nnfp
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233 lines
9.5 KiB
233 lines
9.5 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, Union
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import torch
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import torchaudio
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import numpy
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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, hop25_params, distill_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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model_name: str = 'nnfp_default',
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model_path: str = None,
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framework: str = 'pytorch',
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device: str = None
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):
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super().__init__(framework=framework)
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if device is None:
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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self.device = device
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if model_name == 'nnfp_default':
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self.params = default_params
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elif model_name == 'nnfp_hop25':
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self.params = hop25_params
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elif model_name == 'nnfp_distill':
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self.params == distill_params
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else:
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raise ValueError('Invalid model name. Accept value from ["nnfp_default", "nnfp_hop25", "nnfp_distill"]')
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log.info('Loading model...')
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if model_path is None:
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path = str(Path(__file__).parent)
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model_path = os.path.join(path, 'saved_model', 'nnfp_fma.pt')
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if model_path.endswith('.onnx'):
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log.warning('Using onnx.')
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import onnxruntime
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self.model = onnxruntime.InferenceSession(
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model_path,
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providers=['CPUExecutionProvider'] if self.device == 'cpu' else ['CUDAExecutionProvider']
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)
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else:
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try:
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state_dict = torch.jit.load(model_path, map_location=self.device)
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except Exception:
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state_dict = torch.load(model_path, map_location=self.device)
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if isinstance(state_dict, torch.nn.Module):
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self.model = state_dict
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else:
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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 with parameters...')
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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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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: Union[str, List[AudioFrame]]) -> numpy.ndarray:
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audio_tensors = self.preprocess(data)
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if audio_tensors.device != self.device:
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audio_tensors = audio_tensors.to(self.device)
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# print(audio_tensors.shape)
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if isinstance(self.model, onnxruntime.InferenceSession):
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audio_numpy = audio_tensors.detach().cpu().numpy() if audio_tensors.requires_grad \
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else audio_tensors.cpu().numpy()
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ort_inputs = {self.model.get_inputs()[0].name: audio_numpy}
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outs = self.model.run(None, ort_inputs)[0]
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else:
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features = self.model(audio_tensors)
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outs = features.detach().cpu().numpy()
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return outs
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def preprocess(self, frames: Union[str, List[AudioFrame]]):
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if isinstance(frames, str):
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audio, sr = torchaudio.load(frames)
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else:
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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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if len(audio.shape) == 1:
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audio = audio[None, :]
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audio = self.int2float(audio)
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audio = torch.from_numpy(audio)
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assert len(audio.shape) == 2
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if sr != self.params['sample_rate']:
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resampler = torchaudio.transforms.Resample(sr, self.params['sample_rate'], dtype=audio.dtype)
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audio = resampler(audio)
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# import resampy
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# audio = audio.detach().cpu().numpy()
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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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if wav.dtype != 'int16':
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wav = (wav >> 16).astype(numpy.int16)
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assert wav.dtype == 'int16'
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wav = (wav / 32768.0).astype(dtype)
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return wav
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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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def save_model(self, format: str = 'pytorch', path: str = 'default'):
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if path == 'default':
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path = str(Path(__file__).parent)
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path = os.path.join(path, 'saved', format)
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os.makedirs(path, exist_ok=True)
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name = 'nnfp'
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path = os.path.join(path, name)
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dummy_input = torch.rand(
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(1,) + (self.params['n_mels'], self.params['u'])
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).to(self.device)
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if format == 'pytorch':
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path = path + '.pt'
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torch.save(self.model, path)
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elif format == 'torchscript':
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path = path + '.pt'
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try:
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try:
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jit_model = torch.jit.script(self.model)
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except Exception:
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log.warning(
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'Failed to directly export as torchscript.'
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'Using dummy input in shape of %s now.', dummy_input.shape)
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jit_model = torch.jit.trace(self.model, dummy_input, strict=False)
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torch.jit.save(jit_model, path)
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except Exception as e:
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log.error('Fail to save as torchscript: %s.', e)
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raise RuntimeError(f'Fail to save as torchscript: {e}.')
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elif format == 'onnx':
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path = path + '.onnx'
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try:
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torch.onnx.export(self.model,
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dummy_input,
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path,
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input_names=['input'],
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output_names=['output'],
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opset_version=12,
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do_constant_folding=True,
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dynamic_axes={'input': {0: 'batch_size'},
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'output': {0: 'batch_size'}
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}
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)
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except Exception as e:
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log.error(f'Fail to save as onnx: {e}.')
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raise RuntimeError(f'Fail to save as onnx: {e}.')
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# todo: elif format == 'tensorrt':
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else:
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log.error(f'Unsupported format "{format}".')
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def input_schema(self):
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return [(AudioFrame, (1024,))]
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def output_schema(self):
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return [(numpy.ndarray, (-1, self.params['dim']))]
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