# 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 io
from collections import OrderedDict
import numpy as np
import paddle
from paddleaudio.backends import load as load_audio
from paddleaudio.compliance.librosa import melspectrogram
from paddlespeech.cli.log import logger
from paddlespeech.cli.vector.infer import VectorExecutor
from paddlespeech.server.engine.base_engine import BaseEngine
from paddlespeech.vector.io.batch import feature_normalize
class PaddleVectorConnectionHandler:
def __init__(self, vector_engine):
"""The PaddleSpeech Vector Server Connection Handler
This connection process every server request
Args:
vector_engine (VectorEngine): The Vector engine
"""
super().__init__()
logger.info(
"Create PaddleVectorConnectionHandler to process the vector request")
self.vector_engine = vector_engine
self.executor = self.vector_engine.executor
self.task = self.vector_engine.executor.task
self.model = self.vector_engine.executor.model
self.config = self.vector_engine.executor.config
self._inputs = OrderedDict()
self._outputs = OrderedDict()
@paddle.no_grad()
def run(self, audio_data, task="spk"):
"""The connection process the http request audio
Args:
audio_data (bytes): base64.b64decode
Returns:
str: the punctuation text
"""
logger.info(
f"start to extract the do vector {self.task} from the http request")
if self.task == "spk" and task == "spk":
embedding = self.extract_audio_embedding(audio_data)
return embedding
else:
logger.error(
"The request task is not matched with server model task")
logger.error(
f"The server model task is: {self.task}, but the request task is: {task}"
)
return np.array([
0.0,
])
@paddle.no_grad()
def get_enroll_test_score(self, enroll_audio, test_audio):
"""Get the enroll and test audio score
Args:
enroll_audio (str): the base64 format enroll audio
test_audio (str): the base64 format test audio
Returns:
float: the score between enroll and test audio
"""
logger.info("start to extract the enroll audio embedding")
enroll_emb = self.extract_audio_embedding(enroll_audio)
logger.info("start to extract the test audio embedding")
test_emb = self.extract_audio_embedding(test_audio)
logger.info(
"start to get the score between the enroll and test embedding")
score = self.executor.get_embeddings_score(enroll_emb, test_emb)
logger.info(f"get the enroll vs test score: {score}")
return score
@paddle.no_grad()
def extract_audio_embedding(self, audio: str, sample_rate: int=16000):
"""extract the audio embedding
Args:
audio (str): the audio data
sample_rate (int, optional): the audio sample rate. Defaults to 16000.
"""
# we can not reuse the cache io.BytesIO(audio) data,
# because the soundfile will change the io.BytesIO(audio) to the end
# thus we should convert the base64 string to io.BytesIO when we need the audio data
if not self.executor._check(io.BytesIO(audio), sample_rate):
logger.info("check the audio sample rate occurs error")
return np.array([0.0])
waveform, sr = load_audio(io.BytesIO(audio))
logger.info(f"load the audio sample points, shape is: {waveform.shape}")
# stage 2: get the audio feat
# Note: Now we only support fbank feature
try:
feats = melspectrogram(
x=waveform,
sr=self.config.sr,
n_mels=self.config.n_mels,
window_size=self.config.window_size,
hop_length=self.config.hop_size)
logger.info(f"extract the audio feats, shape is: {feats.shape}")
except Exception as e:
logger.info(f"feats occurs exception {e}")
sys.exit(-1)
feats = paddle.to_tensor(feats).unsqueeze(0)
# in inference period, the lengths is all one without padding
lengths = paddle.ones([1])
# stage 3: we do feature normalize,
# Now we assume that the feats must do normalize
feats = feature_normalize(feats, mean_norm=True, std_norm=False)
# stage 4: store the feats and length in the _inputs,
# which will be used in other function
logger.info(f"feats shape: {feats.shape}")
logger.info("audio extract the feats success")
logger.info("start to extract the audio embedding")
embedding = self.model.backbone(feats, lengths).squeeze().numpy()
logger.info(f"embedding size: {embedding.shape}")
return embedding
class VectorServerExecutor(VectorExecutor):
def __init__(self):
"""The wrapper for TextEcutor
"""
super().__init__()
pass
class VectorEngine(BaseEngine):
def __init__(self):
"""The Vector Engine
"""
super(VectorEngine, self).__init__()
logger.info("Create the VectorEngine Instance")
def init(self, config: dict):
"""Init the Vector Engine
Args:
config (dict): The server configuation
Returns:
bool: The engine instance flag
"""
logger.info("Init the vector engine")
try:
self.config = config
if self.config.device:
self.device = self.config.device
else:
self.device = paddle.get_device()
paddle.set_device(self.device)
logger.info(f"Vector Engine set the device: {self.device}")
except BaseException as e:
logger.error(
"Set device failed, please check if device is already used and the parameter 'device' in the yaml file"
)
logger.error("Initialize Vector server engine Failed on device: %s."
% (self.device))
return False
self.executor = VectorServerExecutor()
self.executor._init_from_path(
model_type=config.model_type,
cfg_path=config.cfg_path,
ckpt_path=config.ckpt_path,
task=config.task)
logger.info("Init the Vector engine successfully")
return True