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# 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 io
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import os
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from typing import List
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from typing import Optional
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from typing import Union
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import librosa
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import paddle
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import soundfile
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from yacs.config import CfgNode
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from paddlespeech.cli.utils import MODEL_HOME
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from paddlespeech.s2t.modules.ctc import CTCDecoder
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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.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
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from paddlespeech.s2t.transform.transformation import Transformation
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from paddlespeech.s2t.utils.dynamic_import import dynamic_import
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from paddlespeech.s2t.utils.utility import UpdateConfig
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from paddlespeech.server.utils.config import get_config
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from paddlespeech.server.utils.paddle_predictor import init_predictor
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from paddlespeech.server.utils.paddle_predictor import run_model
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from paddlespeech.server.engine.base_engine import BaseEngine
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__all__ = ['ASREngine']
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pretrained_models = {
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"deepspeech2offline_aishell-zh-16k": {
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'url':
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'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_aishell_ckpt_0.1.1.model.tar.gz',
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'md5':
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'932c3593d62fe5c741b59b31318aa314',
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'cfg_path':
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'model.yaml',
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'ckpt_path':
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'exp/deepspeech2/checkpoints/avg_1',
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'model':
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'exp/deepspeech2/checkpoints/avg_1.jit.pdmodel',
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'params':
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'exp/deepspeech2/checkpoints/avg_1.jit.pdiparams',
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'lm_url':
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'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
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'lm_md5':
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'29e02312deb2e59b3c8686c7966d4fe3'
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},
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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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pass
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def _check(self, audio_file: str, sample_rate: int, force_yes: bool):
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self.sample_rate = sample_rate
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if self.sample_rate != 16000 and self.sample_rate != 8000:
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logger.error("please input --sr 8000 or --sr 16000")
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return False
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logger.info("checking the audio file format......")
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try:
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audio, audio_sample_rate = soundfile.read(
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audio_file, dtype="int16", always_2d=True)
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except Exception as e:
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logger.exception(e)
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logger.error(
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"can not open the audio file, please check the audio file format is 'wav'. \n \
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you can try to use sox to change the file format.\n \
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For example: \n \
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sample rate: 16k \n \
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sox input_audio.xx --rate 16k --bits 16 --channels 1 output_audio.wav \n \
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sample rate: 8k \n \
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sox input_audio.xx --rate 8k --bits 16 --channels 1 output_audio.wav \n \
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")
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logger.info("The sample rate is %d" % audio_sample_rate)
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if audio_sample_rate != self.sample_rate:
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logger.warning("The sample rate of the input file is not {}.\n \
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The program will resample the wav file to {}.\n \
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If the result does not meet your expectations，\n \
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Please input the 16k 16 bit 1 channel wav file. \
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".format(self.sample_rate, self.sample_rate))
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self.change_format = True
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else:
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logger.info("The audio file format is right")
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self.change_format = False
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return True
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def preprocess(self, model_type: str, input: Union[str, os.PathLike]):
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"""
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Input preprocess and return paddle.Tensor stored in self.input.
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Input content can be a text(tts), a file(asr, cls) or a streaming(not supported yet).
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"""
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audio_file = input
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# Get the object for feature extraction
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if "deepspeech2online" in model_type or "deepspeech2offline" in model_type:
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audio, _ = self.collate_fn_test.process_utterance(
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audio_file=audio_file, transcript=" ")
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audio_len = audio.shape[0]
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audio = paddle.to_tensor(audio, dtype='float32')
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audio_len = paddle.to_tensor(audio_len)
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audio = paddle.unsqueeze(audio, axis=0)
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self._inputs["audio"] = audio
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self._inputs["audio_len"] = audio_len
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logger.info(f"audio feat shape: {audio.shape}")
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elif "conformer" in model_type or "transformer" in model_type or "wenetspeech" in model_type:
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logger.info("get the preprocess conf")
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preprocess_conf = self.config.preprocess_config
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preprocess_args = {"train": False}
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preprocessing = Transformation(preprocess_conf)
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logger.info("read the audio file")
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audio, audio_sample_rate = soundfile.read(
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audio_file, dtype="int16", always_2d=True)
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if self.change_format:
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if audio.shape[1] >= 2:
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audio = audio.mean(axis=1, dtype=np.int16)
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else:
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audio = audio[:, 0]
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# pcm16 -> pcm 32
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audio = self._pcm16to32(audio)
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audio = librosa.resample(audio, audio_sample_rate,
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self.sample_rate)
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audio_sample_rate = self.sample_rate
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# pcm32 -> pcm 16
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audio = self._pcm32to16(audio)
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else:
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audio = audio[:, 0]
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logger.info(f"audio shape: {audio.shape}")
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# fbank
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audio = preprocessing(audio, **preprocess_args)
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audio_len = paddle.to_tensor(audio.shape[0])
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audio = paddle.to_tensor(audio, dtype='float32').unsqueeze(axis=0)
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self._inputs["audio"] = audio
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self._inputs["audio_len"] = audio_len
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logger.info(f"audio feat shape: {audio.shape}")
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else:
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raise Exception("wrong type")
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def _init_from_path(self,
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model_type: str='wenetspeech',
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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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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 cfg_path is None or am_model is None or am_params is None:
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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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res_path = self._get_pretrained_path(tag) # wenetspeech_zh
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self.res_path = res_path
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self.cfg_path = os.path.join(res_path,
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pretrained_models[tag]['cfg_path'])
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self.am_model = os.path.join(res_path,
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pretrained_models[tag]['model'])
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self.am_params = os.path.join(res_path,
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pretrained_models[tag]['params'])
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logger.info(res_path)
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logger.info(self.cfg_path)
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logger.info(self.am_model)
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logger.info(self.am_params)
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else:
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self.cfg_path = os.path.abspath(cfg_path)
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self.am_model = os.path.abspath(am_model)
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self.am_params = os.path.abspath(am_params)
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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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#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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with UpdateConfig(self.config):
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if "deepspeech2online" in model_type or "deepspeech2offline" in model_type:
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from paddlespeech.s2t.io.collator import SpeechCollator
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self.vocab = self.config.vocab_filepath
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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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self.collate_fn_test = SpeechCollator.from_config(self.config)
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self.text_feature = TextFeaturizer(
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unit_type=self.config.unit_type, vocab=self.vocab)
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lm_url = pretrained_models[tag]['lm_url']
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lm_md5 = pretrained_models[tag]['lm_md5']
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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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elif "conformer" in model_type or "transformer" in model_type or "wenetspeech" in model_type:
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raise Exception("wrong type")
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else:
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raise Exception("wrong type")
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# AM predictor
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self.am_predictor_conf = am_predictor_conf
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self.am_predictor = init_predictor(
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model_file=self.am_model,
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params_file=self.am_params,
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predictor_conf=self.am_predictor_conf)
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# decoder
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self.decoder = CTCDecoder(
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odim=self.config.output_dim, # <blank> is in vocab
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enc_n_units=self.config.rnn_layer_size * 2,
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blank_id=self.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.config.get('ctc_grad_norm_type', None))
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@paddle.no_grad()
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def infer(self, model_type: str):
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"""
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Model inference and result stored in self.output.
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"""
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cfg = self.config.decode
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audio = self._inputs["audio"]
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audio_len = self._inputs["audio_len"]
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if "deepspeech2online" in model_type or "deepspeech2offline" in model_type:
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decode_batch_size = audio.shape[0]
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# init once
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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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output_data = run_model(
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self.am_predictor,
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[audio.numpy(), audio_len.numpy()])
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probs = output_data[0]
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eouts_len = output_data[1]
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batch_size = probs.shape[0]
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self.decoder.reset_decoder(batch_size=batch_size)
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self.decoder.next(probs, eouts_len)
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trans_best, trans_beam = self.decoder.decode()
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# self.model.decoder.del_decoder()
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self._outputs["result"] = trans_best[0]
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elif "conformer" in model_type or "transformer" in model_type:
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raise Exception("invalid model name")
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else:
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raise Exception("invalid model name")
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class ASREngine(BaseEngine):
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"""ASR server engine
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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(self, config_file: str) -> 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.input = None
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self.output = None
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self.executor = ASRServerExecutor()
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self.config = get_config(config_file)
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paddle.set_device(paddle.get_device())
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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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am_predictor_conf=self.config.am_predictor_conf)
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logger.info("Initialize ASR server engine successfully.")
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return True
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def run(self, audio_data):
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"""engine run
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Args:
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|
audio_data (bytes): base64.b64decode
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|
"""
|
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|
if self.executor._check(
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|
io.BytesIO(audio_data), self.config.sample_rate,
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|
|
self.config.force_yes):
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|
logger.info("start running asr engine")
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|
self.executor.preprocess(self.config.model_type, io.BytesIO(audio_data))
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|
self.executor.infer(self.config.model_type)
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|
self.output = self.executor.postprocess() # Retrieve result of asr.
|
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|
|
logger.info("end inferring asr engine")
|
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|
|
else:
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|
|
logger.info("file check failed!")
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|
|
self.output = None
|
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|
def postprocess(self):
|
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|
|
"""postprocess
|
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|
"""
|
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|
|
return self.output
|
