{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"audiocraft","slug":"audiocraft","name":"AudioCraft","type":"repo","url":"https://github.com/facebookresearch/audiocraft","page_url":"https://unfragile.ai/audiocraft","categories":["voice-audio"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"audiocraft__cap_0","uri":"capability://image.visual.text.to.music.generation.with.controllable.parameters","name":"text-to-music generation with controllable parameters","description":"Generates high-fidelity music from text descriptions using MusicGen, a transformer-based language model that operates on discrete audio tokens produced by EnCodec. The model uses a two-stage pipeline: text conditioning through embeddings, followed by autoregressive token generation that is decoded back to waveform audio. Supports duration control, temperature sampling, and top-k/top-p filtering for output variation.","intents":["Generate background music for videos from natural language descriptions","Create multiple musical variations from a single text prompt","Control music generation length and sampling parameters for different use cases","Integrate music generation into creative workflows without manual composition"],"best_for":["content creators building video/game audio pipelines","music researchers experimenting with generative models","developers prototyping AI-driven creative applications"],"limitations":["Generation quality depends on text description clarity; vague prompts produce inconsistent results","Inference latency scales with audio duration (30 seconds typically requires 10-30 seconds on GPU)","No real-time streaming generation; full audio must be generated before playback","Limited to 30-second maximum generation length in standard configuration","Model trained on specific music domains; may struggle with niche genres or highly specific styles"],"requires":["PyTorch 2.0+","Python 3.9+","CUDA 11.8+ for GPU acceleration (CPU inference extremely slow)","4GB+ VRAM for small models, 16GB+ for larger variants","Pre-trained MusicGen model weights (~3.5GB download)"],"input_types":["text descriptions (natural language)","optional melody/audio conditioning (for style variants)","generation parameters (duration, temperature, top_k)"],"output_types":["audio waveform (16kHz or 32kHz sample rate)","WAV format","discrete token sequences (intermediate representation)"],"categories":["image-visual","audio-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"audiocraft__cap_1","uri":"capability://image.visual.text.to.sound.effect.generation","name":"text-to-sound effect generation","description":"Generates diverse sound effects and ambient audio from text descriptions using AudioGen, a variant of the MusicGen architecture adapted for non-musical audio. Operates through the same tokenization-generation-decoding pipeline but trained on sound effect datasets with different conditioning strategies optimized for environmental and synthetic sounds.","intents":["Generate sound effects for games, films, or interactive media from text descriptions","Create foley audio programmatically without recording sessions","Produce ambient soundscapes and environmental audio for applications","Batch-generate variations of sound effects for testing or content creation"],"best_for":["game developers needing procedural sound generation","film/video editors prototyping audio before professional recording","accessibility developers creating audio descriptions","researchers studying audio generation beyond music"],"limitations":["Quality varies significantly with prompt specificity; generic descriptions produce generic sounds","No control over sound duration beyond generation length parameter","Cannot guarantee realistic physics-based audio (e.g., impact sounds may not match visual timing)","Limited to 30-second generation window","Training data bias toward common sound effects; rare or specialized sounds may be poorly generated"],"requires":["PyTorch 2.0+","Python 3.9+","CUDA 11.8+ for practical inference speed","4GB+ VRAM minimum","AudioGen pre-trained model weights (~3.5GB)"],"input_types":["text descriptions of desired sounds","generation parameters (duration, sampling temperature)","optional audio conditioning (for style transfer variants)"],"output_types":["audio waveform (16kHz or 32kHz)","WAV format","token sequences"],"categories":["image-visual","audio-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"audiocraft__cap_10","uri":"capability://tool.use.integration.flexible.model.configuration.and.composition","name":"flexible model configuration and composition","description":"Provides a modular configuration system enabling composition of different components (compression models, language models, conditioning systems) into custom audio generation pipelines. Models are defined through YAML/JSON configs that specify architecture, hyperparameters, and component connections. Enables swapping components (e.g., using different encoders or decoders) without code changes.","intents":["Compose custom audio generation models by combining different pre-trained components","Experiment with alternative architectures (different encoders, decoders, language models)","Configure models for different hardware constraints (memory, latency)","Reproduce published models or create variants for research"],"best_for":["researchers experimenting with model architectures","developers customizing AudioCraft for specific use cases","teams managing multiple model variants for different applications","organizations fine-tuning models on custom data"],"limitations":["Configuration complexity increases with model complexity; large configs are difficult to manage","Not all component combinations are tested; some may produce unexpected behavior","Configuration changes may require retraining to achieve optimal performance","Limited documentation on configuration options and valid combinations","Debugging configuration errors can be difficult; error messages may be unclear"],"requires":["PyTorch 2.0+","Python 3.9+","YAML or JSON configuration file","Pre-trained component weights (encoders, decoders, language models)","Understanding of AudioCraft architecture and component interfaces"],"input_types":["YAML/JSON configuration file specifying model architecture","Pre-trained model weights for components","Hyperparameter specifications (learning rate, batch size, etc.)"],"output_types":["instantiated model object","model architecture specification","parameter count and memory requirements"],"categories":["tool-use-integration","model-configuration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"audiocraft__cap_11","uri":"capability://data.processing.analysis.audio.processing.utilities.and.feature.extraction","name":"audio processing utilities and feature extraction","description":"Provides utilities for audio loading, resampling, normalization, and feature extraction (spectrograms, mel-spectrograms, MFCC, chroma features). Includes wrappers around librosa and torchaudio for efficient batch processing. Enables preprocessing of audio for training and inference, and extraction of audio features for analysis or conditioning.","intents":["Load and preprocess audio files in various formats for training or inference","Extract audio features (spectrograms, mel-spectrograms) for analysis or visualization","Normalize and resample audio to consistent format for model input","Batch process large audio datasets efficiently"],"best_for":["developers building audio ML pipelines","researchers analyzing audio datasets","teams preprocessing audio for training","audio engineers extracting features for analysis"],"limitations":["Limited feature extraction compared to specialized audio analysis libraries (librosa, essentia)","No real-time audio processing; designed for batch operations","Resampling quality depends on algorithm choice; some algorithms may introduce artifacts","Memory usage scales with batch size; large batches may exceed GPU memory","No support for streaming audio processing"],"requires":["PyTorch 2.0+","Python 3.9+","librosa or torchaudio for audio I/O","Audio files in supported formats (WAV, MP3, FLAC, etc.)"],"input_types":["audio file paths (WAV, MP3, FLAC, OGG, etc.)","audio waveforms (numpy arrays or torch tensors)","feature extraction parameters (sample rate, n_mels, n_fft, etc.)"],"output_types":["normalized audio waveforms","spectrograms or mel-spectrograms","MFCC or other audio features","metadata (duration, sample rate, etc.)"],"categories":["data-processing-analysis","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"audiocraft__cap_12","uri":"capability://tool.use.integration.pre.trained.model.management.and.inference.api","name":"pre-trained model management and inference api","description":"Provides unified inference API for loading and using pre-trained AudioCraft models (MusicGen, AudioGen, MAGNeT, JASCO, etc.) with automatic model downloading, caching, and device management. Abstracts away model-specific implementation details, providing consistent interface across different generation models. Handles model loading, GPU memory management, and inference batching.","intents":["Load pre-trained models with single function call without manual weight management","Generate audio using consistent API regardless of underlying model architecture","Manage GPU memory efficiently across multiple model loads","Batch process multiple generation requests efficiently"],"best_for":["developers integrating AudioCraft into applications","non-researchers using pre-trained models for generation","teams building inference servers or APIs","rapid prototyping of audio generation features"],"limitations":["API abstractions hide model-specific parameters; advanced tuning requires direct model access","Automatic model downloading requires internet connectivity and sufficient disk space","Model caching can consume significant disk space (3-4GB per model)","Batching efficiency depends on batch size and GPU memory; suboptimal batching reduces throughput","Limited control over inference optimization (quantization, pruning, etc.)"],"requires":["PyTorch 2.0+","Python 3.9+","Internet connectivity for initial model download","4GB+ disk space per model","CUDA 11.8+ for GPU acceleration"],"input_types":["model name (string identifier like 'facebook/musicgen-medium')","generation parameters (text prompt, duration, temperature, etc.)","device specification (GPU or CPU)"],"output_types":["audio waveform","WAV format","generation metadata (model version, parameters used)"],"categories":["tool-use-integration","inference-api"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"audiocraft__cap_2","uri":"capability://data.processing.analysis.neural.audio.compression.with.encodec","name":"neural audio compression with encodec","description":"Compresses audio to discrete token sequences using EnCodec, a neural codec that learns to represent audio as quantized embeddings across multiple codebooks. The codec operates as an autoencoder with a residual vector quantizer, enabling variable bitrate compression (1.5-24 kbps) while maintaining perceptual quality. Serves as the tokenizer for all downstream generation models in AudioCraft.","intents":["Convert raw audio waveforms to discrete tokens for language model processing","Compress audio for efficient storage or transmission while preserving quality","Create a unified audio representation that enables text-to-audio generation","Reconstruct high-fidelity audio from compressed token sequences"],"best_for":["researchers building audio generation models","developers needing efficient audio tokenization for ML pipelines","audio engineers exploring neural compression alternatives to traditional codecs","teams implementing audio streaming with variable bitrate requirements"],"limitations":["Compression quality degrades at very low bitrates (<1.5 kbps); artifacts become audible","Inference requires GPU for practical speed; CPU encoding is 10-50x slower","Quantization introduces irreversible information loss; cannot perfectly reconstruct original audio","Model trained on specific audio domains; may perform poorly on out-of-distribution audio (e.g., extreme frequencies, unusual instruments)","Requires batch processing for efficiency; single-sample encoding is inefficient"],"requires":["PyTorch 2.0+","Python 3.9+","CUDA 11.8+ for GPU acceleration","2GB+ VRAM","EnCodec pre-trained model weights (~200MB)"],"input_types":["audio waveform (16kHz, 24kHz, or 48kHz sample rate)","WAV, MP3, or other audio formats (via librosa/torchaudio)","bitrate specification (1.5, 3, 6, 12, or 24 kbps)"],"output_types":["discrete token sequences (shape: [batch, num_codebooks, time_steps])","reconstructed audio waveform (same sample rate as input)","quantization indices for storage/transmission"],"categories":["data-processing-analysis","audio-compression"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"audiocraft__cap_3","uri":"capability://image.visual.style.conditioned.music.generation","name":"style-conditioned music generation","description":"Generates music from text descriptions while conditioning on a reference audio style using MusicGen-Style. The model extends MusicGen with dual conditioning: text embeddings for semantic content and audio embeddings extracted from a reference track for stylistic characteristics. Style embeddings are computed via a separate audio encoder, then jointly processed with text through the transformer decoder.","intents":["Generate music in a specific style (e.g., 'jazz', 'orchestral') by providing a reference track","Create variations of existing music with different instrumentation or arrangement","Transfer musical style from one track to a new composition described in text","Maintain consistent sonic characteristics across multiple generated tracks"],"best_for":["music producers creating themed content libraries","game developers maintaining audio consistency across levels","content creators needing style-matched background music","researchers studying style transfer in generative audio"],"limitations":["Style transfer quality depends on reference audio relevance; mismatched styles produce unpredictable results","Requires both text description AND reference audio; cannot generate from style alone","Reference audio must be reasonably clean; heavily compressed or noisy audio produces poor style embeddings","Style influence is not directly controllable; no parameter to weight text vs. style conditioning","Inference latency higher than base MusicGen due to dual conditioning processing"],"requires":["PyTorch 2.0+","Python 3.9+","CUDA 11.8+ for GPU acceleration","6GB+ VRAM (higher than base MusicGen)","MusicGen-Style pre-trained model weights (~3.5GB)","Reference audio file (WAV, MP3, or other format)"],"input_types":["text description of desired music content","reference audio file (5-30 seconds recommended)","generation parameters (duration, temperature, style influence weight if supported)"],"output_types":["audio waveform (16kHz or 32kHz sample rate)","WAV format","style embedding vectors (intermediate representation)"],"categories":["image-visual","audio-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"audiocraft__cap_4","uri":"capability://image.visual.non.autoregressive.music.generation.with.magnet","name":"non-autoregressive music generation with magnet","description":"Generates music and sound effects using MAGNeT, a non-autoregressive transformer that predicts all tokens in parallel rather than sequentially. Uses iterative refinement with confidence-based masking: initially predicts all tokens, then iteratively refines low-confidence predictions in subsequent passes. Achieves faster inference than autoregressive models at the cost of potential quality trade-offs.","intents":["Generate audio with lower latency than autoregressive models for real-time or interactive applications","Produce multiple audio variations in parallel for batch processing","Experiment with non-autoregressive generation architectures for research","Balance generation speed and quality through iteration count tuning"],"best_for":["real-time audio generation applications (games, interactive media)","batch processing pipelines requiring high throughput","researchers studying non-autoregressive generation","developers optimizing for latency-sensitive deployments"],"limitations":["Generation quality typically lower than autoregressive MusicGen; more artifacts and less coherent long-form structure","Requires tuning iteration count; too few iterations produce poor quality, too many negate speed benefits","No streaming capability; must generate full audio length upfront","Confidence masking strategy may fail on out-of-distribution prompts","Limited to 30-second generation window like other models"],"requires":["PyTorch 2.0+","Python 3.9+","CUDA 11.8+ for practical inference","4GB+ VRAM","MAGNeT pre-trained model weights (~3.5GB)"],"input_types":["text descriptions","generation parameters (duration, temperature, iteration count for refinement)"],"output_types":["audio waveform (16kHz or 32kHz)","WAV format","token sequences with confidence scores (intermediate)"],"categories":["image-visual","audio-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"audiocraft__cap_5","uri":"capability://image.visual.chord.and.melody.conditioned.music.generation.with.jasco","name":"chord and melody-conditioned music generation with jasco","description":"Generates music conditioned on explicit musical structure using JASCO (Joint Audio-Symbolic Conditioning), which accepts text descriptions alongside chord progressions, melody contours, and drum patterns. The model processes symbolic music inputs (represented as token sequences) through dedicated conditioning encoders, then jointly fuses them with text embeddings in the generation transformer. Enables fine-grained control over harmonic and rhythmic structure.","intents":["Generate music that follows a specific chord progression while matching a text description","Create variations of a melody with different instrumentation or arrangement","Compose music with predefined drum patterns or rhythmic structure","Maintain harmonic consistency across multiple generated sections"],"best_for":["music composers using AI as a creative tool with structural constraints","game developers needing music that synchronizes with gameplay events","music educators demonstrating harmonic concepts with generated examples","researchers studying symbolic-to-audio generation"],"limitations":["Requires knowledge of music theory to specify chords and melodies; not accessible to non-musicians","Chord/melody input format must match expected symbolic representation (MIDI, chord symbols, etc.)","Model may ignore or conflict with symbolic constraints if text description contradicts them","Inference latency higher due to multiple conditioning branches","Limited to 30-second generation; long-form composition requires manual concatenation","Symbolic inputs must be reasonably well-formed; malformed sequences produce unpredictable results"],"requires":["PyTorch 2.0+","Python 3.9+","CUDA 11.8+ for GPU acceleration","6GB+ VRAM","JASCO pre-trained model weights (~3.5GB)","Music representation library (e.g., music21) for symbolic input preparation"],"input_types":["text description of desired music","chord progression (as sequence of chord symbols or MIDI note sequences)","melody contour (as MIDI note sequence or pitch contour)","drum pattern (as MIDI drum track or rhythm specification)","generation parameters (duration, temperature)"],"output_types":["audio waveform (16kHz or 32kHz sample rate)","WAV format","token sequences with symbolic alignment information"],"categories":["image-visual","audio-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"audiocraft__cap_6","uri":"capability://image.visual.diffusion.based.audio.enhancement.with.multiband.diffusion","name":"diffusion-based audio enhancement with multiband diffusion","description":"Enhances audio quality by applying diffusion-based decoding as a post-processing step after EnCodec reconstruction. MultiBand Diffusion operates on frequency bands independently, using a diffusion model to refine reconstructed audio and reduce compression artifacts. Can be used as a drop-in replacement for the standard EnCodec decoder or applied to any compressed audio.","intents":["Improve perceived quality of EnCodec-compressed audio without re-encoding","Reduce compression artifacts in generated audio from MusicGen or AudioGen","Enhance audio quality for specific frequency bands (e.g., improve clarity in vocals)","Apply post-processing enhancement to any audio, not just generated content"],"best_for":["audio engineers optimizing generation quality","developers deploying AudioCraft models where quality is critical","researchers studying diffusion-based audio enhancement","teams with GPU resources to afford additional post-processing"],"limitations":["Adds significant latency (10-30 seconds for 30-second audio); not suitable for real-time applications","Requires additional GPU memory and compute; increases total pipeline latency by 50-100%","Enhancement quality depends on diffusion model training; may introduce artifacts on out-of-distribution audio","No parameter control over enhancement strength; all-or-nothing application","Diffusion sampling is stochastic; same input produces slightly different outputs on repeated runs"],"requires":["PyTorch 2.0+","Python 3.9+","CUDA 11.8+ for practical inference","8GB+ VRAM (higher than base generation)","MultiBand Diffusion pre-trained model weights (~500MB)","EnCodec or other compressed audio as input"],"input_types":["audio waveform (output from EnCodec decoder or any compressed audio)","diffusion sampling parameters (number of steps, temperature)"],"output_types":["enhanced audio waveform (same sample rate as input)","WAV format"],"categories":["image-visual","audio-enhancement"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"audiocraft__cap_7","uri":"capability://safety.moderation.audio.watermarking.with.audioseal","name":"audio watermarking with audioseal","description":"Embeds imperceptible watermarks into generated audio using AudioSeal, a watermarking system that adds inaudible signals to audio while preserving quality. The watermark encodes metadata (e.g., generation timestamp, model version) and is designed to survive common audio transformations (compression, resampling, time-stretching). Enables detection and attribution of AI-generated audio.","intents":["Mark generated audio to indicate AI origin and enable detection of synthetic content","Embed metadata in audio for tracking generation source and timestamp","Protect against unauthorized use of generated audio through watermark verification","Support content authentication and provenance tracking for regulatory compliance"],"best_for":["content platforms implementing AI-generated content disclosure","researchers studying audio provenance and authenticity","organizations requiring audit trails for generated content","developers building content authentication systems"],"limitations":["Watermark robustness depends on audio transformation type; extreme compression or heavy processing may degrade watermark","Watermark detection requires access to AudioSeal detector model; not universally detectable","Adds minimal but measurable latency to generation pipeline (~100-500ms)","Watermark payload limited to small metadata; cannot encode large amounts of information","Adversarial attacks may be able to remove watermarks with sufficient effort","Watermark presence may be detectable by sophisticated analysis even if content cannot be read"],"requires":["PyTorch 2.0+","Python 3.9+","AudioSeal watermarking model weights (~200MB)","AudioSeal detector model for verification (~200MB)","Audio waveform to watermark"],"input_types":["audio waveform (any sample rate)","metadata to embed (generation timestamp, model version, etc.)","watermarking parameters (strength, payload)"],"output_types":["watermarked audio waveform (same sample rate as input)","watermark detection confidence scores","extracted metadata (if watermark detected)"],"categories":["safety-moderation","audio-watermarking"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"audiocraft__cap_8","uri":"capability://automation.workflow.distributed.training.with.fsdp.and.gradient.checkpointing","name":"distributed training with fsdp and gradient checkpointing","description":"Enables training of large audio generation models across multiple GPUs and nodes using Fully Sharded Data Parallel (FSDP) and gradient checkpointing. The framework automatically distributes model parameters, activations, and gradients across devices, reducing per-GPU memory requirements. Gradient checkpointing trades computation for memory by recomputing activations during backpropagation rather than storing them.","intents":["Train large AudioCraft models on limited GPU memory through parameter sharding","Scale training across multi-GPU and multi-node clusters for faster convergence","Fine-tune pre-trained models on custom audio datasets with limited hardware","Reduce memory footprint to enable training on consumer-grade GPUs"],"best_for":["researchers training custom audio generation models","organizations fine-tuning AudioCraft on proprietary datasets","teams with multi-GPU infrastructure optimizing training efficiency","developers implementing distributed training pipelines"],"limitations":["FSDP introduces communication overhead; scaling efficiency decreases with more GPUs (typically 70-85% efficiency at 8 GPUs)","Gradient checkpointing increases training time by 20-30% due to recomputation overhead","Requires careful tuning of batch size, learning rate, and communication frequency for optimal performance","Debugging distributed training is significantly more complex than single-GPU training","Network bandwidth becomes bottleneck at scale; requires high-speed interconnects (NVLink, InfiniBand) for efficiency","Checkpointing and resuming training requires careful state management across all processes"],"requires":["PyTorch 2.0+ with FSDP support","Python 3.9+","Multiple NVIDIA GPUs (2+ recommended, 8+ for significant scaling)","CUDA 11.8+","High-speed GPU interconnect (NVLink preferred, PCIe acceptable)","Training dataset in compatible format (audio files + metadata)"],"input_types":["audio training data (WAV, MP3, or other formats)","text descriptions or conditioning information","training configuration (batch size, learning rate, num_epochs)","model architecture specification"],"output_types":["trained model checkpoints (distributed across devices)","training logs and metrics","validation audio samples"],"categories":["automation-workflow","training-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"audiocraft__cap_9","uri":"capability://automation.workflow.streaming.transformer.inference.for.long.form.audio","name":"streaming transformer inference for long-form audio","description":"Generates audio in streaming fashion using a streaming transformer architecture that processes audio in chunks with limited context window, enabling generation of audio longer than typical 30-second limits. The model maintains a rolling cache of key-value pairs from previous chunks, allowing efficient incremental generation without reprocessing entire sequences.","intents":["Generate long-form audio (minutes or hours) without memory constraints","Stream audio generation for real-time playback without waiting for full completion","Reduce latency for interactive applications by generating incrementally","Enable continuous music or ambient audio generation for extended periods"],"best_for":["streaming music services generating background audio","interactive applications requiring real-time audio generation","researchers studying long-form audio generation","developers building continuous audio generation pipelines"],"limitations":["Streaming generation may produce less coherent long-form structure than full-sequence generation","Context window limitations may cause repetition or discontinuity at chunk boundaries","Streaming inference requires careful tuning of chunk size and overlap for quality","Memory savings are modest compared to standard generation; still requires GPU for practical speed","Streaming generation is non-deterministic; same prompt produces different outputs on repeated runs","Requires custom inference code; not available through standard generation API"],"requires":["PyTorch 2.0+","Python 3.9+","CUDA 11.8+ for practical inference","4GB+ VRAM","Streaming transformer model variant (not all AudioCraft models support streaming)","Custom inference implementation or streaming-enabled wrapper"],"input_types":["text description","target audio length (can be arbitrary, not limited to 30 seconds)","streaming parameters (chunk size, overlap, context window size)"],"output_types":["audio chunks (streamed incrementally)","WAV format (can be written incrementally to file)","token sequences (intermediate)"],"categories":["automation-workflow","audio-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"audiocraft__headline","uri":"capability://voice.audio.ai.audio.generation.framework","name":"ai audio generation framework","description":"AudioCraft is a comprehensive PyTorch library designed for audio generation research, enabling users to create high-quality music and sound effects through advanced AI models like MusicGen and AudioGen.","intents":["best AI audio generation tool","audio generation framework for music","how to generate sound effects with AI","top libraries for audio synthesis","AI models for music generation"],"best_for":["researchers in audio AI","developers creating audio applications"],"limitations":["requires familiarity with PyTorch"],"requires":["Python","PyTorch"],"input_types":["text prompts","audio inputs"],"output_types":["music","sound effects"],"categories":["voice-audio"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"low","permissions":["PyTorch 2.0+","Python 3.9+","CUDA 11.8+ for GPU acceleration (CPU inference extremely slow)","4GB+ VRAM for small models, 16GB+ for larger variants","Pre-trained MusicGen model weights (~3.5GB download)","CUDA 11.8+ for practical inference speed","4GB+ VRAM minimum","AudioGen pre-trained model weights (~3.5GB)","YAML or JSON configuration file","Pre-trained component weights (encoders, decoders, language models)"],"failure_modes":["Generation quality depends on text description clarity; vague prompts produce inconsistent results","Inference latency scales with audio duration (30 seconds typically requires 10-30 seconds on GPU)","No real-time streaming generation; full audio must be generated before playback","Limited to 30-second maximum generation length in standard configuration","Model trained on specific music domains; may struggle with niche genres or highly specific styles","Quality varies significantly with prompt specificity; generic descriptions produce generic sounds","No control over sound duration beyond generation length parameter","Cannot guarantee realistic physics-based audio (e.g., impact sounds may not match visual timing)","Limited to 30-second generation window","Training data bias toward common sound effects; rare or specialized sounds may be poorly generated","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:02.370Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=audiocraft","compare_url":"https://unfragile.ai/compare?artifact=audiocraft"}},"signature":"5m6BNXDs4xSGAkW8gZAWRky56IWUj90x5G7M8h6j6xp+rld8THUY9eJAjkSl5ItOxdgWz9ylWwX182MKP0bMAA==","signedAt":"2026-06-20T21:25:00.916Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/audiocraft","artifact":"https://unfragile.ai/audiocraft","verify":"https://unfragile.ai/api/v1/verify?slug=audiocraft","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"}}