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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 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.engine.base_engine import BaseEngine
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from paddlespeech.server.utils.config import get_config
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__all__ = ['ASREngine']
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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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# vocab_list = collate_fn_test.vocab_list
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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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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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try:
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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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self.config.model, self.config.lang, self.config.sample_rate,
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self.config.cfg_path, self.config.decode_method,
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self.config.ckpt_path)
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except:
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logger.info("Initialize ASR server engine Failed.")
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return False
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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 run asr engine")
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self.executor.preprocess(self.config.model, io.BytesIO(audio_data))
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self.executor.infer(self.config.model)
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self.output = self.executor.postprocess() # Retrieve result of asr.
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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
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