clmr
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95 lines
3.0 KiB
95 lines
3.0 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 os
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import sys
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import logging
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from pathlib import Path
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from typing import Union
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import torchaudio
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import torch
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import numpy
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from towhee.operator import NNOperator
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from towhee import register
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sys.path.append(str(Path(__file__).parent))
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from clmr_checkpoint import load_encoder_checkpoint
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from sample_cnn import SampleCNN
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log = logging.getLogger()
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@register(output_schema=['vecs'])
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class ClmrMagnatagatune(NNOperator):
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"""
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Pretrained clmr
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"""
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def __init__(self, framework="pytorch") -> None:
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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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weight_path = os.path.join(str(Path(__file__).parent),
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'clmr_checkpoint_10000.pt')
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state_dict = load_encoder_checkpoint(weight_path, 1)
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encoder = SampleCNN(strides=[3, 3, 3, 3, 3, 3, 3, 3, 3], supervised=False, out_dim=1)
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encoder.load_state_dict(state_dict)
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new_encoder = torch.nn.Sequential(*(list(encoder.children())[:-1]))
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x = list(new_encoder[0][:10].children())
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y = torch.nn.Sequential(*list(new_encoder[0][10].children())[:-1])
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x.append(y)
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self.model = torch.nn.Sequential(*x)
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self.model.eval()
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self.model.to(self.device)
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def __call__(self, audio: Union[str, numpy.ndarray], sample_rate: int = None) -> numpy.ndarray:
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_sr = 22050
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audio_length = 59049
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if isinstance(audio, str):
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source = os.path.abspath(audio)
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audio, sr = torchaudio.load(source)
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elif isinstance(audio, numpy.ndarray):
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sr = sample_rate
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audio = torch.tensor(audio).to(torch.float32)
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if sr != _sr:
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transform = torchaudio.transforms.Resample(orig_freq=sr, new_freq=_sr)
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audio = transform(audio)
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with torch.no_grad():
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batch = torch.split(audio, audio_length, dim=1)
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batch = torch.cat(batch[:-1])
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batch = batch.unsqueeze(dim=1)
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batch = batch.to(self.device)
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features = numpy.squeeze(self.model(batch))
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embeddings = features.to("cpu")
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return embeddings.detach().numpy()
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# if __name__ == "__main__":
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# encoder = ClmrMagnatagatune()
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#
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# # audio_path = "/audio/path/or/link"
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# # vec = encoder(audio_path)
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#
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# audio_data = numpy.zeros((2, 441344))
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# sample_rate = 44100
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# vec = encoder(audio_data, sample_rate)
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#
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# print(vec.shape)
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