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Merge pull request #1646 from Honei/develop

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[vec]add speaker verification score method
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Honei 2 years ago committed by GitHub
parent 1843bed458 9b5f7f71ac
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5 changed files with 246 additions and 4 deletions
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61
demos/speaker_verification/README.md
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@ -30,6 +30,11 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
paddlespeech vector --task spk --input vec.job
echo -e "demo2 85236145389.wav \n demo3 85236145389.wav" | paddlespeech vector --task spk
paddlespeech vector --task score --input "./85236145389.wav ./123456789.wav"
echo -e "demo4 85236145389.wav 85236145389.wav \n demo5 85236145389.wav 123456789.wav" > vec.job
paddlespeech vector --task score --input vec.job
```
Usage:
@ -103,6 +108,19 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
audio_file='./85236145389.wav',
device=paddle.get_device())
print('Audio embedding Result: \n{}'.format(audio_emb))
test_emb = vector_executor(
model='ecapatdnn_voxceleb12',
sample_rate=16000,
config=None, # Set `config` and `ckpt_path` to None to use pretrained model.
ckpt_path=None,
audio_file='./123456789.wav',
device=paddle.get_device())
print('Test embedding Result: \n{}'.format(test_emb))
# score range [0, 1]
score = vector_executor.get_embeddings_score(audio_emb, test_emb)
print(f"Eembeddings Score: {score}")
```
Output：
@ -149,6 +167,49 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
-3.3925855 5.079156 7.759716 4.677565 5.8457737
2.402413 7.7071047 3.9711342 -6.390043 6.1268735
-3.7760346 -11.118123 ]
# get the test embedding
Test embedding Result:
[ -1.902964 2.0690894 -8.034194 3.5472693 0.18089125
6.9085927 1.4097427 -1.9487704 -10.021278 -0.20755845
-8.04332 4.344489 2.3200977 -14.306299 5.184692
-11.55602 -3.8497238 0.6444722 1.2833948 2.6766639
0.5878921 0.7946299 1.7207596 2.5791872 14.998469
-1.3385371 15.031221 -0.8006958 1.99287 -9.52007
2.435466 4.003221 -4.33817 -4.898601 -5.304714
-18.033886 10.790787 -12.784645 -5.641755 2.9761686
-10.566622 1.4839455 6.152458 -5.7195854 2.8603241
6.112133 8.489869 5.5958056 1.2836679 -1.2293907
0.89927405 7.0288725 -2.854029 -0.9782962 5.8255906
14.905906 -5.025907 0.7866458 -4.2444224 -16.354029
10.521315 0.9604709 -3.3257897 7.144871 -13.592733
-8.568869 -1.7953678 0.26313916 10.916714 -6.9374123
1.857403 -6.2746415 2.8154466 -7.2338667 -2.293357
-0.05452765 5.4287076 5.0849075 -6.690375 -1.6183422
3.654291 0.94352573 -9.200294 -5.4749465 -3.5235846
1.3420814 4.240421 -2.772944 -2.8451524 16.311104
4.2969875 -1.762936 -12.5758915 8.595198 -0.8835239
-1.5708797 1.568961 1.1413603 3.5032008 -0.45251232
-6.786333 16.89443 5.3366146 -8.789056 0.6355629
3.2579517 -3.328322 7.5969577 0.66025066 -6.550468
-9.148656 2.020372 -0.4615173 1.1965656 -3.8764873
11.6562195 -6.0750933 12.182899 3.2218833 0.81969476
5.570001 -3.8459578 -7.205299 7.9262037 -7.6611166
-5.249467 -2.2671914 7.2658715 -13.298164 4.821147
-2.7263982 11.691089 -3.8918593 -2.838112 -1.0336838
-3.8034165 2.8536487 -5.60398 -1.1972581 1.3455094
-3.4903061 2.2408795 5.5010734 -3.970756 11.99696
-7.8858757 0.43160373 -5.5059714 4.3426995 16.322706
11.635366 0.72157705 -9.245714 -3.91465 -4.449838
-1.5716927 7.713747 -2.2430465 -6.198303 -13.481864
2.8156567 -5.7812386 5.1456156 2.7289324 -14.505571
13.270688 3.448231 -7.0659585 4.5886116 -4.466099
-0.296428 -11.463529 -2.6076477 14.110243 -6.9725137
-1.9962958 2.7119343 19.391657 0.01961198 14.607133
-1.6695905 -4.391516 1.3131028 -6.670972 -5.888604
12.0612335 5.9285784 3.3715196 1.492534 10.723728
-0.95514804 -12.085431 ]
# get the score between enroll and test
Eembeddings Score: 0.4292638301849365
```
### 4.Pretrained Models

61
demos/speaker_verification/README_cn.md
Unescape View File

@ -29,6 +29,11 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
paddlespeech vector --task spk --input vec.job
echo -e "demo2 85236145389.wav \n demo3 85236145389.wav" | paddlespeech vector --task spk
paddlespeech vector --task score --input "./85236145389.wav ./123456789.wav"
echo -e "demo4 85236145389.wav 85236145389.wav \n demo5 85236145389.wav 123456789.wav" > vec.job
paddlespeech vector --task score --input vec.job
```
使用方法：
@ -101,6 +106,19 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
audio_file='./85236145389.wav',
device=paddle.get_device())
print('Audio embedding Result: \n{}'.format(audio_emb))
test_emb = vector_executor(
model='ecapatdnn_voxceleb12',
sample_rate=16000,
config=None, # Set `config` and `ckpt_path` to None to use pretrained model.
ckpt_path=None,
audio_file='./123456789.wav',
device=paddle.get_device())
print('Test embedding Result: \n{}'.format(test_emb))
# score range [0, 1]
score = vector_executor.get_embeddings_score(audio_emb, test_emb)
print(f"Eembeddings Score: {score}")
```
输出：
@ -146,6 +164,49 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
-3.3925855 5.079156 7.759716 4.677565 5.8457737
2.402413 7.7071047 3.9711342 -6.390043 6.1268735
-3.7760346 -11.118123 ]
# get the test embedding
Test embedding Result:
[ -1.902964 2.0690894 -8.034194 3.5472693 0.18089125
6.9085927 1.4097427 -1.9487704 -10.021278 -0.20755845
-8.04332 4.344489 2.3200977 -14.306299 5.184692
-11.55602 -3.8497238 0.6444722 1.2833948 2.6766639
0.5878921 0.7946299 1.7207596 2.5791872 14.998469
-1.3385371 15.031221 -0.8006958 1.99287 -9.52007
2.435466 4.003221 -4.33817 -4.898601 -5.304714
-18.033886 10.790787 -12.784645 -5.641755 2.9761686
-10.566622 1.4839455 6.152458 -5.7195854 2.8603241
6.112133 8.489869 5.5958056 1.2836679 -1.2293907
0.89927405 7.0288725 -2.854029 -0.9782962 5.8255906
14.905906 -5.025907 0.7866458 -4.2444224 -16.354029
10.521315 0.9604709 -3.3257897 7.144871 -13.592733
-8.568869 -1.7953678 0.26313916 10.916714 -6.9374123
1.857403 -6.2746415 2.8154466 -7.2338667 -2.293357
-0.05452765 5.4287076 5.0849075 -6.690375 -1.6183422
3.654291 0.94352573 -9.200294 -5.4749465 -3.5235846
1.3420814 4.240421 -2.772944 -2.8451524 16.311104
4.2969875 -1.762936 -12.5758915 8.595198 -0.8835239
-1.5708797 1.568961 1.1413603 3.5032008 -0.45251232
-6.786333 16.89443 5.3366146 -8.789056 0.6355629
3.2579517 -3.328322 7.5969577 0.66025066 -6.550468
-9.148656 2.020372 -0.4615173 1.1965656 -3.8764873
11.6562195 -6.0750933 12.182899 3.2218833 0.81969476
5.570001 -3.8459578 -7.205299 7.9262037 -7.6611166
-5.249467 -2.2671914 7.2658715 -13.298164 4.821147
-2.7263982 11.691089 -3.8918593 -2.838112 -1.0336838
-3.8034165 2.8536487 -5.60398 -1.1972581 1.3455094
-3.4903061 2.2408795 5.5010734 -3.970756 11.99696
-7.8858757 0.43160373 -5.5059714 4.3426995 16.322706
11.635366 0.72157705 -9.245714 -3.91465 -4.449838
-1.5716927 7.713747 -2.2430465 -6.198303 -13.481864
2.8156567 -5.7812386 5.1456156 2.7289324 -14.505571
13.270688 3.448231 -7.0659585 4.5886116 -4.466099
-0.296428 -11.463529 -2.6076477 14.110243 -6.9725137
-1.9962958 2.7119343 19.391657 0.01961198 14.607133
-1.6695905 -4.391516 1.3131028 -6.670972 -5.888604
12.0612335 5.9285784 3.3715196 1.492534 10.723728
-0.95514804 -12.085431 ]
# get the score between enroll and test
Eembeddings Score: 0.4292638301849365
```
### 4.预训练模型

3
demos/speaker_verification/run.sh
Unescape View File

@ -1,6 +1,9 @@
#!/bin/bash
wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
wget -c https://paddlespeech.bj.bcebos.com/vector/audio/123456789.wav
# vector
paddlespeech vector --task spk --input ./85236145389.wav
paddlespeech vector --task score --input "./85236145389.wav ./123456789.wav"

79
paddlespeech/cli/vector/infer.py
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@ -15,6 +15,7 @@ import argparse
import os
import sys
from collections import OrderedDict
from typing import Dict
from typing import List
from typing import Optional
from typing import Union
@ -79,7 +80,7 @@ class VectorExecutor(BaseExecutor):
"--task",
type=str,
default="spk",
choices=["spk"],
choices=["spk", "score"],
help="task type in vector domain")
self.parser.add_argument(
"--input",
@ -147,13 +148,40 @@ class VectorExecutor(BaseExecutor):
logger.info(f"task source: {task_source}")
# stage 3: process the audio one by one
# we do action according the task type
task_result = OrderedDict()
has_exceptions = False
for id_, input_ in task_source.items():
try:
res = self(input_, model, sample_rate, config, ckpt_path,
device)
task_result[id_] = res
# extract the speaker audio embedding
if parser_args.task == "spk":
logger.info("do vector spk task")
res = self(input_, model, sample_rate, config, ckpt_path,
device)
task_result[id_] = res
elif parser_args.task == "score":
logger.info("do vector score task")
logger.info(f"input content {input_}")
if len(input_.split()) != 2:
logger.error(
f"vector score task input {input_} wav num is not two,"
"that is {len(input_.split())}")
sys.exit(-1)
# get the enroll and test embedding
enroll_audio, test_audio = input_.split()
logger.info(
f"score task, enroll audio: {enroll_audio}, test audio: {test_audio}"
)
enroll_embedding = self(enroll_audio, model, sample_rate,
config, ckpt_path, device)
test_embedding = self(test_audio, model, sample_rate,
config, ckpt_path, device)
# get the score
res = self.get_embeddings_score(enroll_embedding,
test_embedding)
task_result[id_] = res
except Exception as e:
has_exceptions = True
task_result[id_] = f'{e.__class__.__name__}: {e}'
@ -172,6 +200,49 @@ class VectorExecutor(BaseExecutor):
else:
return True
def _get_job_contents(
self, job_input: os.PathLike) -> Dict[str, Union[str, os.PathLike]]:
"""
Read a job input file and return its contents in a dictionary.
Refactor from the Executor._get_job_contents
Args:
job_input (os.PathLike): The job input file.
Returns:
Dict[str, str]: Contents of job input.
"""
job_contents = OrderedDict()
with open(job_input) as f:
for line in f:
line = line.strip()
if not line:
continue
k = line.split(' ')[0]
v = ' '.join(line.split(' ')[1:])
job_contents[k] = v
return job_contents
def get_embeddings_score(self, enroll_embedding, test_embedding):
"""get the enroll embedding and test embedding score
Args:
enroll_embedding (numpy.array): shape: (emb_size), enroll audio embedding
test_embedding (numpy.array): shape: (emb_size), test audio embedding
Returns:
score: the score between enroll embedding and test embedding
"""
if not hasattr(self, "score_func"):
self.score_func = paddle.nn.CosineSimilarity(axis=0)
logger.info("create the cosine score function ")
score = self.score_func(
paddle.to_tensor(enroll_embedding),
paddle.to_tensor(test_embedding))
return score.item()
@stats_wrapper
def __call__(self,
audio_file: os.PathLike,

46
paddlespeech/vector/models/ecapa_tdnn.py
Unescape View File

@ -79,6 +79,20 @@ class Conv1d(nn.Layer):
bias_attr=bias, )
def forward(self, x):
"""Do conv1d forward
Args:
x (paddle.Tensor): [N, C, L] input data,
N is the batch,
C is the data dimension,
L is the time
Raises:
ValueError: only support the same padding type
Returns:
paddle.Tensor: the value of conv1d
"""
if self.padding == "same":
x = self._manage_padding(x, self.kernel_size, self.dilation,
self.stride)
@ -88,6 +102,20 @@ class Conv1d(nn.Layer):
return self.conv(x)
def _manage_padding(self, x, kernel_size: int, dilation: int, stride: int):
"""Padding the input data
Args:
x (paddle.Tensor): [N, C, L] input data
N is the batch,
C is the data dimension,
L is the time
kernel_size (int): 1-d convolution kernel size
dilation (int): 1-d convolution dilation
stride (int): 1-d convolution stride
Returns:
paddle.Tensor: the padded input data
"""
L_in = x.shape[-1] # Detecting input shape
padding = self._get_padding_elem(L_in, stride, kernel_size,
dilation) # Time padding
@ -101,6 +129,17 @@ class Conv1d(nn.Layer):
stride: int,
kernel_size: int,
dilation: int):
"""Calculate the padding value in same mode
Args:
L_in (int): the times of the input data,
stride (int): 1-d convolution stride
kernel_size (int): 1-d convolution kernel size
dilation (int): 1-d convolution stride
Returns:
int: return the padding value in same mode
"""
if stride > 1:
n_steps = math.ceil(((L_in - kernel_size * dilation) / stride) + 1)
L_out = stride * (n_steps - 1) + kernel_size * dilation
@ -245,6 +284,13 @@ class SEBlock(nn.Layer):
class AttentiveStatisticsPooling(nn.Layer):
def __init__(self, channels, attention_channels=128, global_context=True):
"""Compute the speaker verification statistics
The detail info is section 3.1 in https://arxiv.org/pdf/1709.01507.pdf
Args:
channels (int): input data channel or data dimension
attention_channels (int, optional): attention dimension. Defaults to 128.
global_context (bool, optional): If use the global context information. Defaults to True.
"""
super().__init__()
self.eps = 1e-12

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