{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-resembleai--chatterbox","slug":"resembleai--chatterbox","name":"chatterbox","type":"model","url":"https://huggingface.co/ResembleAI/chatterbox","page_url":"https://unfragile.ai/resembleai--chatterbox","categories":["voice-audio"],"tags":["chatterbox","text-to-speech","speech","speech-generation","voice-cloning","multilingual-tts","ar","da","de","el","en","es","fi","fr","he","hi","it","ja","ko","ms"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-resembleai--chatterbox__cap_0","uri":"capability://image.visual.multilingual.text.to.speech.synthesis.with.neural.vocoding","name":"multilingual text-to-speech synthesis with neural vocoding","description":"Converts text input into natural-sounding speech audio across 20 languages (AR, DA, DE, EL, EN, ES, FI, FR, HE, HI, IT, JA, KO, MS, and others) using a neural vocoder architecture. The model processes tokenized text through a sequence-to-sequence encoder-decoder with attention mechanisms to generate mel-spectrogram features, which are then converted to waveform audio via a neural vocoder (likely WaveGlow or similar). Language detection or explicit language specification routes text through language-specific phoneme encoders and prosody predictors.","intents":["Generate natural speech audio from text in multiple languages for accessibility features","Create voice-over content for videos, podcasts, or interactive applications without hiring voice actors","Build multilingual voice assistants or chatbots that speak in user-preferred languages","Prototype voice-enabled applications that need to support global audiences"],"best_for":["Developers building accessibility features for web/mobile applications","Content creators producing multilingual video or podcast content at scale","Teams prototyping voice assistants or conversational AI products","Non-technical founders building MVP voice products without voice talent budgets"],"limitations":["No voice cloning or speaker adaptation — generates generic neutral voices per language, not personalized speaker identities","Prosody control is limited — cannot easily adjust emotional tone, emphasis, or speaking rate per sentence","Inference latency likely 2-5 seconds per sentence depending on hardware; not suitable for real-time streaming applications","No fine-tuning API exposed — model weights are frozen; customization requires retraining from scratch","Audio quality degrades on out-of-domain text (e.g., highly technical jargon, code snippets, unusual punctuation)"],"requires":["Python 3.7+ with PyTorch or TensorFlow installed","HuggingFace transformers library (version 4.20+)","GPU with 4GB+ VRAM for reasonable inference speed (CPU inference possible but slow)","Text input in supported language (auto-detection or explicit language tag required)"],"input_types":["plain text (UTF-8 encoded)","text with punctuation and special characters","language-tagged text (e.g., 'en: Hello world', 'fr: Bonjour le monde')"],"output_types":["WAV audio file (16-bit PCM, typically 22.05kHz or 44.1kHz sample rate)","raw audio tensor/array (for downstream processing)","streaming audio chunks (if using streaming inference wrapper)"],"categories":["image-visual","audio-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-resembleai--chatterbox__cap_1","uri":"capability://data.processing.analysis.phoneme.aware.text.preprocessing.and.normalization","name":"phoneme-aware text preprocessing and normalization","description":"Preprocesses raw text input into phoneme sequences and normalized linguistic features required for neural TTS synthesis. The pipeline handles text normalization (expanding abbreviations, numbers-to-words conversion, punctuation handling), language-specific phoneme conversion (grapheme-to-phoneme mapping), and prosody feature extraction (stress markers, syllable boundaries). This preprocessing ensures the neural vocoder receives consistent, well-formed linguistic input regardless of input text irregularities.","intents":["Handle diverse text inputs (numbers, abbreviations, URLs, special characters) without manual preprocessing","Ensure consistent pronunciation across similar words by normalizing text before synthesis","Support language-specific linguistic rules (e.g., German compound words, French liaisons) automatically","Improve synthesis quality by providing phoneme-level linguistic features to the model"],"best_for":["Applications processing user-generated or web-scraped text with inconsistent formatting","Multilingual systems requiring robust text normalization across language-specific rules","Developers who want TTS to handle edge cases (URLs, dates, technical abbreviations) without custom preprocessing"],"limitations":["Phoneme conversion accuracy varies by language — low-resource languages may have lower G2P accuracy","Cannot handle context-dependent pronunciation (e.g., 'read' as past vs. present tense) without explicit markup","Abbreviation expansion is rule-based and may fail on domain-specific or newly-coined abbreviations","No support for custom pronunciation dictionaries — users cannot override phoneme mappings for proper nouns or technical terms"],"requires":["Text input in UTF-8 encoding","Language specification or auto-detection capability","Phoneme inventory for target language (built into model)"],"input_types":["raw text with mixed case, punctuation, numbers, abbreviations","text with special characters and symbols","multilingual text (with language tags or auto-detection)"],"output_types":["normalized text string","phoneme sequence (IPA or language-specific phoneme notation)","linguistic feature vectors (stress, syllable boundaries, prosody markers)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-resembleai--chatterbox__cap_2","uri":"capability://data.processing.analysis.real.time.mel.spectrogram.generation.with.attention.based.alignment","name":"real-time mel-spectrogram generation with attention-based alignment","description":"Generates mel-spectrogram representations of speech from phoneme sequences using an encoder-decoder architecture with attention mechanisms. The encoder processes phoneme embeddings and linguistic features; the decoder generates mel-spectrogram frames autoregressively, with attention weights determining which phonemes to focus on at each synthesis step. This attention-based alignment ensures phonemes are stretched/compressed to match natural speech timing without explicit duration models, enabling natural prosody and pacing.","intents":["Generate speech spectrograms that preserve natural timing and prosody without manual duration annotation","Synthesize speech with natural pauses and emphasis by leveraging learned attention patterns","Enable fast inference by generating spectrograms in a single forward pass rather than iterative refinement"],"best_for":["Developers building real-time or near-real-time TTS systems where attention-based alignment is sufficient","Applications requiring natural prosody without explicit prosody control parameters"],"limitations":["Attention alignment can fail on very long sequences (>500 tokens), causing skipped or repeated phonemes","No explicit duration control — users cannot adjust speech rate or pause length per sentence","Attention mechanism adds ~100-200ms latency per spectrogram generation step","Attention weights are learned during training and cannot be modified at inference time for style transfer"],"requires":["Phoneme sequence input (from preprocessing step)","GPU for efficient spectrogram generation (CPU inference possible but slow)","Mel-spectrogram configuration matching training data (typically 80 mel bins, 12.5ms frame shift)"],"input_types":["phoneme sequence (integer token IDs)","linguistic feature vectors (stress, syllable boundaries)","language embedding (for language-specific prosody)"],"output_types":["mel-spectrogram tensor (shape: [time_steps, 80_mel_bins])","attention weight matrices (for visualization/debugging)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-resembleai--chatterbox__cap_3","uri":"capability://image.visual.neural.vocoding.with.waveform.reconstruction","name":"neural vocoding with waveform reconstruction","description":"Converts mel-spectrogram representations into high-fidelity audio waveforms using a neural vocoder (likely WaveGlow, HiFi-GAN, or similar architecture). The vocoder is a generative model trained to invert the mel-spectrogram representation, learning to add high-frequency details and natural acoustic characteristics that are lost in the mel-spectrogram compression. This two-stage approach (text→spectrogram→waveform) enables faster training and inference compared to end-to-end waveform generation.","intents":["Convert mel-spectrograms into natural-sounding audio waveforms with minimal artifacts","Generate high-quality speech audio (16-bit PCM) suitable for production applications","Achieve fast inference by using a pre-trained vocoder rather than training end-to-end"],"best_for":["Production TTS systems requiring high audio quality with reasonable latency","Developers who want to decouple spectrogram generation from waveform synthesis for modularity"],"limitations":["Vocoder quality is bounded by mel-spectrogram representation — information lost in mel-compression cannot be recovered","Vocoder inference adds 1-3 seconds latency per sentence (depending on audio length and hardware)","Vocoder artifacts (e.g., buzzing, clicking) can occur on out-of-distribution spectrograms","No control over vocoder behavior at inference time — vocoding is deterministic given spectrogram input"],"requires":["Mel-spectrogram input (from spectrogram generation step)","GPU for efficient vocoding (CPU inference possible but very slow, ~10-30x real-time)","Vocoder model weights (pre-trained, included in artifact)"],"input_types":["mel-spectrogram tensor (shape: [time_steps, 80_mel_bins])","sample rate specification (22.05kHz or 44.1kHz)"],"output_types":["audio waveform tensor (shape: [num_samples], float32 or int16 PCM)","WAV file (16-bit PCM, mono or stereo)"],"categories":["image-visual","audio-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-resembleai--chatterbox__cap_4","uri":"capability://text.generation.language.language.specific.speaker.adaptation.and.accent.modeling","name":"language-specific speaker adaptation and accent modeling","description":"Adapts synthesis output to language-specific acoustic characteristics and accent patterns by conditioning the encoder-decoder on language embeddings and speaker identity tokens. The model learns language-specific prosody patterns (intonation contours, stress patterns, speech rate) during training and applies them at inference time based on language specification. Speaker adaptation is implicit — the model generates a generic neutral speaker voice per language, but the acoustic characteristics (formant frequencies, voice quality) are language-specific.","intents":["Generate language-appropriate speech with natural prosody and accent for each language","Ensure synthesized speech sounds native to the target language rather than accented/foreign","Support language-specific speech rate and intonation patterns automatically"],"best_for":["Multilingual applications requiring natural-sounding speech in each language","Developers building global voice assistants or chatbots with language-specific voice characteristics"],"limitations":["No speaker cloning or voice customization — all speakers per language are identical generic voices","Accent modeling is implicit and cannot be controlled — users cannot request specific accents (e.g., British vs. American English)","Language-specific prosody is fixed during training — cannot be adjusted at inference time","Limited to 20 pre-trained languages — new languages require retraining or fine-tuning"],"requires":["Language specification (explicit tag or auto-detection)","Text input in target language","Language embeddings (learned during training, included in model)"],"input_types":["text with language tag (e.g., 'en:', 'fr:')","language ID token (integer)"],"output_types":["language-adapted mel-spectrogram","language-adapted audio waveform"],"categories":["text-generation-language","audio-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-resembleai--chatterbox__cap_5","uri":"capability://automation.workflow.batch.inference.with.variable.length.text.sequences","name":"batch inference with variable-length text sequences","description":"Supports efficient batch processing of multiple text inputs of varying lengths without padding to a fixed maximum length. The model uses dynamic batching and padding strategies (pad to longest sequence in batch, not global maximum) to minimize wasted computation on padding tokens. Batch inference is implemented with attention masking to prevent attention across batch boundaries and padding positions, enabling efficient GPU utilization for multiple concurrent synthesis requests.","intents":["Process multiple text-to-speech requests in parallel for higher throughput","Reduce per-request latency by amortizing model loading and GPU setup costs across multiple requests","Build scalable TTS services that handle multiple concurrent users efficiently"],"best_for":["Developers building TTS APIs or services with multiple concurrent users","Applications processing large volumes of text (e.g., content generation, data annotation) where batch processing is beneficial","Teams optimizing inference cost and latency for production TTS systems"],"limitations":["Batch size is limited by GPU memory — large batches may cause out-of-memory errors","Dynamic padding adds overhead for variable-length sequences — fixed-length batches may be faster","Batch inference requires collecting multiple requests before processing — introduces latency for single-request scenarios","No support for streaming inference within batches — entire batch must complete before returning results"],"requires":["GPU with sufficient VRAM for batch size (4GB+ for batch size 4-8)","Multiple text inputs (list of strings)","HuggingFace transformers library with batch inference support"],"input_types":["list of text strings (variable length)","batch size parameter","language specification (per-batch or per-sequence)"],"output_types":["list of mel-spectrograms (variable length)","list of audio waveforms (variable length)","list of WAV files"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":49,"verified":false,"data_access_risk":"low","permissions":["Python 3.7+ with PyTorch or TensorFlow installed","HuggingFace transformers library (version 4.20+)","GPU with 4GB+ VRAM for reasonable inference speed (CPU inference possible but slow)","Text input in supported language (auto-detection or explicit language tag required)","Text input in UTF-8 encoding","Language specification or auto-detection capability","Phoneme inventory for target language (built into model)","Phoneme sequence input (from preprocessing step)","GPU for efficient spectrogram generation (CPU inference possible but slow)","Mel-spectrogram configuration matching training data (typically 80 mel bins, 12.5ms frame shift)"],"failure_modes":["No voice cloning or speaker adaptation — generates generic neutral voices per language, not personalized speaker identities","Prosody control is limited — cannot easily adjust emotional tone, emphasis, or speaking rate per sentence","Inference latency likely 2-5 seconds per sentence depending on hardware; not suitable for real-time streaming applications","No fine-tuning API exposed — model weights are frozen; customization requires retraining from scratch","Audio quality degrades on out-of-domain text (e.g., highly technical jargon, code snippets, unusual punctuation)","Phoneme conversion accuracy varies by language — low-resource languages may have lower G2P accuracy","Cannot handle context-dependent pronunciation (e.g., 'read' as past vs. present tense) without explicit markup","Abbreviation expansion is rule-based and may fail on domain-specific or newly-coined abbreviations","No support for custom pronunciation dictionaries — users cannot override phoneme mappings for proper nouns or technical terms","Attention alignment can fail on very long sequences (>500 tokens), causing skipped or repeated phonemes","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8166755689283568,"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":2108297,"model_likes":1570}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=resembleai--chatterbox","compare_url":"https://unfragile.ai/compare?artifact=resembleai--chatterbox"}},"signature":"QFAQyfBcV4p5RjiSIDeMKa6B9p0UZmgrPYIpylNvDMDRTvbtWIagkNDQ9tpeuxhxMxqEiflTWBzXnlvEtQ4RBQ==","signedAt":"2026-06-22T10:49:37.956Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/resembleai--chatterbox","artifact":"https://unfragile.ai/resembleai--chatterbox","verify":"https://unfragile.ai/api/v1/verify?slug=resembleai--chatterbox","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"}}