Merge branch 'it19953298' into 'master'

Voice verification

See merge request !4

NodeServer/src/config/types.ts

@@ -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

@@ -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

@@ -11,6 +11,7 @@ const applicationSchema = new Schema<ApplicationType>({
       time: String,
       link: String,
       videoRef: String,
+      voiceVerification: Number,
     },
     require: false,
   },

NodeServer/src/models/Candidate.ts

@@ -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

+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

@@ -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

@@ -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

-import { ResumeDataType } from "../../config/types";
 import { request } from "../requests";
 
 export default class ResumeAPI {

NodeServer/src/utilities/apis/voice.ts

+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);
+}

PythonServer/.gitignore

 __pycache__
 **/__pycache__
+voices/auth/embed/**
+voices/auth/temp/**
+voices/interviews/**
+resumes
+models/**
+data/**
\ No newline at end of file

PythonServer/Candidates_score.csv

-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

PythonServer/Job_description.txt

-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.
-

PythonServer/README.md

 ### 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 & models
+
+voice: https://drive.google.com/file/d/1wWsrN2Ep7x6lWqOXfr4rpKGYrJhWc8z7/view
+models: https://drive.google.com/file/d/1TWjIiyyInXHaXySKymM6VcsIR5YMVQ1V/view?usp=share_link
+
+conda list --export > conda-requirements.txt
+pip freeze > pip-requirements.txt

PythonServer/baseModels/payloads.py

@@ -11,3 +11,17 @@ class ResumeScores(BaseModel):
     primary_skills:List[str] = []
     secondary_skills:List[str] = []
     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

PythonServer/conda-requirements.txt

+# 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
+brotlipy=0.7.0=py39ha55989b_1005
+bzip2=1.0.8=h8ffe710_4
+ca-certificates=2022.12.7=h5b45459_0
+cachetools=5.3.0=pypi_0
+catalogue=1.0.2=pypi_0
+certifi=2022.12.7=pyhd8ed1ab_0
+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
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+font-ttf-ubuntu=0.83=hab24e00_0
+fontconfig=2.14.2=hbde0cde_0
+fonts-conda-ecosystem=1=0
+fonts-conda-forge=1=0
+fonttools=4.39.3=py39ha55989b_0
+freetype=2.12.1=h546665d_1
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+h11=0.14.0=pyhd8ed1ab_0
+h5py=3.8.0=pypi_0
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+libcblas=3.9.0=16_win64_mkl
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+libdeflate=1.18=hcfcfb64_0
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+libglib=2.74.1=he8f3873_1
+libhwloc=2.9.0=h51c2c0f_0
+libiconv=1.17=h8ffe710_0
+libjpeg-turbo=2.1.5.1=hcfcfb64_0
+liblapack=3.9.0=16_win64_mkl
+libogg=1.3.4=h8ffe710_1
+libopus=1.3.1=h8ffe710_1
+libpng=1.6.39=h19919ed_0
+librosa=0.9.2=pypi_0
+libsqlite=3.40.0=hcfcfb64_0
+libtiff=4.5.0=h6c8260b_6
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+libxcb=1.13=hcd874cb_1004
+libxml2=2.10.3=hc3477c8_6
+libzlib=1.2.13=hcfcfb64_4
+llvmlite=0.39.1=pypi_0
+m2w64-gcc-libgfortran=5.3.0=6
+m2w64-gcc-libs=5.3.0=7
+m2w64-gcc-libs-core=5.3.0=7
+m2w64-gmp=6.1.0=2
+m2w64-libwinpthread-git=5.0.0.4634.697f757=2
+mako=1.2.4=pypi_0
+markdown=3.4.3=pypi_0
+markupsafe=2.1.2=pypi_0
+matplotlib=3.7.1=py39hcbf5309_0
+matplotlib-base=3.7.1=py39haf65ace_0
+mkl=2022.1.0=h6a75c08_874
+moviepy=1.0.3=pyhd8ed1ab_1
+mpmath=1.3.0=pypi_0
+msys2-conda-epoch=20160418=1
+multidict=6.0.4=pypi_0
+munkres=1.1.4=pyh9f0ad1d_0
+murmurhash=1.0.9=pypi_0
+networkx=2.8.8=pypi_0
+nltk=3.8.1=pypi_0
+numba=0.56.4=pypi_0
+numpy=1.23.5=pypi_0
+oauthlib=3.2.2=pypi_0
+olefile=0.46=pypi_0
+omegaconf=2.3.0=pypi_0
+openh264=2.3.1=h63175ca_2
+openjpeg=2.5.0=ha2aaf27_2
+openssl=3.1.0=hcfcfb64_0
+opt-einsum=3.3.0=pypi_0
+optuna=3.1.0=pypi_0
+packaging=23.0=pyhd8ed1ab_0
+pandas=1.5.3=py39h2ba5b7c_0
+pcre2=10.40=h17e33f8_0
+pdfminer=20191125=pyhd8ed1ab_1
+pdfminer-six=20221105=pypi_0
+pillow=9.4.0=py39haa1d754_2
+pip=23.0.1=pyhd8ed1ab_0
+plac=1.1.3=pypi_0
+platformdirs=3.2.0=pypi_0
+plotly=5.13.1=pypi_0
+ply=3.11=py_1
+pooch=1.7.0=pypi_0
+preprocess=1.2.3=py_1
+preshed=3.0.8=pypi_0
+primepy=1.3=pypi_0
+proglog=0.1.9=py_0
+protobuf=3.19.6=pypi_0
+pthread-stubs=0.4=hcd874cb_1001
+pthreads-win32=2.9.1=hfa6e2cd_3
+pyannote-audio=2.1.1=pypi_0
+pyannote-core=4.5=pypi_0
+pyannote-database=4.1.3=pypi_0
+pyannote-metrics=3.2.1=pypi_0
+pyannote-pipeline=2.3=pypi_0
+pyasn1=0.4.8=pypi_0
+pyasn1-modules=0.2.8=pypi_0
+pyaudio=0.2.13=pypi_0
+pycparser=2.21=pyhd8ed1ab_0
+pycryptodome=3.17=pypi_0
+pydantic=1.10.7=py39ha55989b_0
+pydeprecate=0.3.2=pypi_0
+pygments=2.14.0=pypi_0
+pyopenssl=23.1.1=pyhd8ed1ab_0
+pyparsing=3.0.9=pyhd8ed1ab_0
+pyqt=5.15.7=py39hb77abff_3
+pyqt5-sip=12.11.0=py39h99910a6_3
+pyresparser=1.0.6=pypi_0
+pyrsistent=0.19.3=pypi_0
+pysocks=1.7.1=pyh0701188_6
+python=3.9.16=h4de0772_0_cpython
+python-dateutil=2.8.2=pyhd8ed1ab_0
+python-speech-features=0.6=pypi_0
+python_abi=3.9=3_cp39
+pytorch-lightning=1.6.5=pypi_0
+pytorch-metric-learning=1.7.3=pypi_0
+pytz=2023.3=pyhd8ed1ab_0
+pyyaml=6.0=pypi_0
+qt-main=5.15.8=h88fe7eb_7
+regex=2023.3.23=pypi_0
+requests=2.28.2=pyhd8ed1ab_0
+requests-oauthlib=1.3.1=pypi_0
+resampy=0.4.2=pypi_0
+rich=12.6.0=pypi_0
+rsa=4.9=pypi_0
+ruamel-yaml=0.17.21=pypi_0
+ruamel-yaml-clib=0.2.7=pypi_0
+scikit-learn=1.2.0=py39hd77b12b_1
+scipy=1.9.3=py39hfbf2dce_2
+semver=2.13.0=pypi_0
+sentencepiece=0.1.97=pypi_0
+setuptools=67.6.1=pyhd8ed1ab_0
+shellingham=1.5.0.post1=pypi_0
+simplejson=3.18.4=pypi_0
+singledispatchmethod=1.0=pypi_0
+sip=6.7.7=py39h99910a6_0
+six=1.16.0=pyh6c4a22f_0
+smart-open=6.3.0=pypi_0
+sniffio=1.3.0=pyhd8ed1ab_0
+sortedcontainers=2.4.0=pypi_0
+soundfile=0.10.3.post1=pypi_0
+spacy=2.3.9=pypi_0
+speechbrain=0.5.14=pypi_0
+sqlalchemy=2.0.7=pypi_0
+sqlite3=3.8.6=0
+srsly=1.0.6=pypi_0
+starlette=0.26.1=pyhd8ed1ab_0
+svt-av1=1.4.1=h63175ca_0
+sympy=1.11.1=pypi_0
+tabulate=0.9.0=pypi_0
+tbb=2021.8.0=h91493d7_0
+tenacity=8.2.2=pypi_0
+tensorboard=2.10.1=pypi_0
+tensorboard-data-server=0.6.1=pypi_0
+tensorboard-plugin-wit=1.8.1=pypi_0
+tensorflow=2.10.1=pypi_0
+tensorflow-estimator=2.10.0=pypi_0
+tensorflow-io-gcs-filesystem=0.31.0=pypi_0
+termcolor=2.2.0=pypi_0
+texthero=1.1.0=pypi_0
+thinc=7.4.6=pypi_0
+threadpoolctl=3.1.0=pyh8a188c0_0
+tk=8.6.12=h8ffe710_0
+toml=0.10.2=pyhd8ed1ab_0
+torch=1.13.1=pypi_0
+torch-audiomentations=0.11.0=pypi_0
+torch-pitch-shift=1.2.2=pypi_0
+torchaudio=0.13.1=pypi_0
+torchmetrics=0.11.4=pypi_0
+tornado=6.2=py39ha55989b_1
+tqdm=4.65.0=pyhd8ed1ab_1
+typer=0.7.0=pypi_0
+typing-extensions=4.5.0=hd8ed1ab_0
+typing_extensions=4.5.0=pyha770c72_0
+tzdata=2023c=h71feb2d_0
+ucrt=10.0.22621.0=h57928b3_0
+unicodedata2=15.0.0=py39ha55989b_0
+unidecode=1.3.6=pypi_0
+urllib3=1.26.15=pyhd8ed1ab_0
+uvicorn=0.21.1=py39hcbf5309_0
+vc=14.3=hb6edc58_10
+vs2015_runtime=14.34.31931=h4c5c07a_10
+wasabi=0.10.1=pypi_0
+werkzeug=2.2.3=pypi_0
+wheel=0.40.0=pyhd8ed1ab_0
+win_inet_pton=1.1.0=pyhd8ed1ab_6
+wordcloud=1.8.2.2=pypi_0
+wrapt=1.15.0=pypi_0
+x264=1!164.3095=h8ffe710_2
+x265=3.5=h2d74725_3
+xorg-libxau=1.0.9=hcd874cb_0
+xorg-libxdmcp=1.1.3=hcd874cb_0
+xz=5.2.6=h8d14728_0
+yarl=1.8.2=pypi_0
+zipp=3.15.0=pyhd8ed1ab_0
+zstd=1.5.2=h12be248_6

PythonServer/main.py

 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():

PythonServer/pip-requirements.txt

+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

PythonServer/requirements.txt

-tqdm==4.61.2
-pdf2image==1.16.0
-pandas==1.3.0
-pytesseract==0.3.8

PythonServer/routes/voice.py

+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

PythonServer/scripts/parameters.py

+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

PythonServer/scripts/processing.py

 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

PythonServer/scripts/train.py

+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

PythonServer/scripts/voice_feature_extraction.py

+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']]

PythonServer/scripts/voice_preprocess.py

+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