soniqo/speech-swift

Features

• Voice Agents - Provides a framework for building interactive, low-latency voice assistants that operate entirely offline.
• Full-Duplex Voice Interactions - Processes continuous audio input to detect speech and generate spoken responses while allowing for real-time interruption.
• Voice Activity Detection - Identifies speech segments within audio streams to manage conversational turn-taking.
• Conversational Voice Pipelines - Coordinates speech recognition, language processing, and speech synthesis with automated turn detection for real-time interactions.
• Hardware Acceleration - Utilizes specialized hardware components to enhance computational throughput in machine learning tasks.
• On-Device Speech Recognizers - Transcribes spoken audio into text entirely on-device without requiring network connectivity.
• Weight Quantization - Compresses model weights into lower-precision formats to reduce memory footprint and accelerate inference.
• Real-Time Speech Transcription - Processes live audio streams to produce partial text output as speech is spoken for immediate feedback.
• Speech Synthesis Models - Converts text input into synthesized speech using configurable models to support both batch generation and real-time streaming.
• Local Speech Synthesis - Generates natural-sounding speech from text using locally executed models for privacy and low latency.
• Voice Cloning - Extracts and saves unique speaker characteristics from reference audio samples to enable consistent, personalized voice synthesis.
• Audio Streaming Pipelines - Processes audio in chunks through a chain of models for real-time generation and transcription with incremental output.
• Audio Signal Enhancements - Processes audio input using noise reduction and voice activity detection to improve clarity.
• Wake Word Detection - Identifies specific spoken keywords in an audio stream using a lightweight neural network optimized for on-device execution.
• Mel-Spectrogram Processing - Transforms audio waveforms into mel-spectrograms for analysis by neural networks.
• Audio Source Separation Models - Isolates specific audio components like vocals from mixed tracks.
• Audio Speaker Embeddings - Generates numerical representations of vocal characteristics to enable speaker identification across different sessions.
• Audio-Transcript Aligners - Produces per-word timestamps and confidence scores by aligning audio recordings with their text transcripts.
• Hotword Boosts - Applies token-level logit bias to favor specific phrases during decoding to improve recognition accuracy for custom terminology.
• Long Audio Chunk Transcribers - Processes large audio files by breaking them into smaller chunks to improve transcription stability.
• Tool Discovery and Invocation - Parses natural language prompts to identify and extract structured function calls for use in voice-agent pipelines.
• Keyword Spotting - Identifies specific phrases within an audio stream by matching acoustic patterns against a configurable list of target keywords.
• Language Model Orchestration - Coordinates complex interactions between language models, external tools, and data sources.
• Transformer Weight Loading - Imports and runs dense transformer architectures from local sources using standardized quantization formats.
• Local Model Execution - Processes text prompts through on-device language models to generate conversational responses and structured output.
• Multilingual Speech Translation - Converts spoken language or text between different languages using on-device models.
• Speech Recognition Libraries - Offers a framework for performing speech recognition, synthesis, and voice cloning locally on Apple hardware.
• Model Quantization - Adjusts memory footprint and processing speed by selecting between different quantization levels for models.
• Sequence-to-Sequence Models - Uses encoder-decoder neural network architectures to transform input sequences into target sequences.
• Speaker Diarization - Analyzes audio recordings to distinguish between different speakers and assign specific time segments to each participant.
• End-of-Speech Detectors - Triggers an immediate end-of-utterance event to commit accumulated text when voice activity detection signals a pause.
• Speech Processing Toolkits - Provides a comprehensive toolkit for speech recognition, synthesis, and audio processing on Apple hardware.
• Multilingual Transcription - Automatically recognizes and transcribes over sixteen hundred languages without requiring explicit language hints.
• Speech Transcription Engines - Implements high-performance engines for converting spoken audio into written text using optimized machine learning models.
• Speech-to-Speech Models - Processes input audio to generate a spoken response directly, enabling real-time conversational interactions without intermediate text conversion.
• Model Weight Conversions - Transforms neural network weights into specialized execution formats for hardware optimization.
• Audio Processing Interfaces - Performs speech recognition, synthesis, diarization, and audio processing operations directly from the command line interface.
• Offline Inference Deployments - Prevents network requests during model initialization by forcing the system to use only locally cached weights.
• Audio Codec Decoders - Translates compressed audio bitstreams back into raw waveforms.
• Echo Cancellation - Coordinates speech synthesis and recognition timing to prevent the system from capturing its own output as new input.
• Voice Quality Enhancement - Applies noise suppression and echo cancellation to improve human speech clarity.
• Codebook Converters - Encodes raw audio into structured codebook representations by resampling, padding, and applying causal vector quantization.
• Audio Feature Extraction - Extracts audio characteristics such as spectrograms and filter banks from raw audio data.
• Audio Input Cleaning - Processes audio streams with noise cancellation and gain control to improve speech recognition.
• Hardware Acceleration - Selects specific hardware compute units to optimize model execution speed and device compatibility.
• Chunk Buffers - Accumulates audio samples into fixed-size segments to ensure compatibility with voice activity detection models.

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