{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"kokoro-tts","slug":"kokoro-tts","name":"Kokoro TTS","type":"repo","url":"https://github.com/hexgrad/kokoro","page_url":"https://unfragile.ai/kokoro-tts","categories":["voice-audio","testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"kokoro-tts__cap_0","uri":"capability://text.generation.language.dual.platform.text.to.speech.synthesis.with.82m.parameter.neural.model","name":"dual-platform text-to-speech synthesis with 82m parameter neural model","description":"Generates natural-sounding speech from text using a lightweight 82-million parameter transformer-based neural model (KModel class) that operates on phoneme sequences rather than raw text, with parallel Python and JavaScript implementations enabling deployment from CLI to web browsers. The KPipeline orchestrates text processing through language-specific G2P conversion (misaki or espeak-ng backends) followed by neural synthesis and ONNX-based audio waveform generation via istftnet modules.","intents":["Generate high-quality speech audio from text input across multiple languages and voice styles","Deploy TTS inference in resource-constrained environments without cloud dependencies","Integrate speech synthesis into web applications, desktop tools, and server-side services","Synthesize speech with controllable voice characteristics and language-specific phonetics"],"best_for":["Developers building offline-first voice applications with commercial licensing requirements","Teams deploying TTS to edge devices or resource-constrained servers","Builders creating multilingual voice interfaces without cloud API costs"],"limitations":["Supports only 8 languages (American/British English, Spanish, French, Hindi, Italian, Japanese, Brazilian Portuguese, Mandarin Chinese) — no other language support","Model inference latency varies by hardware; CPU inference significantly slower than GPU (no benchmarks provided in documentation)","No streaming/real-time synthesis — entire text must be processed before audio generation completes","Voice selection limited to pre-defined voice embeddings; no voice cloning or custom voice training"],"requires":["Python 3.9+ with PyTorch and Transformers library","espeak-ng system package for fallback grapheme-to-phoneme conversion","misaki library for primary G2P processing","soundfile library for audio I/O","HuggingFace Hub connectivity for initial model/voice downloads","Node.js 14+ for JavaScript implementation","2GB minimum RAM for model loading"],"input_types":["Plain text strings (any length, auto-chunked by pipeline)","Language code ('a' for American English, 'b' for British, etc.)","Voice identifier (e.g., 'af_heart' for American female voice)"],"output_types":["Audio waveform as NumPy array (Python) or typed array (JavaScript)","WAV/MP3 files via soundfile integration","Generator yielding tuples of (graphemes, phonemes, audio_segment) for streaming processing"],"categories":["text-generation-language","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kokoro-tts__cap_1","uri":"capability://data.processing.analysis.language.aware.grapheme.to.phoneme.conversion.with.hybrid.g2p.backends","name":"language-aware grapheme-to-phoneme conversion with hybrid g2p backends","description":"Converts text characters to phoneme sequences using a dual-backend architecture: misaki library as primary G2P engine for most languages, with espeak-ng fallback for Hindi and other languages requiring rule-based phonetic conversion. The text processing pipeline (in kokoro/pipeline.py) selects the appropriate G2P backend based on language code, handles text chunking for long inputs, and produces phoneme sequences that feed into neural synthesis.","intents":["Convert arbitrary text to phonetically accurate phoneme sequences for neural TTS synthesis","Support multiple languages with language-specific phonetic rules and character sets","Handle edge cases like abbreviations, numbers, and special characters in multilingual contexts","Enable phoneme-level inspection and debugging of TTS output"],"best_for":["Multilingual TTS applications requiring accurate phonetic representation","Developers needing phoneme-level control over speech synthesis","Systems integrating with downstream phoneme-based audio processing"],"limitations":["Phoneme accuracy depends on G2P backend quality; misaki and espeak-ng have different coverage and accuracy profiles","No custom phoneme mapping or user-defined phoneme inventories","Text preprocessing (punctuation handling, number expansion) is basic and may not handle domain-specific abbreviations","Language detection is manual (via language code parameter) — no automatic language detection"],"requires":["misaki library installed (primary G2P backend)","espeak-ng system package for Hindi and fallback languages","Language code parameter ('a', 'b', 'e', 'f', 'h', 'i', 'j', 'p', 'z')","Input text in UTF-8 encoding"],"input_types":["Plain text strings in supported languages","Language code identifier","Optional text chunking parameters"],"output_types":["Phoneme sequences as lists or strings","Grapheme-phoneme alignment tuples","Phoneme token IDs for neural model input"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kokoro-tts__cap_2","uri":"capability://data.processing.analysis.onnx.based.audio.waveform.generation.from.phoneme.embeddings","name":"onnx-based audio waveform generation from phoneme embeddings","description":"Generates raw audio waveforms from phoneme token sequences using ONNX-optimized istftnet modules that perform inverse short-time Fourier transform (ISTFT) synthesis. The KModel class produces mel-spectrogram embeddings from phoneme tokens, which are then converted to linear spectrograms and finally to waveforms via the ONNX-compiled istftnet vocoder, enabling efficient CPU/GPU inference without PyTorch overhead.","intents":["Convert neural model phoneme embeddings to high-quality audio waveforms efficiently","Deploy audio synthesis on CPU-only or resource-constrained hardware","Generate audio with consistent quality across different inference environments","Export and optimize TTS pipeline for production deployment"],"best_for":["Production deployments requiring optimized inference performance","Edge devices and embedded systems with limited computational resources","Teams needing cross-platform audio synthesis consistency"],"limitations":["ONNX export requires manual model conversion via examples/export.py — no automatic export in standard pipeline","Audio quality depends on istftnet vocoder training; no alternative vocoders provided","Waveform generation is non-streaming — entire mel-spectrogram must be computed before audio output","ONNX Runtime version compatibility may vary across platforms; no version pinning documented"],"requires":["ONNX Runtime library (onnxruntime package)","Pre-exported ONNX model files for istftnet vocoder","PyTorch for initial model inference (before ONNX export)","Soundfile library for audio I/O and format conversion"],"input_types":["Phoneme token sequences (integer arrays)","Mel-spectrogram embeddings from KModel","Audio parameters (sample rate, duration)"],"output_types":["Raw audio waveforms as NumPy arrays (float32)","WAV files (via soundfile)","Streaming audio chunks for real-time playback"],"categories":["data-processing-analysis","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kokoro-tts__cap_3","uri":"capability://text.generation.language.multi.voice.synthesis.with.pre.trained.voice.embeddings","name":"multi-voice synthesis with pre-trained voice embeddings","description":"Enables selection from multiple pre-trained voice styles (e.g., 'af_heart' for American female, various British voices) by conditioning the neural model with voice-specific embeddings. The KModel class accepts a voice identifier parameter that retrieves corresponding embeddings from HuggingFace Hub, which are concatenated with phoneme embeddings during synthesis to produce voice-specific speech characteristics without retraining the base model.","intents":["Generate speech in different voice styles and personas from the same text input","Create diverse voice options for interactive applications without multiple TTS models","Maintain consistent voice identity across multiple text segments","Support voice selection in user-facing applications"],"best_for":["Interactive applications requiring voice variety (audiobooks, games, assistants)","Accessibility applications supporting user voice preferences","Content creation tools with multiple narrator options"],"limitations":["Voice selection limited to pre-defined embeddings; no voice cloning or custom voice training","Voice embeddings are fixed and cannot be fine-tuned per-user","No voice interpolation or blending between voice styles","Voice availability varies by language; not all voices available for all languages"],"requires":["Voice identifier string (e.g., 'af_heart', 'bf_emma')","HuggingFace Hub connectivity to download voice embeddings","Pre-trained voice embedding files in model repository"],"input_types":["Text string","Language code","Voice identifier"],"output_types":["Audio waveform with voice-specific characteristics","Voice embedding vector (for debugging/analysis)"],"categories":["text-generation-language","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kokoro-tts__cap_4","uri":"capability://tool.use.integration.python.and.javascript.dual.implementation.api.with.unified.semantics","name":"python and javascript dual-implementation api with unified semantics","description":"Provides parallel Python (KPipeline, KModel classes) and JavaScript (KokoroTTS class) implementations with identical functional semantics, enabling code portability and consistent behavior across environments. Both implementations share the same text processing pipeline, model inference logic, and audio synthesis approach, with language-specific optimizations (PyTorch for Python, ONNX.js for JavaScript) while maintaining API compatibility.","intents":["Deploy TTS in both server-side Python and browser-based JavaScript environments","Migrate TTS code between Python and JavaScript with minimal refactoring","Build full-stack applications with consistent TTS behavior across frontend and backend","Prototype in Python and deploy in JavaScript (or vice versa) without reimplementation"],"best_for":["Full-stack developers building TTS features across Python and JavaScript","Teams with mixed Python/JavaScript codebases requiring unified TTS","Web applications needing both server-side and client-side TTS capabilities"],"limitations":["JavaScript implementation requires ONNX.js runtime; not all ONNX operations are supported in browser environment","Performance characteristics differ significantly between Python (PyTorch) and JavaScript (ONNX.js) implementations","Browser-based JavaScript TTS limited by available memory and computational resources","Model download and caching behavior differs between Python (HuggingFace Hub) and JavaScript (browser storage)"],"requires":["Python 3.9+ with PyTorch for Python implementation","Node.js 14+ for JavaScript implementation","ONNX.js library for browser-based JavaScript","HuggingFace Hub connectivity for model downloads"],"input_types":["Text string","Language code","Voice identifier"],"output_types":["Audio waveform (NumPy array in Python, typed array in JavaScript)","WAV/MP3 files","Generator/iterator for streaming processing"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kokoro-tts__cap_5","uri":"capability://automation.workflow.command.line.interface.for.batch.and.interactive.tts.synthesis","name":"command-line interface for batch and interactive tts synthesis","description":"Provides CLI tools for text-to-speech synthesis without programmatic API usage, supporting both interactive input and batch file processing. The CLI wraps the KPipeline class, accepting text input via stdin or file arguments, language/voice parameters, and output file specifications, enabling integration into shell scripts and data processing pipelines.","intents":["Generate speech from text files or command-line input without writing code","Integrate TTS into shell scripts and data processing workflows","Batch-process large text files into audio outputs","Prototype and test TTS functionality quickly from terminal"],"best_for":["Non-developer users and content creators using TTS from command line","DevOps and data engineering teams integrating TTS into batch pipelines","Researchers and developers prototyping TTS functionality"],"limitations":["No real-time streaming output — entire text must be processed before audio is written","Limited control over synthesis parameters (no pitch, speed, or prosody control via CLI)","Batch processing lacks parallelization — processes files sequentially","No progress reporting or logging for long-running synthesis tasks"],"requires":["Python 3.9+ with kokoro package installed","Text input file or stdin","Language code and voice identifier parameters"],"input_types":["Plain text files","Stdin text input","Command-line arguments for language and voice"],"output_types":["WAV audio files","MP3 files (if ffmpeg available)","Stdout audio stream"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kokoro-tts__cap_6","uri":"capability://automation.workflow.model.export.and.optimization.for.production.deployment","name":"model export and optimization for production deployment","description":"Provides utilities (examples/export.py) to export the KModel neural network and istftnet vocoder to ONNX format for optimized inference across different hardware and runtime environments. The export process converts PyTorch models to ONNX intermediate representation, enabling deployment on ONNX Runtime (CPU, GPU, mobile) without PyTorch dependency, reducing model size and inference latency.","intents":["Optimize TTS models for production inference performance","Deploy TTS on hardware without PyTorch support (mobile, embedded systems)","Reduce model size and memory footprint for edge deployment","Enable cross-platform model deployment via ONNX Runtime"],"best_for":["Production deployment teams optimizing inference performance","Mobile and embedded systems developers","Teams deploying TTS to resource-constrained environments"],"limitations":["Export process is manual and requires running examples/export.py separately","ONNX export may lose some PyTorch-specific optimizations or operations","Exported models require ONNX Runtime; no built-in fallback to PyTorch","Model versioning and compatibility tracking between PyTorch and ONNX versions not documented"],"requires":["PyTorch with KModel and istftnet modules","ONNX library (onnx package)","ONNX Runtime for inference","examples/export.py script"],"input_types":["PyTorch model checkpoints","Model configuration parameters"],"output_types":["ONNX model files (.onnx format)","Model metadata and configuration"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kokoro-tts__cap_7","uri":"capability://automation.workflow.streaming.audio.generation.with.generator.based.processing","name":"streaming audio generation with generator-based processing","description":"Implements generator-based processing pipeline that yields audio segments incrementally as they are synthesized, rather than buffering entire output. The KPipeline class returns Python generators that yield tuples of (graphemes, phonemes, audio_segment) for each text chunk, enabling memory-efficient processing of long texts and streaming output to audio devices or files.","intents":["Process long text documents without loading entire audio into memory","Stream audio output to playback devices in real-time","Implement progressive audio generation with incremental output","Reduce memory footprint for large-scale TTS applications"],"best_for":["Applications processing long documents or continuous text streams","Real-time audio playback systems with memory constraints","Server-side TTS services handling multiple concurrent requests"],"limitations":["Generator-based processing prevents global prosody optimization across chunk boundaries","Audio quality may degrade at chunk boundaries due to lack of context","Chunk size is fixed and not user-configurable","No streaming support for voice selection — voice must be specified upfront"],"requires":["Python 3.9+ with generator support","Text input (any length)","Audio output handler (file, device, or buffer)"],"input_types":["Text string (any length)","Language code","Voice identifier"],"output_types":["Generator yielding (graphemes, phonemes, audio_segment) tuples","Audio segments as NumPy arrays","Streaming audio to file or device"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kokoro-tts__cap_8","uri":"capability://text.generation.language.browser.based.javascript.tts.with.onnx.js.inference","name":"browser-based javascript tts with onnx.js inference","description":"Enables client-side text-to-speech synthesis in web browsers using ONNX.js runtime for neural model inference and Web Audio API for audio playback. The JavaScript KokoroTTS class implements the same pipeline as Python version but uses ONNX.js for model inference, avoiding server-side processing and enabling offline-capable web applications.","intents":["Synthesize speech directly in web browsers without server dependency","Build offline-capable web applications with local TTS","Reduce server load by offloading TTS computation to client","Create interactive web experiences with real-time voice synthesis"],"best_for":["Web developers building client-side TTS features","Progressive web applications requiring offline functionality","Interactive web applications with voice synthesis needs"],"limitations":["Browser memory constraints limit model size and batch processing","ONNX.js performance significantly slower than native PyTorch inference","Model download in browser requires network connectivity on first load","Browser storage limitations may prevent caching large model files","No GPU acceleration in most browsers; CPU-only inference"],"requires":["Modern web browser with WebAssembly support","ONNX.js library (onnxruntime-web package)","Web Audio API support for audio playback","Network connectivity for initial model download","Minimum 2GB available browser memory"],"input_types":["Text string","Language code","Voice identifier"],"output_types":["Web Audio API AudioBuffer","Typed arrays (Float32Array) for audio data","Audio playback via Web Audio API"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kokoro-tts__cap_9","uri":"capability://tool.use.integration.huggingface.hub.integration.for.model.and.voice.distribution","name":"huggingface hub integration for model and voice distribution","description":"Automatically downloads and caches model weights, voice embeddings, and language-specific assets from HuggingFace Hub on first use, eliminating manual model management. The system uses HuggingFace's hub_download API to fetch the 82M parameter model, istftnet decoder, voice embeddings, and G2P resources, with local caching to avoid repeated downloads. Model versioning is handled via HuggingFace Hub's revision system, enabling easy updates and rollbacks without code changes.","intents":["Simplify model distribution and updates without manual file management","Enable automatic model versioning and rollback via HuggingFace Hub","Support multiple model variants (e.g., quantized versions) from a single codebase","Reduce deployment friction by eliminating manual model download steps"],"best_for":["Developers deploying TTS without pre-downloaded model files","Teams requiring easy model updates and version management","Builders distributing TTS via package managers (pip, npm) without bundling large model files","Organizations using HuggingFace Hub infrastructure for model hosting"],"limitations":["Requires network connectivity on first run; no offline mode without pre-caching models","HuggingFace Hub rate limiting may throttle downloads for high-volume deployments","Model caching location is fixed (typically ~/.cache/huggingface); no built-in configuration for custom cache paths","No built-in model integrity verification (checksums, signatures); relies on HuggingFace Hub security","Large model downloads (~500MB) may be slow on bandwidth-constrained networks; no resume/checkpoint support documented"],"requires":["Network connectivity to HuggingFace Hub","HuggingFace Hub account (optional; public models don't require authentication)","Sufficient disk space for model caching (~1GB for model + voices)","huggingface_hub Python library (auto-installed with kokoro)"],"input_types":["model identifier (e.g., 'hexgrad/kokoro-82m')","voice identifier","language code"],"output_types":["downloaded model weights","cached voice embeddings","language-specific assets"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"kokoro-tts__headline","uri":"capability://voice.audio.lightweight.open.source.text.to.speech.model","name":"lightweight open-source text-to-speech model","description":"Kokoro TTS is a lightweight, open-source text-to-speech model that delivers high-quality speech synthesis with only 82 million parameters, supporting multiple voice styles in American and British English, making it ideal for developers seeking efficient TTS solutions.","intents":["best text-to-speech model","text-to-speech for web applications","open-source TTS solutions","high-quality speech synthesis tools","lightweight TTS for developers"],"best_for":["developers","startups","educational projects"],"limitations":["limited to English and a few other languages"],"requires":["Python 3.9+","PyTorch"],"input_types":["text"],"output_types":["audio"],"categories":["voice-audio"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+ with PyTorch and Transformers library","espeak-ng system package for fallback grapheme-to-phoneme conversion","misaki library for primary G2P processing","soundfile library for audio I/O","HuggingFace Hub connectivity for initial model/voice downloads","Node.js 14+ for JavaScript implementation","2GB minimum RAM for model loading","misaki library installed (primary G2P backend)","espeak-ng system package for Hindi and fallback languages","Language code parameter ('a', 'b', 'e', 'f', 'h', 'i', 'j', 'p', 'z')"],"failure_modes":["Supports only 8 languages (American/British English, Spanish, French, Hindi, Italian, Japanese, Brazilian Portuguese, Mandarin Chinese) — no other language support","Model inference latency varies by hardware; CPU inference significantly slower than GPU (no benchmarks provided in documentation)","No streaming/real-time synthesis — entire text must be processed before audio generation completes","Voice selection limited to pre-defined voice embeddings; no voice cloning or custom voice training","Phoneme accuracy depends on G2P backend quality; misaki and espeak-ng have different coverage and accuracy profiles","No custom phoneme mapping or user-defined phoneme inventories","Text preprocessing (punctuation handling, number expansion) is basic and may not handle domain-specific abbreviations","Language detection is manual (via language code parameter) — no automatic language detection","ONNX export requires manual model conversion via examples/export.py — no automatic export in standard pipeline","Audio quality depends on istftnet vocoder training; no alternative vocoders provided","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.49999999999999994,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.692Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=kokoro-tts","compare_url":"https://unfragile.ai/compare?artifact=kokoro-tts"}},"signature":"Q0XP4+OpCoKtaJEeW3FGO3SscSUaffyCM6hpMV8DQZOog4LncEMNukdqUyTtXp1Be+ZvC/0M0zoebnpn82ftDQ==","signedAt":"2026-06-23T10:55:22.128Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/kokoro-tts","artifact":"https://unfragile.ai/kokoro-tts","verify":"https://unfragile.ai/api/v1/verify?slug=kokoro-tts","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}