xiongxinlei
2 years ago
parent
b5fb2760b8
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# This is the parameter configuration file for PaddleSpeech Serving.
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#################################################################################
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# SERVER SETTING #
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#################################################################################
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host: 0.0.0.0
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port: 8090
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# The task format in the engin_list is: <speech task>_<engine type>
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# protocol = ['http'] (only one can be selected).
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# http only support offline engine type.
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protocol: 'http'
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engine_list: ['vector_python']
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#################################################################################
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# ENGINE CONFIG #
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#################################################################################
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################################### Vector ######################################
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################### Vector task: spk; engine_type: python #######################
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vector_python:
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task: spk
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model_type: 'ecapatdnn_voxceleb12'
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sample_rate: 16000
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cfg_path: # [optional]
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ckpt_path: # [optional]
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device: # set 'gpu:id' or 'cpu'
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@ -0,0 +1,200 @@
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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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from collections import OrderedDict
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import numpy as np
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import paddle
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from paddleaudio.backends import load as load_audio
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from paddleaudio.compliance.librosa import melspectrogram
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from paddlespeech.cli.log import logger
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from paddlespeech.cli.vector.infer import VectorExecutor
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from paddlespeech.server.engine.base_engine import BaseEngine
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from paddlespeech.vector.io.batch import feature_normalize
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class PaddleVectorConnectionHandler:
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def __init__(self, vector_engine):
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"""The PaddleSpeech Vector Server Connection Handler
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This connection process every server request
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Args:
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vector_engine (VectorEngine): The Vector engine
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"""
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super().__init__()
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logger.info(
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"Create PaddleVectorConnectionHandler to process the vector request")
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self.vector_engine = vector_engine
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self.executor = self.vector_engine.executor
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self.task = self.vector_engine.executor.task
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self.model = self.vector_engine.executor.model
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self.config = self.vector_engine.executor.config
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self._inputs = OrderedDict()
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self._outputs = OrderedDict()
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@paddle.no_grad()
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def run(self, audio_data, task="spk"):
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"""The connection process the http request audio
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Args:
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audio_data (bytes): base64.b64decode
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Returns:
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str: the punctuation text
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"""
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logger.info(
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f"start to extract the do vector {self.task} from the http request")
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if self.task == "spk" and task == "spk":
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embedding = self.extract_audio_embedding(audio_data)
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return embedding
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else:
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logger.error(
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"The request task is not matched with server model task")
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logger.error(
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f"The server model task is: {self.task}, but the request task is: {task}"
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)
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return np.array([
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0.0,
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])
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@paddle.no_grad()
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def get_enroll_test_score(self, enroll_audio, test_audio):
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"""Get the enroll and test audio score
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Args:
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enroll_audio (str): the base64 format enroll audio
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test_audio (str): the base64 format test audio
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Returns:
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float: the score between enroll and test audio
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"""
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logger.info("start to extract the enroll audio embedding")
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enroll_emb = self.extract_audio_embedding(enroll_audio)
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logger.info("start to extract the test audio embedding")
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test_emb = self.extract_audio_embedding(test_audio)
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logger.info(
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"start to get the score between the enroll and test embedding")
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score = self.executor.get_embeddings_score(enroll_emb, test_emb)
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logger.info(f"get the enroll vs test score: {score}")
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return score
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@paddle.no_grad()
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def extract_audio_embedding(self, audio: str, sample_rate: int=16000):
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"""extract the audio embedding
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Args:
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audio (_type_): _description_
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sample_rate (int, optional): _description_. Defaults to 16000.
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"""
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# we can not reuse the cache io.BytesIO(audio) data,
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# because the soundfile will change the io.BytesIO(audio) to the end
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# thus we should convert the base64 string to io.BytesIO when we need the audio data
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if not self.executor._check(io.BytesIO(audio), sample_rate):
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logger.info("check the audio sample rate occurs error")
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return np.array([0.0])
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waveform, sr = load_audio(io.BytesIO(audio))
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logger.info(f"load the audio sample points, shape is: {waveform.shape}")
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# stage 2: get the audio feat
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# Note: Now we only support fbank feature
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try:
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feats = melspectrogram(
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x=waveform,
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sr=self.config.sr,
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n_mels=self.config.n_mels,
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window_size=self.config.window_size,
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hop_length=self.config.hop_size)
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logger.info(f"extract the audio feats, shape is: {feats.shape}")
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except Exception as e:
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logger.info(f"feats occurs exception {e}")
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sys.exit(-1)
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feats = paddle.to_tensor(feats).unsqueeze(0)
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# in inference period, the lengths is all one without padding
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lengths = paddle.ones([1])
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# stage 3: we do feature normalize,
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# Now we assume that the feats must do normalize
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feats = feature_normalize(feats, mean_norm=True, std_norm=False)
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# stage 4: store the feats and length in the _inputs,
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# which will be used in other function
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logger.info(f"feats shape: {feats.shape}")
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logger.info("audio extract the feats success")
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logger.info("start to extract the audio embedding")
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embedding = self.model.backbone(feats, lengths).squeeze().numpy()
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logger.info(f"embedding size: {embedding.shape}")
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return embedding
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class VectorServerExecutor(VectorExecutor):
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def __init__(self):
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"""The wrapper for TextEcutor
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"""
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super().__init__()
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pass
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class VectorEngine(BaseEngine):
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def __init__(self):
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"""The Vector Engine
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"""
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super(VectorEngine, self).__init__()
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logger.info("Create the VectorEngine Instance")
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def init(self, config: dict):
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"""Init the Vector Engine
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Args:
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config (dict): The server configuation
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Returns:
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bool: The engine instance flag
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"""
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logger.info("Init the vector engine")
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try:
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self.config = config
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if self.config.device:
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self.device = self.config.device
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else:
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self.device = paddle.get_device()
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paddle.set_device(self.device)
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logger.info(f"Vector Engine set the device: {self.device}")
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except BaseException as e:
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logger.error(
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"Set device failed, please check if device is already used and the parameter 'device' in the yaml file"
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)
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logger.error("Initialize Vector server engine Failed on device: %s."
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% (self.device))
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return False
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self.executor = VectorServerExecutor()
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self.executor._init_from_path(
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model_type=config.model_type,
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cfg_path=config.cfg_path,
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ckpt_path=config.ckpt_path,
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task=config.task)
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logger.info("Init the Vector engine successfully")
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return True
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@ -0,0 +1,151 @@
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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 base64
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import traceback
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from typing import Union
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import numpy as np
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from fastapi import APIRouter
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from paddlespeech.cli.log import logger
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from paddlespeech.server.engine.engine_pool import get_engine_pool
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from paddlespeech.server.engine.vector.python.vector_engine import PaddleVectorConnectionHandler
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from paddlespeech.server.restful.request import VectorRequest
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from paddlespeech.server.restful.request import VectorScoreRequest
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from paddlespeech.server.restful.response import ErrorResponse
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from paddlespeech.server.restful.response import VectorResponse
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from paddlespeech.server.restful.response import VectorScoreResponse
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from paddlespeech.server.utils.errors import ErrorCode
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from paddlespeech.server.utils.errors import failed_response
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from paddlespeech.server.utils.exception import ServerBaseException
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router = APIRouter()
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@router.get('/paddlespeech/vector/help')
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def help():
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"""help
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Returns:
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json: The /paddlespeech/vector api response content
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"""
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response = {
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"success": "True",
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"code": 200,
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"message": {
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"global": "success"
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},
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"vector": [2.3, 3.5, 5.5, 6.2, 2.8, 1.2, 0.3, 3.6]
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}
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return response
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@router.post(
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"/paddlespeech/vector", response_model=Union[VectorResponse, ErrorResponse])
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def vector(request_body: VectorRequest):
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"""vector api
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Args:
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request_body (VectorRequest): the vector request body
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Returns:
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json: the vector response body
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"""
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try:
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# 1. get the audio data
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# the audio must be base64 format
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audio_data = base64.b64decode(request_body.audio)
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# 2. get single engine from engine pool
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# and we use the vector_engine to create an connection handler to process the request
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engine_pool = get_engine_pool()
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vector_engine = engine_pool['vector']
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connection_handler = PaddleVectorConnectionHandler(vector_engine)
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# 3. we use the connection handler to process the audio
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audio_vec = connection_handler.run(audio_data, request_body.task)
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# 4. we need the result of the vector instance be numpy.ndarray
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if not isinstance(audio_vec, np.ndarray):
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logger.error(
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f"the vector type is not numpy.array, that is: {type(audio_vec)}"
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)
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error_reponse = ErrorResponse()
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error_reponse.message.description = f"the vector type is not numpy.array, that is: {type(audio_vec)}"
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return error_reponse
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response = {
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"success": True,
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"code": 200,
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"message": {
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"description": "success"
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},
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"result": {
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"vec": audio_vec.tolist()
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}
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}
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except ServerBaseException as e:
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response = failed_response(e.error_code, e.msg)
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except BaseException:
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response = failed_response(ErrorCode.SERVER_UNKOWN_ERR)
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traceback.print_exc()
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return response
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@router.post(
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"/paddlespeech/vector/score",
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response_model=Union[VectorScoreResponse, ErrorResponse])
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def score(request_body: VectorScoreRequest):
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"""vector api
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Args:
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request_body (VectorScoreRequest): the punctuation request body
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Returns:
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json: the punctuation response body
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"""
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try:
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# 1. get the audio data
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# the audio must be base64 format
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enroll_data = base64.b64decode(request_body.enroll_audio)
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test_data = base64.b64decode(request_body.test_audio)
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# 2. get single engine from engine pool
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# and we use the vector_engine to create an connection handler to process the request
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engine_pool = get_engine_pool()
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vector_engine = engine_pool['vector']
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connection_handler = PaddleVectorConnectionHandler(vector_engine)
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# 3. we use the connection handler to process the audio
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score = connection_handler.get_enroll_test_score(enroll_data, test_data)
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response = {
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"success": True,
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"code": 200,
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"message": {
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"description": "success"
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},
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"result": {
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"score": score
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}
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}
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except ServerBaseException as e:
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response = failed_response(e.error_code, e.msg)
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except BaseException:
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response = failed_response(ErrorCode.SERVER_UNKOWN_ERR)
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traceback.print_exc()
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return response
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