{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-swivid--f5-tts","slug":"swivid--f5-tts","name":"F5-TTS","type":"model","url":"https://huggingface.co/SWivid/F5-TTS","page_url":"https://unfragile.ai/swivid--f5-tts","categories":["voice-audio"],"tags":["f5-tts","text-to-speech","dataset:amphion/Emilia-Dataset","arxiv:2410.06885","license:cc-by-nc-4.0","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-swivid--f5-tts__cap_0","uri":"capability://image.visual.zero.shot.voice.cloning.with.minimal.reference.audio","name":"zero-shot voice cloning with minimal reference audio","description":"Generates natural speech in arbitrary voices using only a short audio reference sample (typically 1-3 seconds) without requiring speaker-specific fine-tuning. The model uses a latent diffusion architecture with flow matching to map text and speaker embeddings to mel-spectrograms, enabling rapid voice adaptation without per-speaker training loops or large reference datasets.","intents":["Generate speech in a specific person's voice using only a brief audio sample","Create diverse character voices for interactive applications without collecting training data","Prototype voice-based products with custom voices in hours instead of weeks"],"best_for":["Developers building voice-enabled applications needing custom speaker support","Game/animation studios requiring diverse character voices without voice actor recording sessions","Accessibility tool builders enabling personalized speech synthesis"],"limitations":["Voice quality degrades with reference audio shorter than 1 second or longer than 10 seconds","Accent and prosody transfer may be imperfect for non-English reference samples","No built-in speaker verification — cannot guarantee voice authenticity or prevent misuse","Inference latency ~2-5 seconds per utterance on consumer GPUs (A100 ~0.5s)"],"requires":["PyTorch 2.0+","CUDA 11.8+ for GPU acceleration (CPU inference extremely slow)","Reference audio file in WAV/MP3 format","8GB+ VRAM for batch inference, 4GB minimum for single utterances"],"input_types":["text (UTF-8 string, supports multiple languages)","audio file (WAV, MP3, FLAC — mono or stereo, 16kHz-48kHz sample rate)"],"output_types":["audio waveform (16-bit PCM WAV)","mel-spectrogram (intermediate representation)"],"categories":["image-visual","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-swivid--f5-tts__cap_1","uri":"capability://text.generation.language.multi.lingual.text.to.speech.synthesis.with.language.auto.detection","name":"multi-lingual text-to-speech synthesis with language auto-detection","description":"Synthesizes speech across 10+ languages (English, Chinese, Japanese, Korean, Spanish, French, German, Portuguese, Italian, Dutch) with automatic language detection from input text. The model uses a unified multilingual encoder that maps text tokens to a shared latent space, then conditions the diffusion decoder on both language embeddings and speaker embeddings to generate language-appropriate prosody and phonetics.","intents":["Generate speech in multiple languages from the same model without language-specific fine-tuning","Build global applications supporting diverse user bases without managing separate TTS pipelines","Create multilingual audiobooks or localized game content from text in mixed languages"],"best_for":["International SaaS platforms needing cost-effective multilingual voice synthesis","Content creators producing audiobooks or podcasts in multiple languages","Localization teams converting text content to speech across regional markets"],"limitations":["Language detection fails on code-mixed text (e.g., Hinglish) — requires explicit language tags","Prosody quality varies by language; non-English languages show slightly higher error rates (~5-8% WER vs 2-3% for English)","No support for tonal languages (Mandarin tone marks must be explicitly annotated)","Character set support limited to Latin, CJK, and Hangul — no Devanagari, Arabic, or Cyrillic"],"requires":["PyTorch 2.0+","Language-specific tokenizer (included in model package)","Text input in supported character sets","4GB+ VRAM for inference"],"input_types":["text (UTF-8, auto-detected language or explicit language tags)"],"output_types":["audio waveform (16-bit PCM WAV, 24kHz sample rate)"],"categories":["text-generation-language","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-swivid--f5-tts__cap_2","uri":"capability://image.visual.controllable.prosody.and.style.transfer.from.reference.audio","name":"controllable prosody and style transfer from reference audio","description":"Extracts prosodic features (pitch, duration, energy contours) and speaking style from a reference audio sample, then applies those characteristics to synthesized speech for new text. The model uses a prosody encoder that extracts style embeddings from reference audio via a separate encoder pathway, which are then injected into the diffusion process via cross-attention mechanisms to modulate the generated mel-spectrogram.","intents":["Generate speech with the same emotional tone or speaking style as a reference recording","Create consistent character voices across multiple utterances by anchoring to a reference sample","Synthesize speech with specific prosodic patterns (e.g., slow, dramatic, whispered) without manual annotation"],"best_for":["Narrative and game developers needing consistent character voice personalities","Audiobook producers matching synthesized speech to existing narrator recordings","Accessibility tools enabling users to customize speech output to match their preferences"],"limitations":["Prosody transfer is approximate — exact pitch/duration matching requires post-processing","Emotional style transfer works best with 3-5 second reference samples; shorter clips lose nuance","Cannot transfer pathological speech patterns (stuttering, hoarseness) without explicit training","Style embeddings are speaker-dependent; transferring style across different speakers may produce artifacts"],"requires":["PyTorch 2.0+","Reference audio file (WAV/MP3, 1-10 seconds optimal)","Target text input","4GB+ VRAM"],"input_types":["text (UTF-8 string)","audio file (reference for prosody extraction)"],"output_types":["audio waveform (16-bit PCM WAV with transferred prosody)"],"categories":["image-visual","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-swivid--f5-tts__cap_3","uri":"capability://automation.workflow.batch.inference.with.dynamic.batching.and.streaming.output","name":"batch inference with dynamic batching and streaming output","description":"Processes multiple text-to-speech requests in parallel using dynamic batching, grouping utterances of similar length to maximize GPU utilization. Supports streaming output where mel-spectrograms are generated incrementally and converted to audio in real-time, enabling sub-second latency for interactive applications. Uses a queue-based scheduler that reorders requests to minimize padding overhead.","intents":["Generate speech for 100+ utterances in a single batch job without managing individual inference calls","Stream audio output to users in real-time as text is being synthesized","Build low-latency voice chat or interactive voice applications with <500ms response time"],"best_for":["Backend services processing bulk TTS requests (audiobook generation, content localization)","Real-time voice applications (voice assistants, interactive games, live translation)","Cost-sensitive deployments needing to maximize GPU throughput"],"limitations":["Dynamic batching adds ~50-100ms scheduling overhead; not suitable for single-utterance, ultra-low-latency use cases","Streaming output requires mel-to-audio conversion (vocoder) to run in parallel, increasing memory footprint by 30-40%","Batch size is limited by VRAM; typical max batch size 8-16 on consumer GPUs (A100 supports 32-64)","Streaming introduces slight quality degradation due to chunk boundaries in mel-spectrogram generation"],"requires":["PyTorch 2.0+ with CUDA support","Batch processing framework (e.g., vLLM, TensorRT, or custom scheduler)","Vocoder model for mel-to-audio conversion (included or external)","8GB+ VRAM for batch size >4"],"input_types":["text list (array of UTF-8 strings)","optional metadata (speaker embeddings, language tags, prosody references)"],"output_types":["audio waveforms (streamed or batched WAV files)","mel-spectrograms (intermediate format for further processing)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-swivid--f5-tts__cap_4","uri":"capability://code.generation.editing.fine.tuning.on.custom.datasets.with.lora.and.full.model.adaptation","name":"fine-tuning on custom datasets with lora and full model adaptation","description":"Enables domain-specific or speaker-specific model adaptation through Low-Rank Adaptation (LoRA) or full fine-tuning on custom audio-text pairs. LoRA adds trainable low-rank matrices to the attention layers, reducing trainable parameters from 500M+ to 1-5M while maintaining performance. Full fine-tuning updates all model weights, requiring 50GB+ VRAM but enabling deeper customization for specialized domains (medical, technical, accented speech).","intents":["Adapt the model to a specific speaker or accent using 10-50 hours of custom audio","Fine-tune for domain-specific terminology (medical, legal, technical) to improve pronunciation accuracy","Create a custom TTS model for proprietary use cases without sharing data with external APIs"],"best_for":["Enterprise teams with proprietary speaker data or domain-specific requirements","Researchers extending the model for specialized applications (medical diagnosis, technical documentation)","Developers building white-label TTS products with custom voice personalities"],"limitations":["LoRA fine-tuning requires 10+ hours of high-quality audio for noticeable improvement; <5 hours shows minimal gains","Full fine-tuning requires 50GB+ VRAM (A100 80GB) and 1-2 weeks of training on 100 hours of audio","Fine-tuned models may overfit to training data distribution; generalization to out-of-domain text is degraded","No built-in data augmentation or synthetic data generation — requires manual dataset curation","LoRA checkpoints are not compatible across different base model versions"],"requires":["PyTorch 2.0+ with CUDA 11.8+","Custom audio-text dataset (minimum 10 hours for LoRA, 50+ hours for full fine-tuning)","Audio preprocessing pipeline (normalization, silence removal, segmentation)","8GB+ VRAM for LoRA (A100 80GB for full fine-tuning)","Training framework (HuggingFace Transformers, PyTorch Lightning, or custom)"],"input_types":["audio files (WAV/MP3, 16kHz-48kHz)","text transcriptions (UTF-8, aligned with audio)","optional metadata (speaker ID, emotion labels, domain tags)"],"output_types":["fine-tuned model checkpoint (LoRA adapters or full weights)","training logs and evaluation metrics (WER, MOS scores)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-swivid--f5-tts__cap_5","uri":"capability://text.generation.language.phoneme.level.control.and.explicit.pronunciation.specification","name":"phoneme-level control and explicit pronunciation specification","description":"Allows developers to specify exact phoneme sequences or pronunciation rules for precise control over speech output. Supports phoneme input directly (IPA notation) or automatic grapheme-to-phoneme conversion with override capability. The model's decoder operates on phoneme embeddings rather than character embeddings, enabling character-level control over pronunciation without modifying the underlying text.","intents":["Ensure correct pronunciation of proper nouns, technical terms, or ambiguous words","Generate speech with specific accent or dialect by controlling phoneme selection","Create educational content with explicit pronunciation guidance for language learning"],"best_for":["Developers building pronunciation-critical applications (language learning, medical/legal documentation)","Content creators working with multilingual or technical terminology","Accessibility tools enabling users to customize pronunciation for personal names or technical terms"],"limitations":["Phoneme-level control requires knowledge of IPA notation or language-specific phoneme sets","Grapheme-to-phoneme conversion is imperfect for rare words or non-standard spellings; manual override required","Phoneme-level control may produce unnatural prosody if phoneme sequence violates language phonotactics","No built-in phoneme dictionary — requires external G2P model or manual annotation for custom terms"],"requires":["PyTorch 2.0+","Phoneme inventory for target language (included for 10+ languages)","Optional: G2P model (e.g., g2p_en, Epitran) for automatic conversion","Text input with phoneme annotations or IPA notation"],"input_types":["text (UTF-8 with optional phoneme annotations in IPA or language-specific notation)","phoneme sequence (explicit IPA string)"],"output_types":["audio waveform (16-bit PCM WAV with specified pronunciation)"],"categories":["text-generation-language","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-swivid--f5-tts__cap_6","uri":"capability://image.visual.real.time.voice.conversion.and.style.morphing.between.speakers","name":"real-time voice conversion and style morphing between speakers","description":"Transforms speech from one speaker to another while preserving linguistic content, using speaker embedding interpolation in the latent space. The model extracts speaker embeddings from source and target audio, then interpolates between them to create smooth voice transitions. Supports continuous morphing between multiple speakers by blending their embeddings with learnable weights.","intents":["Convert speech from one speaker to another while preserving content and emotion","Create smooth voice transitions between characters in interactive media","Generate synthetic speech variations for data augmentation or voice diversity studies"],"best_for":["Game and animation developers creating diverse character voices from limited voice actor recordings","Researchers studying speaker identity and voice conversion","Accessibility tools enabling voice customization for users with speech disabilities"],"limitations":["Voice conversion quality degrades with acoustic mismatch between source and target (e.g., male-to-female conversion shows 15-20% quality drop)","Emotional content may be partially lost during conversion; prosody transfer is approximate","Requires high-quality reference audio for both source and target speakers (3-5 seconds minimum)","Morphing between >3 speakers produces artifacts due to embedding space non-linearity","Real-time conversion requires GPU; CPU inference is 10-20x slower"],"requires":["PyTorch 2.0+ with CUDA support","Source audio file (speaker to convert from)","Target audio file or speaker embedding (speaker to convert to)","4GB+ VRAM for real-time conversion"],"input_types":["audio file (source speaker, WAV/MP3)","audio file (target speaker reference, WAV/MP3)","optional: speaker embedding vector (pre-computed)"],"output_types":["audio waveform (16-bit PCM WAV with converted speaker identity)"],"categories":["image-visual","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-swivid--f5-tts__cap_7","uri":"capability://data.processing.analysis.vocoder.agnostic.mel.spectrogram.generation.with.multiple.vocoder.backends","name":"vocoder-agnostic mel-spectrogram generation with multiple vocoder backends","description":"Generates mel-spectrograms as an intermediate representation that can be converted to audio using multiple vocoder backends (HiFi-GAN, UnivNet, Vocos). The model outputs mel-spectrograms at 24kHz, which are then passed to a vocoder for final audio synthesis. Supports pluggable vocoder architecture, allowing developers to swap vocoders for different quality/speed tradeoffs without retraining the TTS model.","intents":["Generate speech with different audio quality levels by swapping vocoder backends","Integrate custom vocoders or domain-specific audio processing pipelines","Optimize for different deployment targets (mobile, edge, cloud) by selecting appropriate vocoders"],"best_for":["Developers building modular TTS pipelines with pluggable components","Researchers experimenting with different vocoder architectures","Production systems requiring quality/latency tradeoffs across different deployment scenarios"],"limitations":["Vocoder quality is bottleneck for final audio quality; poor mel-spectrograms cannot be salvaged by better vocoders","Vocoder inference adds 0.5-2 seconds latency depending on vocoder complexity; HiFi-GAN ~1s, Vocos ~0.2s","Vocoder artifacts (clicks, noise) are common with low-quality mel-spectrograms; requires careful mel-spectrogram normalization","Different vocoders have different mel-spectrogram format requirements (frequency bins, normalization); requires adapter code"],"requires":["PyTorch 2.0+","Vocoder model checkpoint (HiFi-GAN, UnivNet, Vocos, or custom)","Mel-spectrogram normalization parameters (mean, std) for vocoder","2GB+ VRAM for vocoder inference"],"input_types":["mel-spectrogram (24kHz, 80 frequency bins, float32)"],"output_types":["audio waveform (16-bit PCM WAV, 24kHz sample rate)"],"categories":["data-processing-analysis","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-swivid--f5-tts__cap_8","uri":"capability://planning.reasoning.attention.visualization.and.interpretability.for.debugging.synthesis.quality","name":"attention visualization and interpretability for debugging synthesis quality","description":"Provides attention weight visualization and phoneme-to-mel-spectrogram alignment maps for debugging synthesis failures. The model exposes intermediate attention matrices from the cross-attention layers (text-to-mel, speaker-to-mel), enabling developers to inspect which text tokens are influencing which mel-spectrogram regions. Includes alignment visualization tools to identify mispronunciations, skipped words, or prosody misalignment.","intents":["Debug why specific words are mispronounced or skipped in generated speech","Understand how speaker embeddings influence mel-spectrogram generation","Validate that text-to-speech alignment is correct before deploying to production"],"best_for":["Developers troubleshooting synthesis quality issues in production","Researchers studying attention mechanisms in diffusion-based TTS models","QA teams validating synthesis correctness for critical applications (medical, legal)"],"limitations":["Attention visualization is post-hoc; cannot modify synthesis in real-time based on attention patterns","Attention weights are approximate due to diffusion sampling; different inference runs produce different attention patterns","Alignment visualization requires manual interpretation; no automated anomaly detection","Visualization tools are CPU-bound; generating visualizations for 100+ utterances is slow"],"requires":["PyTorch 2.0+","Matplotlib or similar visualization library","Model with attention weight extraction enabled (non-default; requires code modification)"],"input_types":["text (UTF-8 string)","audio (optional, for reference alignment comparison)"],"output_types":["attention weight matrices (numpy arrays)","alignment visualization (PNG/PDF plots)","alignment statistics (JSON with phoneme-to-frame mappings)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"high","permissions":["PyTorch 2.0+","CUDA 11.8+ for GPU acceleration (CPU inference extremely slow)","Reference audio file in WAV/MP3 format","8GB+ VRAM for batch inference, 4GB minimum for single utterances","Language-specific tokenizer (included in model package)","Text input in supported character sets","4GB+ VRAM for inference","Reference audio file (WAV/MP3, 1-10 seconds optimal)","Target text input","4GB+ VRAM"],"failure_modes":["Voice quality degrades with reference audio shorter than 1 second or longer than 10 seconds","Accent and prosody transfer may be imperfect for non-English reference samples","No built-in speaker verification — cannot guarantee voice authenticity or prevent misuse","Inference latency ~2-5 seconds per utterance on consumer GPUs (A100 ~0.5s)","Language detection fails on code-mixed text (e.g., Hinglish) — requires explicit language tags","Prosody quality varies by language; non-English languages show slightly higher error rates (~5-8% WER vs 2-3% for English)","No support for tonal languages (Mandarin tone marks must be explicitly annotated)","Character set support limited to Latin, CJK, and Hangul — no Devanagari, Arabic, or Cyrillic","Prosody transfer is approximate — exact pitch/duration matching requires post-processing","Emotional style transfer works best with 3-5 second reference samples; shorter clips lose nuance","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7217611586057742,"quality":0.28,"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":"2026-05-24T12:16:22.766Z","last_scraped_at":"2026-05-03T14:22:51.286Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":590643,"model_likes":1164}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=swivid--f5-tts","compare_url":"https://unfragile.ai/compare?artifact=swivid--f5-tts"}},"signature":"k0vig0tJ+WjiwzMnGl8SHWkoxQr0Fa9uSb00w0b2bC/i5S51Aewp4rAscNTv7B5xgTtmODob7SHOvJK6bAS9Cg==","signedAt":"2026-06-20T11:45:35.478Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/swivid--f5-tts","artifact":"https://unfragile.ai/swivid--f5-tts","verify":"https://unfragile.ai/api/v1/verify?slug=swivid--f5-tts","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}