{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-tts","slug":"pypi-tts","name":"TTS","type":"repo","url":"https://github.com/coqui-ai/TTS","page_url":"https://unfragile.ai/pypi-tts","categories":["voice-audio"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-tts__cap_0","uri":"capability://text.generation.language.multi.language.text.to.speech.synthesis.with.pre.trained.models","name":"multi-language text-to-speech synthesis with pre-trained models","description":"Converts text input to natural-sounding speech across 1100+ languages using a unified TTS API that abstracts model selection, text processing, and vocoder execution. The system loads pre-trained model weights and configurations from a centralized catalog (.models.json), applies language-specific text normalization, generates mel-spectrograms via the selected TTS model (VITS, Tacotron2, GlowTTS, etc.), and converts spectrograms to audio waveforms using neural vocoders. The Synthesizer class orchestrates this pipeline, handling sentence segmentation, speaker/language routing, and audio post-processing in a single inference call.","intents":["Generate speech from text in a specific language without managing model selection or configuration","Build a multilingual voice application that supports 1100+ languages with minimal setup","Integrate TTS into an application without understanding the underlying model architecture","Synthesize speech with consistent quality across different languages using pre-trained weights"],"best_for":["Application developers building multilingual voice features","Non-ML engineers integrating TTS into products","Teams prototyping voice-enabled applications quickly"],"limitations":["Pre-trained models are fixed and cannot be fine-tuned without retraining infrastructure","Inference latency varies by model architecture (Tacotron2 slower than VITS for real-time use)","Text normalization is language-specific and may not handle domain-specific terminology","No built-in streaming/chunked synthesis — entire text must be processed before audio output"],"requires":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.x","Sufficient disk space for model weights (100MB-1GB per model)","Internet connection for initial model download"],"input_types":["plain text (UTF-8)","text with language codes","text with speaker IDs (for multi-speaker models)"],"output_types":["WAV audio files (16kHz or 22.05kHz sample rate)","numpy arrays (float32 waveforms)","raw audio bytes"],"categories":["text-generation-language","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tts__cap_1","uri":"capability://text.generation.language.speaker.aware.speech.synthesis.with.multi.speaker.model.support","name":"speaker-aware speech synthesis with multi-speaker model support","description":"Generates speech in specific speaker voices by routing speaker IDs or speaker embeddings through multi-speaker TTS models (VITS, Tacotron2) that were trained on datasets with multiple speakers. The system maintains speaker metadata in model configurations, validates speaker IDs at inference time, and passes speaker embeddings or speaker conditioning vectors to the model's speaker encoder layers. For models without pre-trained speaker support, the framework provides a Speaker Encoder training pipeline to learn speaker embeddings from custom voice data, enabling zero-shot speaker adaptation.","intents":["Generate speech in multiple distinct speaker voices from a single model","Create character voices for interactive applications or audiobooks","Train custom speaker embeddings from new voice samples for personalized synthesis","Adapt a pre-trained model to synthesize in a new speaker's voice without full model retraining"],"best_for":["Developers building interactive voice applications with multiple characters","Teams creating audiobook or podcast production tools","Researchers fine-tuning speaker adaptation for low-resource languages"],"limitations":["Speaker quality depends on training data diversity — models trained on few speakers may generalize poorly to new voices","Speaker Encoder training requires 5-10 minutes of reference audio per speaker for good embeddings","Not all model architectures support multi-speaker synthesis (e.g., some Tacotron variants are single-speaker only)","Zero-shot speaker adaptation quality degrades for speakers very different from training distribution"],"requires":["Multi-speaker TTS model (VITS, Tacotron2, or similar)","Valid speaker ID or pre-computed speaker embedding vector","For custom speakers: Speaker Encoder model + 5-10 minutes of reference audio per speaker"],"input_types":["text with speaker ID parameter","text with pre-computed speaker embedding (numpy array)","reference audio files (WAV, MP3) for speaker encoder"],"output_types":["WAV audio in target speaker's voice","speaker embedding vectors (for reuse across synthesis calls)"],"categories":["text-generation-language","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tts__cap_10","uri":"capability://automation.workflow.configuration.driven.model.and.training.system","name":"configuration-driven model and training system","description":"Uses YAML configuration files to define model architectures, training hyperparameters, and dataset specifications, decoupling configuration from code and enabling reproducible experiments without code changes. Each model architecture (Tacotron2, VITS, GlowTTS, etc.) has a corresponding config class (e.g., Tacotron2Config) that loads YAML files and validates parameters. Training scripts read configuration files to instantiate models, create data loaders, and configure optimizers and learning rate schedules. This approach allows users to experiment with different hyperparameters, model architectures, and datasets by modifying YAML files rather than editing Python code, improving reproducibility and reducing the barrier to entry for non-programmers.","intents":["Configure and train TTS models without modifying Python code","Reproduce published results by sharing configuration files","Experiment with different hyperparameters and model architectures systematically","Version control model configurations separately from code"],"best_for":["Researchers experimenting with TTS hyperparameters and architectures","Teams managing multiple TTS models with different configurations","Non-programmers configuring TTS models via YAML files"],"limitations":["Configuration validation is limited — invalid YAML may not be caught until training starts","Complex hyperparameter dependencies are not enforced — users can create invalid configurations","No built-in configuration versioning — users must manually track configuration changes","Configuration schema is not standardized across model architectures — different models use different config parameters"],"requires":["YAML configuration file with model and training parameters","Understanding of model-specific configuration options (documented in config classes)"],"input_types":["YAML configuration file (model architecture, hyperparameters, dataset paths)"],"output_types":["instantiated model object","data loader","optimizer and learning rate scheduler"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tts__cap_11","uri":"capability://automation.workflow.multi.model.inference.pipeline.with.automatic.model.composition","name":"multi-model inference pipeline with automatic model composition","description":"Orchestrates the inference pipeline by automatically composing TTS models with compatible vocoders, handling text processing, spectrogram generation, and waveform synthesis in a single call. The Synthesizer class manages the pipeline: it loads the TTS model and its paired vocoder from configuration, applies text normalization and sentence segmentation, runs the TTS model to generate mel-spectrograms, applies vocoder-specific normalization, runs the vocoder to generate waveforms, and optionally applies post-processing (silence trimming, loudness normalization). The system validates model compatibility (e.g., spectrogram dimensions match between TTS and vocoder) and provides clear error messages if incompatible models are paired.","intents":["Synthesize speech end-to-end without manually managing TTS and vocoder models","Ensure TTS and vocoder compatibility automatically without manual configuration","Apply consistent text preprocessing and audio post-processing across all synthesis calls","Handle edge cases (very long text, silence trimming) transparently"],"best_for":["Application developers building TTS features without deep knowledge of TTS internals","Teams deploying TTS systems that require consistent, reproducible synthesis","Researchers comparing different TTS/vocoder combinations"],"limitations":["Pipeline is opaque — users cannot easily inspect intermediate outputs (spectrograms, vocoder inputs)","No streaming synthesis — entire text must be processed before audio output","Post-processing options are limited — no built-in audio effects or advanced filtering","Error handling is generic — model incompatibility errors may not provide actionable debugging information"],"requires":["TTS model with compatible vocoder configuration","Text input with language code"],"input_types":["text string","language code","speaker ID (optional, for multi-speaker models)"],"output_types":["audio waveform (numpy array)","WAV file"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tts__cap_2","uri":"capability://memory.knowledge.model.discovery.and.automatic.download.with.catalog.management","name":"model discovery and automatic download with catalog management","description":"Maintains a centralized model catalog (.models.json) containing metadata for 100+ pre-trained TTS and vocoder models, enabling users to list available models, query by language/architecture/dataset, and automatically download model weights and configurations from remote repositories. The ModelManager class handles HTTP-based model fetching, local caching, configuration path updates, and version management. When a user requests a model by name, the system looks up the model in the catalog, downloads weights if not cached locally, and loads the configuration YAML file that specifies model architecture, hyperparameters, and vocoder pairing.","intents":["Discover what TTS models are available for a specific language without manual research","Automatically download and cache model weights on first use without manual file management","Switch between different model architectures (VITS, Tacotron2, GlowTTS) for the same language to compare quality/speed","Query model metadata to understand training dataset, speaker count, and supported features"],"best_for":["Developers building applications that support multiple languages and want automatic model selection","Researchers comparing different model architectures on the same language","Teams deploying TTS without manual model management infrastructure"],"limitations":["Model catalog is static and updated only on library releases — new community models require library update","Download bandwidth depends on remote repository availability — no built-in CDN or mirror support","Model weights are cached locally without automatic cleanup — can consume 10GB+ disk space for all models","No version pinning — updating the library may change which model version is downloaded"],"requires":["Internet connection for initial model download","Disk space for model weights (100MB-1GB per model)","Read/write access to TTS cache directory (~/.TTS)"],"input_types":["model name string (e.g., 'tts_models/en/ljspeech/vits')","language code (e.g., 'en', 'fr')","model architecture filter (e.g., 'vits', 'tacotron2')"],"output_types":["model metadata dictionary (architecture, language, dataset, speaker count)","loaded model object with weights and configuration","list of available models matching query criteria"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tts__cap_3","uri":"capability://data.processing.analysis.text.normalization.and.sentence.segmentation.for.multilingual.input","name":"text normalization and sentence segmentation for multilingual input","description":"Preprocesses raw text input by applying language-specific text normalization (expanding abbreviations, converting numbers to words, handling punctuation) and splitting text into sentences to manage synthesis latency and memory usage. The system uses language-specific text processors (defined in TTS/tts/utils/text/) that handle character sets, phoneme conversion, and linguistic rules for each language. Sentence segmentation uses regex-based splitting with language-aware punctuation rules, preventing incorrect splits on abbreviations or decimal numbers. This preprocessing ensures consistent phoneme generation and prevents out-of-memory errors on very long texts.","intents":["Convert raw user text with numbers, abbreviations, and mixed punctuation into normalized phoneme sequences","Split long documents into manageable chunks for synthesis without memory overflow","Handle language-specific text rules (e.g., French accents, German umlauts, Japanese hiragana) correctly","Ensure consistent pronunciation across multiple synthesis calls with the same text"],"best_for":["Applications processing user-generated text with variable formatting","Multilingual systems that need consistent text preprocessing across languages","Long-form content synthesis (books, articles) that requires chunking"],"limitations":["Text normalization is rule-based and may fail on domain-specific terminology (medical terms, proper nouns, brand names)","Sentence segmentation can break incorrectly on abbreviations not in the language-specific rule set","No context awareness — homographs (words with multiple pronunciations) are normalized to a single form","Language detection is not automatic — language must be specified explicitly or inferred from model selection"],"requires":["Language code matching a supported language in TTS/tts/utils/text/","UTF-8 encoded text input","Language-specific text processor module (bundled with TTS)"],"input_types":["raw text string with mixed punctuation, numbers, abbreviations","text with language code parameter"],"output_types":["normalized text string","list of sentence strings","phoneme sequence (for models using phoneme-based synthesis)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tts__cap_4","uri":"capability://data.processing.analysis.neural.vocoder.based.waveform.generation.from.spectrograms","name":"neural vocoder-based waveform generation from spectrograms","description":"Converts mel-spectrogram outputs from TTS models into high-quality audio waveforms using neural vocoder models (HiFi-GAN, Glow-TTS vocoder, WaveGlow). The vocoder inference pipeline takes spectrograms generated by the TTS model, applies optional normalization and denormalization based on vocoder-specific statistics, and passes them through the vocoder's neural network to produce raw audio samples. The system supports multiple vocoder architectures and automatically selects the appropriate vocoder based on the TTS model's configuration, ensuring spectral compatibility. Vocoders are loaded separately from TTS models, enabling vocoder swapping without retraining the TTS model.","intents":["Convert TTS model outputs (spectrograms) into listenable audio without manual vocoder selection","Swap vocoders to improve audio quality or reduce inference latency without retraining the TTS model","Generate high-fidelity audio (22.05kHz or 44.1kHz) from lower-resolution spectrograms","Use different vocoders for different quality/speed trade-offs in the same application"],"best_for":["Developers building production TTS systems requiring high audio quality","Researchers experimenting with different vocoder architectures","Applications with variable latency budgets (can use faster vocoders for real-time, slower for batch)"],"limitations":["Vocoder quality depends on spectrogram resolution and normalization — mismatched TTS/vocoder configurations produce artifacts","Neural vocoders add 50-200ms latency per synthesis call (HiFi-GAN slower than Glow-TTS vocoder)","Vocoder models require GPU for real-time inference — CPU inference is 10-50x slower","No built-in vocoder training — custom vocoders require external training infrastructure"],"requires":["Pre-trained vocoder model (HiFi-GAN, Glow-TTS, WaveGlow, etc.)","Mel-spectrogram output from compatible TTS model","GPU recommended for real-time inference (CPU inference possible but slow)"],"input_types":["mel-spectrogram tensor (shape: [time_steps, mel_bins])","vocoder model name or path"],"output_types":["audio waveform (numpy array, float32)","WAV file (16-bit PCM or 32-bit float)"],"categories":["data-processing-analysis","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tts__cap_5","uri":"capability://automation.workflow.tts.model.training.with.custom.datasets.and.configurations","name":"tts model training with custom datasets and configurations","description":"Provides a complete training pipeline for building custom TTS models from scratch or fine-tuning pre-trained models on new datasets. The training system uses PyTorch-based model definitions (Tacotron2, VITS, GlowTTS, etc.), configuration files (YAML) that specify hyperparameters, and a DataLoader that handles audio preprocessing (mel-spectrogram computation), text normalization, and speaker/language conditioning. The training loop implements gradient accumulation, mixed precision training, learning rate scheduling, and checkpoint management. Users define custom datasets by creating metadata files (CSV with audio paths and transcriptions) and specifying dataset-specific configuration (sample rate, mel-spectrogram parameters, speaker count).","intents":["Train a custom TTS model on proprietary voice data for domain-specific synthesis","Fine-tune a pre-trained model on a new language or speaker with limited data","Experiment with different model architectures and hyperparameters on custom datasets","Build multi-speaker TTS models from datasets with multiple speakers"],"best_for":["ML teams building proprietary TTS systems for specific languages or domains","Researchers experimenting with TTS model architectures","Organizations with custom voice data that want to avoid cloud TTS services"],"limitations":["Training requires significant computational resources (GPU with 8GB+ VRAM, 24-72 hours for convergence)","Dataset preparation is manual — requires audio files, transcriptions, and speaker metadata in specific formats","Hyperparameter tuning is not automated — requires manual experimentation or grid search","No built-in data augmentation — users must handle data preprocessing and augmentation externally","Training stability depends on dataset quality — noisy or misaligned audio/text pairs cause training failures"],"requires":["Python 3.7+","PyTorch 1.9+ with CUDA support","GPU with 8GB+ VRAM (16GB+ recommended)","Custom dataset with audio files and transcriptions (CSV metadata file)","Configuration YAML file specifying model architecture and hyperparameters"],"input_types":["audio files (WAV, MP3) with 16kHz or 22.05kHz sample rate","transcription metadata (CSV: audio_path, text, speaker_id)","configuration file (YAML with model, training, and dataset parameters)"],"output_types":["trained model checkpoint (PyTorch .pth file)","training logs (loss curves, validation metrics)","configuration file for inference"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tts__cap_6","uri":"capability://automation.workflow.vocoder.model.training.from.audio.datasets","name":"vocoder model training from audio datasets","description":"Provides a specialized training pipeline for building custom neural vocoders (HiFi-GAN, Glow-TTS vocoder) from raw audio data. The vocoder training system takes audio files and corresponding mel-spectrograms, trains the vocoder to minimize reconstruction error (L1 loss on waveforms), and optionally applies adversarial training (discriminator loss) for improved audio quality. The training loop handles audio preprocessing (normalization, mel-spectrogram computation), batch loading, and checkpoint management. Unlike TTS training, vocoder training does not require text transcriptions — only audio files and their spectrograms are needed.","intents":["Train a custom vocoder on proprietary audio data for domain-specific waveform generation","Fine-tune a pre-trained vocoder on a new language or speaker with limited audio data","Build a vocoder optimized for specific audio characteristics (e.g., singing voice, accented speech)","Experiment with vocoder architectures and loss functions on custom audio datasets"],"best_for":["ML teams building proprietary TTS systems requiring custom vocoders","Researchers experimenting with vocoder architectures","Organizations with specific audio quality requirements (e.g., singing synthesis, accent preservation)"],"limitations":["Vocoder training requires significant computational resources (GPU with 8GB+ VRAM, 48-96 hours for convergence)","Adversarial training can be unstable — requires careful hyperparameter tuning and monitoring","Dataset preparation requires only audio files but must be high-quality (low noise, consistent loudness)","No built-in audio augmentation — users must handle preprocessing externally","Vocoder quality depends heavily on mel-spectrogram parameters matching TTS model output"],"requires":["Python 3.7+","PyTorch 1.9+ with CUDA support","GPU with 8GB+ VRAM (16GB+ recommended)","Audio dataset (WAV files, 16kHz or 22.05kHz sample rate)","Configuration YAML file specifying vocoder architecture and training hyperparameters"],"input_types":["audio files (WAV, MP3) with consistent sample rate","mel-spectrogram files (pre-computed or computed on-the-fly)","configuration file (YAML with vocoder architecture and training parameters)"],"output_types":["trained vocoder checkpoint (PyTorch .pth file)","training logs (loss curves, validation metrics)","configuration file for inference"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tts__cap_7","uri":"capability://automation.workflow.speaker.encoder.training.for.zero.shot.speaker.adaptation","name":"speaker encoder training for zero-shot speaker adaptation","description":"Implements a specialized training pipeline for learning speaker embeddings from reference audio samples, enabling zero-shot speaker adaptation without retraining the TTS model. The Speaker Encoder is a neural network (typically a ResNet-based architecture) that maps audio samples to fixed-size speaker embedding vectors. During training, the encoder is optimized using triplet loss or similar metric learning objectives to ensure that embeddings from the same speaker are close together and embeddings from different speakers are far apart. Once trained, the encoder can generate embeddings for new speakers from just 5-10 minutes of reference audio, which are then passed to the TTS model's speaker conditioning layers.","intents":["Train a speaker encoder to enable zero-shot speaker adaptation for new voices","Generate speaker embeddings from reference audio for use in multi-speaker TTS synthesis","Fine-tune a pre-trained speaker encoder on a new language or speaker distribution","Build a voice cloning system that adapts TTS to new speakers without full model retraining"],"best_for":["Teams building voice cloning or speaker adaptation features","Researchers experimenting with speaker embedding methods","Applications requiring personalized TTS without per-speaker model training"],"limitations":["Speaker Encoder training requires a large, diverse speaker dataset (100+ speakers) for good generalization","Embedding quality depends on reference audio duration — 5-10 minutes minimum per speaker for reliable embeddings","Zero-shot adaptation quality degrades for speakers very different from training distribution","Triplet loss training can be unstable — requires careful batch sampling and hard negative mining","No built-in speaker verification — cannot guarantee that reference audio is from the claimed speaker"],"requires":["Python 3.7+","PyTorch 1.9+ with CUDA support","GPU with 8GB+ VRAM","Large speaker dataset (100+ speakers, 5-10 minutes per speaker)","Configuration YAML file specifying encoder architecture and training hyperparameters"],"input_types":["audio files (WAV, MP3) from multiple speakers","speaker ID metadata (mapping audio files to speaker identities)","configuration file (YAML with encoder architecture and training parameters)"],"output_types":["trained speaker encoder checkpoint (PyTorch .pth file)","speaker embedding vectors (numpy arrays, typically 256-512 dimensions)","training logs (loss curves, validation metrics)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tts__cap_8","uri":"capability://tool.use.integration.command.line.interface.for.synthesis.and.model.management","name":"command-line interface for synthesis and model management","description":"Provides a command-line tool (tts command) that wraps the Python API for text-to-speech synthesis, model listing, and model downloading without requiring Python code. The CLI accepts text input via stdin or command-line arguments, model selection via --model_name flag, speaker/language selection via --speaker_idx or --language flags, and output file specification via --out_path. The CLI internally uses the TTS class and ModelManager to handle model loading and synthesis. Additional CLI commands support listing available models (tts --list_models), downloading models (tts --model_name <name> --download), and running a web server (tts-server) for browser-based synthesis.","intents":["Synthesize speech from the command line without writing Python code","Batch process text files into speech files using shell scripts","Quickly test different TTS models and speakers from the terminal","Integrate TTS into shell pipelines and automation scripts"],"best_for":["DevOps engineers and system administrators automating TTS workflows","Researchers quickly testing models without writing Python scripts","Non-programmers using TTS from the command line"],"limitations":["CLI interface is less flexible than Python API — cannot access advanced features like custom text processing or model introspection","No streaming output — entire synthesis must complete before output file is written","Limited error handling — CLI errors may not provide detailed debugging information","No support for batch processing multiple texts in a single command (requires shell loop)"],"requires":["TTS library installed (pip install TTS)","Python 3.7+ in PATH","Text input (via stdin, file, or command-line argument)"],"input_types":["text string (command-line argument or stdin)","text file path","model name (e.g., 'tts_models/en/ljspeech/vits')"],"output_types":["WAV audio file","list of available models (JSON or text)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tts__cap_9","uri":"capability://tool.use.integration.web.server.interface.for.browser.based.synthesis","name":"web server interface for browser-based synthesis","description":"Provides a tts-server command that launches a Flask/FastAPI web server exposing TTS functionality via HTTP endpoints. The server implements REST endpoints for text-to-speech synthesis (/tts), model listing (/models), and speaker listing (/speakers). Clients send POST requests with text, model name, speaker ID, and language parameters, and receive audio files or JSON responses. The server handles concurrent requests using a thread pool or async workers, manages model caching in memory, and provides a simple HTML interface for browser-based testing. The server internally uses the TTS class and Synthesizer for synthesis, ensuring consistency with the Python API.","intents":["Build a web application with TTS synthesis without implementing the synthesis logic","Expose TTS as a microservice that multiple applications can call via HTTP","Provide a browser-based UI for testing different TTS models and speakers","Deploy TTS in a containerized environment (Docker) for cloud or on-premises hosting"],"best_for":["Web developers integrating TTS into web applications","Teams deploying TTS as a microservice or API","Researchers sharing TTS models via a web interface"],"limitations":["Web server adds network latency (50-500ms) compared to local Python API calls","Concurrent request handling depends on server configuration — default may not scale to 100+ concurrent users","No built-in authentication or rate limiting — requires external reverse proxy (nginx) for production","Server memory usage grows with model size — loading multiple large models can exhaust available RAM","No streaming audio output — entire synthesis must complete before response is sent"],"requires":["TTS library installed","Python 3.7+","Flask or FastAPI (bundled with TTS)","Port availability (default 5002 or configurable)"],"input_types":["HTTP POST request with JSON body (text, model_name, speaker_idx, language)","HTTP GET request for model/speaker listing"],"output_types":["WAV audio file (HTTP response with audio/wav MIME type)","JSON response with model/speaker metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.x","Sufficient disk space for model weights (100MB-1GB per model)","Internet connection for initial model download","Multi-speaker TTS model (VITS, Tacotron2, or similar)","Valid speaker ID or pre-computed speaker embedding vector","For custom speakers: Speaker Encoder model + 5-10 minutes of reference audio per speaker","YAML configuration file with model and training parameters","Understanding of model-specific configuration options (documented in config classes)","TTS model with compatible vocoder configuration"],"failure_modes":["Pre-trained models are fixed and cannot be fine-tuned without retraining infrastructure","Inference latency varies by model architecture (Tacotron2 slower than VITS for real-time use)","Text normalization is language-specific and may not handle domain-specific terminology","No built-in streaming/chunked synthesis — entire text must be processed before audio output","Speaker quality depends on training data diversity — models trained on few speakers may generalize poorly to new voices","Speaker Encoder training requires 5-10 minutes of reference audio per speaker for good embeddings","Not all model architectures support multi-speaker synthesis (e.g., some Tacotron variants are single-speaker only)","Zero-shot speaker adaptation quality degrades for speakers very different from training distribution","Configuration validation is limited — invalid YAML may not be caught until training starts","Complex hyperparameter dependencies are not enforced — users can create invalid configurations","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"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":"2026-05-24T12:16:25.061Z","last_scraped_at":"2026-05-03T15:20:21.281Z","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=pypi-tts","compare_url":"https://unfragile.ai/compare?artifact=pypi-tts"}},"signature":"5r97Ol7rHKM3frz6Y+mvB29HYXTeF0D0GSeOWTOEMKqk1Nm0eVnNb+rpLHjrEFRRXiaLvYG3do1RUdDrCcw9Dg==","signedAt":"2026-06-22T09:56:03.686Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-tts","artifact":"https://unfragile.ai/pypi-tts","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-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"}}