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Easy Quest - Smart Recruitment Tool with AI - Backend
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22_23 - J 36
Easy Quest - Smart Recruitment Tool with AI - Backend
Commits
aceb2402
Commit
aceb2402
authored
Mar 30, 2023
by
Emika Chamodi
Browse files
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voice verification
parent
b698b996
Changes
19
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Showing
19 changed files
with
153 additions
and
1885 deletions
+153
-1885
NodeServer/src/config/types.ts
NodeServer/src/config/types.ts
+8
-0
NodeServer/src/index.ts
NodeServer/src/index.ts
+2
-0
NodeServer/src/models/Application.ts
NodeServer/src/models/Application.ts
+1
-0
NodeServer/src/models/Candidate.ts
NodeServer/src/models/Candidate.ts
+1
-0
NodeServer/src/routes/application.ts
NodeServer/src/routes/application.ts
+115
-0
NodeServer/src/routes/jobs.ts
NodeServer/src/routes/jobs.ts
+1
-46
NodeServer/src/routes/user.ts
NodeServer/src/routes/user.ts
+7
-0
NodeServer/src/utilities/apis/resume.ts
NodeServer/src/utilities/apis/resume.ts
+0
-1
NodeServer/src/utilities/apis/voice.ts
NodeServer/src/utilities/apis/voice.ts
+18
-0
voice_analyzer/Voice_Emotion/ReadMe.md
voice_analyzer/Voice_Emotion/ReadMe.md
+0
-1
voice_analyzer/Voice_Emotion/convert_wavs.py
voice_analyzer/Voice_Emotion/convert_wavs.py
+0
-72
voice_analyzer/Voice_Emotion/lib.py
voice_analyzer/Voice_Emotion/lib.py
+0
-96
voice_analyzer/Voice_Emotion/main
voice_analyzer/Voice_Emotion/main
+0
-117
voice_analyzer/Voice_Emotion/train.py
voice_analyzer/Voice_Emotion/train.py
+0
-26
voice_analyzer/Voice_recognizer/Pipfile
voice_analyzer/Voice_recognizer/Pipfile
+0
-19
voice_analyzer/Voice_recognizer/Pipfile.lock
voice_analyzer/Voice_recognizer/Pipfile.lock
+0
-1435
voice_analyzer/Voice_recognizer/ReadMe.md
voice_analyzer/Voice_recognizer/ReadMe.md
+0
-4
voice_analyzer/Voice_recognizer/main.py
voice_analyzer/Voice_recognizer/main.py
+0
-68
voice_analyzer/Voice_recognizer/requirements.txt
voice_analyzer/Voice_recognizer/requirements.txt
+0
-0
No files found.
NodeServer/src/config/types.ts
View file @
aceb2402
...
...
@@ -73,6 +73,7 @@ export type CandidateType = {
state
:
"
INTIAL
"
|
"
READY
"
;
resume
?:
string
;
resumeData
?:
ResumeDataType
;
selfIntro
?:
string
;
};
export
type
OrganizationType
=
{
...
...
@@ -124,6 +125,7 @@ export type ApplicationType = {
time
:
string
;
link
:
string
;
videoRef
?:
string
;
voiceVerification
?:
number
;
};
score
:
{
primary
:
number
;
...
...
@@ -135,6 +137,12 @@ export type ApplicationType = {
};
};
export
type
AnalyseApplicationPayload
=
{
applicationId
:
string
;
startTime
:
number
;
endTime
:
number
;
};
export
interface
TypedRequest
<
T
extends
Query
,
U
>
extends
Request
{
body
:
U
;
query
:
T
;
...
...
NodeServer/src/index.ts
View file @
aceb2402
...
...
@@ -10,6 +10,7 @@ app.use(cors());
const
authRoute
=
require
(
"
./routes/auth
"
);
const
userRoute
=
require
(
"
./routes/user
"
);
const
jobsRoute
=
require
(
"
./routes/jobs
"
);
const
applicationsRoute
=
require
(
"
./routes/application
"
);
// Service Initialisation
mongoose
.
connect
(
MONGO_URL
,
{
...
...
@@ -29,5 +30,6 @@ app.use(express.urlencoded({ extended: false }));
app
.
use
(
"
/auth
"
,
authRoute
);
app
.
use
(
"
/user
"
,
userRoute
);
app
.
use
(
"
/jobs
"
,
jobsRoute
);
app
.
use
(
"
/applications
"
,
applicationsRoute
);
app
.
listen
(
API_PORT
,
()
=>
console
.
log
(
`Listening on port
${
API_PORT
}
`
));
NodeServer/src/models/Application.ts
View file @
aceb2402
...
...
@@ -11,6 +11,7 @@ const applicationSchema = new Schema<ApplicationType>({
time
:
String
,
link
:
String
,
videoRef
:
String
,
voiceVerification
:
Number
,
},
require
:
false
,
},
...
...
NodeServer/src/models/Candidate.ts
View file @
aceb2402
...
...
@@ -42,6 +42,7 @@ const candidateSchema = new Schema<CandidateType>({
state
:
{
type
:
String
,
default
:
"
INTIAL
"
},
resume
:
{
type
:
String
,
require
:
false
},
resumeData
:
{
type
:
ResumeDataSchema
,
require
:
false
},
selfIntro
:
{
type
:
String
,
require
:
false
},
});
const
Candidates
=
model
<
CandidateType
>
(
"
candidates
"
,
candidateSchema
);
...
...
NodeServer/src/routes/application.ts
0 → 100644
View file @
aceb2402
import
{
Router
}
from
"
express
"
;
import
{
ApplicationType
,
TypedRequest
,
AnalyseApplicationPayload
,
}
from
"
../config/types
"
;
import
{
authMiddleware
,
candidateMiddleware
}
from
"
../middlewares/auth
"
;
import
Application
from
"
../models/Application
"
;
import
Jobs
from
"
../models/Job
"
;
import
ResumeAPI
from
"
../utilities/apis/resume
"
;
import
VoiceAPI
from
"
../utilities/apis/voice
"
;
const
router
=
Router
();
router
.
post
(
"
/apply
"
,
authMiddleware
,
candidateMiddleware
,
async
(
req
:
TypedRequest
<
{
userId
:
string
},
{
application
:
ApplicationType
;
resumeUrl
:
string
}
>
,
res
)
=>
{
try
{
const
{
application
,
resumeUrl
}
=
req
.
body
;
const
job
=
await
Jobs
.
findById
(
application
.
job
);
const
data
:
any
=
await
ResumeAPI
.
getResumeScores
({
user_id
:
req
.
query
.
userId
,
resume_url
:
resumeUrl
,
primary_skills
:
job
.
primarySkills
,
secondary_skills
:
job
.
secondarySkills
,
job_desc
:
job
.
description
,
});
const
score
:
ApplicationType
[
"
score
"
]
=
{
primary
:
data
.
primary_score
,
primatyMatch
:
data
.
primary_match
,
secondary
:
data
.
secondary_score
,
secondaryMatch
:
data
.
secondary_match
,
similarity
:
data
.
similarity
,
total
:
data
.
primary_score
+
data
.
secondary_score
+
data
.
similarity
,
};
const
newApplication
=
new
Application
({
...
application
,
score
});
const
_application
=
await
newApplication
.
save
();
job
.
applications
.
push
(
_application
.
id
);
await
job
.
save
();
return
res
.
json
({
success
:
true
,
applicationId
:
_application
.
id
,
});
}
catch
(
error
)
{
return
res
.
json
({
success
:
false
,
error
});
}
}
);
router
.
put
(
"
/update
"
,
authMiddleware
,
async
(
req
:
TypedRequest
<
{},
{
applicationId
:
string
;
update
:
Partial
<
ApplicationType
>
;
candidateId
:
string
;
}
>
,
res
)
=>
{
let
update
=
req
.
body
.
update
;
if
(
update
.
interview
?.
videoRef
)
{
const
score
:
any
=
await
VoiceAPI
.
verifyVoice
({
video_url
:
update
.
interview
?.
videoRef
,
user_id
:
req
.
body
.
candidateId
,
application_id
:
req
.
body
.
applicationId
,
});
update
.
interview
.
voiceVerification
=
score
;
}
Application
.
findByIdAndUpdate
(
req
.
body
.
applicationId
,
{
$set
:
update
,
})
.
then
((
_application
)
=>
{
res
.
json
({
success
:
true
,
application
:
{
...
_application
,
...
update
},
});
})
.
catch
((
err
)
=>
res
.
send
(
err
));
}
);
router
.
post
(
"
/analyse
"
,
authMiddleware
,
async
(
req
:
TypedRequest
<
{},
AnalyseApplicationPayload
>
,
res
)
=>
{
const
{
applicationId
,
startTime
,
endTime
}
=
req
.
body
;
const
data
=
await
VoiceAPI
.
analyseVoice
({
start
:
startTime
,
end
:
endTime
,
application_id
:
applicationId
,
});
return
res
.
json
({
voice
:
data
});
}
);
module
.
exports
=
router
;
NodeServer/src/routes/jobs.ts
View file @
aceb2402
...
...
@@ -45,6 +45,7 @@ router.get(
"
profilePicture
"
,
"
resume
"
,
"
resumeData
"
,
"
selfIntro
"
,
],
},
});
...
...
@@ -105,50 +106,4 @@ router.delete(
}
);
router
.
put
(
"
/apply
"
,
authMiddleware
,
candidateMiddleware
,
async
(
req
:
TypedRequest
<
{
userId
:
string
},
{
application
:
ApplicationType
;
resumeUrl
:
string
}
>
,
res
)
=>
{
try
{
const
{
application
,
resumeUrl
}
=
req
.
body
;
const
job
=
await
Jobs
.
findById
(
application
.
job
);
const
data
:
any
=
await
ResumeAPI
.
getResumeScores
({
user_id
:
req
.
query
.
userId
,
resume_url
:
resumeUrl
,
primary_skills
:
job
.
primarySkills
,
secondary_skills
:
job
.
secondarySkills
,
job_desc
:
job
.
description
,
});
const
score
:
ApplicationType
[
"
score
"
]
=
{
primary
:
data
.
primary_score
,
primatyMatch
:
data
.
primary_match
,
secondary
:
data
.
secondary_score
,
secondaryMatch
:
data
.
secondary_match
,
similarity
:
data
.
similarity
,
total
:
data
.
primary_score
+
data
.
secondary_score
+
data
.
similarity
,
};
const
newApplication
=
new
Application
({
...
application
,
score
});
const
_application
=
await
newApplication
.
save
();
job
.
applications
.
push
(
_application
.
id
);
await
job
.
save
();
return
res
.
json
({
success
:
true
,
applicationId
:
_application
.
id
,
});
}
catch
(
error
)
{
return
res
.
json
({
success
:
false
,
error
});
}
}
);
module
.
exports
=
router
;
NodeServer/src/routes/user.ts
View file @
aceb2402
...
...
@@ -3,6 +3,7 @@ import { CandidateType, TypedRequest } from "../config/types";
import
{
authMiddleware
}
from
"
../middlewares/auth
"
;
import
Candidates
from
"
../models/Candidate
"
;
import
ResumeAPI
from
"
../utilities/apis/resume
"
;
import
VoiceAPI
from
"
../utilities/apis/voice
"
;
const
router
=
Router
();
...
...
@@ -19,6 +20,12 @@ router.post(
});
update
.
resumeData
=
data
;
}
if
(
req
.
body
?.
selfIntro
)
{
await
VoiceAPI
.
enrollVoice
({
user_id
:
req
.
query
.
userId
,
video_url
:
req
.
body
.
selfIntro
,
});
}
await
Candidates
.
findByIdAndUpdate
(
req
.
query
.
userId
,
{
$set
:
req
.
body
});
return
res
.
status
(
200
).
json
({
data
:
req
.
body
});
}
catch
(
error
)
{
...
...
NodeServer/src/utilities/apis/resume.ts
View file @
aceb2402
import
{
ResumeDataType
}
from
"
../../config/types
"
;
import
{
request
}
from
"
../requests
"
;
export
default
class
ResumeAPI
{
...
...
NodeServer/src/utilities/apis/voice.ts
0 → 100644
View file @
aceb2402
import
{
request
}
from
"
../requests
"
;
export
default
class
VoiceAPI
{
static
enrollVoice
=
(
payload
:
{
video_url
:
string
;
user_id
:
string
})
=>
request
(
"
<BASE_URL>/voice/enroll
"
,
"
POST
"
,
payload
);
static
verifyVoice
=
(
payload
:
{
video_url
:
string
;
user_id
:
string
;
application_id
:
string
;
})
=>
request
(
"
<BASE_URL>/voice/verify
"
,
"
POST
"
,
payload
);
static
analyseVoice
=
(
payload
:
{
start
:
number
;
end
:
number
;
application_id
:
string
;
})
=>
request
(
"
<BASE_URL>/voice/analyse
"
,
"
POST
"
,
payload
);
}
voice_analyzer/Voice_Emotion/ReadMe.md
deleted
100644 → 0
View file @
b698b996
DataSet: https://drive.google.com/file/d/1wWsrN2Ep7x6lWqOXfr4rpKGYrJhWc8z7/view
voice_analyzer/Voice_Emotion/convert_wavs.py
deleted
100644 → 0
View file @
b698b996
"""
A utility script used for converting audio samples to be
suitable for feature extraction
"""
import
os
def
convert_audio
(
audio_path
,
target_path
,
remove
=
False
):
"""This function sets the audio `audio_path` to:
- 16000Hz Sampling rate
- one audio channel ( mono )
Params:
audio_path (str): the path of audio wav file you want to convert
target_path (str): target path to save your new converted wav file
remove (bool): whether to remove the old file after converting
Note that this function requires ffmpeg installed in your system."""
os
.
system
(
f
"ffmpeg -i {audio_path} -ac 1 -ar 16000 {target_path}"
)
# os.system(f"ffmpeg -i {audio_path} -ac 1 {target_path}")
if
remove
:
os
.
remove
(
audio_path
)
def
convert_audios
(
path
,
target_path
,
remove
=
False
):
"""Converts a path of wav files to:
- 16000Hz Sampling rate
- one audio channel ( mono )
and then put them into a new folder called `target_path`
Params:
audio_path (str): the path of audio wav file you want to convert
target_path (str): target path to save your new converted wav file
remove (bool): whether to remove the old file after converting
Note that this function requires ffmpeg installed in your system."""
for
dirpath
,
dirnames
,
filenames
in
os
.
walk
(
path
):
for
dirname
in
dirnames
:
dirname
=
os
.
path
.
join
(
dirpath
,
dirname
)
target_dir
=
dirname
.
replace
(
path
,
target_path
)
if
not
os
.
path
.
isdir
(
target_dir
):
os
.
mkdir
(
target_dir
)
for
dirpath
,
_
,
filenames
in
os
.
walk
(
path
):
for
filename
in
filenames
:
file
=
os
.
path
.
join
(
dirpath
,
filename
)
if
file
.
endswith
(
".wav"
):
# it is a wav file
target_file
=
file
.
replace
(
path
,
target_path
)
convert_audio
(
file
,
target_file
,
remove
=
remove
)
if
__name__
==
"__main__"
:
import
argparse
parser
=
argparse
.
ArgumentParser
(
description
=
"""Convert ( compress ) wav files to 16MHz and mono audio channel ( 1 channel )
This utility helps for compressing wav files for training and testing"""
)
parser
.
add_argument
(
"audio_path"
,
help
=
"Folder that contains wav files you want to convert"
)
parser
.
add_argument
(
"target_path"
,
help
=
"Folder to save new wav files"
)
parser
.
add_argument
(
"-r"
,
"--remove"
,
type
=
bool
,
help
=
"Whether to remove the old wav file after converting"
,
default
=
False
)
args
=
parser
.
parse_args
()
audio_path
=
args
.
audio_path
target_path
=
args
.
target_path
if
os
.
path
.
isdir
(
audio_path
):
if
not
os
.
path
.
isdir
(
target_path
):
os
.
makedirs
(
target_path
)
convert_audios
(
audio_path
,
target_path
,
remove
=
args
.
remove
)
elif
os
.
path
.
isfile
(
audio_path
)
and
audio_path
.
endswith
(
".wav"
):
if
not
target_path
.
endswith
(
".wav"
):
target_path
+=
".wav"
convert_audio
(
audio_path
,
target_path
,
remove
=
args
.
remove
)
else
:
raise
TypeError
(
"The audio_path file you specified isn't appropriate for this operation"
)
voice_analyzer/Voice_Emotion/lib.py
deleted
100644 → 0
View file @
b698b996
import
soundfile
import
numpy
as
np
import
librosa
import
glob
import
os
from
sklearn.model_selection
import
train_test_split
EMOTIONS
=
{
"01"
:
"neutral"
,
"02"
:
"calm"
,
"03"
:
"happy"
,
"04"
:
"sad"
,
"05"
:
"angry"
,
"06"
:
"fearful"
,
"07"
:
"disgust"
,
"08"
:
"surprised"
}
AVAILABLE_EMOTIONS
=
{
"angry"
,
"sad"
,
"neutral"
,
"happy"
}
def
extract_feature
(
file_name
,
**
kwargs
):
mfcc
=
kwargs
.
get
(
"mfcc"
)
chroma
=
kwargs
.
get
(
"chroma"
)
mel
=
kwargs
.
get
(
"mel"
)
contrast
=
kwargs
.
get
(
"contrast"
)
tonnetz
=
kwargs
.
get
(
"tonnetz"
)
with
soundfile
.
SoundFile
(
file_name
)
as
sound_file
:
X
=
sound_file
.
read
(
dtype
=
"float32"
)
sample_rate
=
sound_file
.
samplerate
if
chroma
or
contrast
:
stft
=
np
.
abs
(
librosa
.
stft
(
X
))
result
=
np
.
array
([])
if
mfcc
:
mfccs
=
np
.
mean
(
librosa
.
feature
.
mfcc
(
y
=
X
,
sr
=
sample_rate
,
n_mfcc
=
40
)
.
T
,
axis
=
0
)
result
=
np
.
hstack
((
result
,
mfccs
))
if
chroma
:
chroma
=
np
.
mean
(
librosa
.
feature
.
chroma_stft
(
S
=
stft
,
sr
=
sample_rate
)
.
T
,
axis
=
0
)
result
=
np
.
hstack
((
result
,
chroma
))
if
mel
:
mel
=
np
.
mean
(
librosa
.
feature
.
melspectrogram
(
X
,
sr
=
sample_rate
)
.
T
,
axis
=
0
)
result
=
np
.
hstack
((
result
,
mel
))
if
contrast
:
contrast
=
np
.
mean
(
librosa
.
feature
.
spectral_contrast
(
S
=
stft
,
sr
=
sample_rate
)
.
T
,
axis
=
0
)
result
=
np
.
hstack
((
result
,
contrast
))
if
tonnetz
:
tonnetz
=
np
.
mean
(
librosa
.
feature
.
tonnetz
(
y
=
librosa
.
effects
.
harmonic
(
X
),
sr
=
sample_rate
)
.
T
,
axis
=
0
)
result
=
np
.
hstack
((
result
,
tonnetz
))
return
result
# update random_state=9
def
load_data
(
test_size
=
0.2
,
random_state
=
7
):
X
,
y
=
[],
[]
for
file
in
glob
.
glob
(
"data/Actor_*/*.wav"
):
basename
=
os
.
path
.
basename
(
file
)
emotion
=
EMOTIONS
[
basename
.
split
(
"-"
)[
2
]]
if
emotion
not
in
AVAILABLE_EMOTIONS
:
continue
features
=
extract_feature
(
file
,
mfcc
=
True
,
chroma
=
True
,
mel
=
True
)
X
.
append
(
features
)
y
.
append
(
emotion
)
return
train_test_split
(
np
.
array
(
X
),
y
,
test_size
=
test_size
,
random_state
=
random_state
)
import
os
,
glob
,
pickle
import
numpy
as
np
from
sklearn.model_selection
import
train_test_split
from
sklearn.neural_network
import
MLPClassifier
from
sklearn.metrics
import
accuracy_score
def
extract_feature_2
(
file_name
,
mfcc
,
chroma
,
mel
):
with
soundfile
.
SoundFile
(
file_name
)
as
sound_file
:
X
=
sound_file
.
read
(
dtype
=
"float32"
)
sample_rate
=
sound_file
.
samplerate
if
chroma
:
stft
=
np
.
abs
(
librosa
.
stft
(
X
))
result
=
np
.
array
([])
if
mfcc
:
mfccs
=
np
.
mean
(
librosa
.
feature
.
mfcc
(
y
=
X
,
sr
=
sample_rate
,
n_mfcc
=
40
)
.
T
,
axis
=
0
)
result
=
np
.
hstack
((
result
,
mfccs
))
if
chroma
:
chroma
=
np
.
mean
(
librosa
.
feature
.
chroma_stft
(
S
=
stft
,
sr
=
sample_rate
)
.
T
,
axis
=
0
)
result
=
np
.
hstack
((
result
,
chroma
))
if
mel
:
mel
=
np
.
mean
(
librosa
.
feature
.
melspectrogram
(
X
,
sr
=
sample_rate
)
.
T
,
axis
=
0
)
result
=
np
.
hstack
((
result
,
mel
))
return
result
voice_analyzer/Voice_Emotion/main
deleted
100644 → 0
View file @
b698b996
import
pyaudio
import
os
import
wave
import
pickle
from
sys
import
byteorder
from
array
import
array
from
struct
import
pack
from
sklearn
.
neural_network
import
MLPClassifier
from
lib
import
extract_feature
THRESHOLD
=
500
CHUNK_SIZE
=
1024
FORMAT
=
pyaudio
.
paInt16
RATE
=
16000
SILENCE
=
30
def
is_silent
(
snd_data
):
return
max
(
snd_data
)
<
THRESHOLD
def
normalize
(
snd_data
):
MAXIMUM
=
16384
times
=
float
(
MAXIMUM
)/
max
(
abs
(
i
)
for
i
in
snd_data
)
r
=
array
(
'h'
)
for
i
in
snd_data
:
r
.
append
(
int
(
i
*
times
))
return
r
def
trim
(
snd_data
):
def
_trim
(
snd_data
):
snd_started
=
False
r
=
array
(
'h'
)
for
i
in
snd_data
:
if
not
snd_started
and
abs
(
i
)>
THRESHOLD
:
snd_started
=
True
r
.
append
(
i
)
elif
snd_started
:
r
.
append
(
i
)
return
r
snd_data
=
_trim
(
snd_data
)
snd_data
.
reverse
()
snd_data
=
_trim
(
snd_data
)
snd_data
.
reverse
()
return
snd_data
def
add_silence
(
snd_data
,
seconds
):
r
=
array
(
'h'
,
[
0
for
i
in
range
(
int
(
seconds
*
RATE
))])
r
.
extend
(
snd_data
)
r
.
extend
([
0
for
i
in
range
(
int
(
seconds
*
RATE
))])
return
r
def
record
():
p
=
pyaudio
.
PyAudio
()
stream
=
p
.
open
(
format
=
FORMAT
,
channels
=
1
,
rate
=
RATE
,
input
=
True
,
output
=
True
,
frames_per_buffer
=
CHUNK_SIZE
)
num_silent
=
0
snd_started
=
False
r
=
array
(
'h'
)
while
1
:
#
little
endian
,
signed
short
snd_data
=
array
(
'h'
,
stream
.
read
(
CHUNK_SIZE
))
if
byteorder
==
'big'
:
snd_data
.
byteswap
()
r
.
extend
(
snd_data
)
silent
=
is_silent
(
snd_data
)
if
silent
and
snd_started
:
num_silent
+=
1
elif
not
silent
and
not
snd_started
:
snd_started
=
True
if
snd_started
and
num_silent
>
SILENCE
:
break
sample_width
=
p
.
get_sample_size
(
FORMAT
)
stream
.
stop_stream
()
stream
.
close
()
p
.
terminate
()
r
=
normalize
(
r
)
r
=
trim
(
r
)
r
=
add_silence
(
r
,
0.5
)
return
sample_width
,
r
def
record_to_file
(
path
):
sample_width
,
data
=
record
()
data
=
pack
(
'<'
+
(
'h'
*
len
(
data
)),
*
data
)
wf
=
wave
.
open
(
path
,
'wb'
)
wf
.
setnchannels
(
1
)
wf
.
setsampwidth
(
sample_width
)
wf
.
setframerate
(
RATE
)
wf
.
writeframes
(
data
)
wf
.
close
()
if
__name__
==
"__main__"
:
model
=
pickle
.
load
(
open
(
"result/mlp_classifier.model"
,
"rb"
))
print
(
"Please talk"
)
filename
=
"test.wav"
record_to_file
(
filename
)
features
=
extract_feature
(
filename
,
mfcc
=
True
,
chroma
=
True
,
mel
=
True
).
reshape
(
1
,
-
1
)
result
=
model
.
predict
(
features
)[
0
]
print
(
"result:"
,
result
)
\ No newline at end of file
voice_analyzer/Voice_Emotion/train.py
deleted
100644 → 0
View file @
b698b996
from
sklearn.neural_network
import
MLPClassifier
from
sklearn.metrics
import
accuracy_score
from
lib
import
load_data
import
os
import
pickle
X_train
,
X_test
,
y_train
,
y_test
=
load_data
(
test_size
=
0.25
)
model
=
MLPClassifier
(
alpha
=
0.01
,
batch_size
=
256
,
epsilon
=
1e-08
,
hidden_layer_sizes
=
(
300
,),
learning_rate
=
'adaptive'
,
max_iter
=
500
)
print
(
"Training the model..."
)
model
.
fit
(
X_train
,
y_train
)
y_pred
=
model
.
predict
(
X_test
)
accuracy
=
accuracy_score
(
y_true
=
y_test
,
y_pred
=
y_pred
)
print
(
"Accuracy: {:.2f}
%
"
.
format
(
accuracy
*
100
))
if
not
os
.
path
.
isdir
(
"result"
):
os
.
mkdir
(
"result"
)
pickle
.
dump
(
model
,
open
(
"result/mlp_classifier.model"
,
"wb"
))
\ No newline at end of file
voice_analyzer/Voice_recognizer/Pipfile
deleted
100644 → 0
View file @
b698b996
[[source]]
url
=
"https://pypi.org/simple"
verify_ssl
=
true
name
=
"pypi"
[packages]
vosk
=
"*"
pydub
=
"*"
transformers
=
"*"
torch
=
"*"
pyaudio
=
"*"
regex
=
"*"
ipywidgets
=
"*"
spacy
=
"*"
[dev-packages]
[requires]
python_version
=
"3.9"
voice_analyzer/Voice_recognizer/Pipfile.lock
deleted
100644 → 0
View file @
b698b996
{
"_meta"
:
{
"hash"
:
{
"sha256"
:
"9da169520ae5aea58b98af98b80cdb5c908d34b32bb76fad0982c51c0825b1c9"
},
"pipfile-spec"
:
6
,
"requires"
:
{
"python_version"
:
"3.9"
},
"sources"
:
[
{
"name"
:
"pypi"
,
"url"
:
"https://pypi.org/simple"
,
"verify_ssl"
:
true
}
]
},
"default"
:
{
"asttokens"
:
{
"hashes"
:
[
"sha256:4622110b2a6f30b77e1473affaa97e711bc2f07d3f10848420ff1898edbe94f3"
,
"sha256:6b0ac9e93fb0335014d382b8fa9b3afa7df546984258005da0b9e7095b3deb1c"
],
"version"
:
"==2.2.1"
},
"backcall"
:
{
"hashes"
:
[
"sha256:5cbdbf27be5e7cfadb448baf0aa95508f91f2bbc6c6437cd9cd06e2a4c215e1e"
,
"sha256:fbbce6a29f263178a1f7915c1940bde0ec2b2a967566fe1c65c1dfb7422bd255"
],
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:
"==0.2.0"
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:
{
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:
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,
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,
"sha256:d3882b4f44a33367812b5e287c0690027092830ffb1cce124b02f64e761819a4"
,
"sha256:d4eb70a79562a211bd2e6b6db63f1e2eed32c0ab3e9ef921d86f657ae8375845"
,
"sha256:d811e88480203d75e6e959f313fdbf3326393b4e2b317067d952347f5c56216e"
,
"sha256:d81c3f627d33545fc25c9dcb5fee66c476d89288a27d63ac16ea63453401ffd5"
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],
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:
"==0.7.9"
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"catalogue"
:
{
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:
[
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],
"markers"
:
"python_version >= '3.6'"
,
"version"
:
"==2.0.8"
},
"certifi"
:
{
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:
[
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,
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:
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,
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voice_analyzer/Voice_recognizer/ReadMe.md
deleted
100644 → 0
View file @
b698b996
Pretrained models:
English : https://alphacephei.com/vosk/models/vosk-model-en-us-0.22.zip or https://alphacephei.com/vosk/models/vosk-model-small-en-us-0.15.zip
Punctuation : https://alphacephei.com/vosk/models/vosk-recasepunc-en-0.22.zip
voice_analyzer/Voice_recognizer/main.py
deleted
100644 → 0
View file @
b698b996
from
vosk
import
Model
,
KaldiRecognizer
from
pydub
import
AudioSegment
from
transformers
import
pipeline
import
json
import
subprocess
import
spacy
from
spacy.lang.en.stop_words
import
STOP_WORDS
from
string
import
punctuation
from
heapq
import
nlargest
FRAME_RATE
=
16000
CHANNELS
=
1
def
voice_recognition
(
filename
):
model
=
Model
(
model_name
=
"vosk-model-en-us-0.22"
)
rec
=
KaldiRecognizer
(
model
,
FRAME_RATE
)
rec
.
SetWords
(
True
)
mp3
=
AudioSegment
.
from_mp3
(
filename
)
mp3
=
mp3
.
set_channels
(
CHANNELS
)
mp3
=
mp3
.
set_frame_rate
(
FRAME_RATE
)
step
=
45000
transcript
=
""
for
i
in
range
(
0
,
len
(
mp3
),
step
):
print
(
f
"Progress: {i/len(mp3)}"
)
segment
=
mp3
[
i
:
i
+
step
]
rec
.
AcceptWaveform
(
segment
.
raw_data
)
result
=
rec
.
Result
()
text
=
json
.
loads
(
result
)[
"text"
]
transcript
+=
text
cased
=
subprocess
.
check_output
(
'python recasepunc/recasepunc.py predict recasepunc/checkpoint'
,
shell
=
True
,
text
=
True
,
input
=
transcript
)
return
cased
def
summarize
(
text
,
per
):
nlp
=
spacy
.
load
(
'en_core_web_sm'
)
doc
=
nlp
(
text
)
tokens
=
[
token
.
text
for
token
in
doc
]
word_frequencies
=
{}
for
word
in
doc
:
if
word
.
text
.
lower
()
not
in
list
(
STOP_WORDS
):
if
word
.
text
.
lower
()
not
in
punctuation
:
if
word
.
text
not
in
word_frequencies
.
keys
():
word_frequencies
[
word
.
text
]
=
1
else
:
word_frequencies
[
word
.
text
]
+=
1
max_frequency
=
max
(
word_frequencies
.
values
())
for
word
in
word_frequencies
.
keys
():
word_frequencies
[
word
]
=
word_frequencies
[
word
]
/
max_frequency
sentence_tokens
=
[
sent
for
sent
in
doc
.
sents
]
sentence_scores
=
{}
for
sent
in
sentence_tokens
:
for
word
in
sent
:
if
word
.
text
.
lower
()
in
word_frequencies
.
keys
():
if
sent
not
in
sentence_scores
.
keys
():
sentence_scores
[
sent
]
=
word_frequencies
[
word
.
text
.
lower
()]
else
:
sentence_scores
[
sent
]
+=
word_frequencies
[
word
.
text
.
lower
()]
select_length
=
int
(
len
(
sentence_tokens
)
*
per
)
summary
=
nlargest
(
select_length
,
sentence_scores
,
key
=
sentence_scores
.
get
)
final_summary
=
[
word
.
text
for
word
in
summary
]
summary
=
''
.
join
(
final_summary
)
return
summary
transcript
=
voice_recognition
(
"sample_voice.mp3"
)
summary
=
summarize
(
transcript
,
0.05
)
print
(
summary
)
\ No newline at end of file
voice_analyzer/Voice_recognizer/requirements.txt
deleted
100644 → 0
View file @
b698b996
Binary files a/voice_analyzer/Voice_recognizer/requirements.txt and /dev/null differ
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