{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"coqui-tts","slug":"coqui-tts","name":"Coqui TTS","type":"framework","url":"https://github.com/coqui-ai/TTS","page_url":"https://unfragile.ai/coqui-tts","categories":["voice-audio"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"coqui-tts__cap_0","uri":"capability://text.generation.language.multilingual.text.to.speech.synthesis.with.1100.language.support","name":"multilingual text-to-speech synthesis with 1100+ language support","description":"Converts text input to natural-sounding speech across 1100+ languages using a modular TTS pipeline that chains text processing, acoustic modeling, and vocoding stages. The system uses a unified BaseTTS class hierarchy supporting multiple model architectures (VITS, Tacotron, Glow-TTS, FastPitch) with language-specific text processors that handle phoneme conversion, grapheme normalization, and sentence segmentation before feeding spectrograms to neural vocoders for waveform generation.","intents":["I need to generate speech in a language my current TTS provider doesn't support","I want to build a multilingual voice assistant without licensing per-language models","I need to synthesize speech for low-resource languages with minimal training data"],"best_for":["developers building multilingual applications (chatbots, accessibility tools, localization)","researchers working with underrepresented languages","teams needing cost-effective TTS without per-language licensing"],"limitations":["Quality varies significantly across languages — high-resource languages (English, Mandarin) produce near-human speech while low-resource languages may have noticeable artifacts","No built-in language detection — requires explicit language specification in API calls","Inference latency scales with text length and model size (typically 0.5-2s for short sentences on CPU)","Pre-trained models are fixed-size; custom language support requires training from scratch"],"requires":["Python 3.9+","PyTorch 1.9+ (CPU or GPU)","~500MB disk space for base models, additional space per language model (~50-200MB each)"],"input_types":["plain text (UTF-8)","text with language codes (ISO 639-1 format)","text with speaker identifiers for multi-speaker models"],"output_types":["WAV audio files (16kHz, 22.05kHz, or 44.1kHz sample rates)","raw numpy arrays","streaming audio chunks"],"categories":["text-generation-language","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"coqui-tts__cap_1","uri":"capability://text.generation.language.voice.cloning.and.speaker.adaptation.via.speaker.encoder","name":"voice cloning and speaker adaptation via speaker encoder","description":"Enables synthesis of speech in a target speaker's voice by encoding reference audio samples through a speaker encoder network that extracts speaker embeddings, which are then injected into the TTS model's decoder during inference. The system supports both speaker-conditional models (VITS, Tacotron2) that accept speaker embeddings as conditioning input and fine-tuning of speaker encoders on custom speaker datasets to improve voice similarity for out-of-distribution speakers.","intents":["I want to generate speech that sounds like a specific person using just a few seconds of their voice","I need to create personalized voice assistants for multiple users without recording each user individually","I want to preserve speaker identity across multiple sentences in a long-form synthesis task"],"best_for":["developers building personalized voice applications (custom assistants, character voices for games)","content creators needing consistent voice synthesis across multiple videos","accessibility teams creating personalized text-to-speech for users with speech disabilities"],"limitations":["Voice cloning quality depends heavily on reference audio quality — noisy or compressed audio degrades speaker similarity","Requires 5-30 seconds of reference audio per speaker for acceptable quality; shorter clips produce less stable embeddings","Speaker encoder training requires 100+ hours of labeled speaker data; pre-trained encoders may not generalize well to accented or non-native speakers","No speaker identity preservation across model architecture changes — switching from VITS to Tacotron requires re-encoding reference audio"],"requires":["Python 3.9+","PyTorch 1.9+","reference audio file (WAV, MP3) with clear speech from target speaker","optional: GPU for faster speaker encoding (CPU inference ~1-5s per reference sample)"],"input_types":["reference audio files (WAV, MP3, FLAC)","text to synthesize","speaker identifier (for multi-speaker models)"],"output_types":["WAV audio with cloned speaker voice","speaker embedding vectors (numpy arrays, ~256-512 dimensions)"],"categories":["text-generation-language","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"coqui-tts__cap_10","uri":"capability://text.generation.language.multi.speaker.synthesis.with.speaker.conditioning.and.speaker.embedding.injection","name":"multi-speaker synthesis with speaker conditioning and speaker embedding injection","description":"Enables synthesis of speech from multiple speakers using speaker-conditional TTS models (VITS, Tacotron2) that accept speaker embeddings or speaker IDs as conditioning input during inference. The system supports both discrete speaker IDs (for models trained on multi-speaker datasets) and continuous speaker embeddings (from speaker encoders), allowing users to generate speech in any speaker's voice by providing either a speaker ID or reference audio; the Synthesizer class handles speaker embedding extraction and injection transparently.","intents":["I want to generate speech in different speaker voices from the same TTS model","I need to create audiobooks with multiple character voices using a single model","I want to synthesize speech in a speaker's voice without fine-tuning the model"],"best_for":["content creators producing audiobooks, podcasts, or videos with multiple speakers","developers building interactive voice applications with multiple character voices","teams creating accessible audio content with diverse speaker representation"],"limitations":["Multi-speaker models require training on datasets with multiple speakers — single-speaker pre-trained models cannot be used for multi-speaker synthesis","Speaker quality depends on the diversity and size of the training dataset — models trained on 10 speakers may not generalize well to new speakers","Discrete speaker IDs are limited to speakers in the training set; new speakers require speaker embedding injection which depends on speaker encoder quality","No automatic speaker selection or recommendation — users must manually specify speaker IDs or provide reference audio","Speaker consistency is not guaranteed across long synthesis tasks — speaker embeddings may drift if reference audio is noisy"],"requires":["Python 3.9+","multi-speaker TTS model (e.g., 'tts_models/en/ljspeech/vits' with speaker conditioning)","optional: reference audio for speaker embedding extraction","optional: speaker ID (for discrete speaker models)"],"input_types":["text to synthesize","speaker ID (integer, for discrete speaker models)","reference audio (WAV, MP3) for speaker embedding extraction","speaker embedding vector (numpy array, for continuous speaker models)"],"output_types":["WAV audio with specified speaker voice","speaker embedding vectors"],"categories":["text-generation-language","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"coqui-tts__cap_11","uri":"capability://automation.workflow.streaming.audio.synthesis.and.real.time.inference","name":"streaming audio synthesis and real-time inference","description":"Supports streaming synthesis where audio is generated and returned in chunks rather than waiting for the entire synthesis to complete, enabling real-time TTS applications. The system processes text in sentence-length chunks, generates spectrograms incrementally, and streams audio chunks to the client as they become available; this reduces latency for long-form synthesis and enables interactive applications like voice assistants that need to start playing audio before synthesis completes.","intents":["I want to build a voice assistant that starts speaking immediately without waiting for full synthesis","I need to stream TTS audio to a client application in real-time","I want to reduce perceived latency in interactive TTS applications"],"best_for":["developers building real-time voice assistants and conversational AI","teams streaming TTS audio to web/mobile clients","applications requiring low latency like live translation or interactive games"],"limitations":["Streaming synthesis requires sentence-level segmentation which may produce unnatural pauses between sentences","Audio quality may be lower due to lack of global context — models trained on full documents may produce worse quality on sentence fragments","Streaming adds complexity to error handling — if synthesis fails mid-stream, partial audio has already been sent to client","No built-in buffering or jitter control — network delays may cause audio playback stuttering","Streaming is not compatible with all model architectures — some models require full text context for accurate synthesis"],"requires":["Python 3.9+","PyTorch 1.9+","TTS model compatible with streaming (most models support it)","client application capable of handling streaming audio (web browser, mobile app, etc.)"],"input_types":["text to synthesize (can be long-form)","streaming configuration (chunk size, buffer size)","optional: speaker ID or reference audio"],"output_types":["audio chunks (numpy arrays or WAV bytes)","streaming HTTP response (for tts-server)"],"categories":["automation-workflow","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"coqui-tts__cap_12","uri":"capability://data.processing.analysis.language.specific.phoneme.conversion.and.text.to.phoneme.processing","name":"language-specific phoneme conversion and text-to-phoneme processing","description":"Converts text to phoneme sequences using language-specific phoneme inventories and grapheme-to-phoneme (G2P) conversion rules. The system supports multiple phoneme sets (IPA, language-specific phoneme sets) and uses rule-based or neural G2P models to convert text to phonemes. Phoneme sequences are then used as input to TTS models instead of raw text, improving pronunciation accuracy.","intents":["Improve pronunciation accuracy by using phoneme-based TTS instead of character-based","Handle languages with complex grapheme-to-phoneme mappings (e.g., English, French)","Support custom phoneme inventories for specialized applications"],"best_for":["developers building high-quality TTS for languages with complex phonetics","researchers working on phoneme-based TTS models","teams handling languages with non-phonetic writing systems"],"limitations":["G2P conversion is language-specific — no universal G2P model across all languages","G2P accuracy varies by language — some languages have better G2P models than others","Phoneme inventories are fixed — custom phonemes require modifying phoneme sets","No automatic phoneme selection — users must specify phoneme set for each language","Homograph disambiguation is not supported — words with same spelling but different pronunciation are not handled"],"requires":["Language code (ISO 639-1 format)","G2P model or rule set for target language","Phoneme inventory for target language"],"input_types":["text input","language code"],"output_types":["phoneme sequence (list of phoneme symbols)","phoneme durations (optional)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"coqui-tts__cap_2","uri":"capability://planning.reasoning.model.architecture.selection.and.configuration.management","name":"model architecture selection and configuration management","description":"Provides a pluggable model architecture system where users select from multiple TTS model families (VITS, Tacotron, Glow-TTS, FastPitch, FastSpeech) through a configuration-driven approach. Each architecture inherits from BaseTTS and is instantiated via a config object (e.g., VitsConfig, Tacotron2Config) that specifies hyperparameters, layer counts, and training objectives; the ModelManager loads pre-trained weights and configs from a .models.json catalog, and the Synthesizer transparently handles architecture-specific inference logic.","intents":["I want to compare different TTS architectures (VITS vs Tacotron) on my language without rewriting code","I need to fine-tune a pre-trained model but customize its architecture for my hardware constraints","I want to understand which model architecture is best for my use case (speed vs quality tradeoff)"],"best_for":["researchers experimenting with TTS architectures","developers optimizing for specific hardware (edge devices, mobile, GPU clusters)","teams migrating between TTS systems and needing architecture flexibility"],"limitations":["Configuration objects are tightly coupled to model implementations — changing architecture requires understanding model-specific config parameters","No automatic architecture recommendation; users must manually select based on speed/quality tradeoffs","Config validation is minimal — invalid hyperparameter combinations may fail silently during training","Switching architectures mid-training requires restarting from scratch; no checkpoint compatibility across architectures"],"requires":["Python 3.9+","PyTorch 1.9+","understanding of TTS model families and their tradeoffs"],"input_types":["model name string (e.g., 'tts_models/en/ljspeech/vits')","custom config objects (VitsConfig, Tacotron2Config, etc.)","training dataset and hyperparameters"],"output_types":["instantiated model objects (inheriting from BaseTTS)","config dictionaries (JSON-serializable)","trained model checkpoints"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"coqui-tts__cap_3","uri":"capability://automation.workflow.fine.tuning.and.transfer.learning.on.custom.datasets","name":"fine-tuning and transfer learning on custom datasets","description":"Supports training TTS models on custom datasets through a modular training system that loads pre-trained model checkpoints and continues training on user-provided audio/text pairs. The training pipeline includes data loading via PyTorch DataLoaders with custom samplers, loss computation specific to each model architecture, gradient-based optimization, and checkpoint management; users can fine-tune entire models or specific components (e.g., speaker encoder only) by selectively freezing layers and adjusting learning rates.","intents":["I want to adapt a pre-trained English TTS model to my specific accent or speaking style","I need to train a TTS model for a language with limited pre-trained models by starting from a related language","I want to fine-tune only the speaker encoder on my custom speaker dataset without retraining the entire model"],"best_for":["developers building domain-specific voice assistants (medical, legal, customer service)","researchers adapting TTS to new languages or accents","teams with proprietary speaker datasets wanting to preserve model ownership"],"limitations":["Requires 10+ hours of aligned audio/text data for meaningful fine-tuning; less data produces overfitting","Training is computationally expensive — fine-tuning on GPU takes 2-48 hours depending on dataset size and model architecture","No automatic hyperparameter tuning; users must manually set learning rates, batch sizes, and training schedules","Checkpoint compatibility is architecture-specific — cannot transfer weights between VITS and Tacotron models","No built-in data augmentation or noise robustness training; requires manual dataset preprocessing"],"requires":["Python 3.9+","PyTorch 1.9+","GPU with 8GB+ VRAM (recommended; CPU training is 10-50x slower)","custom dataset: aligned audio files (WAV) and text transcriptions (CSV or JSON)","pre-trained model checkpoint (downloaded via ModelManager or custom)"],"input_types":["audio files (WAV, MP3) with sample rates 16kHz-44.1kHz","text transcriptions (UTF-8, with optional phoneme annotations)","training configuration (YAML or Python config objects)","pre-trained model checkpoint"],"output_types":["fine-tuned model checkpoint (PyTorch .pth files)","training logs (TensorBoard events, JSON metrics)","validation audio samples"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"coqui-tts__cap_4","uri":"capability://data.processing.analysis.text.processing.and.phoneme.conversion.with.language.specific.rules","name":"text processing and phoneme conversion with language-specific rules","description":"Normalizes and converts input text to phoneme sequences using language-specific text processors that handle grapheme-to-phoneme conversion, number/date expansion, abbreviation resolution, and sentence segmentation. The system maintains a registry of language-specific processors (e.g., EnglishProcessor, Mandarin Processor) that inherit from a BaseProcessor class and apply rules like converting '123' to 'one hundred twenty-three' and splitting long text into sentences to prevent acoustic artifacts from long sequences.","intents":["I want to ensure numbers, dates, and abbreviations are pronounced correctly in synthesized speech","I need to handle text with mixed scripts (e.g., English + Mandarin) without manual preprocessing","I want to split long documents into sentence-length chunks for more natural-sounding synthesis"],"best_for":["developers building production TTS systems handling real-world text (emails, documents, web content)","teams synthesizing content with numbers, dates, or domain-specific abbreviations","accessibility applications needing robust text normalization"],"limitations":["Language-specific processors only exist for ~50 languages; unsupported languages fall back to basic ASCII processing which may mispronounce special characters","Phoneme conversion quality depends on language-specific rules and dictionaries — homographs (words with same spelling, different pronunciation) are not disambiguated","No context-aware text processing — abbreviations like 'St.' are always expanded to 'Saint' regardless of context (street vs. saint)","Sentence segmentation uses simple heuristics (periods, exclamation marks) and fails on edge cases like 'Dr. Smith' or 'U.S.A.'","Custom text processing rules require modifying processor classes; no user-friendly rule definition interface"],"requires":["Python 3.9+","language code (ISO 639-1 format, e.g., 'en', 'zh')","optional: custom text processor class for unsupported languages"],"input_types":["plain text (UTF-8)","text with numbers, dates, abbreviations","multi-script text (if language processor supports it)"],"output_types":["normalized text strings","phoneme sequences (IPA or language-specific phoneme sets)","sentence-segmented text"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"coqui-tts__cap_5","uri":"capability://data.processing.analysis.vocoder.based.waveform.generation.from.spectrograms","name":"vocoder-based waveform generation from spectrograms","description":"Converts acoustic spectrograms (mel-spectrograms or linear spectrograms) generated by TTS models into raw audio waveforms using neural vocoder models (HiFi-GAN, Glow-TTS vocoder, WaveGlow). The vocoder inference pipeline loads pre-trained vocoder checkpoints, applies spectral normalization/denormalization to match training conditions, and runs the vocoder network to produce high-quality audio; the system supports multiple vocoder architectures and automatically selects compatible vocoders for each TTS model.","intents":["I want to generate high-quality audio from TTS spectrograms without artifacts or noise","I need to use a different vocoder than the default to optimize for speed or quality","I want to understand the vocoder's impact on synthesis quality and experiment with alternatives"],"best_for":["developers building production TTS systems requiring high audio quality","researchers studying vocoder architectures and their impact on speech quality","teams optimizing TTS latency by selecting faster vocoders"],"limitations":["Vocoder quality is highly dependent on training data — vocoders trained on clean speech may produce artifacts on noisy spectrograms","Spectral mismatch between TTS model training and vocoder training causes audio artifacts; requires careful alignment of mel-spectrogram parameters","Vocoder inference adds 0.5-2s latency per sentence; slower vocoders (WaveGlow) may be impractical for real-time applications","No vocoder adaptation or fine-tuning interface — users cannot customize vocoders for domain-specific audio characteristics","Limited vocoder selection for non-English languages; many languages reuse English-trained vocoders which may not generalize well"],"requires":["Python 3.9+","PyTorch 1.9+","pre-trained vocoder checkpoint (downloaded via ModelManager)","spectrograms from TTS model with matching mel-spectrogram parameters (sample rate, n_mels, n_fft)"],"input_types":["mel-spectrograms (numpy arrays, shape [time_steps, n_mels])","linear spectrograms (for some vocoders)","vocoder model name or checkpoint path"],"output_types":["raw audio waveforms (numpy arrays, float32)","WAV files (16-bit PCM or float32)"],"categories":["data-processing-analysis","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"coqui-tts__cap_6","uri":"capability://tool.use.integration.model.discovery.and.automatic.downloading.via.centralized.catalog","name":"model discovery and automatic downloading via centralized catalog","description":"Maintains a .models.json catalog of pre-trained TTS and vocoder models with metadata (architecture, language, dataset, download URL) and provides a ModelManager class that lists available models, downloads them on-demand from remote repositories, caches them locally, and automatically loads model configurations and weights. Users specify models via strings like 'tts_models/en/ljspeech/vits' which are resolved to download URLs and cached in ~/.local/share/tts_models/ for offline reuse.","intents":["I want to quickly try different pre-trained TTS models without manually downloading and configuring each one","I need to know which pre-trained models are available for my language and use case","I want to ensure my application uses the latest model versions without manual updates"],"best_for":["developers prototyping TTS applications and wanting quick model experimentation","teams deploying TTS without custom model training","researchers comparing multiple pre-trained models"],"limitations":["Model catalog is static and updated infrequently — new models may not appear in .models.json for weeks after release","No automatic model versioning — switching between model versions requires manual catalog updates or downloading specific checkpoint URLs","Download URLs are hardcoded in .models.json; if a model is moved or deleted, downloads fail without fallback mechanisms","No model quality metrics or benchmarks in catalog — users cannot compare models before downloading","Cached models consume significant disk space (~50-200MB per model); no automatic cleanup or quota management"],"requires":["Python 3.9+","internet connection for initial model download","~500MB-2GB disk space for model cache (depending on number of models)","write access to ~/.local/share/tts_models/ or custom cache directory"],"input_types":["model identifier string (e.g., 'tts_models/en/ljspeech/vits')","optional: custom cache directory path"],"output_types":["list of available models (with metadata)","downloaded model files (PyTorch checkpoints, config files)","instantiated model objects ready for inference"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"coqui-tts__cap_7","uri":"capability://automation.workflow.command.line.interface.for.batch.synthesis.and.model.management","name":"command-line interface for batch synthesis and model management","description":"Provides a tts command-line tool (implemented in TTS/bin/synthesize.py) that enables text-to-speech synthesis, model listing, and model downloading without writing Python code. The CLI supports reading text from files or stdin, specifying model/speaker/language via flags, and writing output to audio files; it also includes subcommands for listing available models, downloading models, and running a TTS server for HTTP-based synthesis.","intents":["I want to synthesize speech from a text file without writing Python code","I need to batch-process multiple text files into audio files using a shell script","I want to run a TTS server that other applications can query via HTTP"],"best_for":["non-technical users and content creators needing quick TTS without coding","DevOps engineers integrating TTS into shell scripts or CI/CD pipelines","teams deploying TTS as a microservice via the built-in HTTP server"],"limitations":["CLI has limited customization options compared to Python API — advanced features like custom text processors or vocoder selection require Python code","Batch processing is sequential (one file at a time); no built-in parallelization for large batches","Error handling is minimal — invalid inputs produce cryptic error messages without suggestions","No progress reporting for long synthesis tasks; users cannot estimate completion time","TTS server is single-threaded and not suitable for high-concurrency production deployments"],"requires":["Python 3.9+ with Coqui TTS installed","text input file (plain text, UTF-8)","optional: GPU for faster synthesis"],"input_types":["text files (plain text, UTF-8)","command-line flags (--model, --speaker, --language, --output_path)","stdin (for piping text)"],"output_types":["WAV audio files","model list (JSON or text)","HTTP server (for tts-server subcommand)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"coqui-tts__cap_8","uri":"capability://automation.workflow.speaker.encoder.training.and.custom.speaker.representation.learning","name":"speaker encoder training and custom speaker representation learning","description":"Provides a training pipeline for speaker encoder networks that learn to extract speaker embeddings from audio samples, enabling zero-shot speaker adaptation. The training system loads speaker datasets, computes speaker embeddings via the encoder, applies speaker-specific loss functions (e.g., speaker verification losses), and optimizes the encoder to produce discriminative speaker representations that generalize to unseen speakers. Users can fine-tune pre-trained speaker encoders on custom speaker datasets to improve voice cloning quality.","intents":["I want to train a speaker encoder on my custom speaker dataset to improve voice cloning accuracy","I need to extract speaker embeddings from audio for speaker verification or identification tasks","I want to understand how speaker encoders work and experiment with different architectures"],"best_for":["researchers studying speaker representation learning","teams building speaker verification or speaker identification systems","developers optimizing voice cloning for specific speaker populations (e.g., accented speakers)"],"limitations":["Speaker encoder training requires 100+ hours of labeled speaker data; small datasets produce poor generalization","Training is computationally expensive — requires GPU and 10-50 hours of training time","No automatic hyperparameter tuning; users must manually set learning rates, batch sizes, and loss weights","Speaker embeddings are not interpretable — users cannot understand which acoustic features drive speaker similarity","No built-in evaluation metrics for speaker encoder quality; users must manually evaluate on held-out test speakers"],"requires":["Python 3.9+","PyTorch 1.9+","GPU with 8GB+ VRAM","speaker dataset: audio files (WAV) with speaker labels (CSV or directory structure)","training configuration (YAML or Python config objects)"],"input_types":["audio files (WAV, MP3) with speaker labels","training configuration (learning rate, batch size, loss function)","optional: pre-trained speaker encoder checkpoint"],"output_types":["trained speaker encoder checkpoint","speaker embeddings (numpy arrays, ~256-512 dimensions)","training logs (TensorBoard events, JSON metrics)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"coqui-tts__cap_9","uri":"capability://automation.workflow.inference.optimization.and.latency.reduction.through.model.quantization.and.pruning","name":"inference optimization and latency reduction through model quantization and pruning","description":"Supports inference-time optimizations including model quantization (converting float32 weights to int8 or float16) and layer pruning to reduce model size and latency. The system provides utilities for converting pre-trained models to quantized formats compatible with PyTorch's quantization API, enabling faster inference on CPU and edge devices; users can trade off audio quality for speed by selecting quantized model variants.","intents":["I want to deploy TTS on edge devices (mobile, IoT) with limited compute and memory","I need to reduce TTS latency for real-time applications like voice assistants","I want to understand the quality-speed tradeoff of different quantization strategies"],"best_for":["developers deploying TTS on edge devices (mobile phones, smart speakers, embedded systems)","teams optimizing TTS latency for real-time applications","researchers studying model compression techniques"],"limitations":["Quantization support is limited — only PyTorch quantization API is supported, no ONNX or TensorRT support for hardware-specific optimization","Quantized models may produce lower audio quality due to reduced precision; quality degradation is model and quantization strategy dependent","No automatic quantization — users must manually convert models and test quality impact","Quantization is not compatible with all model architectures — some models may fail to quantize due to unsupported operations","No benchmarking tools to measure latency/quality tradeoffs; users must manually profile on target hardware"],"requires":["Python 3.9+","PyTorch 1.9+ with quantization support","pre-trained model checkpoint","optional: target hardware for profiling (mobile device, edge device)"],"input_types":["pre-trained model checkpoint (float32)","quantization configuration (bit-width, strategy)","optional: calibration dataset for post-training quantization"],"output_types":["quantized model checkpoint (int8 or float16)","latency/quality metrics (JSON)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"coqui-tts__headline","uri":"capability://voice.audio.open.source.text.to.speech.framework","name":"open-source text-to-speech framework","description":"Coqui TTS is an open-source text-to-speech framework that supports over 1100 languages and offers features like voice cloning and fine-tuning, making it ideal for developers looking to integrate TTS capabilities into their applications.","intents":["best text-to-speech framework","text-to-speech for multilingual applications","open-source TTS solutions","TTS framework with voice cloning","best TTS library for Python"],"best_for":["developers","researchers"],"limitations":[],"requires":["Python"],"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+","PyTorch 1.9+ (CPU or GPU)","~500MB disk space for base models, additional space per language model (~50-200MB each)","PyTorch 1.9+","reference audio file (WAV, MP3) with clear speech from target speaker","optional: GPU for faster speaker encoding (CPU inference ~1-5s per reference sample)","multi-speaker TTS model (e.g., 'tts_models/en/ljspeech/vits' with speaker conditioning)","optional: reference audio for speaker embedding extraction","optional: speaker ID (for discrete speaker models)","TTS model compatible with streaming (most models support it)"],"failure_modes":["Quality varies significantly across languages — high-resource languages (English, Mandarin) produce near-human speech while low-resource languages may have noticeable artifacts","No built-in language detection — requires explicit language specification in API calls","Inference latency scales with text length and model size (typically 0.5-2s for short sentences on CPU)","Pre-trained models are fixed-size; custom language support requires training from scratch","Voice cloning quality depends heavily on reference audio quality — noisy or compressed audio degrades speaker similarity","Requires 5-30 seconds of reference audio per speaker for acceptable quality; shorter clips produce less stable embeddings","Speaker encoder training requires 100+ hours of labeled speaker data; pre-trained encoders may not generalize well to accented or non-native speakers","No speaker identity preservation across model architecture changes — switching from VITS to Tacotron requires re-encoding reference audio","Multi-speaker models require training on datasets with multiple speakers — single-speaker pre-trained models cannot be used for multi-speaker synthesis","Speaker quality depends on the diversity and size of the training dataset — models trained on 10 speakers may not generalize well to new speakers","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.690Z","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=coqui-tts","compare_url":"https://unfragile.ai/compare?artifact=coqui-tts"}},"signature":"GWvahLu4izqAfYbGtKNUrfcVMuUg/HNO8XVI9OLXKQmX058ROMy2n2Z3m9YWURLPsADW5CMGXetdtXb9VektCg==","signedAt":"2026-06-22T01:50:55.726Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/coqui-tts","artifact":"https://unfragile.ai/coqui-tts","verify":"https://unfragile.ai/api/v1/verify?slug=coqui-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"}}