Open-source customizable AI voice dictation built on Pipecat

Captures audio input from microphone or system audio and converts it to text in real-time using streaming transcription APIs. Built on Pipecat's audio pipeline architecture, which handles buffering, frame aggregation, and asynchronous transcription without blocking the audio capture loop. Supports multiple transcription backends (OpenAI Whisper, Google Cloud Speech-to-Text, or local models) through pluggable provider abstraction.

Applies user-defined transformation rules to transcribed text before output, including punctuation restoration, capitalization correction, abbreviation expansion, and domain-specific text normalization. Implemented as a composable chain of processors that can be enabled/disabled and reordered, allowing developers to inject custom formatting logic at any stage. Integrates with LLM-based processors for intelligent punctuation and grammar correction.

Tracks end-to-end latency from audio capture to final text output, with per-stage breakdowns (audio buffering, transcription, post-processing, output routing). Exposes metrics through Pipecat's monitoring hooks, allowing integration with observability platforms (Prometheus, DataDog, New Relic). Includes built-in performance profiling to identify bottlenecks. Configurable sampling to avoid overhead in production.

Supports multiple languages and locales for transcription and text processing, with dynamic switching without restarting the application. Manages language-specific models and post-processing rules (e.g., different punctuation rules for different languages). Implements language detection to automatically select the appropriate language model. Built as a Pipecat service with language-specific processor chains.

Abstracts transcription provider implementations behind a unified interface, allowing seamless switching between OpenAI Whisper, Google Cloud Speech-to-Text, Azure Speech Services, or local models without changing application code. Implements provider-agnostic request/response mapping and includes automatic fallback logic that routes to a secondary provider if the primary fails or times out. Built using Pipecat's service abstraction pattern with pluggable provider classes.

Detects when the user is actively speaking vs. silent, automatically pausing transcription during silence periods to reduce API costs and latency. Uses either energy-based VAD (voice activity detection) on raw audio frames or integrates with provider-native VAD if available (e.g., Whisper's built-in silence detection). Configurable sensitivity thresholds and minimum speech duration to avoid false positives from background noise.

Streams transcribed and formatted text to the application UI in real-time as it becomes available, supporting both partial (interim) results and final confirmed text. Implements output routing through Pipecat's message pipeline, allowing text to be sent to multiple destinations simultaneously (UI text field, file, external API, clipboard). Supports configurable buffering and batching strategies to balance latency vs. update frequency.

Interprets transcribed text as voice commands or intents within a configurable command schema, extracting parameters and routing to appropriate handlers. Uses pattern matching, fuzzy matching, or LLM-based intent classification to map user utterances to defined commands. Maintains conversation context to handle multi-turn interactions and anaphora (e.g., 'delete that' referring to the previous message). Implemented as a Pipecat processor that sits downstream of transcription and post-processing.

Abstracts audio input from various sources (system microphone, USB headset, virtual audio device, audio file) through a unified interface. Handles device enumeration, format negotiation (sample rate, bit depth, channels), and graceful fallback if the selected device becomes unavailable. Supports simultaneous capture from multiple audio sources for multi-participant scenarios. Built on Pipecat's audio input abstraction with platform-specific implementations (PyAudio for cross-platform, native APIs for macOS/Windows).

Provides hooks and event callbacks for integrating the voice dictation engine with custom UI frameworks (Qt, Tkinter, web frameworks like Flask/FastAPI). Exposes events for transcription start/stop, text updates, command recognition, and error conditions. Allows UI to control the dictation engine (start/stop recording, change provider, adjust settings) through a clean API. Implemented as a Pipecat service with async event emission and callback registration.

Manages application settings (transcription provider, language, VAD sensitivity, post-processing rules, UI preferences) through a configuration file or database, with support for multiple named profiles. Allows users to save/load configurations without code changes, and provides sensible defaults for common use cases. Implements configuration validation and schema versioning to handle upgrades. Built on standard Python config libraries (ConfigParser, YAML, or JSON) with Pipecat service initialization hooks.

Implements comprehensive error handling for transcription failures, network issues, invalid audio input, and API errors. Provides user-friendly error messages and automatic recovery strategies (retry with exponential backoff, fallback to alternative provider, graceful degradation to text input). Logs detailed error information for debugging. Built as a Pipecat error handler middleware that intercepts exceptions and decides whether to retry, fallback, or fail gracefully.