add extract audio embedding api, test=doc

examples/voxceleb/sv0/local/extract_speaker_embedding.py

+# Copyright (c) 2021 PaddlePaddle Authors. 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 argparse
+import ast
+import os
+
+import numpy as np
+import paddle
+import paddle.nn.functional as F
+from paddle.io import BatchSampler
+from paddle.io import DataLoader
+from tqdm import tqdm
+
+from paddleaudio.datasets.voxceleb import VoxCeleb1
+from paddleaudio.features.core import melspectrogram
+from paddleaudio.backends import load as load_audio
+from paddlespeech.vector.io.batch import feature_normalize
+from paddlespeech.s2t.utils.log import Log
+from paddlespeech.vector.models.ecapa_tdnn import EcapaTdnn
+from paddlespeech.vector.modules.sid_model import SpeakerIdetification
+from paddlespeech.vector.training.metrics import compute_eer
+from paddlespeech.vector.training.seeding import seed_everything
+
+logger = Log(__name__).getlog()
+
+# feat configuration
+cpu_feat_conf = {
+    'n_mels': 80,
+    'window_size': 400,  #ms
+    'hop_length': 160,  #ms
+}
+
+def extract_audio_embedding(args):
+    # stage 0: set the training device, cpu or gpu
+    paddle.set_device(args.device)
+    # set the random seed, it is a must for multiprocess training
+    seed_everything(args.seed)
+
+    # stage 1: build the dnn backbone model network
+    ##"channels": [1024, 1024, 1024, 1024, 3072],
+    model_conf = {
+        "input_size": 80,
+        "channels": [512, 512, 512, 512, 1536],
+        "kernel_sizes": [5, 3, 3, 3, 1],
+        "dilations": [1, 2, 3, 4, 1],
+        "attention_channels": 128,
+        "lin_neurons": 192,
+    }
+    ecapa_tdnn = EcapaTdnn(**model_conf)
+
+    # stage 2: load the pre-trained model
+    args.load_checkpoint = os.path.abspath(
+        os.path.expanduser(args.load_checkpoint))
+
+    # load model checkpoint to sid model
+    state_dict = paddle.load(
+        os.path.join(args.load_checkpoint, 'model.pdparams'))
+    model.set_state_dict(state_dict)
+    logger.info(f'Checkpoint loaded from {args.load_checkpoint}')
+
+    # stage 3: we must set the model to eval mode
+    model.eval()
+    
+    # stage 4: read the audio data and extract the embedding
+    waveform, sr = load_audio(args.audio_path)
+    feat = melspectrogram(x=waveform, **cpu_feat_conf)
+    feat = paddle.to_tensor(feat).unsqueeze(0)
+    lengths = paddle.ones([1]) # in paddle inference model, the lengths is all one without padding
+    feat = feature_normalize(feat, mean_norm=True, std_norm=False)
+    embedding = ecapa_tdnn(feat, lengths
+                ).squeeze().numpy() # (1, emb_size, 1) -> (emb_size)
+
+    # stage 5: do global norm with external mean and std
+    # todo
+    return embedding
+
+
+if __name__ == "__main__":
+    # yapf: disable
+    parser = argparse.ArgumentParser(__doc__)
+    parser.add_argument('--device',
+                        choices=['cpu', 'gpu'],
+                        default="gpu",
+                        help="Select which device to train model, defaults to gpu.")
+    parser.add_argument("--seed",
+                        default=0,
+                        type=int,
+                        help="random seed for paddle, numpy and python random package")
+    parser.add_argument("--load-checkpoint",
+                        type=str,
+                        default='',
+                        help="Directory to load model checkpoint to contiune trainning.")
+    parser.add_argument("--global-embedding-norm",
+                        type=str,
+                        default=None,
+                        help="Apply global normalization on speaker embeddings.")
+    parser.add_argument("--audio-path",
+                        default="./data/demo.wav",
+                        type=str,
+                        help="Single audio file path")
+    args = parser.parse_args()
+    # yapf: enable
+
+    extract_audio_embedding(args)