{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-hexgrad--kokoro-82m","slug":"hexgrad--kokoro-82m","name":"Kokoro-82M","type":"model","url":"https://huggingface.co/hexgrad/Kokoro-82M","page_url":"https://unfragile.ai/hexgrad--kokoro-82m","categories":["voice-audio"],"tags":["text-to-speech","en","arxiv:2306.07691","arxiv:2203.02395","base_model:yl4579/StyleTTS2-LJSpeech","base_model:finetune:yl4579/StyleTTS2-LJSpeech","doi:10.57967/hf/4329","license:apache-2.0","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-hexgrad--kokoro-82m__cap_0","uri":"capability://text.generation.language.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 neural vocoder architecture based on StyleTTS2, enabling fine-grained control over prosody, pitch, and speaking style through latent style embeddings. The model operates in two stages: a text encoder that processes linguistic features into mel-spectrograms, and a neural vocoder that converts spectrograms to waveform audio at 22.05kHz sample rate. Style vectors are learned during training on LJSpeech dataset and can be manipulated to produce variations in emotional tone, speaking rate, and voice characteristics.","intents":["Generate natural-sounding speech from arbitrary text input for accessibility or voice-over applications","Create multiple speaking style variations from the same text without retraining","Integrate TTS into applications requiring low-latency audio generation on consumer hardware","Fine-tune the model on custom voice datasets while preserving style control capabilities"],"best_for":["developers building accessibility features for text-heavy applications","indie game developers needing dynamic NPC dialogue without voice actors","content creators producing multilingual or multi-voice narration at scale","researchers experimenting with prosody control and emotional speech synthesis"],"limitations":["Monolingual English-only — no native support for other languages without additional fine-tuning","Single speaker voice trained on LJSpeech dataset — limited to female voice characteristics without retraining","Inference latency ~2-5 seconds per sentence on CPU, GPU acceleration recommended for real-time applications","Style control is learned from training data distribution — out-of-distribution style requests may produce artifacts","No built-in support for SSML markup or fine-grained phoneme-level control","Audio quality degrades on very long documents (>500 words) due to attention mechanism limitations"],"requires":["Python 3.8+","PyTorch 1.9+ with CUDA 11.0+ for GPU acceleration (CPU inference possible but slow)","transformers library 4.20+","librosa for audio processing","~500MB disk space for model weights","4GB+ RAM for inference, 8GB+ recommended for batch processing"],"input_types":["plain text (UTF-8 encoded)","text with optional style control parameters (numeric vectors or style descriptors)"],"output_types":["WAV audio files (22.05kHz, 16-bit PCM)","raw waveform tensors (PyTorch or NumPy arrays)","mel-spectrogram intermediate representations"],"categories":["text-generation-language","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-hexgrad--kokoro-82m__cap_1","uri":"capability://text.generation.language.batch.text.to.speech.processing.with.style.interpolation","name":"batch text-to-speech processing with style interpolation","description":"Processes multiple text inputs sequentially or in batches, generating corresponding speech outputs with optional style interpolation between reference audio samples. The model accepts a list of text strings and optional style vectors, returning synchronized audio outputs that can be concatenated or processed independently. Style interpolation works by computing weighted combinations of learned style embeddings from reference audio, enabling smooth transitions between different speaking styles across a document or dialogue.","intents":["Generate audiobook narration with consistent voice across multiple chapters","Create dialogue between multiple characters with distinct but related speaking styles","Produce variations of the same script with different emotional tones for A/B testing","Batch-process large document collections into speech with style consistency"],"best_for":["content production teams creating long-form audio content (audiobooks, podcasts)","game developers generating NPC dialogue with style variation","accessibility teams converting documentation to audio at scale"],"limitations":["Batch processing requires loading entire batch into memory — maximum batch size limited by available VRAM (typically 8-16 samples on 8GB GPU)","Style interpolation assumes linear interpolation in embedding space — non-linear style transitions may produce unnatural artifacts","No automatic style detection from reference audio — requires manual style vector extraction or external speaker embedding model","Concatenation of batch outputs may have audible discontinuities at segment boundaries without post-processing"],"requires":["Python 3.8+","PyTorch 1.9+ with CUDA support for batch processing","transformers 4.20+","scipy for audio concatenation and resampling","8GB+ VRAM for batches >4 samples"],"input_types":["list of text strings (variable length)","optional style vectors (float arrays, dimension matching model architecture)","optional reference audio files for style extraction"],"output_types":["list of WAV files or in-memory audio tensors","concatenated audio stream with optional silence padding between segments"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-hexgrad--kokoro-82m__cap_2","uri":"capability://code.generation.editing.fine.tuning.on.custom.voice.datasets.with.style.preservation","name":"fine-tuning on custom voice datasets with style preservation","description":"Enables adaptation of the base Kokoro model to new speaker voices or acoustic characteristics by fine-tuning on custom audio-text pairs while preserving the learned style control mechanism. The fine-tuning process updates the vocoder and text encoder weights while maintaining the style embedding space, allowing the adapted model to generate speech in the new voice while retaining the ability to manipulate prosody and emotional tone. Training uses the same loss functions as the base model (reconstruction loss on mel-spectrograms plus style consistency regularization) but operates on custom data.","intents":["Adapt the model to a specific speaker's voice for personalized TTS applications","Create brand-specific voice profiles for corporate applications or game characters","Improve audio quality for domain-specific vocabulary (medical, technical, legal terminology)","Build multilingual TTS by fine-tuning on non-English language datasets"],"best_for":["enterprises building branded voice assistants","game studios creating character-specific dialogue systems","accessibility teams building personalized TTS for individual users","researchers exploring cross-lingual or cross-speaker transfer learning"],"limitations":["Requires minimum 10-30 minutes of high-quality audio per speaker for stable fine-tuning (more data needed for non-English languages)","Audio data must be aligned with text transcriptions — manual annotation required if automatic alignment fails","Fine-tuning on very small datasets (<5 minutes) risks overfitting and loss of style generalization","No built-in data augmentation or synthetic data generation for low-resource scenarios","Style preservation is not guaranteed — aggressive fine-tuning may collapse style embeddings to speaker-specific features","Requires GPU with 8GB+ VRAM and 4-8 hours training time for typical dataset sizes"],"requires":["Python 3.8+","PyTorch 1.9+ with CUDA 11.0+","transformers 4.20+","librosa and soundfile for audio I/O","Montreal Forced Aligner or similar tool for audio-text alignment","8GB+ VRAM GPU (RTX 3060 or equivalent minimum)","Custom audio dataset with corresponding text transcriptions"],"input_types":["WAV or MP3 audio files (22.05kHz or resampled to 22.05kHz)","text transcriptions (plain text or JSON with timing information)","optional style annotations or speaker metadata"],"output_types":["fine-tuned model checkpoint (PyTorch state dict)","training logs with loss curves and validation metrics","inference-ready model compatible with base Kokoro API"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-hexgrad--kokoro-82m__cap_3","uri":"capability://text.generation.language.real.time.streaming.audio.generation.with.low.latency","name":"real-time streaming audio generation with low latency","description":"Generates speech audio in a streaming fashion with minimal latency by processing text incrementally and outputting audio chunks as they become available, rather than waiting for the entire text to be processed. The implementation uses a sliding window approach where the model processes text in overlapping segments, generating mel-spectrograms that are immediately passed to the vocoder for waveform synthesis. Audio chunks are buffered and output with configurable overlap to minimize discontinuities, enabling near-real-time speech generation suitable for interactive applications.","intents":["Build interactive voice assistants with natural conversational latency (<500ms response time)","Stream live transcription output to speech for real-time translation or captioning","Create responsive chatbot interfaces where users hear speech as it's being generated","Implement voice-based gaming with dynamic NPC dialogue generation"],"best_for":["developers building real-time voice assistant applications","teams creating interactive gaming experiences with dynamic dialogue","accessibility teams building live transcription-to-speech systems","researchers exploring streaming neural audio synthesis"],"limitations":["Streaming latency is 2-3 seconds minimum on CPU, 500ms-1s on GPU due to model inference time","Segment boundaries may introduce audible artifacts or prosody discontinuities if text is split mid-sentence","Requires careful tuning of overlap window size — too small causes artifacts, too large increases latency","No built-in sentence boundary detection — external NLP required for optimal segment splitting","Memory overhead from maintaining multiple overlapping segments in the vocoder pipeline","Quality degrades if text arrives faster than the model can process (backpressure handling required)"],"requires":["Python 3.8+","PyTorch 1.9+ with CUDA support for acceptable latency","transformers 4.20+","queue-based architecture for buffering text and audio chunks","GPU with 4GB+ VRAM for <1s latency targets","external sentence segmentation library (spaCy, NLTK, or similar)"],"input_types":["text stream (character-by-character or sentence-by-sentence)","optional style parameters updated per segment","optional timing constraints (maximum latency budget)"],"output_types":["audio chunks (WAV format or raw PCM samples)","streaming audio buffer compatible with audio playback APIs","timing metadata (chunk boundaries, latency measurements)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-hexgrad--kokoro-82m__cap_4","uri":"capability://data.processing.analysis.speaker.embedding.extraction.and.style.vector.computation","name":"speaker embedding extraction and style vector computation","description":"Extracts learned style embeddings from reference audio samples, enabling style transfer and style interpolation without explicit speaker conditioning. The model computes style vectors by encoding reference audio through the trained encoder network, producing a fixed-dimensional embedding that captures prosodic and acoustic characteristics. These embeddings can be averaged across multiple reference samples, interpolated between different speakers, or manipulated directly to control output speech characteristics. The extraction process is deterministic and reproducible, allowing consistent style application across multiple synthesis runs.","intents":["Extract style vectors from reference speaker audio for voice cloning or style transfer","Compute average style embeddings across multiple speakers for blended voice synthesis","Create style interpolation paths between different speakers for smooth voice transitions","Build style libraries for reuse across multiple TTS applications or projects"],"best_for":["developers building voice cloning or style transfer features","content creators producing multi-speaker audio with consistent style","researchers studying prosody and speaking style in neural TTS","teams building voice customization features for end users"],"limitations":["Style extraction requires high-quality reference audio (>5 seconds recommended) — noisy or heavily compressed audio produces poor embeddings","Extracted embeddings are specific to the Kokoro model architecture — not transferable to other TTS systems","Style vectors capture only prosodic characteristics learned during training — cannot encode arbitrary acoustic features not present in training data","No built-in validation or quality metrics for extracted embeddings — poor quality embeddings may not be detected until synthesis time","Interpolation between very different speakers may produce unnatural intermediate styles","Requires reference audio in the same domain (speech) — music or non-speech audio produces meaningless embeddings"],"requires":["Python 3.8+","PyTorch 1.9+","transformers 4.20+","librosa for audio loading and preprocessing","reference audio files (WAV or MP3, 22.05kHz or resampled)","2GB+ RAM for batch embedding extraction"],"input_types":["audio files (WAV, MP3, FLAC) with speech content","optional speaker metadata or labels for organization","optional interpolation parameters (blend weights between multiple speakers)"],"output_types":["style embeddings (float vectors, dimension matching model architecture)","embedding metadata (source audio filename, duration, quality metrics)","interpolated embeddings (weighted combinations of multiple style vectors)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-hexgrad--kokoro-82m__cap_5","uri":"capability://data.processing.analysis.multilingual.text.preprocessing.and.phoneme.handling","name":"multilingual text preprocessing and phoneme handling","description":"Processes input text through linguistic analysis to extract phonetic and prosodic features required for synthesis, including grapheme-to-phoneme conversion, stress marking, and language-specific text normalization. The preprocessing pipeline handles abbreviations, numbers, punctuation, and special characters by converting them to phonetically meaningful representations. While the base model is English-only, the preprocessing architecture supports extension to other languages through language-specific rule sets and phoneme inventories. The system produces normalized text and corresponding phoneme sequences that feed into the neural encoder.","intents":["Normalize diverse text inputs (URLs, numbers, abbreviations) into phonetically meaningful representations","Handle edge cases like acronyms, currency symbols, and domain-specific terminology","Prepare text for synthesis in non-English languages through language-specific preprocessing","Extract phoneme sequences for analysis or debugging of synthesis quality"],"best_for":["developers building TTS for applications with diverse text inputs (web content, technical documentation)","teams extending Kokoro to non-English languages","researchers analyzing phonetic features of synthesized speech","accessibility teams handling real-world text with abbreviations and special characters"],"limitations":["English-only grapheme-to-phoneme conversion — non-English text requires language-specific phoneme inventories and rules","No built-in handling of homographs (words with identical spelling but different pronunciation) — context-aware disambiguation not supported","Abbreviation expansion relies on heuristics — domain-specific abbreviations may be mishandled without custom rules","No support for prosodic markup (SSML) — fine-grained control over pause duration, pitch, or speaking rate not available","Phoneme inventory is fixed to English phonemes — cannot represent non-English phonetic distinctions without model retraining"],"requires":["Python 3.8+","g2p_en library for grapheme-to-phoneme conversion","regex library for text normalization","optional: language-specific phoneme inventories for multilingual extension"],"input_types":["raw text strings (UTF-8 encoded)","text with optional language tags or metadata","optional custom abbreviation dictionaries"],"output_types":["normalized text strings","phoneme sequences (IPA or model-specific phoneme inventory)","stress and intonation markers"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-hexgrad--kokoro-82m__cap_6","uri":"capability://data.processing.analysis.audio.quality.assessment.and.artifact.detection","name":"audio quality assessment and artifact detection","description":"Evaluates synthesized audio quality through analysis of spectral characteristics, prosodic continuity, and acoustic artifacts. The assessment uses mel-spectrogram analysis to detect common synthesis artifacts (clicks, pops, discontinuities at segment boundaries) and compares output spectrograms against reference patterns learned during training. Prosodic continuity is evaluated through pitch contour analysis and energy envelope smoothness. While not a formal MOS (Mean Opinion Score) evaluation, the system provides quantitative metrics for quality assurance and debugging of synthesis failures.","intents":["Detect synthesis failures or artifacts before audio is delivered to users","Compare quality across different model configurations or fine-tuning approaches","Identify problematic text inputs that consistently produce poor audio","Monitor synthesis quality in production systems for degradation detection"],"best_for":["teams deploying TTS in production requiring quality assurance","researchers comparing synthesis quality across model variants","developers debugging synthesis failures or audio artifacts","content production teams validating large-scale audio generation"],"limitations":["Artifact detection is heuristic-based — may miss subtle quality issues or produce false positives","No perceptual quality metrics (MOS, PESQ) — assessment is acoustic rather than human-perceived quality","Metrics are model-specific — cannot compare quality across different TTS systems","Requires reference spectrograms or training data statistics for comparison — not suitable for standalone quality assessment","No support for subjective quality dimensions (naturalness, speaker identity preservation) — only objective acoustic metrics"],"requires":["Python 3.8+","librosa for spectrogram computation","scipy for signal processing and pitch extraction","numpy for statistical analysis","optional: reference audio or training data statistics"],"input_types":["synthesized audio files (WAV format)","optional reference audio for comparison","optional quality thresholds or configuration parameters"],"output_types":["quality metrics (numeric scores for artifact presence, prosodic continuity, spectral smoothness)","diagnostic reports with artifact locations and severity","comparison matrices for multiple audio samples"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-hexgrad--kokoro-82m__headline","uri":"capability://voice.audio.text.to.speech.model","name":"text-to-speech model","description":"Kokoro-82M is an advanced text-to-speech model that converts written text into natural-sounding speech, supporting multiple languages and offering high-quality audio output.","intents":["best text-to-speech model","text-to-speech for creating audiobooks","text-to-speech solutions for accessibility","top text-to-speech tools for developers","text-to-speech with high download counts"],"best_for":["audiobook creation","accessibility applications"],"limitations":[],"requires":[],"input_types":["text"],"output_types":["audio"],"categories":["voice-audio"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":54,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","PyTorch 1.9+ with CUDA 11.0+ for GPU 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limitations","Batch processing requires loading entire batch into memory — maximum batch size limited by available VRAM (typically 8-16 samples on 8GB GPU)","Style interpolation assumes linear interpolation in embedding space — non-linear style transitions may produce unnatural artifacts","No automatic style detection from reference audio — requires manual style vector extraction or external speaker embedding model","Concatenation of batch outputs may have audible discontinuities at segment boundaries without post-processing","builder identity is not verified yet","no observed match outcomes 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