torch-vggish/torch_vggish.py

# 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.hstack([item.audio for item in datas])
        sr = datas[0].sample_rate
        audio_array = numpy.reshape(audios, (-1, 1))
        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: numpy.ndarray, sr: int = None):
        ii = numpy.iinfo(audio.dtype)
        samples = 2 * audio / (ii.max - ii.min + 1)
        return vggish_input.waveform_to_examples(samples, sr, return_tensor=True)