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| #!/usr/bin/env python3
| # -*- encoding: utf-8 -*-
| # Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
| # MIT License (https://opensource.org/licenses/MIT)
| # Modified from 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker)
|
| import time
| import torch
| import numpy as np
| from collections import OrderedDict
| from contextlib import contextmanager
| from distutils.version import LooseVersion
|
| from funasr.register import tables
| from funasr.models.campplus.utils import extract_feature
| from funasr.utils.load_utils import load_audio_text_image_video
| from funasr.models.campplus.components import (
| DenseLayer,
| StatsPool,
| TDNNLayer,
| CAMDenseTDNNBlock,
| TransitLayer,
| get_nonlinear,
| FCM,
| )
|
|
| if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
| from torch.cuda.amp import autocast
| else:
| # Nothing to do if torch<1.6.0
| @contextmanager
| def autocast(enabled=True):
| yield
|
|
| @tables.register("model_classes", "CAMPPlus")
| class CAMPPlus(torch.nn.Module):
| def __init__(
| self,
| feat_dim=80,
| embedding_size=192,
| growth_rate=32,
| bn_size=4,
| init_channels=128,
| config_str="batchnorm-relu",
| memory_efficient=True,
| output_level="segment",
| **kwargs,
| ):
| super().__init__()
|
| self.head = FCM(feat_dim=feat_dim)
| channels = self.head.out_channels
| self.output_level = output_level
|
| self.xvector = torch.nn.Sequential(
| OrderedDict(
| [
| (
| "tdnn",
| TDNNLayer(
| channels,
| init_channels,
| 5,
| stride=2,
| dilation=1,
| padding=-1,
| config_str=config_str,
| ),
| ),
| ]
| )
| )
| channels = init_channels
| for i, (num_layers, kernel_size, dilation) in enumerate(
| zip((12, 24, 16), (3, 3, 3), (1, 2, 2))
| ):
| block = CAMDenseTDNNBlock(
| num_layers=num_layers,
| in_channels=channels,
| out_channels=growth_rate,
| bn_channels=bn_size * growth_rate,
| kernel_size=kernel_size,
| dilation=dilation,
| config_str=config_str,
| memory_efficient=memory_efficient,
| )
| self.xvector.add_module("block%d" % (i + 1), block)
| channels = channels + num_layers * growth_rate
| self.xvector.add_module(
| "transit%d" % (i + 1),
| TransitLayer(channels, channels // 2, bias=False, config_str=config_str),
| )
| channels //= 2
|
| self.xvector.add_module("out_nonlinear", get_nonlinear(config_str, channels))
|
| if self.output_level == "segment":
| self.xvector.add_module("stats", StatsPool())
| self.xvector.add_module(
| "dense", DenseLayer(channels * 2, embedding_size, config_str="batchnorm_")
| )
| else:
| assert (
| self.output_level == "frame"
| ), "`output_level` should be set to 'segment' or 'frame'. "
|
| for m in self.modules():
| if isinstance(m, (torch.nn.Conv1d, torch.nn.Linear)):
| torch.nn.init.kaiming_normal_(m.weight.data)
| if m.bias is not None:
| torch.nn.init.zeros_(m.bias)
|
| def forward(self, x):
| x = x.permute(0, 2, 1) # (B,T,F) => (B,F,T)
| x = self.head(x)
| x = self.xvector(x)
| if self.output_level == "frame":
| x = x.transpose(1, 2)
| return x
|
| def inference(
| self,
| data_in,
| data_lengths=None,
| key: list = None,
| tokenizer=None,
| frontend=None,
| **kwargs,
| ):
| # extract fbank feats
| meta_data = {}
| time1 = time.perf_counter()
| audio_sample_list = load_audio_text_image_video(
| data_in, fs=16000, audio_fs=kwargs.get("fs", 16000), data_type="sound"
| )
| time2 = time.perf_counter()
| meta_data["load_data"] = f"{time2 - time1:0.3f}"
| speech, speech_lengths, speech_times = extract_feature(audio_sample_list)
| speech = speech.to(device=kwargs["device"])
| time3 = time.perf_counter()
| meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
| meta_data["batch_data_time"] = np.array(speech_times).sum().item() / 16000.0
| results = [{"spk_embedding": self.forward(speech.to(torch.float32))}]
| return results, meta_data
|
|
