Spaces:
Runtime error
Runtime error
File size: 4,883 Bytes
0102e16 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 |
#!/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_detach.register import tables
from funasr_detach.models.campplus.utils import extract_feature
from funasr_detach.utils.load_utils import load_audio_text_image_video
from funasr_detach.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
|