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saadmannan
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ASR-finetuning

Sleeping

Week 1-2: Setup + Data prep
  - Create conda environment (PyTorch 2.0, CUDA 12.5)
  - Download Common Voice German (~40 hours)
  - Implement data loading pipeline
  
Week 3-4: Fine-tuning
  - Fine-tune Whisper-small on German data
  - Use mixed precision (FP16) + gradient checkpointing
  - Expected: 15% WER improvement
  
Week 5: Evaluation & Optimization
  - Calculate WER/CER metrics
  - Compare to baseline
  - Optimize inference latency
  
Week 6: Deployment
  - Deploy to Hugging Face Spaces (free)
  - Create REST API with FastAPI
  - Push to GitHub with full documentation

Deliverables:

GitHub repo: whisper-german-asr
Hugging Face Space with live demo
README with benchmarks and usage
Blog post: "Fine-tuning Whisper for German ASR"

Project 2: Real-Time VAD + Speaker Diarization (Weeks 1-6 parallel)

Week 1-2: VAD System (Silero VAD)
  - Implement Silero Voice Activity Detection
  - Test on various audio conditions
  - Measure latency (<100ms target)
  
Week 3-4: Speaker Diarization (Pyannote)
  - Set up Pyannote.audio pipeline
  - Test on multi-speaker scenarios
  - Measure DER (Diarization Error Rate)
  
Week 5: Integration
  - Combine VAD + Diarization
  - Build end-to-end pipeline
  - Real-time streaming support
  
Week 6: Deployment
  - Containerize with Docker
  - Deploy to Hugging Face Spaces
  - Create Gradio interface

Deliverables:

GitHub repo: realtime-speaker-diarization
Gradio demo with streaming audio
Docker image for deployment
Benchmarks on FEARLESS STEPS data (reference your existing project)

Project 3: Speech Emotion Recognition (Weeks 1-6 parallel)

Week 1-2: Dataset prep (RAVDESS)
  - Download RAVDESS emotion dataset (1400 files)
  - Extract mel-spectrograms + MFCCs
  - Create train/val/test splits
  
Week 3-4: Model training
  - Build CNN architecture
  - Train on emotion classification (8 classes)
  - Target: 75%+ accuracy
  
Week 5: Evaluation & visualization
  - Confusion matrix
  - Class-wise metrics
  - Attention visualization
  
Week 6: Demo & deployment
  - Streamlit app for real-time demo
  - Deploy to Streamlit Cloud (free)
  - Upload to Hugging Face Model Hub

Deliverables:

GitHub repo: speech-emotion-recognition
Live Streamlit demo
Trained model on Hugging Face
Blog post: "Building Emotion Recognition from Speech"

Supporting Tasks (Weeks 1-8)

Create professional portfolio website (GitHub Pages)
Write 2 technical blog posts (Medium/Dev.to)
Update LinkedIn profile with project links
Set up GitHub profile (pin 6 best repos)
Create Hugging Face account and upload models

PORTFOLIO SHOWCASE CHECKLIST (End of Month 2)

GitHub:

3 repositories with comprehensive READMEs
Each with: requirements.txt, Dockerfile, model cards
Code is clean, documented, well-structured
At least 50 stars total (organic growth OK)

Blog:

2-3 posts on Medium/Dev.to with code examples
500+ words each
Include: problem statement, architecture, results, lessons learned

Deployed Demos:

Project 1: Live Whisper demo (Hugging Face Spaces)
Project 2: Diarization demo with streaming (Gradio)
Project 3: Emotion detection demo (Streamlit)

Portfolio Website:

Professional design (minimal, clean)
Project descriptions with links to code + demos
About section (story + skills)
Contact information
Mobile-responsive

MONTH 2-3: ACTIVE JOB SEARCH PHASE

Application Wave 1: Tier 1 Companies (December)

Target Companies: 5 companies

ElevenLabs (London + Remote)
Parloa (Berlin)
voize (Berlin)
audEERING (Munich)
ai|coustics (Berlin)

For Each Company:

Research: Learn about company, products, team
Customize: Tailor resume + cover letter (100%)
Personal touch: Reference specific projects or team members
Application: Submit through official channels + follow up

Effort: 10 hours per application (5 × 10 = 50 hours total)

Expected Outcome:

0-1 first-round interviews (not guaranteed, but possible)
Feedback/rejections (valuable for iteration)

LinkedIn Outreach Strategy (December)

Goal: Connect with 10 engineers at target companies

Process:

Find engineers on LinkedIn (search: "ElevenLabs" + "Engineer")

Personalized message (NOT generic):

"Hi [Name], I was impressed by your work on [specific project/achievement].
I'm building voice AI projects (multilingual ASR, speaker diarization) and
would love to learn about your experience at ElevenLabs. Would you have 15
minutes for a chat?"

Wait 2-3 days before follow-up
Offer value: Share your project or article, not just asking for help

Expected Response Rate: 10-20% (1-2 connections)

MONTH 3-4: PORTFOLIO TIER 2 + APPLICATIONS

Project 4: Text-to-Speech with Voice Cloning (Weeks 9-12)

Quick Timeline (because Tier 1 is already strong):

Week 9: Setup Coqui TTS framework
Week 10: Voice encoding + few-shot adaptation
Week 11: Multi-speaker TTS system
Week 12: Deploy + create demo

Deliverables:

GitHub repo: voice-cloning-tts
Live demo (try 3-5 different voices)
Blog post: "Voice Cloning at Home: Technical Deep Dive"

Project 5: Voice-Based Chatbot (Weeks 13-16 start)

High-level architecture:

User Voice Input
    ↓
[ASR] (Whisper)
    ↓
[NLU] (Intent recognition)
    ↓
[LLM] (GPT-4 / Open LLM)
    ↓
[TTS] (Coqui / ElevenLabs API)
    ↓
Voice Output

Timeline:

Week 13-14: Integrate ASR + TTS + LLM
Week 15: Test + optimize latency
Week 16: Deploy (API + web interface)

Application Wave 2: Tier 2 Companies (January-February)

Target Companies: 10-15 companies

Cerence (automotive)
Continental R&D (automotive)
Synthflow AI (Berlin)
Deutsche Telekom AI Lab
SAP AI Research
German tech consulting firms

Strategy:

60-80% customization (template base, customize key sections)
Leverage network: Ask LinkedIn connections for referrals
Direct outreach: Email hiring managers directly (find on LinkedIn)

Volume: 3-4 applications per week

MONTH 4-5: INTERVIEW PREPARATION

LeetCode & Coding Interview (Weeks 17-20)

Target: 50 problems, all categories

Weekly breakdown:

10 problems/week (3 hours)
Focus: Arrays, Strings, Trees, Graphs, DP
Difficulty: 60% Easy, 30% Medium, 10% Hard
Platform: LeetCode, HackerRank

Resources:

Blind 75 (optimized problem list)
Neetcode.io (video explanations)
Grind 75 (extended version)

ML System Design (Weeks 17-20)

Practice scenarios (prepare for each):

"Design an ASR system at scale"
- Problem statement: Real-time speech → text
- Architecture: Frontend (audio capture) → ASR model → Backend
- Challenges: Latency, accuracy, scalability
- Your answer: Walk through Whisper fine-tuning approach
"Design a voice cloning system"
- Problem: Few-shot voice adaptation
- Approach: Speaker embeddings + TTS
- Trade-offs: Quality vs. latency
"Design a speaker diarization system"
- Problem: Identify who spoke when
- Your project: Diarization using Pyannote

Practice: Do 1 mock interview per week (use Pramp or interviewing.io)

Behavioral Interview Prep

Your STAR Stories (prepare 5):

Challenge & Solution Story
- Story: "My Master's thesis involved solving inverse EM problems with deep learning"
- Challenge: Massive computational cost, data generation difficulty
- Action: Used synthetic data + U-Net + optimization techniques
- Result: 4000x speedup
Collaboration Story
- Story: "FEARLESS STEPS project with 5 teammates"
- Challenge: Coordinating complex pipeline (SAD → SID → ASR)
- Action: Clear communication, documentation, regular syncs
- Result: Published paper, successful deployment
Learning & Growth Story
- Story: "Learned deployment best practices while building portfolio"
- Challenge: Limited resources (RTX 5060 Ti)
- Action: Optimization techniques (mixed precision, quantization)
- Result: Deployed 3 models to production on free platforms
Conflict Resolution Story
- Story: "Debugged production issue in speech processing pipeline"
- Challenge: Model was producing random outputs
- Action: Systematic debugging, data validation
- Result: Fixed data preprocessing issue, improved robustness
Impact Story
- Story: "Building portfolio projects to enter AI industry"
- Challenge: Competitive market, need to stand out
- Action: Built 5 production-ready projects, deployed, documented
- Result: Getting interviews, building professional reputation

Mock Interview Schedule (Weeks 17-24)

Week 17-18: 2 coding interviews (LeetCode-style)
Week 19-20: 2 system design interviews
Week 21-22: 2 behavioral interviews
Week 23-24: 2 full interview simulations (all 3 rounds)

Resources:

Pramp (free mock interviews)
Interviewing.io
Interview Kickstart (paid, but high quality)

MONTH 5-6: FINAL PHASE & OFFERS

Application Wave 3: Tier 3 + Final Push (March-April)

Target: 20-30 applications to smaller companies, startups, consultancies

Strategy:

30-50% customization (mostly templates)
Focus on volume
Target: 1-2 offers

Companies:

YC-backed startups (AngelList.com)
Tech consulting (Accenture, Deloitte AI practices)
Corporate R&D labs (Siemens, Bosch, Volkswagen)
Growth-stage companies on Crunchbase

Interview Pipeline Management

Track everything in spreadsheet:

Company	Position	Date Applied	Status	Interview 1	Interview 2	Status	Notes
ElevenLabs	ML Engineer	Dec 15	Submitted	Jan 5	Jan 15	Passed R2	Waiting for R3
Parloa	ASR Engineer	Dec 20	Submitted	-	-	Rejected	Good learning
voize	ML Eng	Jan 5	Submitted	Jan 20	-	Pending R2	Good fit

Weekly review:

How many first-round interviews?
What's the response rate? (should be 5-10%)
Are rejections pattern-based?
Adjust strategy if needed

Offer Negotiation

When you get an offer:

Don't accept immediately
- "Thank you! I'm very excited. Can I think about it for 2-3 days?"
Understand the offer:
- Base salary
- Bonus structure (if any)
- Benefits (health insurance, vacation, home office)
- Stock options (if startup)
- Remote policy
- Budget for learning/conferences
Research market rate:
- German salary: €50,000-80,000 for ML Engineer (depending on experience)
- Add 10-20% premium for startups (equity trade-off)
- Compare on Glassdoor, Levels.fyi
Negotiate:
- "I'm very interested in this role. Based on my experience and market research, I was hoping for X salary. Would that be possible?"
- Negotiate everything: salary, remote flexibility, learning budget, vacation days
Get everything in writing:
- Before resigning from any current role

WEEKLY RHYTHM TEMPLATE

Monday

Review previous week's progress
Plan week ahead (5 key tasks)
Check applications status (new responses?)
2-3 hours: Project development

Tuesday-Thursday

5 hours/day: Project development (main work)
1 hour/day: Learning (courses, papers)
30 min/day: LeetCode or system design
30 min/day: LinkedIn engagement (comment, share, connect)

Friday

3 hours: Project optimization/deployment
1 hour: Blog writing or documentation
1 hour: Applications + outreach (if in active phase)

Saturday

4-6 hours: Deep work on complex project
1-2 hours: Open-source contributions
1 hour: Content creation (record video, write article)

Sunday

2-3 hours: Interview prep (LeetCode, system design, mock interviews)
1-2 hours: Planning for next week
1-2 hours: Optional blogging/content

SUCCESS INDICATORS BY MONTH

Month 2 (End of December 2025)

3 projects deployed and working
Portfolio website live
2 blog posts published
5 applications sent
10 LinkedIn connections to target companies
0-1 interview requests (bonus)

Status Check: Are projects working? Is portfolio visible? Is anything preventing applications?

Month 3 (End of January 2026)

Projects 1-3 polished and showcased
20 applications sent total
1-3 first-round interviews
3-5 LinkedIn conversations
3 blog posts published

Status Check: Getting any response? If not, something is wrong. Debug immediately.

Month 4 (End of February 2026)

Projects 4-5 started/deployed
30 applications sent total
3-5 first-round interviews
1-2 second-round interviews
30+ LeetCode problems completed
4+ mock interviews done

Status Check: Should have at least 1-2 companies seriously interested.

Month 5 (End of March 2026)

All projects completed
40-50 applications sent
5+ interviews at various stages
2-3 offer conversations
LeetCode: 50 problems
Mock interviews: 8+ sessions

Status Check: Should be in final rounds with 1-2 companies.

Month 6 (End of April 2026)

Offers received from 1-2 companies
Negotiating terms
Preparing for first day
Celebrating! 🎉

RED FLAGS & COURSE CORRECTIONS

"I'm not getting any responses after 2 weeks"

Check ATS compatibility of resume
Get resume reviewed by someone
Verify cover letters are customized
Make sure portfolio is visible
Try direct outreach instead of job board portals

"I'm getting rejections but no interviews"

Problem: Resume/portfolio not matching role requirements
Solution:
- Emphasize specific tech stack company uses
- Highlight most relevant projects first
- Customize cover letter more

"I'm getting interviews but no offers"

Problem: Failing technical or behavioral interview
Solution:
- Record yourself doing mock interviews
- Get feedback from mentors
- Focus weak area intensively
- Practice more (LeetCode, system design)

"Projects are taking too long"

Solution: Ship MVP version first, polish later
Focus on "good enough to deploy" not "perfect code"
Reduce scope (3 excellent > 6 mediocre)
Use existing models/frameworks (don't build from scratch)

ESSENTIAL RESOURCES

Code Repositories (Bookmark these)

HuggingFace Transformers: https://github.com/huggingface/transformers
Pyannote.audio: https://github.com/pyannote/pyannote-audio
Silero VAD: https://github.com/snakers4/silero-vad
Coqui TTS: https://github.com/coqui-ai/TTS

Learning (Free)

HuggingFace Audio Course: https://huggingface.co/course
Made with ML (ML systems): https://madewithml.com/
Papers with Code (speech): https://paperswithcode.com/

Job Search

AngelList Talent: https://wellfound.com/
German Tech Jobs: https://germantechjobs.de/
LinkedIn Jobs: https://www.linkedin.com/jobs/

Applications

Hugging Face Spaces: https://huggingface.co/spaces
Streamlit Cloud: https://streamlit.io/cloud
GitHub Pages: https://pages.github.com/

YOUR COMPETITIVE ADVANTAGES

Master's degree in Signal Processing (credibility)
Published research (thesis + project papers)
Real-world data experience (FEARLESS STEPS, Apollo-11)
End-to-end skills (research → production)
German location (speaks to German companies naturally)
Specific domain expertise (speech AI, not generic "AI engineer")

FINAL WORDS

This is an aggressive but achievable plan. You're not competing against:

Course graduates (you have a Master's)
Theory-only researchers (you deploy code)
Generic "AI engineers" (you have specialized skills)

You're competing against:

Other qualified ML engineers (maybe 50 total in German market)
Most of whom are already employed (internal promotion competition is low)

The market is hungry for ML engineers. Germany has 935+ AI startups. They need people like you.

Execute this plan diligently, and you'll have offers by May 2026.

Execution starts now. Ship it! 🚀