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torch-vggish
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Signed-off-by: Jael Gu <mengjia.gu@zilliz.com>
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Jael Gu 4 years ago
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3 changed files with 59 additions and 190 deletions
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  1. 82
      torch_vggish.py
  2. 84
      vggish.bak
  3. 83
      vggish.py

82
torch_vggish.py
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@ -12,36 +12,72 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import warnings
import os
import sys
import torch
from typing import NamedTuple
from pathlib import Path
import numpy
import os
from pathlib import Path
from typing import Union, List, NamedTuple
from towhee.operator import Operator
import torch
import warnings
warnings.filterwarnings("ignore")
from towhee.operator.base import NNOperator
from towhee.models.vggish.torch_vggish import VGG
from towhee import register
sys.path.append(str(Path(__file__).parent))
import vggish_input
warnings.filterwarnings('ignore')
log = logging.getLogger()
class TorchVggish(Operator):
AudioOutput = NamedTuple('AudioOutput', [('vec', 'ndarray')])
class Vggish(NNOperator):
"""
"""
def __init__(self, framework: str = 'pytorch', weights_path: str=None) -> None:
super().__init__()
if framework == 'pytorch':
import importlib.util
path = os.path.join(str(Path(__file__).parent), 'pytorch', 'model.py')
opname = os.path.basename(str(Path(__file__))).split('.')[0]
spec = importlib.util.spec_from_file_location(opname, path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
self.model = module.Model(weights_path)
def __call__(self, audio_path: str) -> NamedTuple('Outputs', [('embs', numpy.ndarray)]):
audio_tensors = self.model.preprocess(audio_path)
def __init__(self, weights_path: str = None, framework: str = 'pytorch') -> None:
super().__init__(framework=framework)
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.model = VGG()
if not weights_path:
path = str(Path(__file__).parent)
weights_path = os.path.join(path, 'vggish.pth')
state_dict = torch.load(weights_path, map_location=torch.device('cpu'))
self.model.load_state_dict(state_dict)
self.model.eval()
self.model.to(self.device)
def __call__(self, datas: List[NamedTuple('data', [('audio', 'ndarray'), ('sample_rate', 'int')])]) -> numpy.ndarray:
audios = numpy.stack([item.audio for item in datas])
sr = datas[0].sample_rate
audio_array = numpy.reshape(audios, (-1, 2))
audio_tensors = self.preprocess(audio_array, sr).to(self.device)
features = self.model(audio_tensors)
Outputs = NamedTuple('Outputs', [('embs', numpy.ndarray)])
return Outputs(features.detach().numpy())
outs = features.to("cpu")
return [AudioOutput(outs.detach().numpy())]
def preprocess(self, audio: Union[str, numpy.ndarray], sr: int = None):
if audio.dtype == numpy.int32:
samples = audio / 2147483648.0
elif audio.dtype == numpy.int16:
samples = audio / 32768.0
return vggish_input.waveform_to_examples(samples, sr, return_tensor=True)
# if __name__ == '__main__':
# encoder = Vggish()
#
# # audio_path = '/path/to/audio'
# # vec = encoder(audio_path)
#
# audio_data = numpy.zeros((2, 441344))
# sample_rate = 44100
# vec = encoder(audio_data, sample_rate)
# print(vec)

84
vggish.bak
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@ -1,84 +0,0 @@
# Copyright 2021 Zilliz. 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 logging
import warnings
import os
import sys
import numpy
from pathlib import Path
from typing import Union
import torch
from towhee.operator.base import NNOperator
from towhee.models.vggish.torch_vggish import VGG
from towhee import register
sys.path.append(str(Path(__file__).parent))
import vggish_input
warnings.filterwarnings('ignore')
log = logging.getLogger()
@register(output_schema=['vec'])
class Vggish(NNOperator):
"""
"""
def __init__(self, weights_path: str = None, framework: str = 'pytorch') -> None:
super().__init__(framework=framework)
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.model = VGG()
if not weights_path:
path = str(Path(__file__).parent)
weights_path = os.path.join(path, 'vggish.pth')
state_dict = torch.load(weights_path, map_location=torch.device('cpu'))
self.model.load_state_dict(state_dict)
self.model.eval()
self.model.to(self.device)
def __call__(self, audio: Union[str, numpy.ndarray], sr: int = None) -> numpy.ndarray:
audio_tensors = self.preprocess(audio, sr).to(self.device)
features = self.model(audio_tensors)
outs = features.to("cpu")
return outs.detach().numpy()
def preprocess(self, audio: Union[str, numpy.ndarray], sr: int = None):
if isinstance(audio, str):
audio_tensors = vggish_input.wavfile_to_examples(audio)
elif isinstance(audio, numpy.ndarray):
try:
audio = audio.transpose()
audio_tensors = vggish_input.waveform_to_examples(audio, sr, return_tensor=True)
except Exception as e:
log.error("Fail to load audio data.")
raise e
else:
log.error(f"Invalid input audio: {type(audio)}")
return audio_tensors
# if __name__ == '__main__':
# encoder = Vggish()
#
# # audio_path = '/path/to/audio'
# # vec = encoder(audio_path)
#
# audio_data = numpy.zeros((2, 441344))
# sample_rate = 44100
# vec = encoder(audio_data, sample_rate)
# print(vec)

83
vggish.py
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@ -1,83 +0,0 @@
# Copyright 2021 Zilliz. 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 logging
import warnings
import os
import sys
import numpy
from pathlib import Path
from typing import Union, List, NamedTuple
import torch
from towhee.operator.base import NNOperator
from towhee.models.vggish.torch_vggish import VGG
from towhee import register
sys.path.append(str(Path(__file__).parent))
import vggish_input
warnings.filterwarnings('ignore')
log = logging.getLogger()
AudioOutput = NamedTuple('AudioOutput', [('vec', 'ndarray')])
class Vggish(NNOperator):
"""
"""
def __init__(self, weights_path: str = None, framework: str = 'pytorch') -> None:
super().__init__(framework=framework)
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.model = VGG()
if not weights_path:
path = str(Path(__file__).parent)
weights_path = os.path.join(path, 'vggish.pth')
state_dict = torch.load(weights_path, map_location=torch.device('cpu'))
self.model.load_state_dict(state_dict)
self.model.eval()
self.model.to(self.device)
def __call__(self, datas: List[NamedTuple('data', [('audio', 'ndarray'), ('sample_rate', 'int')])]) -> numpy.ndarray:
audios = numpy.stack([item.audio for item in datas])
sr = datas[0].sample_rate
audio_array = numpy.reshape(audios, (-1, 2))
audio_tensors = self.preprocess(audio_array, sr).to(self.device)
features = self.model(audio_tensors)
outs = features.to("cpu")
return [AudioOutput(outs.detach().numpy())]
def preprocess(self, audio: Union[str, numpy.ndarray], sr: int = None):
if audio.dtype == numpy.int32:
samples = audio / 2147483648.0
elif audio.dtype == numpy.int16:
samples = audio / 32768.0
return vggish_input.waveform_to_examples(samples, sr, return_tensor=True)
# if __name__ == '__main__':
# encoder = Vggish()
#
# # audio_path = '/path/to/audio'
# # vec = encoder(audio_path)
#
# audio_data = numpy.zeros((2, 441344))
# sample_rate = 44100
# vec = encoder(audio_data, sample_rate)
# print(vec)
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