Mastering Speech Language Models : From ASR to Emotion AI
Build end-to-end speech systems: streaming Whisper ASR, prosody modeling, Speech Emotion Recognition (SER), speaker diarization, and zero-shot voice cloning.
Two Flexible Ways to Master Speech Language Models
Choose between self-paced on-demand videos on Udemy or live weekly cohort mentorship with real-time SpeechLM labs.
Buy Directly on Udemy
Learn at your own pace with lifetime access to 19+ hours of video, 100+ lectures, Whisper streaming repos, and Q&A support.
- Real-time streaming Whisper ASR & CTranslate2
- Speaker Diarization with PyAnnote & multi-speaker separation
- Wav2Vec 2.0 & HuBERT emotion/sentiment classifiers
- Zero-shot voice cloning with XTTS & Udemy Certificate
Join the Live Cohort
Weekly live Zoom voice architecture sessions with Vinit Singh, 1-on-1 office hours, private Discord audio lab, and custom VoIP reviews.
- Includes everything in the On-Demand course +
- 6 weeks of live interactive Zoom coding sessions & Q&A
- 1-on-1 weekly instructor office hours & live VoIP debugging
- Private Discord peer cohort & telephony voice bot reviews
- Official gadgap AI Certified Speech AI Specialist Credential
Course Description & Overview
Voice is the most natural human interface, and speech models are undergoing a massive revolution with unified Speech Language Models (SpeechLMs) and streaming acoustic neural networks.
This course provides a comprehensive hands-on deep dive into modern speech engineering. You will learn the physics of sound and spectrogram representations, fine-tune and deploy real-time streaming Whisper ASR pipelines, implement speaker diarization with PyAnnote, extract vocal biomarkers and Speech Emotion Recognition (SER) with Wav2Vec 2.0 / HuBERT, and build zero-shot neural voice cloning systems integrated with VoIP and WebRTC telephony.
Course Requirements & Prerequisites
- Basic Python: Basic Python programming knowledge.
- Machine Learning: Familiarity with deep learning basics (PyTorch or TensorFlow is helpful).
- Setup: Any computer with a working microphone and internet access.
Who This Course Is For (Intended Learners)
Designed for engineers building real-world speech AI, voice assistants, and audio intelligence products.
Audio & Speech Engineers
Developers and ML engineers interested in building real-time audio transcription systems, streaming Whisper servers, and acoustic pipelines.
Customer Support & Call Center Architects
Engineers building automated telephony phone bots, caller sentiment/emotion detection dashboards, and meeting diarization tools.
Podcasters & Media Localization Creators
Audio engineers and creators looking to automate podcast editing, noise removal, multi-lingual dubbing, and neural voice conversion.
ML Students & Researchers
Learners seeking hands-on experience fine-tuning Whisper on speech datasets (Librispeech, Common Voice) and evaluating acoustic metrics.
Comprehensive 6-Module Curriculum (100+ Lectures)
- Physics of human voice production, vocal tract modeling, and formants
- Audio preprocessing: Denoising, spectral subtraction, voice activity detection (VAD)
- Feature engineering: MFCCs, Chroma, Spectral Centroid, and Mel-spectrograms
- Audio augmentation: Pitch shifting, time stretching, room impulse response (RIR) convolution
- Encoder-Decoder transformer speech recognition architectures
- Deep dive into Whisper: Architecture, multi-task conditioning, and tokenization
- Real-time streaming speech transcription using faster-whisper, CTranslate2, and VAD chunking
- Domain adaptation: Fine-tuning Whisper for specialized medical, legal, and regional accents
- Who spoke when? Architecture of speaker diarization pipelines (PyAnnote)
- Speaker embeddings: x-vectors, d-vectors, and ECAPA-TDNN cosine distance
- Agglomerative hierarchical clustering and spectral clustering for speaker grouping
- Overlap speech detection and beamforming for multi-microphone arrays
- Acoustic correlates of emotion: Pitch variation, speech rate, energy jitter, and shimmer
- Deep learning for Speech Emotion Recognition (SER): Wav2Vec 2.0, HuBERT, and WavLM embeddings
- Multi-modal emotion analysis: Combining vocal tone with transcribed text sentiment
- Real-time customer satisfaction and stress detection in live call centers
- Zero-shot and few-shot voice cloning architectures (XTTS, OpenVoice, Tortoise)
- Speaker conditioning: Conditioning neural vocoders with target speaker latents
- Real-time Any-to-Any Voice Conversion (RVC): Changing voice identity while preserving emotion
- Evaluating voice similarity: Cosine distance on deep speaker embeddings
- Connecting AI voice pipelines to SIP / PSTN telephony (Twilio, LiveKit, Daily)
- Asynchronous pipeline orchestration: VAD -> Streaming ASR -> LLM -> Streaming TTS
- Achieving <300ms end-to-end conversational turn-taking latency
- Capstone: Production-ready Voice Bot with custom cloned voice and sentiment dashboard
Vinit Singh
AI Consultant & Principal AI Architect, gadgap AI
Vinit is an AI Consultant and Educator specializing in LLM Fine-Tuning, Voice AI, Speech Language Models, Agentic AI, and Computer Vision — with over 18 years of experience in Data Science and Artificial Intelligence. A graduate of IIT Bombay with Stanford Machine Learning and Deep Learning certifications, he works at the intersection of AI research and real-world deployment.
Currently a Consultant in the Speech & Language team at Sony India Software Centre, his prior work spans Computer Vision on Nvidia edge hardware at Assert AI, and end-to-end AI consulting at tvam Technologies — where he built agentic FinTech workflows, a robo-advisor via LoRA fine-tuning on DeepSeek-R1, and a Voice AI telecaller pipeline.
A top 3% Udemy creator globally, trusted by learners across 150+ countries and enterprises including Adidas, Barclays, and Volkswagen — covering Voice AI, Agentic AI, Computer Vision, and NLP & LLMs.
Enroll in Speech Language Models
Buy on Udemy for on-demand self-paced access, or join our live interactive cohort for hands-on mentorship.