Commit e50db0a7 authored by Emika Chamodi's avatar Emika Chamodi

voice verification

parent b6570edd
__pycache__
**/__pycache__
\ No newline at end of file
**/__pycache__
voices/**
resumes
models/**
data/**
\ No newline at end of file
filename,name,mobile_number,email,company_names,college_name,experience,skills,experience_age,degree,words,primary_score,primary_match,secondary_score,secondary_match,no_of_pages,document_similarity,document_score,Score
resumes/Dhaval_Thakkar_Resume.pdf,Dhaval Thakkar,9191729595,thakkar.dhaval.haresh@gmail.com,['UNIFYND TECHNOLOGIES PVT. LTD'],None,"['UNIFYND TECHNOLOGIES PVT. LTD. | Data Scientist', 'Mumbai, MH, India | June 2018 – Present', '• Led the development of a Templatized OCR Engine with GUI to onboard 2000+ retailers from different malls. The', 'microservice deployed is currently operating at an accuracy of 81%', '• Built a Customer Segmentation model to target customers with relevant coupons, rewards, and content resulting', 'in a 3x increase in revenue and 2x increase in coupon utilization', '• Built a Dynamic Coupon Pricing Engine for malls that led to a 5x increase in coupon consumption on the coupon', 'marketplace', '• Built a Pricing Engine and Customer Segmentation Model for a logistics company which saw a 32% reduction in', 'Customer Attrition and a 12% increase in Repeat Purchase Rate', '• Developed an Automated End to End Reporting system to track KPIs performance for 10 malls that saves 60', 'hours of manual labour each month', 'UNIFYND TECHNOLOGIES PVT. LTD. | Intern Data Scientist Mumbai, MH, India | Sept 2017 - June 2018', '• Built a Smart Cryptocurrency trading platform which used social data and historical prices to optimize current', 'portfolio. Boosted overall profit from the portfolio by 30%', '• Worked with Product and Marketing teams to identify the power users of an app which resulted in 43% increase in', 'activity and a 65% increase in revenue from these users', 'ZIFF, INC | Deep Learning Intern', 'Provo, UT, USA | May 2017 – Aug 2017', '• Demonstrated competency in Hyperparameter Optimization, Image Augmentation and Learning Rate decay', 'strategies using the Keras Library', '• Deployed a Multi-Class Image classifier microservice written on Flask as a container on AWS EC2 using Docker']","['Html', 'Data analytics', 'Marketing', 'Segmentation', 'Content', 'Algorithms', 'Numpy', 'Pandas', 'Github', 'R', 'Logistics', 'Css', 'Operating systems', 'Testing', 'Flask', 'Mysql', 'Scrapy', 'Machine learning', 'Security', 'Keras', 'Python', 'Kpis', 'System', 'Docker', 'Reporting', 'Analytics', 'Aws', 'Engineering', 'Anaconda', 'Networking', 'Sql']",5.75,['Bachelor of Engineering'],350,44,"['ocr', 'aws', 'python', 'gcp']",42,"['data', 'ocr', 'science']",1,32,50.0,168.0
resumes/python-developer-resume-2.pdf,Python Developer,456-7890,ggonzalez@email.com,None,None,"['Python Developer Intern', 'Knewton', 'April 2016 - April 2017', '· Worked alongside another developer to implement RESTful APIs', 'Chicago, IL', 'in Django that enabled internal analytics team to increase', 'reporting speed by 24%', '· Using Selenium, built out a unit testing infrastructure for a client', 'web application that reduced the number of bugs reported by', 'the client by 11% month over month']","['Django', 'Math', 'Oracle', 'Requests', 'Github', 'Api', 'Database', 'Css', 'Design', 'Postgresql', 'Testing', 'Agile', 'Apis', 'Selenium', 'Rest', 'Python', 'Writing', 'System', 'Updates', 'Javascript', 'Reporting', 'Analytics', 'Aws', 'Sql', 'Process']",1.0,"['B.S.', 'M.S.']",223,22,"['python', 'aws']",28,"['science', 'data']",1,20,50.0,120.0
resumes/software-engineer-resume-1.pdf,New York,456-7890,cmcturland@email.com,None,None,"['Software Engineer', 'Embark', 'January 2015 - current / New York, NY', 'Worked with product managers to re-architect a multi-page web', 'app into a single page web-app, boosting yearly revenue by $1.4M', 'Constructed the logic for a streamlined ad-serving platform that', 'scaled to our 35M users, which improved the page speed by 15%', 'after implementation', 'Tested software for bugs and operating speed, fixing bugs and', 'documenting processes to increase efficiency by 18%', 'Iterated platform for college admissions, collaborating with a group', 'of 4 engineers to create features across the software', 'Software Engineer', 'MarketSmart', 'April 2012 - January 2015 / Washington, DC', 'Built RESTful APIs that served data to the JavaScript front-end', 'based on dynamically chosen user inputs that handled over 500,000', 'concurrent users', 'Built internal tool using NodeJS and Pupeteer.js to automate QA and', 'monitoring of donor-facing web app, which improved CTR by 3%', 'Reviewed code and conducted testing for 3 additional features on', 'donor-facing web app that increased contributions by 12%', 'Software Engineer Intern', 'Marketing Science Company', 'April 2011 - March 2012 / Pittsburgh, PA', 'Partnered with a developer to implement RESTful APIs in Django,', 'enabling analytics team to increase reporting speed by 24%', 'Using Selenium I built out a unit testing infrastructure for a client', 'application that reduced the number of bugs reported by the client', 'by 11% month over month']","['Django', 'Marketing', 'Unix', 'Nosql', 'R', 'Css', 'Postgresql', 'Testing', 'Mysql', 'Sci', 'Apis', 'Selenium', 'Admissions', 'Python', 'Html5', 'Javascript', 'Reporting', 'Analytics', 'C', 'Sql', 'Aws']",3.67,['B.S.'],233,22,"['python', 'aws']",28,"['science', 'data']",1,10,50.0,110.0
resumes/Santhosh_Narayanan.pdf,SANTHOSH NARAYANAN,417-6755,santhosn@usc.edu,None,None,"['on an EC2 server supported by S3 and RDS.', '\uf0a7 Maintained AWS infrastructure for institute’s annual technical festival website, by hosting the website', 'on an EC2 Ubuntu server.', 'K J Somaiya Inst. of Engg. & I.T – Penetration tester', 'December 2016 – January 2016', '\uf0a7 Conducted penetration testing for institute’s online admission and examination portal.', '\uf0a7 Performed authentication checks, access control checks, per screen checks (XSS, SQL injection.).', '\uf0a7 Delivered error free application, incorporating patches for the respective bugs using ASP.NET']","['Html', 'Jupyter', 'Access', 'Numpy', 'Php', 'Matplotlib', 'Oracle', 'Pandas', 'Computer science', 'Css', 'Purchasing', 'Schedule', 'Scheduling', 'Flask', 'Testing', 'Lan', 'Mysql', 'Scrapy', 'Security', 'Programming', 'Website', 'Keras', 'Python', 'System', 'Wordpress', 'Spyder', 'Technical', 'Ubuntu', 'Javascript', 'Java', 'Aws', 'Engineering', 'Sql', 'Certification']",,None,367,22,"['python', 'aws']",14,['science'],1,7,50.0,93.0
About the job
Borneo.io is building the next-generation ML Powered data privacy platform for hyper-growth companies. The Data Scientist role is at the core of Borneo's engineering. You will be building models, manipulating big data, and working with APIs essential to the Borneo product.
We are growing fast and expanding our data science family with outstanding minds and diverse personalities.
As a Data Scientist at Borneo, you'll have the opportunity to:
Work with some of the largest data sets used by some of the leading global technology companies
Help build a predictive product and inform features at the ground level.
Lead the way in leveraging unstructured data for predictive modeling, anomaly detection, and drive privacy compliance.
Responsibilities:
Identify, automate data collection processes
Dive into complex data sets to analyze trends and identify opportunities for improvement.
Build predictive models and machine-learning algorithms
Present information using data visualization techniques
Propose solutions and strategies to business challenges
Have a data-driven decision making approach
Requirements:
5-8 years of relevant experience, B2B startup experience preferred
Proven experience as a Data Scientist or Data Analyst
Experience in building ML models and deploying them to production
A solid understanding of data science fundamentals, statistical techniques, NLP algorithms
Understand research papers and create quick proof of concept relevant to the product
Expert in implementing quick prototypes that shows business value
Experience with programming languages such as NodeJs /Python/JavaScript: Cloud technologies: AWS/GCP/K8 etc.
### Create conda python 3.9 env
conda create -n server python=3.9
conda create -n server tensorflow python=3.9
### PIP Packages
gensim==3.8.1
gensim==3.8.1<br/>
texthero==1.1.0
pyresparser
- install gfortran : https://fortran-lang.org/learn/os_setup/install_gfortran
- download and install correct scipy wheel https://pypi.org/project/scipy/#files : pip install <filename>.whl
- conda install scipy=1.9.3
pyresparser<br/>
- conda install -c conda-forge importlib_metadata
soundfile
pip install librosa==0.9.2
### CONDA Packages
tqdm
pdf2image
pandas
pytesseract
"uvicorn[standard]"
fastapi
moviepy
conda install -c blaze sqlite3
### Run server
uvicorn main:app --reload
### Datasets
voice: https://drive.google.com/file/d/1wWsrN2Ep7x6lWqOXfr4rpKGYrJhWc8z7/view
conda list --export > conda-requirements.txt
pip freeze > pip-requirements.txt
......@@ -10,4 +10,18 @@ class ResumeScores(BaseModel):
user_id:str
primary_skills:List[str] = []
secondary_skills:List[str] = []
job_desc:str
\ No newline at end of file
job_desc:str
class EnrollVoice(BaseModel):
video_url: str
user_id:str
class VerifyVoice(BaseModel):
video_url: str
application_id:str
user_id:str
class AnalyseVoice(BaseModel):
start: int
end:int
application_id:str
\ No newline at end of file
# This file may be used to create an environment using:
# $ conda create --name <env> --file <this file>
# platform: win-64
absl-py=1.4.0=pypi_0
aiohttp=3.8.4=pypi_0
aiosignal=1.3.1=pypi_0
alembic=1.10.2=pypi_0
antlr4-python3-runtime=4.9.3=pypi_0
anyio=3.6.2=pyhd8ed1ab_0
aom=3.5.0=h63175ca_0
asteroid-filterbanks=0.4.0=pypi_0
astunparse=1.6.3=pypi_0
async-timeout=4.0.2=pypi_0
attrs=22.2.0=pypi_0
audioread=3.0.0=pypi_0
backports-cached-property=1.0.2=pypi_0
blis=0.7.9=pypi_0
brotli=1.0.9=hcfcfb64_8
brotli-bin=1.0.9=hcfcfb64_8
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bzip2=1.0.8=h8ffe710_4
ca-certificates=2022.12.7=h5b45459_0
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catalogue=1.0.2=pypi_0
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cffi=1.15.1=py39h68f70e3_3
chardet=5.1.0=pypi_0
charset-normalizer=3.1.0=pypi_0
click=8.1.3=win_pyhd8ed1ab_2
cmaes=0.9.1=pypi_0
colorama=0.4.6=pyhd8ed1ab_0
colorlog=6.7.0=pypi_0
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moviepy=1.0.3=pyhd8ed1ab_1
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pyopenssl=23.1.1=pyhd8ed1ab_0
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python-speech-features=0.6=pypi_0
python_abi=3.9=3_cp39
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yarl=1.8.2=pypi_0
zipp=3.15.0=pyhd8ed1ab_0
zstd=1.5.2=h12be248_6
from fastapi import FastAPI
import routes.resume as resumes
import routes.voice as voice
app = FastAPI()
app.include_router(resumes.router)
app.include_router(voice.router)
@app.get("/")
def read_root():
......
absl-py==1.4.0
aiohttp==3.8.4
aiosignal==1.3.1
alembic==1.10.2
antlr4-python3-runtime==4.9.3
anyio @ file:///home/conda/feedstock_root/build_artifacts/anyio_1666191106763/work/dist
asteroid-filterbanks==0.4.0
astunparse==1.6.3
async-timeout==4.0.2
attrs==22.2.0
audioread==3.0.0
backports.cached-property==1.0.2
blis==0.7.9
brotlipy @ file:///D:/bld/brotlipy_1666764815687/work
cachetools==5.3.0
catalogue==1.0.2
certifi==2022.12.7
cffi @ file:///D:/bld/cffi_1671179514672/work
chardet==5.1.0
charset-normalizer==3.1.0
click @ file:///D:/bld/click_1666798499870/work
cmaes==0.9.1
colorama @ file:///home/conda/feedstock_root/build_artifacts/colorama_1666700638685/work
colorlog==6.7.0
commonmark==0.9.1
contourpy @ file:///D:/bld/contourpy_1673633852898/work
cryptography @ file:///D:/bld/cryptography-split_1679811407000/work
cycler @ file:///home/conda/feedstock_root/build_artifacts/cycler_1635519461629/work
cymem==2.0.7
decorator @ file:///home/conda/feedstock_root/build_artifacts/decorator_1641555617451/work
docopt==0.6.2
docx2txt==0.8
einops==0.3.2
en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.3.1/en_core_web_sm-2.3.1.tar.gz
fastapi @ file:///home/conda/feedstock_root/build_artifacts/fastapi_1679196090342/work
filelock==3.10.7
flatbuffers==23.3.3
fonttools @ file:///D:/bld/fonttools_1680021390608/work
frozenlist==1.3.3
fsspec==2023.3.0
future @ file:///home/conda/feedstock_root/build_artifacts/future_1673596611778/work
gast==0.4.0
gensim==3.8.1
google-auth==2.17.0
google-auth-oauthlib==0.4.6
google-pasta==0.2.0
greenlet==2.0.2
grpcio==1.53.0
h11 @ file:///home/conda/feedstock_root/build_artifacts/h11_1664132893548/work
h5py==3.8.0
hmmlearn==0.2.8
huggingface-hub==0.13.3
HyperPyYAML==1.1.0
idna @ file:///home/conda/feedstock_root/build_artifacts/idna_1663625384323/work
imageio @ file:///home/conda/feedstock_root/build_artifacts/imageio_1679914882579/work
imageio-ffmpeg @ file:///home/conda/feedstock_root/build_artifacts/imageio-ffmpeg_1673483481485/work
importlib-metadata @ file:///home/conda/feedstock_root/build_artifacts/importlib-metadata_1679167925176/work
importlib-resources @ file:///home/conda/feedstock_root/build_artifacts/importlib_resources_1676919000169/work
joblib @ file:///home/conda/feedstock_root/build_artifacts/joblib_1663332044897/work
jsonschema==4.17.3
julius==0.2.7
keras==2.10.0
Keras-Preprocessing==1.1.2
kiwisolver @ file:///D:/bld/kiwisolver_1666805897768/work
libclang==16.0.0
librosa==0.9.2
llvmlite==0.39.1
Mako==1.2.4
Markdown==3.4.3
MarkupSafe==2.1.2
matplotlib @ file:///D:/bld/matplotlib-suite_1678135799522/work
moviepy @ file:///home/conda/feedstock_root/build_artifacts/moviepy_1665160419595/work
mpmath==1.3.0
multidict==6.0.4
munkres==1.1.4
murmurhash==1.0.9
networkx==2.8.8
nltk==3.8.1
numba==0.56.4
numpy==1.23.5
oauthlib==3.2.2
olefile==0.46
omegaconf==2.3.0
opt-einsum==3.3.0
optuna==3.1.0
packaging @ file:///home/conda/feedstock_root/build_artifacts/packaging_1673482170163/work
pandas @ file:///D:/bld/pandas_1674136542219/work
pdfminer @ file:///home/conda/feedstock_root/build_artifacts/pdfminer_1613401440402/work
pdfminer.six==20221105
Pillow @ file:///D:/bld/pillow_1678273632076/work
plac==1.1.3
platformdirs==3.2.0
plotly==5.13.1
ply==3.11
pooch==1.7.0
preprocess==1.2.3
preshed==3.0.8
primePy==1.3
proglog==0.1.9
protobuf==3.19.6
pyannote.audio==2.1.1
pyannote.core==4.5
pyannote.database==4.1.3
pyannote.metrics==3.2.1
pyannote.pipeline==2.3
pyasn1==0.4.8
pyasn1-modules==0.2.8
PyAudio==0.2.13
pycparser @ file:///home/conda/feedstock_root/build_artifacts/pycparser_1636257122734/work
pycryptodome==3.17
pydantic @ file:///D:/bld/pydantic_1679565539355/work
pyDeprecate==0.3.2
Pygments==2.14.0
pyOpenSSL @ file:///home/conda/feedstock_root/build_artifacts/pyopenssl_1680037383858/work
pyparsing @ file:///home/conda/feedstock_root/build_artifacts/pyparsing_1652235407899/work
PyQt5==5.15.7
PyQt5-sip @ file:///D:/bld/pyqt-split_1674666735227/work/pyqt_sip
pyresparser==1.0.6
pyrsistent==0.19.3
PySocks @ file:///D:/bld/pysocks_1661604991356/work
python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/python-dateutil_1626286286081/work
python-speech-features==0.6
pytorch-lightning==1.6.5
pytorch-metric-learning==1.7.3
pytz @ file:///home/conda/feedstock_root/build_artifacts/pytz_1680088766131/work
PyYAML==6.0
regex==2023.3.23
requests @ file:///home/conda/feedstock_root/build_artifacts/requests_1673863902341/work
requests-oauthlib==1.3.1
resampy==0.4.2
rich==12.6.0
rsa==4.9
ruamel.yaml==0.17.21
ruamel.yaml.clib==0.2.7
scikit-learn @ file:///C:/b/abs_e01rh8f1vi/croot/scikit-learn_1675454931501/work
scipy==1.9.3
semver==2.13.0
sentencepiece==0.1.97
shellingham==1.5.0.post1
simplejson==3.18.4
singledispatchmethod==1.0
sip @ file:///D:/bld/sip_1675696791179/work
six @ file:///home/conda/feedstock_root/build_artifacts/six_1620240208055/work
smart-open==6.3.0
sniffio @ file:///home/conda/feedstock_root/build_artifacts/sniffio_1662051266223/work
sortedcontainers==2.4.0
SoundFile==0.10.3.post1
spacy==2.3.9
speechbrain==0.5.14
SQLAlchemy==2.0.7
srsly==1.0.6
starlette @ file:///home/conda/feedstock_root/build_artifacts/starlette-recipe_1678817698143/work
sympy==1.11.1
tabulate==0.9.0
tenacity==8.2.2
tensorboard==2.10.1
tensorboard-data-server==0.6.1
tensorboard-plugin-wit==1.8.1
tensorflow==2.10.1
tensorflow-estimator==2.10.0
tensorflow-io-gcs-filesystem==0.31.0
termcolor==2.2.0
texthero==1.1.0
thinc==7.4.6
threadpoolctl @ file:///home/conda/feedstock_root/build_artifacts/threadpoolctl_1643647933166/work
toml @ file:///home/conda/feedstock_root/build_artifacts/toml_1604308577558/work
torch==1.13.1
torch-audiomentations==0.11.0
torch-pitch-shift==1.2.2
torchaudio==0.13.1
torchmetrics==0.11.4
tornado @ file:///D:/bld/tornado_1666788767305/work
tqdm @ file:///home/conda/feedstock_root/build_artifacts/tqdm_1677948868469/work
typer==0.7.0
typing_extensions @ file:///home/conda/feedstock_root/build_artifacts/typing_extensions_1678559861143/work
unicodedata2 @ file:///D:/bld/unicodedata2_1667240049903/work
Unidecode==1.3.6
urllib3 @ file:///home/conda/feedstock_root/build_artifacts/urllib3_1678635778344/work
uvicorn @ file:///D:/bld/uvicorn-split_1678984112139/work
wasabi==0.10.1
Werkzeug==2.2.3
win-inet-pton @ file:///D:/bld/win_inet_pton_1667051142467/work
wordcloud==1.8.2.2
wrapt==1.15.0
yarl==1.8.2
zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1677313463193/work
tqdm==4.61.2
pdf2image==1.16.0
pandas==1.3.0
pytesseract==0.3.8
from fastapi import APIRouter
import moviepy.editor
import pickle
import os
import urllib
import numpy as np
from baseModels.payloads import EnrollVoice, AnalyseVoice, VerifyVoice
from scipy.spatial.distance import euclidean
from scripts.processing import extract_input_feature
from keras.models import load_model
from scripts.parameters import MODEL_FILE, MAX_SEC
from scripts.voice_feature_extraction import get_embedding
router = APIRouter(prefix='/voice')
@router.post("/enroll")
def enroll(payload:EnrollVoice):
video_filename = 'voices/'+payload.user_id+'.mp4'
urllib.request.urlretrieve(payload.video_url, video_filename)
# Download video and save audio
video = moviepy.editor.VideoFileClip(video_filename)
audio = video.audio
audio_filename = 'voices/auth/temp'+payload.user_id+'.wav'
audio.write_audiofile(audio_filename, codec='pcm_s16le', bitrate='50k')
# Extract and entrol audio features
model = load_model(MODEL_FILE)
enroll_result = get_embedding(model, audio_filename, MAX_SEC)
enroll_embs = np.array(enroll_result.tolist())
np.save("voices/auth/embed/"+ payload.user_id+".npy", enroll_embs)
try:
os.remove(video_filename)
except:
print('error')
try:
os.remove(audio_filename)
except:
print('error')
return 'SUCCESS'
@router.post("/verify")
def verify(payload:VerifyVoice):
video_filename = 'voices/interviews/'+payload.application_id+'.mp4'
urllib.request.urlretrieve(payload.video_url, video_filename)
# Download video and save audio
video = moviepy.editor.VideoFileClip(video_filename)
audio = video.audio
audio_filename = 'voices/interviews/'+payload.application_id+'.wav'
audio.write_audiofile(audio_filename, codec='pcm_s16le', bitrate='50k')
model = load_model(MODEL_FILE)
test_result = get_embedding(model, audio_filename, MAX_SEC)
test_embs = np.array(test_result.tolist())
enroll_embs = np.load("voices/auth/embed/"+ payload.user_id+".npy")
distance = euclidean(test_embs, enroll_embs)
try:
os.remove(video_filename)
except:
print('error')
return round(1-distance, 5)
@router.post("/analyse")
def analys(payload:AnalyseVoice):
model = pickle.load(open("models/voice_classifier.model", "rb"))
filename = 'voices/interviews/'+payload.application_id+'.wav'
features = extract_input_feature(filename, mfcc=True, chroma=True, mel=True, start=float(payload.start), end=float(payload.end)).reshape(1, -1)
result = model.predict(features)[0]
return result
\ No newline at end of file
from pyaudio import paInt16
# Signal processing
SAMPLE_RATE = 16000
PREEMPHASIS_ALPHA = 0.97
FRAME_LEN = 0.025
FRAME_STEP = 0.01
NUM_FFT = 512
BUCKET_STEP = 1
MAX_SEC = 10
# Model
MODEL_FILE = "models/voice_auth_model_cnn"
COST_METRIC = "cosine" # euclidean or cosine
INPUT_SHAPE=(NUM_FFT,None,1)
# IO
EMBED_LIST_FILE = "voices/auth/embed"
# Recognition
THRESHOLD = 0.2
\ No newline at end of file
import pandas as pd
import texthero as hero
import numpy as np
import librosa
from pyresparser.utils import extract_text
from PIL import Image
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity
# skills = {
# "primary" : ['Python', 'Machine Learning', 'node.js', 'AWS', 'Kubernetese', 'NLP', 'GCP', 'predective', 'OCR'],
# "secondary" : ['data', 'science', 'modeling', 'anomaly', 'privacy', 'visualization', 'OCR'],
# }
from pyannote.audio import Audio
from pyannote.core import Segment
class document_processing:
......@@ -119,3 +118,39 @@ class document_processing:
'secondary_score': sec_score,
'secondary_match': sec_match,
'similarity': int(doc_sim)}
def extract_input_feature(file_name, **kwargs):
mfcc = kwargs.get("mfcc")
chroma = kwargs.get("chroma")
mel = kwargs.get("mel")
contrast = kwargs.get("contrast")
tonnetz = kwargs.get("tonnetz")
start = kwargs.get("start")
end = kwargs.get("end")
sample_rate = 16000
audio = Audio(sample_rate=sample_rate, mono=True)
segment = Segment(start, end)
sound, sample_rate = audio.crop(file_name, segment)
X = sound[0].numpy()
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
import glob
import os
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score
import librosa
import soundfile
import pickle
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")
sample_rate = 16000
with soundfile.SoundFile(file_name) as sound_file:
X = sound_file.read(dtype="float32")
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/voice/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)
def train_voice():
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)
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"))
train_voice()
\ No newline at end of file
import os
import numpy as np
import pandas as pd
# from scipy.spatial.distance import cdist, euclidean, cosine
from scripts.voice_preprocess import get_fft_spectrum
from scripts.parameters import BUCKET_STEP,FRAME_STEP,MAX_SEC
def buckets(max_time, steptime, frameskip):
buckets = {}
frames_per_sec = int(1/frameskip)
end_frame = int(max_time*frames_per_sec)
step_frame = int(steptime*frames_per_sec)
for i in range(0, end_frame+1, step_frame):
s = i
s = np.floor((s-7+2)/2) + 1 # for first conv layer
s = np.floor((s-3)/2) + 1 # for first maxpool layer
s = np.floor((s-5+2)/2) + 1 # for second conv layer
s = np.floor((s-3)/2) + 1 # for second maxpool layer
s = np.floor((s-3+2)/1) + 1 # for third conv layer
s = np.floor((s-3+2)/1) + 1 # for fourth conv layer
s = np.floor((s-3+2)/1) + 1 # for fifth conv layer
s = np.floor((s-3)/2) + 1 # for fifth maxpool layer
s = np.floor((s-1)/1) + 1 # for sixth fully connected layer
if s > 0:
buckets[i] = int(s)
return buckets
def get_embedding(model, wav_file, max_time):
buckets_var = buckets(MAX_SEC, BUCKET_STEP, FRAME_STEP)
signal = get_fft_spectrum(wav_file, buckets_var)
embedding = np.squeeze(model.predict(signal.reshape(1,*signal.shape,1)))
return embedding
def get_embedding_batch(model, wav_files, max_time):
return [ get_embedding(model, wav_file, max_time) for wav_file in wav_files ]
def get_embeddings_from_list_file(model, list_file, max_time):
buckets_var = buckets(MAX_SEC, BUCKET_STEP, FRAME_STEP)
result = pd.read_csv(list_file, delimiter=",")
result['features'] = result['filename'].apply(lambda x: get_fft_spectrum(x, buckets_var))
result['embedding'] = result['features'].apply(lambda x: np.squeeze(model.predict(x.reshape(1,*x.shape,1))))
return result[['filename','speaker','embedding']]
import librosa
import numpy as np
from scipy.signal import lfilter, butter
from python_speech_features import sigproc
from scripts.parameters import SAMPLE_RATE, PREEMPHASIS_ALPHA, FRAME_LEN, FRAME_STEP, NUM_FFT
def load(filename, sample_rate):
audio, sr = librosa.load(filename, sr=sample_rate, mono=True)
audio = audio.flatten()
return audio
def normalize_frames(m,epsilon=1e-12):
return np.array([(v - np.mean(v)) / max(np.std(v),epsilon) for v in m])
# Valuable dc and dither removal function implemented
# https://github.com/christianvazquez7/ivector/blob/master/MSRIT/rm_dc_n_dither.m
def remove_dc_and_dither(sin, sample_rate):
if sample_rate == 16e3:
alpha = 0.99
elif sample_rate == 8e3:
alpha = 0.999
else:
print("Sample rate must be 16kHz or 8kHz only")
exit(1)
sin = lfilter([1,-1], [1,-alpha], sin)
dither = np.random.random_sample(len(sin)) + np.random.random_sample(len(sin)) - 1
spow = np.std(dither)
sout = sin + 1e-6 * spow * dither
return sout
def get_fft_spectrum(filename, buckets):
signal = load(filename, SAMPLE_RATE)
signal *= 2**15
# get FFT spectrum
signal = remove_dc_and_dither(signal, SAMPLE_RATE)
signal = sigproc.preemphasis(signal, coeff=PREEMPHASIS_ALPHA)
frames = sigproc.framesig(signal, frame_len=FRAME_LEN*SAMPLE_RATE, frame_step=FRAME_STEP*SAMPLE_RATE, winfunc=np.hamming)
fft = abs(np.fft.fft(frames,n=NUM_FFT))
fft_norm = normalize_frames(fft.T)
# truncate to max bucket sizes
rsize = max(k for k in buckets if k <= fft_norm.shape[1])
rstart = int((fft_norm.shape[1]-rsize)/2)
out = fft_norm[:,rstart:rstart+rsize]
return out
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