Design a Voice/Audio Processing Pipeline

To design this pipeline for Spotify, I would start by defining the non-functional requirements: handling high throughput ingestion while maintaining sub-second latency for user feedback. First, we ingest audio via a stateless REST API that validates format and immediately pushes the file reference to a durable message queue like Kafka. This decouples ingestion from processing. For the processing stage, we implement a chunking strategy where large files are split into 30-second segments. These segments are dispatched to a pool of asynchronous workers. Each worker performs noise reduction using GPU-accelerated libraries, followed by transcription via a cloud-based speech-to-text service, and finally re-encoding into adaptive bitrate streams. We store the processed audio chunks in S3 with a manifest file in DynamoDB tracking their status and location. To ensure reliability, we use dead-letter queues for failed chunks and implement idempotent processing to handle retries gracefully. Finally, the system exposes a read API that aggregates these chunks seamlessly for the client player, ensuring a smooth listening experience even during high-load events like new album releases.