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NLP & Speech Processing

From word embeddings to voice AI. Build models that understand language

8 phases. 46 lessons. 46 labs. 1 capstone. NLP and speech from foundations to production — text processing & embeddings, sequence models (RNNs, LSTMs, transformers for NLP), speech processing (audio fundamentals, ASR with Whisper, TTS), and production NLP (RAG, agents, evaluation). You build a voice assistant that transcribes speech, understands intent, and responds with synthesized voice.

8 phases · 46 lessons · 46 labs · 1 capstone

Take Transformers & LLMs from Scratch first — this course builds on it.

Outcomes you will have by the end

• A voice assistant — A complete speech-to-speech pipeline — Whisper for ASR, LLM + RAG for understanding, TTS for response — that you can talk to and get intelligent responses.
• A working mental model of how machines understand language — From word embeddings to transformers to speech models — you understand how text and audio become numerical representations that models can process.
• A production-ready NLP toolkit — Semantic search, text classification, NER, RAG, ASR, TTS — you've built the components that make up modern NLP and voice AI systems.
• Verified certificate of completion — Issued by DeVenture Academy, tied to your completion record. Lists the NLP, speech, RAG, and evaluation skills you built.

What you will be able to do

Text Preprocessing & Tokenization · Word & Sentence Embeddings · Vector Databases & Semantic Search · Transformer Fine-tuning for NLP · Named Entity Recognition · Whisper ASR Pipeline · Text-to-Speech Synthesis · RAG Systems · NLP Agents & Tool Use · NLP Evaluation (BLEU, ROUGE, BERTScore)

Every phase, every lesson, every project

• NLP Foundations (6 lessons) — free — Text processing, tokenization, and word embeddings from scratch.
  • Text Processing — Tokenization, Stemming, Lemmatization
  • Bag of Words, TF-IDF, and Text Representation
  • Word Embeddings — Word2Vec from Scratch
  • GloVe, FastText, and Subword Embeddings
  • Sentiment Analysis
  • Named Entity Recognition
• Text Representation (6 lessons) — Word2Vec, GloVe, and contextual embeddings from scratch.
  • POS Tagging and Syntactic Parsing
  • CNNs and RNNs for Text
  • Sequence-to-Sequence Models
  • Attention Mechanism — The Breakthrough
  • Machine Translation
  • Text Summarization
• Sequence Models (6 lessons) — RNNs, LSTMs, and sequence-to-sequence models from scratch.
  • Question Answering Systems
  • Information Retrieval and Search
  • Topic Modeling — LDA and BERTopic
  • Text Generation Before Transformers — N-gram Language Models
  • Chatbots — Rule-Based to Neural to LLM Agents
  • Multilingual NLP
• Transformers for NLP (6 lessons) — BERT, GPT, and transformer-based NLP from scratch.
  • Subword Tokenization — BPE, WordPiece, Unigram, SentencePiece
  • Structured Outputs & Constrained Decoding
  • Natural Language Inference — Textual Entailment
  • Embedding Models — The 2026 Deep Dive
  • Chunking Strategies for RAG
  • Coreference Resolution
• NLP Applications (5 lessons) — Text classification, NER, summarization, and translation.
  • Entity Linking & Disambiguation
  • Relation Extraction & Knowledge Graph Construction
  • LLM Evaluation — RAGAS, DeepEval, G-Eval
  • Long-Context Evaluation — NIAH, RULER, LongBench, MRCR
  • Dialogue State Tracking
• Speech Foundations (6 lessons) — Audio processing, FFT, and spectrograms from scratch.
  • Audio Fundamentals — Waveforms, Sampling, Fourier Transform
  • Spectrograms, Mel Scale & Audio Features
  • Audio Classification — From k-NN on MFCCs to AST and BEATs
  • Speech Recognition (ASR) — CTC, RNN-T, Attention
  • Whisper — Architecture & Fine-Tuning
  • Speaker Recognition & Verification
• Automatic Speech Recognition (6 lessons) — ASR systems, CTC, and Whisper architecture from scratch.
  • Text-to-Speech (TTS) — From Tacotron to F5 and Kokoro
  • Voice Cloning & Voice Conversion
  • Music Generation — MusicGen, Stable Audio, Suno, and the Licensing Earthquake
  • Audio-Language Models — Qwen2.5-Omni, Audio Flamingo, GPT-4o Audio
  • Real-Time Audio Processing
  • Build a Voice Assistant Pipeline — The Phase 6 Capstone
• Speech Synthesis & Applications (5 lessons) — TTS, voice cloning, and speech applications.
  • Neural Audio Codecs — EnCodec, SNAC, Mimi, DAC and the Semantic-Acoustic Split
  • Voice Activity Detection & Turn-Taking — Silero, Cobra, and the Flush Trick
  • Streaming Speech-to-Speech — Moshi, Hibiki, and Full-Duplex Dialogue
  • Voice Anti-Spoofing & Audio Watermarking — ASVspoof 5, AudioSeal, WaveVerify
  • Audio Evaluation — WER, MOS, UTMOS, MMAU, FAD, and the Open Leaderboards

The technologies you will use

PyTorch · Hugging Face Transformers · Whisper · librosa · FAISS · LangChain

Roles this course prepares you for

• NLP Engineer ($120k–$180k) — Build NLP systems — text classification, entity extraction, semantic search, RAG. Understanding embeddings and transformers from scratch means you can choose the right model and debug quality issues.
• Speech AI Engineer ($125k–$190k) — Build voice AI systems — transcription, synthesis, speaker identification. Understanding audio processing and Whisper architecture means you can build production voice pipelines.
• Conversational AI Engineer ($115k–$175k) — Build chatbots and voice assistants that understand intent, retrieve information, and generate responses. RAG and agent expertise are essential for this role.

What NLP & speech processing actually is

It's building systems that understand human language — both text and speech. From word embeddings that capture semantic meaning to Whisper that transcribes speech to RAG systems that retrieve and generate answers, you build the full pipeline of modern language AI.

What you do every day

You build NLP systems — semantic search, text classification, RAG, voice assistants. You process text and audio, choose the right models, fine-tune for specific tasks, and evaluate quality. When a chatbot gives bad answers or ASR mis-transcribes, you know whether it's the data, the model, or the pipeline.

Why companies hire for this

Every company has text data and many are building voice interfaces. NLP engineers build search systems, chatbots, document processing pipelines, and voice assistants. The engineer who understands embeddings, transformers, and speech models from scratch is the one who can build production NLP systems, not just call APIs.

What this course is not

It is not a LangChain tutorial. It is not about prompt engineering. It is about building the NLP and speech components that make language AI work — from embeddings to ASR to RAG. If you want to understand how machines understand language, this is the course.

Common questions

Do I need Transformers & LLMs from Scratch first?

Yes — this course uses transformers, tokenization, and fine-tuning throughout. Transformers & LLMs from Scratch builds those foundations; this course applies them to NLP and speech tasks.

Is this a natural language processing course or a speech course?

Both. Phases 1–2 cover text NLP (embeddings, classification, NER), Phase 3 covers speech (ASR, TTS, speaker ID), and Phase 4 covers production NLP (RAG, agents, evaluation). The capstone combines both into a voice assistant.

How is this different from the NLP phase in ML & AI Engineering?

ML & AI Engineering covers NLP in 5 lessons — enough to use pre-trained NLP models. This course spends 16 lessons on embeddings, sequence models, speech processing, RAG, and production deployment. It's the difference between using NLP APIs and building NLP systems.

What do I end up with?

A voice assistant that transcribes speech, understands intent with RAG, and responds with synthesized voice — plus a complete NLP toolkit covering text, speech, and production deployment.

Key terms in this course

RAG (Retrieval-Augmented Generation) · Transformer · Fine-Tuning · Semantic Search · Tool Use

Continue your learning path

Transformers & LLMs from Scratch · Multimodal AI Systems · Generative AI Fundamentals · ML & AI Engineering

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