "deepspeech2" in self.model_type:
|
|
assert self.continuous_decoding is False, "ds2 model not support endpoint"
|
|
self.am_predictor = self.asr_engine.executor.am_predictor
|
|
|
|
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)
|
|
else:
|
|
raise ValueError(f"Not supported: {self.model_type}")
|
|
|
|
def model_reset(self):
|
|
# cache for audio and feat
|
|
self.remained_wav = None
|
|
self.cached_feat = None
|
|
|
|
def output_reset(self):
|
|
## outputs
|
|
# partial/ending decoding results
|
|
self.result_transcripts = ['']
|
|
|
|
def reset_continuous_decoding(self):
|
|
"""
|
|
when in continous decoding, reset for next utterance.
|
|
"""
|
|
self.global_frame_offset = self.num_frames
|
|
self.model_reset()
|
|
|
|
def reset(self):
|
|
if "deepspeech2" in self.model_type:
|
|
# for deepspeech2
|
|
# init state
|
|
self.chunk_state_h_box = np.zeros(
|
|
(self.model_config.num_rnn_layers, 1,
|
|
self.model_config.rnn_layer_size),
|
|
dtype=float32)
|
|
self.chunk_state_c_box = np.zeros(
|
|
(self.model_config.num_rnn_layers, 1,
|
|
self.model_config.rnn_layer_size),
|
|
dtype=float32)
|
|
self.decoder.reset_decoder(batch_size=1)
|
|
else:
|
|
raise NotImplementedError(f"{self.model_type} not support.")
|
|
|
|
self.device = None
|
|
|
|
## common
|
|
# global sample and frame step
|
|
self.num_samples = 0
|
|
self.global_frame_offset = 0
|
|
# frame step of cur utterance
|
|
self.num_frames = 0
|
|
|
|
## endpoint
|
|
self.endpoint_state = False # True for detect endpoint
|
|
|
|
## conformer
|
|
self.model_reset()
|
|
|
|
## outputs
|
|
self.output_reset()
|
|
|
|
def extract_feat(self, samples: ByteString):
|
|
logger.debug("Online ASR extract the feat")
|
|
samples = np.frombuffer(samples, dtype=np.int16)
|
|
assert samples.ndim == 1
|
|
|
|
self.num_samples += samples.shape[0]
|
|
logger.debug(
|
|
f"This package receive {samples.shape[0]} pcm data. Global samples:{self.num_samples}"
|
|
)
|
|
|
|
# self.reamined_wav stores all the samples,
|
|
# include the original remained_wav and this package samples
|
|
if self.remained_wav is None:
|
|
self.remained_wav = samples
|
|
else:
|
|
assert self.remained_wav.ndim == 1 # (T,)
|
|
self.remained_wav = np.concatenate([self.remained_wav, samples])
|
|
logger.debug(
|
|
f"The concatenation of remain and now audio samples length is: {self.remained_wav.shape}"
|
|
)
|
|
|
|
if len(self.remained_wav) < self.win_length:
|
|
# samples not enough for feature window
|
|
return 0
|
|
|
|
# fbank
|
|
x_chunk = self.preprocessing(self.remained_wav, **self.preprocess_args)
|
|
x_chunk = paddle.to_tensor(x_chunk, dtype="float32").unsqueeze(axis=0)
|
|
|
|
# feature cache
|
|
if self.cached_feat is None:
|
|
self.cached_feat = x_chunk
|
|
else:
|
|
assert (len(x_chunk.shape) == 3) # (B,T,D)
|
|
assert (len(self.cached_feat.shape) == 3) # (B,T,D)
|
|
self.cached_feat = paddle.concat(
|
|
[self.cached_feat, x_chunk], axis=1)
|
|
|
|
# set the feat device
|
|
if self.device is None:
|
|
self.device = self.cached_feat.place
|
|
|
|
# cur frame step
|
|
num_frames = x_chunk.shape[1]
|
|
|
|
# global frame step
|
|
self.num_frames += num_frames
|
|
|
|
# update remained wav
|
|
self.remained_wav = self.remained_wav[self.n_shift * num_frames:]
|
|
|
|
logger.debug(
|
|
f"process the audio feature success, the cached feat shape: {self.cached_feat.shape}"
|
|
)
|
|
logger.debug(
|
|
f"After extract feat, the cached remain the audio samples: {self.remained_wav.shape}"
|
|
)
|
|
logger.debug(f"global samples: {self.num_samples}")
|
|
logger.debug(f"global frames: {self.num_frames}")
|
|
|
|
def decode(self, is_finished=False):
|
|
"""advance decoding
|
|
|
|
Args:
|
|
is_finished (bool, optional): Is last frame or not. Defaults to False.
|
|
|
|
Returns:
|
|
None:
|
|
"""
|
|
if "deepspeech2" in self.model_type:
|
|
decoding_chunk_size = 1 # decoding chunk size = 1. int decoding frame unit
|
|
|
|
context = 7 # context=7, in audio frame unit
|
|
subsampling = 4 # subsampling=4, in audio frame unit
|
|
|
|
cached_feature_num = context - subsampling
|
|
# decoding window for model, in audio frame unit
|
|
decoding_window = (decoding_chunk_size - 1) * subsampling + context
|
|
# decoding stride for model, in audio frame unit
|
|
stride = subsampling * decoding_chunk_size
|
|
|
|
if self.cached_feat is None:
|
|
logger.info("no audio feat, please input more pcm data")
|
|
return
|
|
|
|
num_frames = self.cached_feat.shape[1]
|
|
logger.debug(
|
|
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
|
|
|
|
end = None
|
|
for cur in range(0, num_frames - left_frames + 1, stride):
|
|
end = min(cur + decoding_window, num_frames)
|
|
|
|
# extract the audio
|
|
x_chunk = self.cached_feat[:, cur:end, :].numpy()
|
|
x_chunk_lens = np.array([x_chunk.shape[1]])
|
|
|
|
trans_best = self.decode_one_chunk(x_chunk, x_chunk_lens)
|
|
|
|
self.result_transcripts = [trans_best]
|
|
|
|
# update feat cache
|
|
self.cached_feat = self.cached_feat[:, end - cached_feature_num:, :]
|
|
|
|
# return trans_best[0]
|
|
else:
|
|
raise Exception(f"{self.model_type} not support paddleinference.")
|
|
|
|
@paddle.no_grad()
|
|
def decode_one_chunk(self, x_chunk, x_chunk_lens):
|
|
"""forward one chunk frames
|
|
|
|
Args:
|
|
x_chunk (np.ndarray): (B,T,D), audio frames.
|
|
x_chunk_lens ([type]): (B,), audio frame lens
|
|
|
|
Returns:
|
|
logprob: poster probability.
|
|
"""
|
|
logger.info("start to decoce one chunk for deepspeech2")
|
|
# state_c, state_h, audio_lens, audio
|
|
# 'chunk_state_c_box', 'chunk_state_h_box', 'audio_chunk_lens', 'audio_chunk'
|
|
input_names = [n.name for n in self.am_predictor.get_inputs()]
|
|
logger.info(f"ort inputs: {input_names}")
|
|
# 'softmax_0.tmp_0', 'tmp_5', 'concat_0.tmp_0', 'concat_1.tmp_0'
|
|
# audio, audio_lens, state_h, state_c
|
|
output_names = [n.name for n in self.am_predictor.get_outputs()]
|
|
logger.info(f"ort outpus: {output_names}")
|
|
assert (len(input_names) == len(output_names))
|
|
assert isinstance(input_names[0], str)
|
|
|
|
input_datas = [
|
|
self.chunk_state_c_box, self.chunk_state_h_box, x_chunk_lens,
|
|
x_chunk
|
|
]
|
|
feeds = dict(zip(input_names, input_datas))
|
|
|
|
outputs = self.am_predictor.run([*output_names], {**feeds})
|
|
|
|
output_chunk_probs, output_chunk_lens, self.chunk_state_h_box, self.chunk_state_c_box = outputs
|
|
self.decoder.next(output_chunk_probs, output_chunk_lens)
|
|
trans_best, trans_beam = self.decoder.decode()
|
|
logger.info(f"decode one best result for deepspeech2: {trans_best[0]}")
|
|
return trans_best[0]
|
|
|
|
def get_result(self):
|
|
"""return partial/ending asr result.
|
|
|
|
Returns:
|
|
str: one best result of partial/ending.
|
|
"""
|
|
if len(self.result_transcripts) > 0:
|
|
return self.result_transcripts[0]
|
|
else:
|
|
return ''
|
|
|
|
def get_word_time_stamp(self):
|
|
return []
|
|
|
|
@paddle.no_grad()
|
|
def rescoring(self):
|
|
...
|
|
|
|
|
|
class ASRServerExecutor(ASRExecutor):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.task_resource = CommonTaskResource(
|
|
task='asr', model_format='onnx', inference_mode='online')
|
|
|
|
def update_config(self) -> None:
|
|
if "deepspeech2" in self.model_type:
|
|
with UpdateConfig(self.config):
|
|
# download lm
|
|
self.config.decode.lang_model_path = os.path.join(
|
|
MODEL_HOME, 'language_model',
|
|
self.config.decode.lang_model_path)
|
|
|
|
lm_url = self.task_resource.res_dict['lm_url']
|
|
lm_md5 = self.task_resource.res_dict['lm_md5']
|
|
logger.debug(f"Start to load language model {lm_url}")
|
|
self.download_lm(
|
|
lm_url,
|
|
os.path.dirname(self.config.decode.lang_model_path), lm_md5)
|
|
else:
|
|
raise NotImplementedError(
|
|
f"{self.model_type} not support paddleinference.")
|
|
|
|
def init_model(self) -> None:
|
|
|
|
if "deepspeech2" in self.model_type:
|
|
# AM predictor
|
|
logger.debug("ASR engine start to init the am predictor")
|
|
self.am_predictor = onnx_infer.get_sess(
|
|
model_path=self.am_model, sess_conf=self.am_predictor_conf)
|
|
else:
|
|
raise NotImplementedError(
|
|
f"{self.model_type} not support paddleinference.")
|
|
|
|
def _init_from_path(self,
|
|
model_type: str=None,
|
|
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',
|
|
num_decoding_left_chunks: int=-1,
|
|
am_predictor_conf: dict=None):
|
|
"""
|
|
Init model and other resources from a specific path.
|
|
"""
|
|
if not model_type or not lang or not sample_rate:
|
|
logger.error(
|
|
"The model type or lang or sample rate is None, please input an valid server parameter yaml"
|
|
)
|
|
return False
|
|
assert am_params is None, "am_params not used in onnx engine"
|
|
|
|
self.model_type = model_type
|
|
self.sample_rate = sample_rate
|
|
self.decode_method = decode_method
|
|
self.num_decoding_left_chunks = num_decoding_left_chunks
|
|
# conf for paddleinference predictor or onnx
|
|
self.am_predictor_conf = am_predictor_conf
|
|
logger.debug(f"model_type: {self.model_type}")
|
|
|
|
sample_rate_str = '16k' if sample_rate == 16000 else '8k'
|
|
tag = model_type + '-' + lang + '-' + sample_rate_str
|
|
self.task_resource.set_task_model(model_tag=tag)
|
|
|
|
if cfg_path is None:
|
|
self.res_path = self.task_resource.res_dir
|
|
self.cfg_path = os.path.join(
|
|
self.res_path, self.task_resource.res_dict['cfg_path'])
|
|
else:
|
|
self.cfg_path = os.path.abspath(cfg_path)
|
|
self.res_path = os.path.dirname(
|
|
os.path.dirname(os.path.abspath(self.cfg_path)))
|
|
|
|
self.am_model = os.path.join(self.res_path, self.task_resource.res_dict[
|
|
'onnx_model']) if am_model is None else os.path.abspath(am_model)
|
|
|
|
# self.am_params = os.path.join(
|
|
# self.res_path, self.task_resource.res_dict[
|
|
# 'params']) if am_params is None else os.path.abspath(am_params)
|
|
|
|
logger.debug("Load the pretrained model:")
|
|
logger.debug(f" tag = {tag}")
|
|
logger.debug(f" res_path: {self.res_path}")
|
|
logger.debug(f" cfg path: {self.cfg_path}")
|
|
logger.debug(f" am_model path: {self.am_model}")
|
|
|
|
#Init body.
|
|
self.config = CfgNode(new_allowed=True)
|
|
self.config.merge_from_file(self.cfg_path)
|
|
|
|
if self.config.spm_model_prefix:
|
|
self.config.spm_model_prefix = os.path.join(
|
|
self.res_path, self.config.spm_model_prefix)
|
|
logger.debug(f"spm model path: {self.config.spm_model_prefix}")
|
|
|
|
self.vocab = self.config.vocab_filepath
|
|
|
|
self.text_feature = TextFeaturizer(
|
|
unit_type=self.config.unit_type,
|
|
vocab=self.config.vocab_filepath,
|
|
spm_model_prefix=self.config.spm_model_prefix)
|
|
|
|
self.update_config()
|
|
|
|
# AM predictor
|
|
self.init_model()
|
|
|
|
logger.debug(f"create the {model_type} model success")
|
|
return True
|
|
|
|
|
|
class ASREngine(BaseEngine):
|
|
"""ASR model resource
|
|
|
|
Args:
|
|
metaclass: Defaults to Singleton.
|
|
"""
|
|
|
|
def __init__(self):
|
|
super(ASREngine, self).__init__()
|
|
|
|
def init_model(self) -> bool:
|
|
if not 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,
|
|
num_decoding_left_chunks=self.config.num_decoding_left_chunks,
|
|
am_predictor_conf=self.config.am_predictor_conf):
|
|
return False
|
|
return True
|
|
|
|
def init(self, config: dict) -> bool:
|
|
"""init engine resource
|
|
|
|
Args:
|
|
config_file (str): config file
|
|
|
|
Returns:
|
|
bool: init failed or success
|
|
"""
|
|
self.config = config
|
|
self.executor = ASRServerExecutor()
|
|
|
|
try:
|
|
self.device = self.config.get("device", paddle.get_device())
|
|
paddle.set_device(self.device)
|
|
except BaseException as e:
|
|
logger.error(
|
|
f"Set device failed, please check if device '{self.device}' is already used and the parameter 'device' in the yaml file"
|
|
)
|
|
logger.error(
|
|
"If all GPU or XPU is used, you can set the server to 'cpu'")
|
|
sys.exit(-1)
|
|
|
|
logger.debug(f"paddlespeech_server set the device: {self.device}")
|
|
|
|
if not self.init_model():
|
|
logger.error(
|
|
"Init the ASR server occurs error, please check the server configuration yaml"
|
|
)
|
|
return False
|
|
|
|
logger.info("Initialize ASR server engine successfully on device: %s." %
|
|
(self.device))
|
|
return True
|
|
|
|
def new_handler(self):
|
|
"""New handler from model.
|
|
|
|
Returns:
|
|
PaddleASRConnectionHanddler: asr handler instance
|
|
"""
|
|
return PaddleASRConnectionHanddler(self)
|
|
|
|
def preprocess(self, *args, **kwargs):
|
|
raise NotImplementedError("Online not using this.")
|
|
|
|
def run(self, *args, **kwargs):
|
|
raise NotImplementedError("Online not using this.")
|
|
|
|
def postprocess(self):
|
|
raise NotImplementedError("Online not using this.")
|