[100% Off] Mastering Voice Ai : From Asr To Emotion Ai To Voice Cloning
Master cutting-edge SpeechLMs and build next-generation voice AI applications with end-to-end speech capabilities
What you’ll learn
- Develop end-to-end speech language models using Python and Transformer architectures.
- Master audio feature extraction and tokenization for speech recognition and synthesis.
- Build AI for emotion recognition and personalized speech with real-world applications.
- Evaluate SpeechLMs with metrics like WER and explore ethical AI design practices.
Requirements
- No prior speech AI experience required – beginner-friendly with hands-on guidance!
- A computer with Python 3.7+
- TensorFlow/PyTorch
- and audio libraries (e.g.
- Librosa).
- Basic Python programming (familiarity with loops
- functions
- and libraries like NumPy).
Description
Transform your understanding of voice AI with this comprehensive course on Speech Language Models (SLMs) – the revolutionary technology that’s replacing traditional speech processing pipelines with powerful end-to-end solutions.What You’ll Master:
Speech Language Models represent the next frontier in AI, moving beyond the limitations of traditional ASR→LLM→TTS pipelines. This course takes you from fundamental concepts to advanced applications, covering everything from speech tokenization and transformer architectures to emotion AI and real-time voice interactions.
Why This Course Matters:
Traditional speech processing suffers from information loss, high latency, and error accumulation across multiple stages. SLMs solve these problems by processing speech directly, capturing not just words but emotions, speaker identity, and paralinguistic cues that make human communication rich and nuanced.
What Makes This Course Unique:
-
Hands-on Learning: Work with state-of-the-art models like YourTTS, Whisper, and HuBERT
-
Complete Pipeline Coverage: From raw audio to deployed applications
-
Real-world Applications: Build ASR systems, voice cloning, emotion recognition, and interactive voice agents
-
Latest Research: Covers cutting-edge developments in the rapidly evolving SLM field
-
Practical Implementation: Learn training methodologies, evaluation metrics, and deployment strategies
Key Technologies You’ll Work With:
-
Speech tokenizers (EnCodec, HuBERT, Wav2Vec 2.0)
-
Transformer architectures adapted for speech (Whisper , Conformer models etc)
-
Vocoder technologies (Tacotron, Hi-Fi GAN, MelGAN etc)
-
Multi-modal training approaches (CTC, UCTC etc
-
Parameter-efficient fine-tuning (LoRA)
Perfect For:
-
AI/ML engineers wanting to specialize in speech technology
-
Students or Career Changers
-
Researchers exploring next-generation voice AI
-
Developers building voice-first applications
-
Anyone curious about how modern voice assistants really work
Course Outcome:
By completion, you’ll have the skills to design, train, and deploy Speech Language Models for diverse applications – from basic speech recognition to sophisticated emotion-aware voice agents. You’ll understand both the theoretical foundations and practical implementation details needed to contribute to this exciting field.
Join the voice AI revolution and master the technology that’s reshaping human-computer interaction!
