{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-mlx-community--kokoro-82m-bf16","slug":"mlx-community--kokoro-82m-bf16","name":"Kokoro-82M-bf16","type":"model","url":"https://huggingface.co/mlx-community/Kokoro-82M-bf16","page_url":"https://unfragile.ai/mlx-community--kokoro-82m-bf16","categories":["voice-audio"],"tags":["mlx","text-to-speech","en","base_model:yl4579/StyleTTS2-LJSpeech","base_model:finetune:yl4579/StyleTTS2-LJSpeech","license:apache-2.0","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-mlx-community--kokoro-82m-bf16__cap_0","uri":"capability://image.visual.neural.text.to.speech.synthesis.with.style.control","name":"neural text-to-speech synthesis with style control","description":"Converts input text to natural-sounding speech audio using a fine-tuned StyleTTS2 architecture optimized for the MLX framework. The model employs a dual-encoder design with style embedding extraction from reference audio, enabling prosodic variation and emotional tone control without explicit phoneme-level annotations. Inference runs efficiently on Apple Silicon via MLX's GPU-accelerated tensor operations, reducing latency compared to CPU-bound alternatives.","intents":["Generate natural-sounding voice narration from text content for accessibility or multimedia applications","Create multiple speaking styles from the same text by conditioning on different reference audio samples","Deploy TTS locally on macOS/iOS devices without cloud API dependencies or latency overhead","Fine-tune the base model on custom voice datasets while maintaining computational efficiency"],"best_for":["macOS/iOS developers building voice-enabled applications with on-device inference requirements","Accessibility teams needing customizable, low-latency speech synthesis for real-time applications","Researchers experimenting with style-controlled TTS without large-scale infrastructure"],"limitations":["Trained exclusively on LJSpeech dataset (single female speaker) — limited voice diversity without additional fine-tuning","English-only language support; no multilingual capability in base model","Requires MLX framework and Apple Silicon hardware for optimal performance; CPU inference significantly slower","Style control quality depends on reference audio quality; poor-quality samples degrade prosody transfer","No built-in speaker adaptation or multi-speaker support in base model"],"requires":["Python 3.8+","MLX framework (Apple Silicon optimized)","macOS 12+ or iOS 15+ for hardware acceleration","4GB+ RAM for model loading and inference","Reference audio file (WAV/MP3) for style conditioning (optional but recommended)"],"input_types":["plain text (UTF-8 encoded)","audio file (WAV, MP3) for style reference"],"output_types":["audio waveform (WAV format, 22050 Hz sample rate)","raw PCM samples"],"categories":["image-visual","voice-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-mlx-community--kokoro-82m-bf16__cap_1","uri":"capability://data.processing.analysis.efficient.model.quantization.and.deployment.via.mlx","name":"efficient model quantization and deployment via mlx","description":"The model is distributed in bfloat16 precision format, leveraging MLX's unified memory architecture to enable efficient inference on Apple Silicon GPUs without separate VRAM allocation. This quantization approach reduces model size by ~50% compared to float32 while maintaining audio quality, and MLX's automatic differentiation framework allows for gradient-based fine-tuning on consumer hardware.","intents":["Deploy a 82M-parameter TTS model on MacBook Air/Pro without GPU memory constraints","Fine-tune the model on custom voice data using on-device training without cloud infrastructure","Reduce model download size and storage footprint for embedded or mobile applications","Maintain inference speed and audio quality while minimizing power consumption on battery-powered devices"],"best_for":["Individual developers and small teams with Apple Silicon hardware seeking cost-effective TTS deployment","Edge AI applications requiring local model updates without cloud synchronization","Resource-constrained environments (MacBook Air M1/M2, iPad Pro) where VRAM is shared with system memory"],"limitations":["bfloat16 precision may introduce subtle audio artifacts in edge cases (e.g., very high-pitched phonemes)","MLX framework is Apple Silicon-exclusive; no cross-platform portability to Linux/Windows without conversion","Fine-tuning requires MLX ecosystem knowledge; limited documentation compared to PyTorch/TensorFlow","Model size (82M parameters) still requires 200-300MB disk space and 400-600MB RAM during inference"],"requires":["Apple Silicon processor (M1, M2, M3 or later)","macOS 12.3+","MLX library (pip install mlx)","200MB+ available disk space","Python 3.8+"],"input_types":["model weights in MLX safetensors format","training data (audio + text pairs) for fine-tuning"],"output_types":["quantized model checkpoint (bfloat16)","inference-optimized model graph"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-mlx-community--kokoro-82m-bf16__cap_2","uri":"capability://image.visual.reference.audio.style.embedding.extraction","name":"reference audio style embedding extraction","description":"Extracts prosodic and tonal characteristics from a reference audio sample using an encoder network, producing a style embedding vector that conditions the decoder during synthesis. The StyleTTS2 architecture uses adversarial training to learn disentangled style representations independent of content, enabling the model to apply one speaker's prosody to another speaker's text without explicit phoneme alignment or duration modeling.","intents":["Clone the speaking style (pitch, pace, emotion) from a short audio clip and apply it to new text","Generate speech with consistent prosody across multiple utterances by reusing the same style embedding","Create expressive TTS output without manual annotation of prosodic features or phoneme durations","Adapt the model to new speakers with minimal reference audio (few-shot style transfer)"],"best_for":["Content creators needing consistent voice personality across long-form narration or audiobook production","Accessibility applications requiring emotional tone preservation from original speaker intent","Voice cloning applications where style transfer is prioritized over speaker identity preservation"],"limitations":["Style embedding quality degrades with noisy or heavily accented reference audio; background noise is not filtered","Requires 3-10 seconds of reference audio minimum for stable style extraction; shorter clips produce inconsistent results","Style transfer is one-directional (reference → synthesis); cannot blend multiple style embeddings","Emotional tone transfer is approximate; subtle emotional nuances may not transfer accurately","No explicit control over individual prosodic dimensions (pitch, duration, energy) — style is monolithic"],"requires":["Reference audio file (WAV, MP3, or similar) with clear speech","Audio duration: 3-30 seconds optimal; outside this range may produce degraded results","Audio sample rate: 22050 Hz or higher (will be resampled internally)","MLX framework with encoder network weights loaded"],"input_types":["audio file (WAV, MP3, FLAC)","raw PCM samples as numpy array"],"output_types":["style embedding vector (float32, dimension ~256-512)","conditioning tensor for decoder"],"categories":["image-visual","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-mlx-community--kokoro-82m-bf16__cap_3","uri":"capability://image.visual.batch.text.to.speech.synthesis.with.streaming.output","name":"batch text-to-speech synthesis with streaming output","description":"Processes multiple text inputs sequentially or in batches, generating corresponding audio outputs with optional streaming/chunked delivery for real-time applications. The model supports variable-length input text and produces audio with consistent quality regardless of utterance length, using attention mechanisms to handle long-range dependencies in text without explicit segmentation.","intents":["Generate audio for multiple text segments (e.g., paragraphs in a document) in a single inference pass","Stream audio output in real-time as it is generated, enabling playback before full synthesis completes","Process long-form text (articles, books) by automatically chunking into sentences while maintaining prosodic continuity","Build interactive applications where users hear speech output with minimal latency after text input"],"best_for":["Real-time voice assistant applications requiring sub-500ms latency between text input and audio output","Batch processing pipelines for converting large document collections to audiobooks or podcasts","Streaming applications (live transcription, real-time narration) where audio must be delivered incrementally"],"limitations":["Batch processing requires careful memory management; batch size is limited by available RAM (typically 1-4 utterances on 8GB systems)","Streaming output introduces latency variability; first audio chunk may be delayed 200-400ms while model processes full input","Long text (>500 words) may produce prosodic inconsistencies at chunk boundaries if not carefully segmented","No built-in sentence segmentation; requires external NLP library (e.g., NLTK, spaCy) for optimal chunking","Audio concatenation at chunk boundaries may introduce audible artifacts if prosody is not smoothed"],"requires":["Text input (UTF-8 encoded, any length)","Optional: sentence segmentation library (NLTK, spaCy, or regex-based)","Audio streaming library (e.g., sounddevice, pyaudio) for real-time playback","Sufficient RAM for batch size (400MB+ for batch size 4)"],"input_types":["plain text (single or multiple utterances)","structured text with metadata (e.g., JSON with speaker ID, style reference)"],"output_types":["audio waveform (WAV file)","streaming audio chunks (PCM samples)","audio metadata (duration, prosody features)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-mlx-community--kokoro-82m-bf16__cap_4","uri":"capability://image.visual.mel.spectrogram.to.waveform.vocoding","name":"mel-spectrogram to waveform vocoding","description":"Converts mel-spectrogram representations (intermediate acoustic features) generated by the text encoder into high-quality audio waveforms using a neural vocoder. The model likely uses a HiFi-GAN or similar architecture to perform fast, high-fidelity waveform synthesis from mel-spectrograms, enabling real-time audio generation without autoregressive decoding.","intents":["Convert intermediate acoustic features (mel-spectrograms) into natural-sounding speech waveforms","Achieve real-time or near-real-time audio synthesis by avoiding autoregressive waveform generation","Maintain audio quality and intelligibility across different text inputs and speaking styles","Enable efficient inference on resource-constrained devices by using a lightweight vocoder"],"best_for":["Real-time TTS applications where latency is critical (interactive voice assistants, live narration)","Mobile and edge devices where computational resources are limited","Applications requiring high audio quality (audiobooks, professional narration) without sacrificing speed"],"limitations":["Vocoder quality is dependent on mel-spectrogram accuracy; errors in acoustic feature generation are amplified in waveform output","Fixed sample rate (22050 Hz); higher quality audio (44.1 kHz or 48 kHz) requires additional upsampling or model retraining","Vocoder may introduce artifacts (e.g., aliasing, noise) if mel-spectrogram contains out-of-distribution features","No explicit control over audio loudness or dynamic range; output levels are determined by training data statistics","Vocoder is non-invertible; cannot recover mel-spectrograms from generated audio for analysis or debugging"],"requires":["Mel-spectrogram input (numpy array, shape [time_steps, 80] for 80-bin mel-scale)","Vocoder weights (HiFi-GAN or equivalent) loaded in memory","Sample rate: 22050 Hz (fixed)","MLX framework for efficient inference"],"input_types":["mel-spectrogram (2D numpy array, float32)","acoustic feature tensor"],"output_types":["audio waveform (1D numpy array, float32, normalized to [-1, 1])","WAV file (16-bit PCM)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-mlx-community--kokoro-82m-bf16__cap_5","uri":"capability://code.generation.editing.fine.tuning.on.custom.voice.datasets","name":"fine-tuning on custom voice datasets","description":"Enables adaptation of the base model to new speakers or speaking styles by training on user-provided audio-text pairs. The fine-tuning process uses gradient-based optimization with MLX's automatic differentiation, allowing efficient parameter updates on consumer hardware. The model supports transfer learning where only the style encoder or decoder is fine-tuned, preserving the base model's generalization while adapting to new voices.","intents":["Adapt the model to a specific speaker's voice using 10-30 minutes of audio-text data","Create a custom TTS voice for a brand, character, or individual without training from scratch","Improve synthesis quality for underrepresented accents or speaking styles by fine-tuning on domain-specific data","Enable rapid iteration on voice characteristics by fine-tuning on small datasets (few-shot adaptation)"],"best_for":["Content creators and voice actors wanting to create a personal TTS voice for commercial use","Companies building branded voice assistants with consistent personality","Researchers experimenting with voice adaptation and speaker-specific TTS"],"limitations":["Requires paired audio-text data; automatic speech recognition (ASR) transcription may introduce errors that degrade fine-tuning quality","Minimum 10-15 minutes of audio recommended; less data results in overfitting and poor generalization to new text","Fine-tuning on very small datasets (<5 minutes) may collapse the model's ability to synthesize diverse phonemes","No built-in data augmentation; requires manual audio preprocessing (normalization, silence removal) for best results","Fine-tuning hyperparameters (learning rate, batch size, epochs) require manual tuning; no automated hyperparameter search","Training time is 2-8 hours on Apple Silicon for 20 minutes of audio, making rapid iteration slow"],"requires":["Audio-text pairs: minimum 10-15 minutes of paired audio and transcriptions","Audio format: WAV, MP3, or similar; 22050 Hz sample rate recommended","Text format: plain text or JSON with audio file paths and transcriptions","MLX framework with training support","8GB+ RAM for training; 16GB+ recommended for batch size >2","Python 3.8+","Optional: audio preprocessing tools (librosa, soundfile)"],"input_types":["audio files (WAV, MP3)","text transcriptions (plain text or JSON)","metadata (speaker ID, emotion labels, optional)"],"output_types":["fine-tuned model checkpoint (bfloat16 weights)","training logs (loss curves, validation metrics)","inference-ready model"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","MLX framework (Apple Silicon optimized)","macOS 12+ or iOS 15+ for hardware acceleration","4GB+ RAM for model loading and inference","Reference audio file (WAV/MP3) for style conditioning (optional but recommended)","Apple Silicon processor (M1, M2, M3 or later)","macOS 12.3+","MLX library (pip install mlx)","200MB+ available disk space","Reference audio file (WAV, MP3, or similar) with clear speech"],"failure_modes":["Trained exclusively on LJSpeech dataset (single female speaker) — limited voice diversity without additional fine-tuning","English-only language support; no multilingual capability in base model","Requires MLX framework and Apple Silicon hardware for optimal performance; CPU inference significantly slower","Style control quality depends on reference audio quality; poor-quality samples degrade prosody transfer","No built-in speaker adaptation or multi-speaker support in base model","bfloat16 precision may introduce subtle audio artifacts in edge cases (e.g., very high-pitched phonemes)","MLX framework is Apple Silicon-exclusive; no cross-platform portability to Linux/Windows without conversion","Fine-tuning requires MLX ecosystem knowledge; limited documentation compared to PyTorch/TensorFlow","Model size (82M parameters) still requires 200-300MB disk space and 400-600MB RAM during inference","Style embedding quality degrades with noisy or heavily accented reference audio; background noise is not filtered","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6370352823527559,"quality":0.22,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"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":"2026-05-24T12:16:22.765Z","last_scraped_at":"2026-05-03T14:22:51.286Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":469583,"model_likes":49}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mlx-community--kokoro-82m-bf16","compare_url":"https://unfragile.ai/compare?artifact=mlx-community--kokoro-82m-bf16"}},"signature":"nuAulezpJvuvWUNgz7+Mw2bJord/Z3yEMpb/zO82J3kxMOYVE9lCHL4fqAHi+wa2vmJoIHM5qRMUFWf0HQ1JBQ==","signedAt":"2026-06-23T03:12:16.901Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mlx-community--kokoro-82m-bf16","artifact":"https://unfragile.ai/mlx-community--kokoro-82m-bf16","verify":"https://unfragile.ai/api/v1/verify?slug=mlx-community--kokoro-82m-bf16","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"}}