{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-humeai--tada-3b-ml","slug":"humeai--tada-3b-ml","name":"tada-3b-ml","type":"model","url":"https://huggingface.co/HumeAI/tada-3b-ml","page_url":"https://unfragile.ai/humeai--tada-3b-ml","categories":["voice-audio"],"tags":["safetensors","llama","tts","text-to-speech","speech-language-model","en","ja","de","fr","es","ch","ar","it","pl","pt","arxiv:2602.23068","base_model:meta-llama/Llama-3.2-3B","base_model:finetune:meta-llama/Llama-3.2-3B","license:llama3.2","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-humeai--tada-3b-ml__cap_0","uri":"capability://text.generation.language.multilingual.text.to.speech.synthesis.with.speech.language.modeling","name":"multilingual text-to-speech synthesis with speech-language modeling","description":"Generates natural-sounding speech from text input across 10 languages (English, Japanese, German, French, Spanish, Chinese, Arabic, Italian, Polish, Portuguese) using a fine-tuned Llama 3.2 3B base model adapted for speech token prediction. The model operates as a speech language model that predicts acoustic tokens from text, enabling end-to-end neural TTS without separate acoustic and vocoder stages. Architecture leverages transformer-based sequence-to-sequence modeling with language-specific tokenization and acoustic feature prediction.","intents":["Generate natural speech audio from text in multiple languages without maintaining separate language-specific models","Build multilingual voice applications with a single unified model checkpoint","Create speech synthesis pipelines that preserve semantic meaning across language boundaries","Deploy TTS inference on resource-constrained devices using a 3B parameter model"],"best_for":["Developers building multilingual voice assistants and chatbots","Teams deploying TTS in production with limited GPU/CPU budgets","Researchers experimenting with speech language models and acoustic token prediction","Organizations needing open-source TTS without commercial licensing restrictions"],"limitations":["3B parameter size may produce lower quality speech compared to larger proprietary models (>7B parameters)","Inference latency and real-time factor unknown — likely requires GPU acceleration for acceptable streaming performance","No documented support for voice cloning, speaker adaptation, or prosody control beyond text input","Training data composition and language coverage balance not publicly detailed — may have uneven quality across 10 languages","Requires acoustic token decoder/vocoder downstream to convert model outputs to waveform audio — not included in base model"],"requires":["Python 3.8+","PyTorch 2.0+ or compatible deep learning framework","Transformers library (HuggingFace) 4.30+","GPU with 8GB+ VRAM recommended for inference (CPU inference possible but slow)","Safetensors library for model loading","Audio processing library (librosa, soundfile, or equivalent) for waveform handling"],"input_types":["text (UTF-8 encoded strings in supported languages)","language identifier or language-specific tokenization hints"],"output_types":["acoustic tokens (discrete token sequences representing speech features)","audio waveform (after downstream vocoder decoding to 16kHz or 24kHz PCM)"],"categories":["text-generation-language","voice-audio","speech-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-humeai--tada-3b-ml__cap_1","uri":"capability://text.generation.language.language.aware.acoustic.token.prediction.with.transformer.attention","name":"language-aware acoustic token prediction with transformer attention","description":"Predicts sequences of discrete acoustic tokens from input text by leveraging transformer self-attention mechanisms to model long-range dependencies between phonetic content and acoustic features. The model learns language-specific phoneme-to-acoustic mappings through fine-tuning on multilingual speech corpora, enabling it to generate contextually appropriate acoustic tokens that capture prosody, duration, and spectral characteristics. Token prediction operates at frame-level granularity (typically 50-100ms acoustic frames) with attention masking to enforce causal generation.","intents":["Convert text directly to acoustic token sequences without intermediate phoneme or linguistic feature extraction","Leverage transformer attention to capture long-range prosodic dependencies (stress patterns, intonation contours)","Enable language-specific acoustic modeling within a single unified architecture","Generate variable-length acoustic sequences that match input text length and linguistic structure"],"best_for":["Researchers studying end-to-end speech synthesis and discrete acoustic representations","Developers building TTS systems that require fine-grained control over acoustic token sequences","Teams implementing custom vocoders or acoustic decoders that consume token streams"],"limitations":["Token prediction quality depends heavily on acoustic tokenizer training — no details on tokenizer architecture or codebook size","Attention mechanism scales quadratically with sequence length — may struggle with very long documents or high-frequency acoustic frames","No documented mechanism for controlling speaking rate, pitch, or other prosodic parameters beyond text content","Discrete token representation may lose fine-grained acoustic details compared to continuous acoustic features (mel-spectrograms)"],"requires":["Pre-trained acoustic tokenizer (codebook-based, likely VQ-VAE or similar) — must be compatible with model's token vocabulary","Text tokenizer supporting 10 languages with proper Unicode handling","Transformer inference framework with attention implementation (PyTorch, JAX, or TensorFlow)"],"input_types":["text tokens (language-specific tokenized text sequences)","language identifier (to enable language-specific attention patterns or embeddings)"],"output_types":["acoustic token sequences (integer token IDs from discrete codebook, typically 1000-4096 vocabulary size)","token logits or probability distributions (for sampling or beam search decoding)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-humeai--tada-3b-ml__cap_2","uri":"capability://text.generation.language.cross.lingual.acoustic.feature.transfer.with.shared.embedding.space","name":"cross-lingual acoustic feature transfer with shared embedding space","description":"Encodes text from different languages into a shared semantic embedding space where acoustic token predictions generalize across languages, enabling zero-shot or few-shot TTS for languages with limited training data. The fine-tuned Llama 3.2 model leverages multilingual pre-training to map phonetically similar sounds across languages to similar acoustic tokens, using shared transformer layers with language-specific input embeddings or adapter modules. This approach allows the model to transfer acoustic knowledge from high-resource languages (English) to lower-resource languages (Arabic, Polish) without retraining.","intents":["Synthesize speech in languages with limited training data by leveraging acoustic patterns from high-resource languages","Build TTS systems that handle code-switching (mixing multiple languages in single utterance) gracefully","Reduce training data requirements for adding new languages to existing TTS system","Enable consistent acoustic characteristics across multilingual applications"],"best_for":["Developers building TTS for low-resource or endangered languages","Teams supporting multilingual applications with uneven data availability per language","Researchers studying cross-lingual transfer in speech synthesis"],"limitations":["Transfer quality depends on phonetic similarity between source and target languages — may fail for typologically distant languages","No documented evaluation metrics for cross-lingual transfer performance — unclear which language pairs work well","Shared embedding space may create acoustic artifacts when languages have conflicting phonotactic patterns","Fine-tuning on multilingual data may reduce per-language quality compared to language-specific models"],"requires":["Multilingual pre-trained transformer (Llama 3.2 provides this foundation)","Phonetic inventory mapping or linguistic features for target languages","Training data from at least one high-resource language for acoustic token codebook training"],"input_types":["text in any of 10 supported languages","language identifier to select appropriate input embedding"],"output_types":["acoustic tokens (shared codebook across all languages)","language-specific acoustic token sequences"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-humeai--tada-3b-ml__cap_3","uri":"capability://automation.workflow.efficient.3b.parameter.inference.with.quantization.and.batching.support","name":"efficient 3b-parameter inference with quantization and batching support","description":"Optimizes inference latency and memory footprint through 3B parameter model size (vs. 7B+ alternatives) while supporting batch processing of multiple text inputs simultaneously. The model can be loaded with quantization techniques (int8, fp16, or bfloat16) to reduce memory requirements from ~6GB (fp32) to ~3GB (fp16) or lower, enabling deployment on consumer GPUs and edge devices. Batching support allows processing multiple text-to-speech requests in parallel, amortizing model loading overhead and improving throughput for production TTS services.","intents":["Deploy TTS inference on resource-constrained devices (laptops, mobile, edge servers) with limited VRAM","Build high-throughput TTS services that process multiple synthesis requests concurrently","Reduce inference latency per request through batch processing and optimized model size","Minimize operational costs by running TTS on cheaper hardware (CPU or small GPUs)"],"best_for":["Solo developers and small teams with limited GPU budgets","Edge deployment scenarios (on-device TTS for accessibility apps, voice assistants)","Production TTS services requiring high throughput with cost optimization","Researchers benchmarking model efficiency vs. quality tradeoffs"],"limitations":["3B parameter size likely produces lower audio quality than 7B+ models — no published quality benchmarks (MOS scores) available","Batch processing introduces latency for real-time streaming use cases — requires buffering multiple requests","Quantization may introduce subtle audio artifacts or reduce acoustic detail — no evaluation of quantization impact on speech quality","Inference speed and real-time factor (RTF) not documented — unclear if model meets real-time requirements on typical hardware","No built-in support for dynamic batching or request queuing — requires external orchestration for production deployment"],"requires":["GPU with 4GB+ VRAM (fp16) or 8GB+ (fp32), or CPU with 16GB+ RAM for CPU inference","PyTorch or compatible framework with quantization support (bitsandbytes, GPTQ, or similar)","Batch processing framework or custom batching logic for concurrent request handling","Profiling tools to measure latency and throughput on target hardware"],"input_types":["text (single or batch of multiple text strings)","batch size parameter (number of concurrent synthesis requests)"],"output_types":["acoustic token sequences (single or batch of token sequences)","latency metrics (inference time per request, throughput in requests/second)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-humeai--tada-3b-ml__cap_4","uri":"capability://automation.workflow.safetensors.model.serialization.with.reproducible.checkpoint.loading","name":"safetensors model serialization with reproducible checkpoint loading","description":"Stores model weights in safetensors format (memory-safe, fast-loading binary format) instead of PyTorch pickle format, enabling secure model distribution and reproducible inference across different hardware and software environments. Safetensors provides built-in integrity checking, prevents arbitrary code execution during model loading, and supports lazy loading of large models without loading entire checkpoint into memory. This approach ensures model reproducibility and security for production TTS deployments.","intents":["Load pre-trained TTS models securely without risk of arbitrary code execution from untrusted checkpoints","Ensure reproducible inference results across different machines and PyTorch versions","Reduce model loading time and memory overhead through lazy loading and efficient serialization","Distribute models safely in production environments with security auditing requirements"],"best_for":["Production TTS services with security and reproducibility requirements","Teams distributing models across heterogeneous hardware (different GPUs, CPUs, cloud providers)","Organizations with strict security policies prohibiting pickle-based model loading","Researchers requiring reproducible model checkpoints for publication and peer review"],"limitations":["Safetensors format requires explicit library support — not all inference frameworks support native safetensors loading (requires safetensors Python library)","Lazy loading may introduce latency on first access to model weights — not suitable for ultra-low-latency inference","No built-in versioning or checkpoint metadata — requires external tracking of model versions and training hyperparameters","Safetensors format is immutable after creation — cannot patch or modify weights without regenerating entire checkpoint"],"requires":["safetensors Python library (pip install safetensors)","PyTorch 1.12+ or compatible framework with safetensors integration","Sufficient disk space for model checkpoint (~6-12GB for 3B parameter model in safetensors format)"],"input_types":["safetensors checkpoint file (.safetensors extension)","model configuration (JSON or YAML specifying architecture, tokenizer, etc.)"],"output_types":["loaded model state dict (PyTorch nn.Module or equivalent)","model metadata (parameter count, architecture details, training configuration)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 2.0+ or compatible deep learning framework","Transformers library (HuggingFace) 4.30+","GPU with 8GB+ VRAM recommended for inference (CPU inference possible but slow)","Safetensors library for model loading","Audio processing library (librosa, soundfile, or equivalent) for waveform handling","Pre-trained acoustic tokenizer (codebook-based, likely VQ-VAE or similar) — must be compatible with model's token vocabulary","Text tokenizer supporting 10 languages with proper Unicode handling","Transformer inference framework with attention implementation (PyTorch, JAX, or TensorFlow)","Multilingual pre-trained transformer (Llama 3.2 provides this foundation)"],"failure_modes":["3B parameter size may produce lower quality speech compared to larger proprietary models (>7B parameters)","Inference latency and real-time factor unknown — likely requires GPU acceleration for acceptable streaming performance","No documented support for voice cloning, speaker adaptation, or prosody control beyond text input","Training data composition and language coverage balance not publicly detailed — may have uneven quality across 10 languages","Requires acoustic token decoder/vocoder downstream to convert model outputs to waveform audio — not included in base model","Token prediction quality depends heavily on acoustic tokenizer training — no details on tokenizer architecture or codebook size","Attention mechanism scales quadratically with sequence length — may struggle with very long documents or high-frequency acoustic frames","No documented mechanism for controlling speaking rate, pitch, or other prosodic parameters beyond text content","Discrete token representation may lose fine-grained acoustic details compared to continuous acoustic features (mel-spectrograms)","Transfer quality depends on phonetic similarity between source and target languages — may fail for typologically distant languages","builder identity is not verified yet","no observed match outcomes 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