{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-2noise--chattts","slug":"2noise--chattts","name":"ChatTTS","type":"agent","url":"https://2noise.com","page_url":"https://unfragile.ai/2noise--chattts","categories":["voice-audio"],"tags":["agent","chat","chatgpt","chattts","chinese","chinese-language","english","english-language","gpt","llm","llm-agent","natural-language-inference","python","text-to-speech","torch","torchaudio","tts"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-2noise--chattts__cap_0","uri":"capability://text.generation.language.dialogue.optimized.text.to.speech.synthesis.with.prosody.control","name":"dialogue-optimized text-to-speech synthesis with prosody control","description":"Generates natural speech from text using a GPT-based architecture specifically trained for conversational dialogue, with fine-grained control over prosodic features including laughter, pauses, and interjections. The system uses a two-stage pipeline: optional GPT-based text refinement that injects prosody markers into the input, followed by discrete audio token generation via a transformer-based audio codec. This approach enables expressive, contextually-aware speech synthesis rather than flat, robotic output typical of generic TTS systems.","intents":["Generate natural-sounding speech for LLM chatbot responses with emotional expressiveness","Create dialogue audio with realistic pauses, laughter, and conversational interjections","Synthesize speech that sounds like a human having a natural conversation rather than reading text","Build voice interfaces for AI assistants that respond with appropriate prosody and timing"],"best_for":["AI/LLM product teams building voice-enabled chatbots and conversational agents","Developers creating interactive voice applications requiring natural dialogue synthesis","Teams building multilingual voice assistants for Chinese and English languages"],"limitations":["Text refinement step adds ~500-1000ms latency per inference due to GPT processing (can be skipped with skip_refine_text=True for faster but less expressive output)","Prosody control is implicit through text markers rather than explicit parameter tuning — limited direct control over speech rate, pitch, or emotion intensity","Optimized for dialogue/conversational speech; may not perform well for formal narration, technical documentation, or non-dialogue content","Requires GPU (CUDA) for reasonable inference speed; CPU inference is significantly slower"],"requires":["Python 3.9+","PyTorch with CUDA support (or CPU fallback)","torchaudio library","4GB+ VRAM for GPU inference (8GB+ recommended for batch processing)","~2GB disk space for model weights"],"input_types":["plain text (English or Chinese)","text with optional prosody markers (e.g., [laugh], [pause])"],"output_types":["WAV audio files (16kHz or 24kHz sample rate)","numpy arrays (raw waveforms)","mel spectrograms (intermediate representation)"],"categories":["text-generation-language","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-2noise--chattts__cap_1","uri":"capability://text.generation.language.gpt.based.text.refinement.with.automatic.prosody.annotation","name":"gpt-based text refinement with automatic prosody annotation","description":"Refines raw input text by running it through a fine-tuned GPT model that adds prosody markers (e.g., [laugh], [pause], [breath]) and improves phrasing for natural speech synthesis. The GPT model operates on discrete tokens and outputs enriched text that guides the downstream audio codec toward more expressive speech. This refinement is optional and can be disabled via skip_refine_text=True for latency-critical applications, but enabling it significantly improves speech naturalness by making the model aware of conversational context.","intents":["Automatically add laughter, pauses, and interjections to text for more natural dialogue","Improve text phrasing and structure to sound more conversational when spoken aloud","Skip refinement for low-latency applications where speed is more important than expressiveness","Control the level of prosodic enrichment in generated speech"],"best_for":["Developers building voice chatbots where naturalness is critical (customer service, entertainment)","Teams with latency budgets of 500ms+ per response","Applications where dialogue context and emotional tone matter more than response speed"],"limitations":["Adds 500-1000ms latency per inference call due to GPT forward pass","Refinement quality depends on GPT model training data — may not handle domain-specific jargon or technical content well","Prosody markers are learned implicitly; no direct API to request specific prosody (e.g., 'add 3 laughs' or 'slow down by 20%')","Refinement is language-specific; English and Chinese models are separate and cannot be mixed"],"requires":["GPT model weights loaded in memory (~1-2GB)","GPU recommended (CPU inference for refinement is very slow)","Input text must be in supported language (English or Chinese)"],"input_types":["plain text (English or Chinese)","text up to ~1000 tokens (longer text may be truncated or require batching)"],"output_types":["refined text with prosody markers embedded","text tokens (internal representation)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-2noise--chattts__cap_10","uri":"capability://automation.workflow.cuda.optimized.inference.with.gpu.acceleration","name":"cuda-optimized inference with gpu acceleration","description":"Implements GPU acceleration for all computationally expensive stages (text refinement, token generation, spectrogram decoding, vocoding) using PyTorch and CUDA, enabling real-time or near-real-time synthesis on modern GPUs. The system automatically detects GPU availability and moves models to GPU memory, with fallback to CPU inference if needed. GPU optimization includes batch processing, kernel fusion, and memory management to maximize throughput and minimize latency.","intents":["Accelerate synthesis for real-time or near-real-time voice applications","Maximize throughput for high-volume synthesis workloads","Enable efficient batch processing with GPU memory management","Support deployment on GPU-equipped servers or edge devices"],"best_for":["Teams building real-time voice applications with GPU infrastructure","High-volume synthesis services requiring maximum throughput","Developers deploying on GPU-equipped servers or cloud instances"],"limitations":["GPU memory is limited; large models or batch sizes may cause out-of-memory errors (typically 4-8GB required)","GPU inference requires CUDA-compatible hardware (NVIDIA GPUs); no support for AMD or Intel GPUs","CPU fallback is significantly slower (10-100x slower depending on model size and CPU)","GPU memory management is automatic but not always optimal; users may need to manually manage batch sizes for large-scale deployments"],"requires":["NVIDIA GPU with CUDA compute capability 3.5+ (e.g., Tesla K40, GTX 1080, A100)","CUDA Toolkit 11.8+ and cuDNN 8.0+","PyTorch built with CUDA support","4-8GB GPU VRAM minimum (8GB+ recommended for batch processing)"],"input_types":["text and speaker embeddings (automatically moved to GPU)"],"output_types":["audio waveforms (on GPU, then transferred to CPU for output)"],"categories":["automation-workflow","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-2noise--chattts__cap_11","uri":"capability://automation.workflow.onnx.export.for.cross.platform.deployment","name":"onnx export for cross-platform deployment","description":"Exports trained models to ONNX (Open Neural Network Exchange) format, enabling deployment on diverse platforms and runtimes without PyTorch dependency. The system supports exporting the GPT model, DVAE decoder, and Vocos vocoder to ONNX, enabling inference on CPU-only servers, edge devices, or specialized hardware (e.g., NVIDIA Triton, ONNX Runtime). ONNX export includes quantization and optimization options for reducing model size and inference latency.","intents":["Deploy ChatTTS on CPU-only servers or edge devices without PyTorch","Export models for use with ONNX Runtime or other inference frameworks","Reduce model size and inference latency through quantization and optimization","Enable cross-platform deployment (Windows, Linux, macOS, mobile)"],"best_for":["Teams deploying on CPU-only infrastructure or edge devices","Developers building mobile or embedded voice applications","Organizations requiring cross-platform deployment without PyTorch dependency"],"limitations":["ONNX export is not fully automated; requires manual model conversion and testing","Some PyTorch operations may not have ONNX equivalents; custom operations require additional work","ONNX Runtime performance varies by platform; CPU inference is still slower than GPU","Quantized models may have reduced quality; requires testing and validation","ONNX export is not officially documented or supported; users must rely on community examples"],"requires":["PyTorch models (GPT, DVAE, Vocos) in original format","ONNX export tools (onnx, onnxruntime)","Understanding of ONNX format and inference runtime"],"input_types":["PyTorch model files (.pt or .pth)"],"output_types":["ONNX model files (.onnx)","optional: quantized ONNX models for reduced size"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-2noise--chattts__cap_12","uri":"capability://text.generation.language.multilingual.support.for.english.and.chinese.synthesis","name":"multilingual support for english and chinese synthesis","description":"Supports synthesis for both English and Chinese languages with language-specific text normalization, tokenization, and prosody handling. The system automatically detects input language or allows explicit language specification, routing text through appropriate language-specific pipelines. Language support includes both Simplified and Traditional Chinese, with separate models and tokenizers for each language to ensure accurate pronunciation and prosody.","intents":["Synthesize speech in English or Chinese with language-appropriate prosody and pronunciation","Build multilingual voice applications supporting English and Chinese users","Handle mixed-language input (code-switching) in dialogue applications","Support both Simplified and Traditional Chinese variants"],"best_for":["Teams building multilingual voice applications for English and Chinese markets","Developers supporting international users with language-specific voice synthesis","Applications requiring accurate Chinese pronunciation and prosody"],"limitations":["Only English and Chinese are supported; no other languages","Language detection is not automatic; users must specify language or provide language hints","Mixed-language input (code-switching) is not well-supported; requires separate synthesis for each language","Chinese support is limited to Simplified and Traditional variants; other Chinese dialects are not supported","Language-specific models are separate; cannot share embeddings or parameters across languages"],"requires":["Input text in English or Chinese","Language-specific tokenizers and models loaded in memory","Optional: language specification parameter for explicit language selection"],"input_types":["English text (ASCII or UTF-8)","Chinese text (Simplified or Traditional Chinese characters)"],"output_types":["audio waveforms with language-appropriate pronunciation and prosody"],"categories":["text-generation-language","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-2noise--chattts__cap_13","uri":"capability://tool.use.integration.web.interface.for.interactive.synthesis.and.testing","name":"web interface for interactive synthesis and testing","description":"Provides a web-based user interface for interactive text-to-speech synthesis, speaker management, and parameter tuning without requiring programming knowledge. The web interface enables users to input text, select or generate speakers, adjust synthesis parameters, and listen to generated audio in real-time. The interface is built with modern web technologies and communicates with the backend Chat class via HTTP API, enabling easy deployment and sharing.","intents":["Test and demo ChatTTS without writing code","Interactively explore speaker embeddings and voice characteristics","Tune synthesis parameters and listen to results in real-time","Share ChatTTS with non-technical users for feedback and testing"],"best_for":["Developers and researchers testing ChatTTS interactively","Teams demoing ChatTTS to stakeholders or users","Non-technical users exploring voice synthesis capabilities"],"limitations":["Web interface is not optimized for high-volume synthesis; suitable for interactive testing only","No built-in authentication or access control; not suitable for public deployment without additional security","Web interface performance depends on browser and network latency; real-time feedback may be slow","Limited parameter tuning options compared to Python API; advanced users may need to use Python directly"],"requires":["Web server running ChatTTS backend (Python with Flask or similar)","Modern web browser (Chrome, Firefox, Safari, Edge)","Network connectivity between browser and server"],"input_types":["text input via web form","speaker selection or generation via web interface"],"output_types":["audio playback in web browser","downloadable audio files (WAV format)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-2noise--chattts__cap_14","uri":"capability://automation.workflow.command.line.interface.for.batch.synthesis.and.scripting","name":"command-line interface for batch synthesis and scripting","description":"Provides a command-line interface (CLI) for batch synthesis, enabling users to synthesize multiple utterances from text files or command-line arguments without writing Python code. The CLI supports common options like input/output paths, speaker selection, sample rate, and refinement control, making it suitable for scripting and automation. The CLI is built on top of the Chat class and exposes its core functionality through command-line arguments.","intents":["Synthesize multiple utterances from text files in batch mode","Integrate ChatTTS into shell scripts or CI/CD pipelines","Process large-scale synthesis tasks without writing Python code","Automate voice synthesis for content generation workflows"],"best_for":["Developers integrating ChatTTS into shell scripts or automation workflows","Teams processing large-scale synthesis tasks","Users without Python programming experience"],"limitations":["CLI is less flexible than Python API; advanced use cases require Python","Batch processing is sequential; no built-in parallelization across multiple processes","Error handling is basic; failures in one utterance may stop the entire batch","Limited parameter tuning options compared to Python API"],"requires":["ChatTTS installed and configured","Command-line shell (bash, zsh, PowerShell, etc.)","Input text file or command-line text argument"],"input_types":["text file (one utterance per line)","command-line text argument","optional: speaker embedding file"],"output_types":["audio files (WAV format, one per input utterance)","optional: output directory specification"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-2noise--chattts__cap_2","uri":"capability://data.processing.analysis.discrete.audio.token.generation.with.speaker.embedding.control","name":"discrete audio token generation with speaker embedding control","description":"Generates sequences of discrete audio tokens (codes) from refined text and speaker embeddings using a transformer-based audio codec. The system encodes speaker characteristics (voice identity, timbre, pitch range) as continuous embeddings that condition the token generation process, enabling voice cloning and speaker variation without retraining the model. Audio tokens are discrete (typically 1024-4096 vocabulary size) rather than continuous, making them more stable and enabling better control over audio quality and speaker consistency.","intents":["Generate consistent audio with a specific speaker identity across multiple utterances","Clone a speaker's voice from a reference audio sample","Vary speaker characteristics (voice type, gender, age) while maintaining text content","Control audio generation at the token level for fine-grained quality tuning"],"best_for":["Teams building multi-speaker voice applications (e.g., audiobook narration with different characters)","Developers implementing voice cloning features for personalized voice assistants","Applications requiring consistent speaker identity across long conversations or sessions"],"limitations":["Speaker embeddings are fixed-size vectors; no direct control over individual voice parameters (pitch, speed, emotion) — only indirect control through embedding space","Voice cloning quality depends on reference audio quality and duration; poor-quality or very short samples (<5 seconds) may produce inconsistent results","Discrete token vocabulary is fixed at model training time; cannot add new speakers or voice characteristics without retraining","Speaker embeddings are not interpretable; difficult to understand what voice characteristics each dimension controls"],"requires":["Speaker embeddings (either random, from sample_random_speaker(), or extracted from audio via sample_audio_speaker())","Audio codec model weights loaded in memory","GPU recommended for real-time generation"],"input_types":["refined text (string or token sequence)","speaker embedding (numpy array, typically 768-1024 dimensions)","optional: reference audio for speaker extraction (WAV format)"],"output_types":["discrete audio tokens (integer sequences, typically 1024-4096 vocabulary)","token logits (probability distributions over vocabulary)"],"categories":["data-processing-analysis","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-2noise--chattts__cap_3","uri":"capability://data.processing.analysis.speaker.embedding.extraction.from.reference.audio","name":"speaker embedding extraction from reference audio","description":"Extracts speaker characteristics (voice identity, timbre, pitch range) from a reference audio sample and encodes them as a continuous embedding vector that can be used to condition subsequent speech synthesis. The system uses the DVAE encoder to process the reference audio and extract speaker-specific features, enabling voice cloning without explicit speaker labels or manual parameter tuning. This embedding can then be reused across multiple synthesis calls to maintain speaker consistency.","intents":["Clone a speaker's voice from a reference audio sample for personalized voice synthesis","Extract speaker identity from existing audio and apply it to new text","Build voice cloning features without requiring manual speaker parameter tuning","Maintain speaker consistency across multiple synthesis calls using a single reference sample"],"best_for":["Developers building voice cloning features for consumer voice apps","Teams implementing speaker-adaptive voice assistants","Applications requiring personalized voice synthesis from user-provided audio samples"],"limitations":["Reference audio quality directly impacts cloning quality; noisy, compressed, or heavily processed audio produces poor embeddings","Requires at least 5-10 seconds of reference audio for reliable speaker extraction; shorter samples may produce inconsistent results","Speaker embeddings are not interpretable or editable — cannot manually adjust voice characteristics after extraction","Extraction assumes reference audio is primarily speech; music, background noise, or non-speech audio may produce invalid embeddings","No built-in validation to check if extracted embedding is valid or high-quality"],"requires":["Reference audio file in WAV format (16kHz or 24kHz sample rate)","DVAE model weights loaded in memory","GPU recommended for fast extraction (CPU extraction is slow)","Reference audio must be primarily speech (minimal background noise)"],"input_types":["audio file path (string) or numpy array (waveform)","sample rate (integer, typically 16000 or 24000 Hz)"],"output_types":["speaker embedding (numpy array, typically 768-1024 dimensions)","embedding can be saved and reused across multiple synthesis calls"],"categories":["data-processing-analysis","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-2noise--chattts__cap_4","uri":"capability://data.processing.analysis.random.speaker.embedding.generation","name":"random speaker embedding generation","description":"Generates random speaker embeddings from a learned distribution, enabling diverse voice synthesis without reference audio or manual speaker specification. The system samples from the speaker embedding space (typically a Gaussian or learned distribution) to create novel speaker identities that are compatible with the synthesis model. This allows applications to generate speech with varied voices without requiring pre-recorded reference samples or explicit speaker parameters.","intents":["Generate speech with diverse, varied voices for multi-character dialogue or audiobook narration","Create novel speaker identities without reference audio or manual parameter tuning","Explore the speaker embedding space to understand voice diversity","Build applications with random voice assignment for each utterance or character"],"best_for":["Developers building multi-character voice applications (audiobooks, games, storytelling)","Teams exploring speaker embedding space for research or debugging","Applications where voice diversity is more important than speaker consistency"],"limitations":["Generated embeddings may produce unusual or unnatural voices at the extremes of the embedding space","No control over voice characteristics (gender, age, accent) — purely random sampling","Generated voices may not be reproducible without storing the embedding vector","No guarantee that random embeddings will produce high-quality audio; some samples may have artifacts or unnatural prosody"],"requires":["Speaker embedding distribution parameters (typically learned during model training)","No external dependencies — purely algorithmic sampling"],"input_types":["optional: random seed for reproducibility (integer)"],"output_types":["speaker embedding (numpy array, typically 768-1024 dimensions)","embedding can be reused across multiple synthesis calls"],"categories":["data-processing-analysis","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-2noise--chattts__cap_5","uri":"capability://text.generation.language.text.normalization.with.language.specific.homophone.handling","name":"text normalization with language-specific homophone handling","description":"Cleans and standardizes input text before synthesis, handling language-specific features such as homophone replacement, number-to-word conversion, and punctuation normalization. The Normalizer component processes text to ensure consistent input to downstream models, handling edge cases like abbreviations, special characters, and language-specific conventions (e.g., Chinese number formatting). This preprocessing step is transparent to users but critical for robust synthesis across diverse input text.","intents":["Handle diverse input text formats (numbers, abbreviations, special characters) consistently","Convert homophones to correct forms for accurate pronunciation in target language","Normalize punctuation and whitespace for consistent speech synthesis","Support both English and Chinese text with language-specific normalization rules"],"best_for":["Applications receiving user-generated text with varied formatting and special characters","Teams building multilingual voice assistants supporting English and Chinese","Systems requiring robust text preprocessing before synthesis"],"limitations":["Normalization rules are fixed and language-specific; cannot customize rules for domain-specific terminology","Homophone replacement is rule-based and may not handle context-dependent homophones correctly","No support for languages other than English and Chinese","Normalization is lossy — original formatting (emphasis, capitalization) is not preserved in output"],"requires":["Input text in English or Chinese","Normalizer model/rules loaded in memory (lightweight, <10MB)"],"input_types":["raw text (string) with any formatting, numbers, abbreviations, special characters"],"output_types":["normalized text (string) ready for synthesis","text tokens (internal representation)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-2noise--chattts__cap_6","uri":"capability://data.processing.analysis.mel.spectrogram.generation.from.discrete.audio.tokens","name":"mel spectrogram generation from discrete audio tokens","description":"Decodes discrete audio tokens into mel spectrograms using a DVAE (Discrete Variational Autoencoder) decoder, converting the compact token representation into a continuous acoustic representation suitable for vocoding. The DVAE decoder maps from discrete token space to continuous spectrogram space, enabling the separation of content (tokens) from acoustic details (spectrogram). This intermediate representation allows for flexible audio processing and quality control before final waveform generation.","intents":["Convert discrete audio tokens into continuous spectrograms for vocoding","Enable inspection and manipulation of spectrograms before waveform generation","Separate content (tokens) from acoustic details (spectrograms) for flexible audio processing","Support alternative vocoders or post-processing on spectrograms"],"best_for":["Developers building custom audio processing pipelines with spectrogram manipulation","Teams implementing alternative vocoders or audio post-processing","Research applications requiring access to intermediate acoustic representations"],"limitations":["Spectrogram generation is deterministic given tokens; no stochasticity or variation in acoustic details","Spectrograms are in mel-scale; conversion to linear scale requires additional processing","No direct control over spectrogram characteristics (frequency resolution, time resolution) — fixed by model architecture","Spectrograms are not human-interpretable; difficult to debug or understand acoustic issues"],"requires":["Discrete audio tokens (from _infer_code() or external source)","DVAE decoder model weights loaded in memory (~500MB-1GB)","GPU recommended for fast decoding"],"input_types":["discrete audio tokens (integer sequences, typically 1024-4096 vocabulary)","optional: speaker embeddings (for conditioning, if supported)"],"output_types":["mel spectrograms (numpy arrays, shape: [time_steps, mel_bins])","typically 80-128 mel bins, time resolution ~20ms per frame"],"categories":["data-processing-analysis","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-2noise--chattts__cap_7","uri":"capability://data.processing.analysis.neural.vocoding.with.vocos.for.waveform.generation","name":"neural vocoding with vocos for waveform generation","description":"Converts mel spectrograms into high-quality audio waveforms using Vocos, a neural vocoder trained on large-scale speech data. Vocos operates on mel spectrograms and generates raw waveforms at the target sample rate (16kHz or 24kHz), enabling fast, high-quality audio synthesis without traditional signal processing. The vocoder is a separate component that can be swapped or fine-tuned independently, providing flexibility for quality tuning or domain adaptation.","intents":["Convert mel spectrograms into high-quality audio waveforms at target sample rate","Generate audio with minimal artifacts and natural-sounding quality","Support multiple sample rates (16kHz, 24kHz) for different quality/bandwidth tradeoffs","Enable fast waveform generation for real-time or near-real-time applications"],"best_for":["Developers building real-time or near-real-time voice synthesis applications","Teams requiring high-quality audio output with minimal artifacts","Applications supporting multiple sample rates for different use cases"],"limitations":["Vocoder quality depends on mel spectrogram quality; poor spectrograms produce poor audio","Vocos is trained on general speech data; may not generalize well to non-speech audio or highly specialized domains","No direct control over vocoding parameters (e.g., noise level, artifacts) — vocoding is deterministic given spectrogram","Vocoder is fixed at model training time; cannot adapt to new domains without retraining or fine-tuning"],"requires":["Mel spectrograms (from DVAE decoder or external source)","Vocos model weights loaded in memory (~200-500MB)","GPU recommended for fast vocoding (CPU vocoding is slow)"],"input_types":["mel spectrograms (numpy arrays, shape: [time_steps, mel_bins])","sample rate specification (16000 or 24000 Hz)"],"output_types":["audio waveforms (numpy arrays, dtype: float32 or int16)","WAV files (optional, written directly to disk)"],"categories":["data-processing-analysis","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-2noise--chattts__cap_8","uri":"capability://automation.workflow.batch.inference.with.multi.utterance.synthesis","name":"batch inference with multi-utterance synthesis","description":"Processes multiple text utterances in a single inference call, enabling efficient batch synthesis with shared model state and optimized GPU utilization. The system batches text normalization, refinement, token generation, and decoding steps, reducing per-utterance overhead and enabling faster throughput for multi-utterance synthesis. Batch processing is transparent to users — the infer() method handles batching automatically based on input type (list of strings).","intents":["Synthesize multiple utterances efficiently in a single call","Reduce per-utterance latency overhead by batching model operations","Generate dialogue with multiple speakers or turns in one batch","Maximize GPU utilization for high-throughput synthesis applications"],"best_for":["Applications generating multiple utterances per request (e.g., dialogue systems, audiobook narration)","Teams optimizing for throughput over latency","Systems with GPU resources that benefit from batch processing"],"limitations":["Batch size is limited by GPU memory; large batches may cause out-of-memory errors","All utterances in a batch must use the same speaker embedding; different speakers require separate batches","Batch processing adds complexity to error handling — one failed utterance may affect the entire batch","Latency for first utterance in batch is similar to single inference; batch benefits only appear with multiple utterances"],"requires":["Input as list of strings (for batch processing) or single string (for single inference)","GPU with sufficient memory for batch size (typically 4-16 utterances per batch on 8GB VRAM)","Optional: speaker embeddings (same for all utterances in batch)"],"input_types":["list of text strings (for batch processing)","single text string (for single inference, automatically batched as size-1 batch)"],"output_types":["list of audio waveforms (one per input utterance)","list of WAV files (optional, written to disk)"],"categories":["automation-workflow","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-2noise--chattts__cap_9","uri":"capability://automation.workflow.configurable.inference.parameters.with.skip.refinement.option","name":"configurable inference parameters with skip-refinement option","description":"Provides fine-grained control over the inference pipeline through configuration parameters, including the ability to skip text refinement for latency-critical applications, control sample rates, and adjust decoding strategies. The Chat class exposes parameters like skip_refine_text, sample_rate, and decoder selection, enabling users to trade off between quality and latency. 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