{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-qwen--qwen3-tts-12hz-1.7b-customvoice","slug":"qwen--qwen3-tts-12hz-1.7b-customvoice","name":"Qwen3-TTS-12Hz-1.7B-CustomVoice","type":"model","url":"https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice","page_url":"https://unfragile.ai/qwen--qwen3-tts-12hz-1.7b-customvoice","categories":["voice-audio"],"tags":["safetensors","qwen3_tts","text-to-speech","arxiv:2601.15621","license:apache-2.0","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-qwen--qwen3-tts-12hz-1.7b-customvoice__cap_0","uri":"capability://image.visual.low.latency.text.to.speech.synthesis.with.12hz.audio.streaming","name":"low-latency text-to-speech synthesis with 12hz audio streaming","description":"Generates natural speech audio from text input using a 1.7B parameter transformer-based architecture optimized for 12Hz (120ms chunk) streaming inference. The model processes text through an encoder-decoder attention mechanism with streaming-compatible positional encodings, enabling real-time audio generation without buffering entire utterances. Outputs 16kHz mono PCM audio in streaming chunks compatible with WebRTC and live playback systems.","intents":["I need to generate speech from text with minimal latency for real-time conversational AI applications","I want to stream audio output to users without waiting for full synthesis completion","I need a lightweight TTS model that runs on edge devices or resource-constrained servers","I'm building a live translation or real-time transcription system that needs synchronized speech output"],"best_for":["developers building real-time conversational AI agents and chatbots","teams deploying edge TTS for mobile or IoT applications","builders creating live streaming or WebRTC-based communication platforms","researchers optimizing inference latency for speech synthesis"],"limitations":["12Hz streaming chunk size introduces ~120ms minimum latency per audio segment; not suitable for sub-100ms latency requirements","1.7B parameter model may produce less natural prosody and emotion variation compared to larger models (>3B parameters)","Streaming architecture requires stateful inference session management; incompatible with stateless serverless deployments without session persistence","No built-in support for voice cloning or speaker adaptation without fine-tuning on custom voice datasets","Audio quality degrades on out-of-domain text (e.g., highly technical jargon, non-Latin scripts without explicit training)"],"requires":["Python 3.8+","PyTorch 2.0+ or compatible ONNX runtime","transformers library 4.30+","safetensors library for model loading","16GB+ VRAM for GPU inference or 8GB+ RAM for CPU inference","HuggingFace Hub access for model download (1.7B model weights)"],"input_types":["plain text (UTF-8 encoded)","SSML markup (limited support for prosody tags)","text with language tags for multilingual synthesis"],"output_types":["PCM audio (16-bit, 16kHz mono)","streaming audio chunks (120ms segments at 12Hz)","WAV file format","raw audio tensor (PyTorch/NumPy)"],"categories":["image-visual","audio-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen3-tts-12hz-1.7b-customvoice__cap_1","uri":"capability://image.visual.custom.voice.adaptation.and.speaker.embedding.injection","name":"custom voice adaptation and speaker embedding injection","description":"Supports voice customization through speaker embedding injection into the synthesis pipeline, allowing users to clone or adapt voice characteristics from reference audio samples. The model accepts pre-computed speaker embeddings (typically 256-512 dimensional vectors) that condition the decoder to produce speech with target speaker characteristics. Embeddings can be extracted from reference audio using a companion speaker encoder or provided directly via API.","intents":["I want to generate speech that sounds like a specific person or voice profile without retraining the model","I need to maintain consistent voice identity across multiple TTS calls in a conversational system","I'm building a personalized voice assistant that adapts to user preferences","I want to create audiobook narration with multiple distinct character voices from a single TTS model"],"best_for":["developers building personalized voice assistant applications","content creators producing audiobooks or podcasts with multiple voice characters","teams implementing voice cloning features in consumer applications","researchers studying speaker adaptation in neural speech synthesis"],"limitations":["Requires reference audio samples (minimum 5-10 seconds recommended) to extract speaker embeddings; zero-shot voice cloning not supported","Voice adaptation quality depends on speaker embedding quality; poor reference audio produces degraded synthesis","No explicit speaker identity preservation across very long utterances (>2 minutes); speaker drift may occur in streaming mode","Custom voice adaptation requires fine-tuning or embedding extraction pipeline; not available as simple parameter in base model","Limited to speaker characteristics learned during training; cannot synthesize entirely novel voice profiles outside training distribution"],"requires":["Pre-computed speaker embeddings (256-512 dimensional vectors) or reference audio for embedding extraction","Speaker encoder model (separate component, not included in base TTS model)","Audio preprocessing pipeline for reference audio normalization (16kHz, mono, 5-10 seconds duration)","PyTorch or ONNX runtime for embedding computation"],"input_types":["speaker embedding vectors (float32, 256-512 dimensions)","reference audio files (WAV, MP3, OGG formats, 16kHz preferred)","speaker identity identifiers (if using pre-computed embedding cache)"],"output_types":["PCM audio with adapted speaker characteristics","speaker embedding vectors (extracted from reference audio)","metadata indicating speaker adaptation parameters used"],"categories":["image-visual","audio-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen3-tts-12hz-1.7b-customvoice__cap_2","uri":"capability://image.visual.multilingual.text.to.speech.synthesis.with.language.aware.tokenization","name":"multilingual text-to-speech synthesis with language-aware tokenization","description":"Synthesizes natural speech across multiple languages using a unified transformer architecture with language-aware tokenization and script-specific processing. The model includes language identification and automatic script detection, routing text through appropriate phoneme or character encoders before synthesis. Supports mixing languages within single utterances with automatic language boundary detection.","intents":["I need to generate speech in multiple languages from a single model without switching between different TTS systems","I want to create multilingual voice assistants that handle code-switched text (mixing languages in one sentence)","I'm building a global application that needs TTS support for 10+ languages with consistent voice identity","I need automatic language detection and appropriate phoneme handling for each language"],"best_for":["developers building global voice applications serving multiple language markets","teams creating multilingual chatbots and voice assistants","content creators producing multilingual audiobooks or educational materials","researchers studying cross-lingual speech synthesis"],"limitations":["Synthesis quality varies significantly across languages; languages with less training data (e.g., low-resource languages) produce less natural output","Code-switching (language mixing) support is limited; model may struggle with rapid language alternation within single utterances","Phoneme inventory differs across languages; some language pairs may have pronunciation conflicts or ambiguities","Accent and prosody characteristics are averaged across training languages; cannot produce language-specific accent variations","No explicit support for language-specific punctuation or text normalization rules; requires preprocessing"],"requires":["Text input with language tags or automatic language detection enabled","Phoneme or character inventory for each supported language","Language-specific text normalization rules (number-to-word conversion, abbreviation expansion, etc.)","Training data coverage for target languages (model quality depends on training data availability)"],"input_types":["plain text with language tags (e.g., <lang:en>Hello</lang:en> <lang:zh>你好</lang:zh>)","text with automatic language detection","SSML with language attributes","phoneme sequences (for advanced users)"],"output_types":["PCM audio with multilingual synthesis","language boundary markers in output metadata","per-language confidence scores (if language detection enabled)"],"categories":["image-visual","audio-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen3-tts-12hz-1.7b-customvoice__cap_3","uri":"capability://image.visual.streaming.inference.with.stateful.attention.caching.for.real.time.synthesis","name":"streaming inference with stateful attention caching for real-time synthesis","description":"Implements streaming-compatible inference using KV-cache (key-value cache) for attention layers, enabling incremental audio generation as text tokens arrive. The model maintains state across 12Hz chunks, computing only new attention interactions for incoming tokens rather than recomputing full attention matrices. Compatible with online text streaming (e.g., from live transcription or token-by-token LLM output).","intents":["I want to generate speech in real-time as text tokens arrive from an LLM or live transcription system","I need to minimize latency by starting audio playback before the entire text is available","I'm building a live translation system that needs synchronized speech output with incoming text","I want to reduce memory usage during inference by avoiding full attention recomputation"],"best_for":["developers building real-time LLM-powered voice assistants","teams implementing live transcription with synchronized speech output","builders creating low-latency streaming audio applications","researchers optimizing inference efficiency for speech synthesis"],"limitations":["Streaming inference requires maintaining state across chunks; incompatible with stateless serverless architectures without external state persistence","KV-cache memory overhead grows linearly with utterance length; very long utterances (>5 minutes) may exceed GPU memory","Attention patterns computed on partial context may differ from full-context synthesis; minor quality degradation possible","Streaming chunks are fixed at 12Hz (120ms); cannot adjust chunk size without retraining","State synchronization across distributed inference nodes is complex; not suitable for multi-GPU inference without careful orchestration"],"requires":["GPU with sufficient VRAM for KV-cache storage (minimum 8GB for typical utterances)","PyTorch 2.0+ with CUDA support for efficient cache operations","Streaming-aware inference framework (custom implementation or compatible library)","Text input stream with token-level granularity (not batch processing)"],"input_types":["streaming text tokens (one or more tokens per inference step)","text chunks with variable length","token IDs from tokenizer"],"output_types":["streaming audio chunks (120ms segments at 12Hz)","intermediate attention states (for debugging or analysis)","cache statistics (cache size, memory usage)"],"categories":["image-visual","audio-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen3-tts-12hz-1.7b-customvoice__cap_4","uri":"capability://image.visual.efficient.inference.optimization.with.quantization.and.model.compression","name":"efficient inference optimization with quantization and model compression","description":"Provides optimized inference through quantization-aware training and model compression techniques, reducing model size from full precision to 8-bit or 4-bit integer representations while maintaining synthesis quality. Supports multiple quantization backends (ONNX, TensorRT, vLLM) for hardware-specific optimization. Enables deployment on resource-constrained devices (mobile, edge) with minimal quality degradation.","intents":["I need to deploy TTS on mobile devices or edge hardware with limited VRAM and compute","I want to reduce model size for faster downloads and lower storage requirements","I'm optimizing inference latency for production deployments with strict SLA requirements","I need to run multiple TTS instances on a single GPU or CPU with limited resources"],"best_for":["mobile developers deploying on-device TTS for iOS/Android applications","edge computing teams deploying TTS on IoT devices or embedded systems","infrastructure teams optimizing cost and latency for large-scale TTS deployments","researchers studying model compression and quantization for speech synthesis"],"limitations":["Quantization introduces minor quality degradation; 4-bit quantization may produce audible artifacts in some cases","Quantized models require specific hardware support (e.g., INT8 CUDA cores); not all devices benefit equally","Quantization-aware training requires retraining or fine-tuning; pre-quantized models may not be available for all variants","Quantized inference frameworks have limited operator support; some custom layers may not be quantizable","Dynamic quantization (post-training) produces lower quality than static quantization; requires calibration dataset"],"requires":["Quantization framework (ONNX, TensorRT, or PyTorch quantization tools)","Calibration dataset for static quantization (optional but recommended)","Hardware support for target quantization format (INT8, INT4, etc.)","Quantization-aware inference runtime (ONNX Runtime, TensorRT, or vLLM)"],"input_types":["full-precision model weights (FP32)","quantization configuration (bit-width, calibration method)","calibration dataset (for static quantization)"],"output_types":["quantized model weights (INT8, INT4, or mixed precision)","quantization metadata (scale factors, zero points)","performance benchmarks (latency, memory usage, quality metrics)"],"categories":["image-visual","audio-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen3-tts-12hz-1.7b-customvoice__cap_5","uri":"capability://image.visual.ssml.based.prosody.and.speech.control.with.fine.grained.markup","name":"ssml-based prosody and speech control with fine-grained markup","description":"Supports SSML (Speech Synthesis Markup Language) annotations for controlling prosody, speech rate, pitch, and emphasis at sub-utterance granularity. Parses SSML tags and converts them into continuous control signals injected into the decoder, enabling precise control over speech characteristics without model retraining. Supports standard SSML tags (speak, prosody, emphasis, break) plus custom extensions for speaker and voice control.","intents":["I need to control speech rate, pitch, and volume for specific words or phrases in synthesized speech","I want to add emphasis or emotional coloring to certain parts of the text","I'm creating audiobooks or educational content that requires precise prosody control","I need to insert pauses or breaks at specific points in the synthesis"],"best_for":["content creators producing audiobooks, podcasts, or educational materials","developers building voice assistants with fine-grained prosody control","teams creating accessible audio content with specific prosody requirements","researchers studying prosody modeling in neural speech synthesis"],"limitations":["SSML parsing and control signal generation adds ~50-100ms latency per utterance","Prosody control is approximate; model may not achieve exact pitch or rate targets due to acoustic constraints","Complex SSML with many nested tags may produce unexpected interactions or artifacts","Custom SSML extensions are not standardized; portability to other TTS systems is limited","Prosody control effectiveness depends on training data; underrepresented prosody patterns may not be synthesizable"],"requires":["SSML-formatted text input with valid XML structure","SSML parser (included in model implementation)","Prosody control signal generation module","Training data with prosody annotations (for model to learn prosody patterns)"],"input_types":["SSML-formatted text with prosody tags","plain text with inline SSML markup","structured prosody control parameters (rate, pitch, volume as numeric values)"],"output_types":["PCM audio with applied prosody modifications","prosody control signal visualization (for debugging)","metadata indicating applied prosody parameters"],"categories":["image-visual","audio-generation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":52,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","PyTorch 2.0+ or compatible ONNX runtime","transformers library 4.30+","safetensors library for model loading","16GB+ VRAM for GPU inference or 8GB+ RAM for CPU inference","HuggingFace Hub access for model download (1.7B model weights)","Pre-computed speaker embeddings (256-512 dimensional vectors) or reference audio for embedding extraction","Speaker encoder model (separate component, not included in base TTS model)","Audio preprocessing pipeline for reference audio normalization (16kHz, mono, 5-10 seconds duration)","PyTorch or ONNX runtime for embedding computation"],"failure_modes":["12Hz streaming chunk size introduces ~120ms minimum latency per audio segment; not suitable for sub-100ms latency requirements","1.7B parameter model may produce less natural prosody and emotion variation compared to larger models (>3B parameters)","Streaming architecture requires stateful inference session management; incompatible with stateless serverless deployments without session persistence","No built-in support for voice cloning or speaker adaptation without fine-tuning on custom voice datasets","Audio quality degrades on out-of-domain text (e.g., highly technical jargon, non-Latin scripts without explicit training)","Requires reference audio samples (minimum 5-10 seconds recommended) to extract speaker embeddings; zero-shot voice cloning not supported","Voice adaptation quality depends on speaker embedding quality; poor reference audio produces degraded synthesis","No explicit speaker identity preservation across very long utterances (>2 minutes); speaker drift may occur in streaming mode","Custom voice adaptation requires fine-tuning or embedding extraction pipeline; not available as simple parameter in base model","Limited to speaker characteristics learned during training; cannot synthesize entirely novel voice profiles outside training distribution","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8026141317520388,"quality":0.37,"ecosystem":0.48000000000000004,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.765Z","last_scraped_at":"2026-05-03T14:22:51.286Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":1766526,"model_likes":1447}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=qwen--qwen3-tts-12hz-1.7b-customvoice","compare_url":"https://unfragile.ai/compare?artifact=qwen--qwen3-tts-12hz-1.7b-customvoice"}},"signature":"NFN8C7522WwW1dHbJk3hThpM9HuW+UFjBAbA7rKsoqyo9ezp4n+1aFO97P6jxrj0PKvYMSrPE3EJt1pKScZlAw==","signedAt":"2026-06-20T23:06:48.654Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qwen--qwen3-tts-12hz-1.7b-customvoice","artifact":"https://unfragile.ai/qwen--qwen3-tts-12hz-1.7b-customvoice","verify":"https://unfragile.ai/api/v1/verify?slug=qwen--qwen3-tts-12hz-1.7b-customvoice","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"}}