YangZhou
3 years ago
committed by
GitHub
commit
ec759094ad
@ -0,0 +1,7 @@
|
|||||||
|
#!/bin/bash
|
||||||
|
|
||||||
|
wget -c https://paddlespeech.bj.bcebos.com/kws/hey_snips.wav https://paddlespeech.bj.bcebos.com/kws/non-keyword.wav
|
||||||
|
|
||||||
|
# kws
|
||||||
|
paddlespeech kws --input ./hey_snips.wav
|
||||||
|
paddlespeech kws --input non-keyword.wav
|
||||||
@ -0,0 +1,14 @@
|
|||||||
|
# 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.
|
||||||
|
from .infer import KWSExecutor
|
||||||
@ -0,0 +1,219 @@
|
|||||||
|
# Copyright (c) 2021 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 argparse
|
||||||
|
import os
|
||||||
|
from collections import OrderedDict
|
||||||
|
from typing import List
|
||||||
|
from typing import Optional
|
||||||
|
from typing import Union
|
||||||
|
|
||||||
|
import paddle
|
||||||
|
import yaml
|
||||||
|
|
||||||
|
from ..executor import BaseExecutor
|
||||||
|
from ..log import logger
|
||||||
|
from ..utils import stats_wrapper
|
||||||
|
from paddlespeech.audio import load
|
||||||
|
from paddlespeech.audio.compliance.kaldi import fbank as kaldi_fbank
|
||||||
|
|
||||||
|
__all__ = ['KWSExecutor']
|
||||||
|
|
||||||
|
|
||||||
|
class KWSExecutor(BaseExecutor):
|
||||||
|
def __init__(self):
|
||||||
|
super().__init__(task='kws')
|
||||||
|
self.parser = argparse.ArgumentParser(
|
||||||
|
prog='paddlespeech.kws', add_help=True)
|
||||||
|
self.parser.add_argument(
|
||||||
|
'--input',
|
||||||
|
type=str,
|
||||||
|
default=None,
|
||||||
|
help='Audio file to keyword spotting.')
|
||||||
|
self.parser.add_argument(
|
||||||
|
'--threshold',
|
||||||
|
type=float,
|
||||||
|
default=0.8,
|
||||||
|
help='Score threshold for keyword spotting.')
|
||||||
|
self.parser.add_argument(
|
||||||
|
'--model',
|
||||||
|
type=str,
|
||||||
|
default='mdtc_heysnips',
|
||||||
|
choices=[
|
||||||
|
tag[:tag.index('-')]
|
||||||
|
for tag in self.task_resource.pretrained_models.keys()
|
||||||
|
],
|
||||||
|
help='Choose model type of kws task.')
|
||||||
|
self.parser.add_argument(
|
||||||
|
'--config',
|
||||||
|
type=str,
|
||||||
|
default=None,
|
||||||
|
help='Config of kws task. Use deault config when it is None.')
|
||||||
|
self.parser.add_argument(
|
||||||
|
'--ckpt_path',
|
||||||
|
type=str,
|
||||||
|
default=None,
|
||||||
|
help='Checkpoint file of model.')
|
||||||
|
self.parser.add_argument(
|
||||||
|
'--device',
|
||||||
|
type=str,
|
||||||
|
default=paddle.get_device(),
|
||||||
|
help='Choose device to execute model inference.')
|
||||||
|
self.parser.add_argument(
|
||||||
|
'-d',
|
||||||
|
'--job_dump_result',
|
||||||
|
action='store_true',
|
||||||
|
help='Save job result into file.')
|
||||||
|
self.parser.add_argument(
|
||||||
|
'-v',
|
||||||
|
'--verbose',
|
||||||
|
action='store_true',
|
||||||
|
help='Increase logger verbosity of current task.')
|
||||||
|
|
||||||
|
def _init_from_path(self,
|
||||||
|
model_type: str='mdtc_heysnips',
|
||||||
|
cfg_path: Optional[os.PathLike]=None,
|
||||||
|
ckpt_path: Optional[os.PathLike]=None):
|
||||||
|
"""
|
||||||
|
Init model and other resources from a specific path.
|
||||||
|
"""
|
||||||
|
if hasattr(self, 'model'):
|
||||||
|
logger.info('Model had been initialized.')
|
||||||
|
return
|
||||||
|
|
||||||
|
if ckpt_path is None:
|
||||||
|
tag = model_type + '-' + '16k'
|
||||||
|
self.task_resource.set_task_model(tag)
|
||||||
|
self.cfg_path = os.path.join(
|
||||||
|
self.task_resource.res_dir,
|
||||||
|
self.task_resource.res_dict['cfg_path'])
|
||||||
|
self.ckpt_path = os.path.join(
|
||||||
|
self.task_resource.res_dir,
|
||||||
|
self.task_resource.res_dict['ckpt_path'] + '.pdparams')
|
||||||
|
else:
|
||||||
|
self.cfg_path = os.path.abspath(cfg_path)
|
||||||
|
self.ckpt_path = os.path.abspath(ckpt_path)
|
||||||
|
|
||||||
|
# config
|
||||||
|
with open(self.cfg_path, 'r') as f:
|
||||||
|
config = yaml.safe_load(f)
|
||||||
|
|
||||||
|
# model
|
||||||
|
backbone_class = self.task_resource.get_model_class(
|
||||||
|
model_type.split('_')[0])
|
||||||
|
model_class = self.task_resource.get_model_class(
|
||||||
|
model_type.split('_')[0] + '_for_kws')
|
||||||
|
backbone = backbone_class(
|
||||||
|
stack_num=config['stack_num'],
|
||||||
|
stack_size=config['stack_size'],
|
||||||
|
in_channels=config['in_channels'],
|
||||||
|
res_channels=config['res_channels'],
|
||||||
|
kernel_size=config['kernel_size'],
|
||||||
|
causal=True, )
|
||||||
|
self.model = model_class(
|
||||||
|
backbone=backbone, num_keywords=config['num_keywords'])
|
||||||
|
model_dict = paddle.load(self.ckpt_path)
|
||||||
|
self.model.set_state_dict(model_dict)
|
||||||
|
self.model.eval()
|
||||||
|
|
||||||
|
self.feature_extractor = lambda x: kaldi_fbank(
|
||||||
|
x, sr=config['sample_rate'],
|
||||||
|
frame_shift=config['frame_shift'],
|
||||||
|
frame_length=config['frame_length'],
|
||||||
|
n_mels=config['n_mels']
|
||||||
|
)
|
||||||
|
|
||||||
|
def preprocess(self, audio_file: Union[str, os.PathLike]):
|
||||||
|
"""
|
||||||
|
Input preprocess and return paddle.Tensor stored in self.input.
|
||||||
|
Input content can be a text(tts), a file(asr, cls) or a streaming(not supported yet).
|
||||||
|
"""
|
||||||
|
assert os.path.isfile(audio_file)
|
||||||
|
waveform, _ = load(audio_file)
|
||||||
|
if isinstance(audio_file, (str, os.PathLike)):
|
||||||
|
logger.info("Preprocessing audio_file:" + audio_file)
|
||||||
|
|
||||||
|
# Feature extraction
|
||||||
|
waveform = paddle.to_tensor(waveform).unsqueeze(0)
|
||||||
|
self._inputs['feats'] = self.feature_extractor(waveform).unsqueeze(0)
|
||||||
|
|
||||||
|
@paddle.no_grad()
|
||||||
|
def infer(self):
|
||||||
|
"""
|
||||||
|
Model inference and result stored in self.output.
|
||||||
|
"""
|
||||||
|
self._outputs['logits'] = self.model(self._inputs['feats'])
|
||||||
|
|
||||||
|
def postprocess(self, threshold: float) -> Union[str, os.PathLike]:
|
||||||
|
"""
|
||||||
|
Output postprocess and return human-readable results such as texts and audio files.
|
||||||
|
"""
|
||||||
|
kws_score = max(self._outputs['logits'][0, :, 0]).item()
|
||||||
|
return 'Score: {:.3f}, Threshold: {}, Is keyword: {}'.format(
|
||||||
|
kws_score, threshold, kws_score > threshold)
|
||||||
|
|
||||||
|
def execute(self, argv: List[str]) -> bool:
|
||||||
|
"""
|
||||||
|
Command line entry.
|
||||||
|
"""
|
||||||
|
parser_args = self.parser.parse_args(argv)
|
||||||
|
|
||||||
|
model_type = parser_args.model
|
||||||
|
cfg_path = parser_args.config
|
||||||
|
ckpt_path = parser_args.ckpt_path
|
||||||
|
device = parser_args.device
|
||||||
|
threshold = parser_args.threshold
|
||||||
|
|
||||||
|
if not parser_args.verbose:
|
||||||
|
self.disable_task_loggers()
|
||||||
|
|
||||||
|
task_source = self.get_input_source(parser_args.input)
|
||||||
|
task_results = OrderedDict()
|
||||||
|
has_exceptions = False
|
||||||
|
|
||||||
|
for id_, input_ in task_source.items():
|
||||||
|
try:
|
||||||
|
res = self(input_, threshold, model_type, cfg_path, ckpt_path,
|
||||||
|
device)
|
||||||
|
task_results[id_] = res
|
||||||
|
except Exception as e:
|
||||||
|
has_exceptions = True
|
||||||
|
task_results[id_] = f'{e.__class__.__name__}: {e}'
|
||||||
|
|
||||||
|
self.process_task_results(parser_args.input, task_results,
|
||||||
|
parser_args.job_dump_result)
|
||||||
|
|
||||||
|
if has_exceptions:
|
||||||
|
return False
|
||||||
|
else:
|
||||||
|
return True
|
||||||
|
|
||||||
|
@stats_wrapper
|
||||||
|
def __call__(self,
|
||||||
|
audio_file: os.PathLike,
|
||||||
|
threshold: float=0.8,
|
||||||
|
model: str='mdtc_heysnips',
|
||||||
|
config: Optional[os.PathLike]=None,
|
||||||
|
ckpt_path: Optional[os.PathLike]=None,
|
||||||
|
device: str=paddle.get_device()):
|
||||||
|
"""
|
||||||
|
Python API to call an executor.
|
||||||
|
"""
|
||||||
|
audio_file = os.path.abspath(os.path.expanduser(audio_file))
|
||||||
|
paddle.set_device(device)
|
||||||
|
self._init_from_path(model, config, ckpt_path)
|
||||||
|
self.preprocess(audio_file)
|
||||||
|
self.infer()
|
||||||
|
res = self.postprocess(threshold)
|
||||||
|
|
||||||
|
return res
|
||||||
Loading…
Reference in new issue