{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-coqui--xtts-v2","slug":"coqui--xtts-v2","name":"XTTS-v2","type":"model","url":"https://huggingface.co/coqui/XTTS-v2","page_url":"https://unfragile.ai/coqui--xtts-v2","categories":["voice-audio"],"tags":["coqui","text-to-speech","license:other","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-coqui--xtts-v2__cap_0","uri":"capability://text.generation.language.multilingual.text.to.speech.synthesis.with.speaker.cloning","name":"multilingual text-to-speech synthesis with speaker cloning","description":"Generates natural-sounding speech in 11+ languages from text input using a transformer-based architecture trained on diverse multilingual datasets. The model performs speaker adaptation by analyzing a short reference audio clip (6-30 seconds) to extract speaker characteristics and apply them to synthesized speech, enabling voice cloning without fine-tuning. Uses a two-stage pipeline: text encoding to phoneme/linguistic features, then acoustic modeling to mel-spectrogram generation, followed by vocoder conversion to waveform.","intents":["Generate speech in multiple languages while preserving a specific speaker's voice characteristics from a reference sample","Clone a speaker's voice for new text without retraining the model","Create multilingual voiceovers for video content with consistent speaker identity across languages","Build voice-enabled applications that support global audiences with natural-sounding, speaker-consistent output"],"best_for":["developers building multilingual voice applications (chatbots, audiobooks, accessibility tools)","content creators needing fast speaker cloning without GPU training infrastructure","teams deploying TTS at scale across multiple languages with consistent voice identity"],"limitations":["Reference audio quality directly impacts cloning fidelity — noisy or heavily accented samples degrade output","Inference latency scales with text length; real-time synthesis of long passages requires streaming or batching optimization","Speaker cloning works best with 6-30 second reference clips; shorter clips lose prosodic nuance, longer clips may introduce artifacts","No built-in emotion/prosody control — output prosody is learned from reference audio and text context only","Multilingual switching within a single utterance not supported; requires separate synthesis passes per language"],"requires":["Python 3.8+","PyTorch 1.13+ (CPU or CUDA 11.8+)","librosa for audio processing","Reference audio file in WAV/MP3 format (6-30 seconds recommended)","~4GB VRAM for GPU inference, or CPU with 8GB+ RAM for slower inference"],"input_types":["text (UTF-8, supports 11+ languages: English, Spanish, French, German, Italian, Portuguese, Polish, Dutch, Russian, Turkish, Chinese, Japanese, Korean, Hindi, Arabic)","audio file (WAV, MP3, FLAC for speaker reference)"],"output_types":["audio waveform (WAV format, 22050 Hz sample rate)","mel-spectrogram (intermediate representation for custom vocoding)"],"categories":["text-generation-language","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-coqui--xtts-v2__cap_1","uri":"capability://text.generation.language.reference.audio.conditioned.voice.adaptation","name":"reference-audio-conditioned voice adaptation","description":"Extracts speaker identity and prosodic characteristics from a reference audio sample using a speaker encoder network, then conditions the TTS decoder to reproduce those characteristics in synthesized speech. The encoder produces a fixed-size speaker embedding that captures voice timbre, pitch range, and speaking style without explicit parameter tuning. This embedding is concatenated with linguistic features during decoding, enabling the model to adapt output speech to match the reference speaker's acoustic properties.","intents":["Match a specific person's voice characteristics for personalized TTS output","Preserve speaker identity when generating speech in different languages or with different text","Create consistent voice across multiple TTS calls without retraining or fine-tuning","Enable voice conversion workflows where text is synthesized in a target speaker's voice"],"best_for":["applications requiring consistent speaker identity across multiple synthesis calls (e.g., personalized audiobooks, branded voice assistants)","voice conversion pipelines where speaker characteristics must be preserved across language or content changes","developers building voice cloning features without access to GPU training infrastructure"],"limitations":["Speaker embedding quality depends on reference audio duration and quality — clips under 6 seconds may not capture full speaker characteristics","Accent and speech patterns in reference audio influence output; strong accents may be partially reproduced in synthesized speech","No explicit control over speaker characteristics (pitch, speed, emotion) — adaptation is implicit from reference audio","Cross-lingual speaker adaptation may introduce subtle artifacts if reference audio is in a different language than synthesis target"],"requires":["Reference audio file (6-30 seconds, WAV/MP3/FLAC format)","Speaker encoder model weights (included in XTTS-v2 release)","Audio preprocessing pipeline (librosa or equivalent) to normalize reference audio"],"input_types":["audio file (reference speaker sample)","text (target synthesis content)"],"output_types":["speaker embedding (fixed-size vector, ~256 dimensions)","synthesized audio (WAV, 22050 Hz)"],"categories":["text-generation-language","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-coqui--xtts-v2__cap_2","uri":"capability://text.generation.language.streaming.text.to.speech.synthesis.with.chunked.generation","name":"streaming text-to-speech synthesis with chunked generation","description":"Generates speech output in real-time by processing input text in chunks rather than waiting for complete text input, enabling low-latency streaming audio output. The model uses a sliding window approach where linguistic features are computed incrementally, and mel-spectrograms are generated chunk-by-chunk, then passed to the vocoder for immediate waveform generation. This architecture allows audio to begin playback before the entire text is synthesized, reducing perceived latency in interactive applications.","intents":["Stream audio output in real-time for interactive voice applications (voice assistants, live narration)","Reduce latency in voice-enabled chatbots by generating audio as text is produced by the LLM","Enable low-latency voice synthesis for accessibility features in real-time applications","Build responsive voice interfaces where users hear audio output immediately without waiting for full synthesis"],"best_for":["real-time voice assistant applications where latency is critical","streaming LLM outputs that need concurrent voice synthesis","accessibility features requiring immediate audio feedback","interactive applications where perceived responsiveness is important"],"limitations":["Chunk boundaries may introduce subtle prosodic discontinuities if text is split at unnatural linguistic boundaries","Streaming mode requires careful buffer management to avoid audio dropouts or stuttering","Optimal chunk size depends on text content and target latency; no automatic chunk optimization provided","Speaker cloning quality may degrade slightly in streaming mode due to reduced context for prosody modeling"],"requires":["Streaming audio output interface (e.g., audio buffer, speaker device, or network stream)","Text chunking logic (application-specific; model does not provide automatic chunking)","Sufficient CPU/GPU resources to maintain real-time synthesis throughput"],"input_types":["text (streamed or pre-chunked)"],"output_types":["audio chunks (WAV format, 22050 Hz, variable duration)"],"categories":["text-generation-language","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-coqui--xtts-v2__cap_3","uri":"capability://text.generation.language.multilingual.text.normalization.and.phoneme.conversion","name":"multilingual text normalization and phoneme conversion","description":"Converts raw text input in 11+ languages into normalized linguistic features (phonemes, stress markers, language tags) that the acoustic model uses for synthesis. The pipeline includes language detection, text normalization (handling numbers, abbreviations, punctuation), grapheme-to-phoneme conversion using language-specific rules or neural models, and prosody annotation. This preprocessing ensures consistent, natural-sounding output across different text formats and languages without requiring manual annotation.","intents":["Automatically convert text in multiple languages to phonetic representations suitable for TTS synthesis","Handle diverse text formats (numbers, abbreviations, URLs, special characters) without manual preprocessing","Ensure consistent pronunciation across different text inputs in the same language","Support multilingual synthesis pipelines where text language is automatically detected and processed"],"best_for":["applications processing user-generated text in multiple languages","systems requiring robust handling of diverse text formats (social media, web content, technical documentation)","multilingual TTS pipelines where language detection and normalization must be automatic"],"limitations":["Language detection may fail on short text or code-mixed content (multiple languages in single input)","Grapheme-to-phoneme conversion quality varies by language; less common languages may have lower accuracy","Abbreviations and acronyms may be mispronounced if not in the normalization dictionary","Proper nouns and domain-specific terms are not automatically handled; may require custom pronunciation dictionaries"],"requires":["Language detection model (included in XTTS-v2)","Phoneme inventory for target languages (included)","Text normalization rules (language-specific, included)"],"input_types":["raw text (UTF-8, multiple languages supported)"],"output_types":["phoneme sequences (language-specific phoneme inventory)","linguistic features (stress, duration, language tags)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-coqui--xtts-v2__cap_4","uri":"capability://text.generation.language.local.inference.with.cpu.and.gpu.acceleration","name":"local inference with cpu and gpu acceleration","description":"Runs the entire TTS pipeline (text encoding, acoustic modeling, vocoding) locally on user hardware without requiring cloud API calls. Supports both CPU inference (slower but accessible) and GPU acceleration (CUDA 11.8+, faster inference). The model uses quantization and optimization techniques to reduce memory footprint, enabling inference on consumer-grade hardware. Inference is fully deterministic and reproducible, with no external dependencies on cloud services or API rate limits.","intents":["Deploy TTS in offline or air-gapped environments without cloud connectivity","Avoid API costs and latency associated with cloud TTS services","Maintain data privacy by processing audio locally without sending to external servers","Build TTS features into edge devices or embedded systems with limited connectivity"],"best_for":["privacy-sensitive applications (healthcare, legal, financial) requiring local processing","cost-conscious teams deploying TTS at scale without per-request API charges","offline or edge deployment scenarios where cloud connectivity is unavailable or unreliable","developers building TTS into desktop or mobile applications"],"limitations":["CPU inference is slow (~5-10x slower than GPU); real-time synthesis of long text requires GPU acceleration","GPU memory requirements scale with batch size; large batches may exceed consumer GPU VRAM (typical: 4-8GB)","Model weights (~2GB) must be downloaded and stored locally; no streaming model loading from cloud","Inference performance varies significantly based on hardware; no automatic hardware detection or optimization"],"requires":["Python 3.8+","PyTorch 1.13+ (CPU or CUDA 11.8+)","~4GB disk space for model weights","4GB+ RAM for CPU inference, 4GB+ VRAM for GPU inference","Optional: CUDA 11.8+ and cuDNN for GPU acceleration"],"input_types":["text (UTF-8)"],"output_types":["audio waveform (WAV, 22050 Hz)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-coqui--xtts-v2__cap_5","uri":"capability://text.generation.language.batch.synthesis.with.multi.sample.processing","name":"batch synthesis with multi-sample processing","description":"Processes multiple text-to-speech synthesis requests in a single batch operation, leveraging GPU parallelization to improve throughput compared to sequential synthesis. The model accepts batched text inputs and speaker embeddings, processes them through the acoustic model in parallel, and outputs batched mel-spectrograms that are vocoded simultaneously. This approach reduces per-sample overhead and enables efficient processing of large synthesis workloads.","intents":["Synthesize multiple audio samples efficiently for batch processing workflows (e.g., generating audiobooks, voice datasets)","Maximize GPU utilization when processing large numbers of TTS requests","Reduce total synthesis time for applications that can tolerate slight latency (non-real-time use cases)","Build efficient TTS pipelines for content generation at scale"],"best_for":["batch processing workflows where multiple audio samples are needed (audiobook generation, dataset creation)","server-side TTS services handling multiple concurrent requests","content generation pipelines where throughput is more important than latency"],"limitations":["Batch processing introduces latency compared to streaming; not suitable for real-time interactive applications","Batch size is limited by available GPU VRAM; larger batches require more memory","All samples in a batch must complete before output is available; one slow sample delays the entire batch","Batch processing requires careful memory management to avoid out-of-memory errors on consumer GPUs"],"requires":["GPU with sufficient VRAM for batch size (4GB+ for batch size 4-8)","Batching logic in application code (model does not provide automatic batching)","Multiple text inputs and optional speaker embeddings"],"input_types":["batched text (list of UTF-8 strings)","batched speaker embeddings (optional, for speaker cloning)"],"output_types":["batched audio waveforms (list of WAV arrays, 22050 Hz)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-coqui--xtts-v2__cap_6","uri":"capability://text.generation.language.cross.lingual.speaker.adaptation.with.language.agnostic.embeddings","name":"cross-lingual speaker adaptation with language-agnostic embeddings","description":"Clones a speaker's voice across different languages by using language-agnostic speaker embeddings extracted from reference audio. The speaker encoder is trained to produce embeddings that capture voice identity (timbre, pitch range, speaking style) independent of the language or content of the reference audio. This enables synthesizing speech in any supported language while preserving the speaker's voice characteristics from a reference sample in a different language.","intents":["Clone a speaker's voice from a reference in one language and synthesize speech in a different language","Create multilingual content with consistent speaker identity across languages","Build voice conversion pipelines where speaker identity is preserved across language boundaries","Enable personalized multilingual TTS where a user's voice is cloned across all supported languages"],"best_for":["multilingual content creation where speaker consistency is important (e.g., dubbed videos, multilingual audiobooks)","personalized voice assistants that support multiple languages with consistent speaker identity","voice conversion applications requiring cross-lingual speaker preservation"],"limitations":["Cross-lingual speaker adaptation quality depends on reference audio quality and speaker characteristics","Strong accents in reference audio may influence output in target language, potentially introducing non-native pronunciation","Speaker characteristics may be partially lost if reference audio is very short (under 6 seconds) or low quality","Some speaker characteristics (e.g., language-specific prosody patterns) may not transfer perfectly across languages"],"requires":["Reference audio in any supported language (6-30 seconds)","Target language supported by XTTS-v2 (11+ languages)","Speaker encoder model (included in XTTS-v2)"],"input_types":["reference audio (any supported language)","target text (any supported language)"],"output_types":["synthesized audio (target language, speaker-adapted, 22050 Hz)"],"categories":["text-generation-language","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-coqui--xtts-v2__cap_7","uri":"capability://text.generation.language.mel.spectrogram.to.waveform.vocoding.with.glow.based.architecture","name":"mel-spectrogram to waveform vocoding with glow-based architecture","description":"Converts mel-spectrogram representations (acoustic features) into high-quality audio waveforms using a glow-based neural vocoder. The vocoder uses invertible neural network layers (glow) to model the distribution of raw audio samples conditioned on mel-spectrograms, enabling fast, parallel waveform generation without autoregressive decoding. This architecture produces natural-sounding audio with minimal artifacts while maintaining fast inference speed suitable for real-time applications.","intents":["Convert acoustic features (mel-spectrograms) from the TTS model into high-quality audio waveforms","Generate audio with minimal vocoding artifacts and natural prosody","Enable fast, parallel waveform generation for real-time TTS applications","Support custom mel-spectrogram inputs from other acoustic models or signal processing pipelines"],"best_for":["TTS systems requiring high-quality waveform generation with minimal latency","applications where vocoding artifacts must be minimized (e.g., music, high-fidelity audio)","real-time TTS applications where parallel waveform generation is critical"],"limitations":["Vocoder quality depends on mel-spectrogram quality; poor acoustic features produce poor audio","Glow-based vocoders may introduce subtle artifacts if mel-spectrograms have discontinuities or unusual patterns","Vocoder is trained on specific audio characteristics (sample rate, frequency range); inputs outside training distribution may degrade","No explicit control over vocoding parameters; output quality is implicit from mel-spectrogram input"],"requires":["Mel-spectrogram input (22050 Hz sample rate, specific frequency range)","Vocoder model weights (included in XTTS-v2)","PyTorch for inference"],"input_types":["mel-spectrogram (2D array, frequency × time)"],"output_types":["audio waveform (WAV, 22050 Hz sample rate)"],"categories":["text-generation-language","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-coqui--xtts-v2__cap_8","uri":"capability://memory.knowledge.speaker.embedding.extraction.and.storage.for.voice.cloning","name":"speaker embedding extraction and storage for voice cloning","description":"Extracts fixed-size speaker embeddings from reference audio using a trained speaker encoder, enabling efficient storage and reuse of speaker characteristics for repeated voice cloning. The encoder produces a compact embedding (typically 256 dimensions) that captures speaker identity without storing the full audio. These embeddings can be cached, indexed, and reused across multiple synthesis calls, enabling efficient voice cloning workflows where the same speaker is used repeatedly.","intents":["Extract and cache speaker embeddings for efficient reuse in repeated voice cloning","Build speaker libraries where multiple speaker embeddings are indexed and retrieved for synthesis","Enable efficient voice cloning workflows where speaker embeddings are precomputed and stored","Create personalized TTS systems where user voice embeddings are stored and reused"],"best_for":["applications with repeated voice cloning from the same speakers (e.g., personalized voice assistants)","systems managing multiple speaker voices (e.g., voice libraries, multi-speaker TTS)","workflows where speaker embeddings are precomputed and cached for efficiency"],"limitations":["Embedding quality depends on reference audio quality; poor quality audio produces poor embeddings","Embeddings are specific to the speaker encoder model; different encoder versions produce incompatible embeddings","No built-in embedding storage or indexing; application must implement persistence and retrieval","Embeddings do not capture all speaker characteristics; some prosodic variation is lost in the fixed-size representation"],"requires":["Reference audio (6-30 seconds, WAV/MP3/FLAC)","Speaker encoder model (included in XTTS-v2)","Storage mechanism for embeddings (application-specific)"],"input_types":["audio file (reference speaker sample)"],"output_types":["speaker embedding (fixed-size vector, ~256 dimensions)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-coqui--xtts-v2__cap_9","uri":"capability://text.generation.language.deterministic.and.reproducible.synthesis.with.seed.control","name":"deterministic and reproducible synthesis with seed control","description":"Enables reproducible audio synthesis by supporting seed-based random number generation, ensuring that identical inputs (text, speaker embedding, seed) produce identical audio output. This is critical for testing, debugging, and creating consistent outputs in production systems. The model uses PyTorch's random seed control to ensure deterministic behavior across inference runs, with no randomness in the synthesis pipeline when a seed is specified.","intents":["Generate reproducible audio for testing and validation of TTS systems","Ensure consistent output across multiple inference runs for the same input","Debug TTS issues by reproducing exact synthesis conditions","Create deterministic voice synthesis pipelines for production systems"],"best_for":["testing and validation workflows where reproducibility is critical","production systems requiring consistent output for the same input","debugging TTS issues by reproducing exact synthesis conditions","quality assurance pipelines where audio output must be deterministic"],"limitations":["Determinism requires explicit seed specification; default behavior may be non-deterministic","Determinism is only guaranteed within the same hardware and software environment; different GPUs or PyTorch versions may produce slightly different results","Streaming synthesis may introduce non-determinism if chunk boundaries are not fixed","Batch processing order affects output if batches are processed in different orders"],"requires":["PyTorch with seed control enabled","Explicit seed specification in synthesis code","Consistent hardware and software environment across runs"],"input_types":["text (UTF-8)","speaker embedding (optional)","seed (integer)"],"output_types":["deterministic audio waveform (WAV, 22050 Hz)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-coqui--xtts-v2__headline","uri":"capability://voice.audio.text.to.speech.model","name":"text-to-speech model","description":"XTTS-v2 is an open-source text-to-speech model that converts written text into natural-sounding speech, making it ideal for applications in accessibility, voiceovers, and interactive systems.","intents":["best text-to-speech model","text-to-speech for accessibility","text-to-speech for voiceovers","top open-source TTS solutions","text-to-speech model comparison"],"best_for":["developers looking for TTS solutions","content creators needing voiceovers"],"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":"high","permissions":["Python 3.8+","PyTorch 1.13+ (CPU or CUDA 11.8+)","librosa for audio processing","Reference audio file in WAV/MP3 format (6-30 seconds recommended)","~4GB VRAM for GPU inference, or CPU with 8GB+ RAM for slower inference","Reference audio file (6-30 seconds, WAV/MP3/FLAC format)","Speaker encoder model weights (included in XTTS-v2 release)","Audio preprocessing pipeline (librosa or equivalent) to normalize reference audio","Streaming audio output interface (e.g., audio buffer, speaker device, or network stream)","Text chunking logic (application-specific; model does not provide automatic chunking)"],"failure_modes":["Reference audio quality directly impacts cloning fidelity — noisy or heavily accented samples degrade output","Inference latency scales with text length; real-time synthesis of long passages requires streaming or batching optimization","Speaker cloning works best with 6-30 second reference clips; shorter clips lose prosodic nuance, longer clips may introduce artifacts","No built-in emotion/prosody control — output prosody is learned from reference audio and text context only","Multilingual switching within a single utterance not supported; requires separate synthesis passes per language","Speaker embedding quality depends on reference audio duration and quality — clips under 6 seconds may not capture full speaker characteristics","Accent and speech patterns in reference audio influence output; strong accents may be partially reproduced in synthesized speech","No explicit control over speaker characteristics (pitch, speed, emotion) — adaptation is implicit from reference audio","Cross-lingual speaker adaptation may introduce subtle artifacts if reference audio is in a different language than synthesis target","Chunk boundaries may introduce subtle prosodic discontinuities if text is split at unnatural linguistic boundaries","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.9228783965754106,"quality":0.3,"ecosystem":0.42,"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":7555083,"model_likes":3517}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=coqui--xtts-v2","compare_url":"https://unfragile.ai/compare?artifact=coqui--xtts-v2"}},"signature":"oEeoObeGGiTPcgjZyKWf/DniVH1KOaDb2egqj8cUgMxjsJ7RoHb4BMUy1x2b3RT/5uf9jJnEe9dGpBCK92uDBw==","signedAt":"2026-06-21T14:35:56.712Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/coqui--xtts-v2","artifact":"https://unfragile.ai/coqui--xtts-v2","verify":"https://unfragile.ai/api/v1/verify?slug=coqui--xtts-v2","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"}}