nnfp
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135 lines
4.9 KiB
135 lines
4.9 KiB
# Copyright 2021 Zilliz. 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 logging
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import warnings
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import os
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from pathlib import Path
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from typing import List
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import torch
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import numpy
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import resampy
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from towhee.operator.base import NNOperator
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from towhee import register
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from towhee.types.audio_frame import AudioFrame
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from towhee.models.nnfp import NNFp
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from towhee.models.utils.audio_preprocess import preprocess_wav, MelSpec
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from .configs import default_params
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warnings.filterwarnings('ignore')
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log = logging.getLogger()
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@register(output_schema=['vecs'])
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class NNFingerprint(NNOperator):
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"""
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Audio embedding operator using Neural Network Fingerprint
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"""
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def __init__(self,
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params: dict = None,
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checkpoint_path: str = None,
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framework: str = 'pytorch'):
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super().__init__(framework=framework)
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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if params is None:
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self.params = default_params
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else:
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self.params = params
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dim = self.params['dim']
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h = self.params['h']
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u = self.params['u']
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f_bin = self.params['n_mels']
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n_seg = int(self.params['segment_size'] * self.params['sample_rate'])
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t = (n_seg + self.params['hop_length'] - 1) // self.params['hop_length']
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log.info('Creating model...')
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self.model = NNFp(
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dim=dim, h=h, u=u,
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in_f=f_bin, in_t=t,
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fuller=self.params['fuller'],
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activation=self.params['activation']
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).to(self.device)
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log.info('Loading weights...')
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if checkpoint_path is None:
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path = str(Path(__file__).parent)
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checkpoint_path = os.path.join(path, './checkpoints/pfann_fma_m.pt')
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state_dict = torch.load(checkpoint_path, map_location=self.device)
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self.model.load_state_dict(state_dict)
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self.model.eval()
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log.info('Model is loaded.')
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def __call__(self, data: List[AudioFrame]) -> numpy.ndarray:
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audio_tensors = self.preprocess(data).to(self.device)
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features = self.model(audio_tensors)
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return features.detach().cpu().numpy()
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def preprocess(self, frames: List[AudioFrame]):
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sr = frames[0].sample_rate
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layout = frames[0].layout
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if layout == 'stereo':
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frames = [frame.reshape(-1, 2) for frame in frames]
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audio = numpy.vstack(frames).transpose()
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else:
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audio = numpy.hstack(frames)
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audio = audio[None, :]
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audio = self.int2float(audio)
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if sr != self.params['sample_rate']:
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audio = resampy.resample(audio, sr, self.params['sample_rate'])
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wav = preprocess_wav(audio,
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segment_size=int(self.params['sample_rate'] * self.params['segment_size']),
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hop_size=int(self.params['sample_rate'] * self.params['hop_size']),
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frame_shift_mul=self.params['frame_shift_mul']).to(self.device)
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wav = wav.to(torch.float32)
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mel = MelSpec(sample_rate=self.params['sample_rate'],
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window_length=self.params['window_length'],
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hop_length=self.params['hop_length'],
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f_min=self.params['f_min'],
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f_max=self.params['f_max'],
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n_mels=self.params['n_mels'],
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naf_mode=self.params['naf_mode'],
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mel_log=self.params['mel_log'],
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spec_norm=self.params['spec_norm']).to(self.device)
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wav = mel(wav)
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return wav
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@staticmethod
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def int2float(wav: numpy.ndarray, dtype: str = 'float64'):
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"""
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Convert audio imgs from int to float.
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The input dtype must be integers.
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The output dtype is controlled by the parameter `dtype`, defaults to 'float64'.
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The code is inspired by https://github.com/mgeier/python-audio/blob/master/audio-files/utility.py
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"""
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dtype = numpy.dtype(dtype)
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assert dtype.kind == 'f'
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if wav.dtype.kind in 'iu':
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ii = numpy.iinfo(wav.dtype)
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abs_max = 2 ** (ii.bits - 1)
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offset = ii.min + abs_max
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return (wav.astype(dtype) - offset) / abs_max
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else:
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log.warning('Converting float dtype from %s to %s.', wav.dtype, dtype)
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return wav.astype(dtype)
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