AI TRAINING
Voice AI and Speech Pipeline Engineering
Build production-grade voice pipelines combining ASR, TTS, and real-time audio processing with confidence.
What it covers
This practitioner-level programme equips engineering teams with the skills to design, build, and deploy end-to-end voice AI systems. Participants work hands-on with leading ASR engines (Whisper, Deepgram), TTS providers (ElevenLabs, PlayHT, Coqui), and real-time streaming architectures. The curriculum covers latency optimisation, speaker diarisation, voice-cloning ethics, and integration patterns for production environments. By the end, teams can architect and ship robust voice products that meet quality, performance, and compliance requirements.
What you'll be able to do
- Integrate and benchmark at least two ASR engines against a custom audio dataset using WER and latency metrics
- Build a real-time voice pipeline with sub-500ms end-to-end latency using WebSocket streaming
- Fine-tune or prompt a TTS model to produce a consistent brand voice and evaluate output with MOS scoring
- Apply speaker diarisation and transcript post-processing to multi-speaker audio recordings
- Articulate the ethical and legal boundaries of voice cloning and implement consent-verification guardrails in a pipeline
Topics covered
- ASR fundamentals and engine comparison: Whisper, Deepgram, Azure Speech, AWS Transcribe
- TTS system selection and voice quality tuning: ElevenLabs, PlayHT, Coqui, XTTS
- Real-time audio streaming pipelines and WebSocket/WebRTC integration
- Speaker diarisation, punctuation restoration, and transcript post-processing
- Voice cloning: technical workflow, ethical constraints, and legal considerations
- Latency budgeting and optimisation for conversational AI use cases
- Evaluation metrics: WER, MOS, latency P95, and hallucination detection
- Deployment patterns: on-premise vs. cloud API vs. self-hosted model serving
Delivery
Delivered as a 3-4 day intensive bootcamp, available in-person or fully remote via collaborative IDE (e.g. GitHub Codespaces). Each day combines 40% concept sessions with 60% hands-on lab work. Participants receive a pre-configured cloud environment with API credits for Deepgram, ElevenLabs, and OpenAI Whisper. A capstone project — building a minimal end-to-end voice agent — is completed on the final day and reviewed by the instructor. All materials, notebooks, and reference architectures are provided and retained by participants.
What makes it work
- Establishing a shared audio evaluation dataset from actual production samples before the training begins
- Assigning a clear pipeline owner per team who can maintain and iterate on the voice stack after the bootcamp
- Running a latency budget review as a standard design step for any new voice feature
- Embedding ethical review checkpoints for any voice-cloning or voice-synthesis feature into the existing development workflow
Common mistakes
- Choosing a TTS or ASR provider solely on demo quality without benchmarking against real production audio conditions (noise, accents, domain vocabulary)
- Ignoring latency budgeting early in design, leading to pipelines that are technically correct but unusable in real-time conversation
- Deploying voice-cloning features without documented consent workflows, creating legal and reputational exposure
- Underestimating the post-processing work required (punctuation, disfluency removal, diarisation) to make raw transcripts usable downstream
When NOT to take this
This bootcamp is not the right fit for teams that have not yet shipped any backend service — foundational software engineering upskilling should come first before tackling real-time audio pipeline complexity.
Providers to consider
Sources
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