{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-microsoft--speecht5_tts","slug":"microsoft--speecht5_tts","name":"speecht5_tts","type":"model","url":"https://huggingface.co/microsoft/speecht5_tts","page_url":"https://unfragile.ai/microsoft--speecht5_tts","categories":["voice-audio"],"tags":["transformers","pytorch","speecht5","text-to-audio","audio","text-to-speech","dataset:libritts","arxiv:2110.07205","arxiv:1910.09700","license:mit","endpoints_compatible","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-microsoft--speecht5_tts__cap_0","uri":"capability://text.generation.language.transformer.based.text.to.speech.synthesis.with.speaker.embedding.control","name":"transformer-based text-to-speech synthesis with speaker embedding control","description":"Converts input text to natural-sounding speech audio using a transformer encoder-decoder architecture trained on LibriTTS dataset. The model accepts text tokens and optional speaker embeddings (x-vectors) to control voice characteristics, producing mel-spectrogram features that are then converted to waveform audio via a vocoder. The architecture separates linguistic content processing from speaker identity, enabling flexible voice cloning and multi-speaker synthesis without retraining.","intents":["Generate natural-sounding speech from arbitrary text input with controllable speaker identity","Create multi-speaker audio content by conditioning synthesis on different speaker embeddings","Build voice cloning applications by extracting speaker embeddings from reference audio","Integrate TTS into accessibility tools, voice assistants, or content creation pipelines"],"best_for":["Developers building accessibility features requiring natural speech synthesis","Teams creating multi-lingual or multi-speaker audio content at scale","Researchers prototyping voice cloning and speaker adaptation systems","Open-source projects requiring permissive MIT-licensed TTS without commercial restrictions"],"limitations":["Requires external vocoder (HiFi-GAN or similar) to convert mel-spectrograms to waveform audio — model outputs intermediate representation only","Speaker embedding extraction requires separate speaker encoder model (e.g., x-vector extractor) not included in base package","Inference latency ~2-5 seconds per sentence on CPU; GPU acceleration recommended for real-time applications","Training data (LibriTTS) is English-only; multilingual support requires fine-tuning or separate models","No built-in prosody control (pitch, speed, emotion) — requires post-processing or model fine-tuning for nuanced expression"],"requires":["Python 3.8+","PyTorch 1.9+ (CPU or GPU)","transformers library 4.20+","scipy for audio processing","Optional: CUDA 11.0+ for GPU acceleration","Optional: vocoder model (HiFi-GAN checkpoint) for waveform generation"],"input_types":["text (string, arbitrary length)","speaker_embeddings (float tensor, shape [1, 512] for x-vector format)","speaker_id (integer, if using pre-extracted speaker embeddings from dataset)"],"output_types":["mel-spectrogram (float tensor, shape [time_steps, 80])","waveform audio (float tensor, shape [samples], requires vocoder post-processing)","audio file (WAV/MP3, after vocoder conversion and optional normalization)"],"categories":["text-generation-language","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-microsoft--speecht5_tts__cap_1","uri":"capability://text.generation.language.speaker.embedding.extraction.and.speaker.conditional.audio.generation","name":"speaker embedding extraction and speaker-conditional audio generation","description":"Accepts speaker embeddings (x-vectors or similar speaker representations) as conditional input to modulate voice characteristics during synthesis. The model uses a cross-attention mechanism to inject speaker identity into the decoder, allowing the same text to be synthesized in different voices by swapping embeddings. This decouples speaker identity from text content, enabling zero-shot voice cloning when paired with a speaker encoder.","intents":["Synthesize the same text in multiple different voices by providing different speaker embeddings","Clone a speaker's voice from a short reference audio sample without retraining","Create consistent multi-speaker audiobooks or dialogue where each character has a distinct voice","Build voice conversion systems that preserve linguistic content while changing speaker identity"],"best_for":["Audio engineers building voice cloning and voice conversion applications","Content creators producing multi-speaker audiobooks or podcasts with consistent character voices","Accessibility developers creating personalized voice synthesis for users with speech disabilities","Research teams exploring speaker disentanglement and zero-shot voice adaptation"],"limitations":["Speaker embeddings must be pre-extracted using a separate speaker encoder model (not included) — adds pipeline complexity","Embedding quality directly impacts synthesis quality; poor speaker encoder produces degraded audio","Zero-shot voice cloning requires high-quality reference audio (3-10 seconds minimum) for reliable embedding extraction","Speaker embeddings are fixed-dimensional (512-dim x-vectors); incompatible with other embedding formats without conversion","No explicit control over speaker characteristics (age, gender, accent) — only implicit control via embedding space"],"requires":["Pre-trained speaker encoder model (e.g., PyannoteAudio, SpeakerNet, or x-vector extractor)","Reference audio sample (3-10 seconds) for zero-shot voice cloning","Speaker embedding tensor of shape [1, 512] in x-vector format","PyTorch and transformers library as above"],"input_types":["speaker_embeddings (float tensor, shape [batch_size, 512])","reference_audio (waveform tensor or file path, for embedding extraction)","text (string, to be synthesized in the speaker's voice)"],"output_types":["mel-spectrogram conditioned on speaker identity (float tensor)","synthesized waveform in target speaker's voice (after vocoder)","audio file with speaker-specific characteristics preserved"],"categories":["text-generation-language","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-microsoft--speecht5_tts__cap_2","uri":"capability://text.generation.language.non.autoregressive.mel.spectrogram.generation.with.duration.prediction","name":"non-autoregressive mel-spectrogram generation with duration prediction","description":"Generates mel-spectrogram features in parallel (non-autoregressive) rather than sequentially, using a transformer encoder-decoder with duration prediction to align text tokens to acoustic frames. The model predicts phoneme durations, then expands the encoder output accordingly, allowing the decoder to generate all acoustic frames simultaneously. This approach reduces inference latency compared to autoregressive models while maintaining audio quality through explicit duration modeling.","intents":["Synthesize speech with lower latency than autoregressive TTS models for near-real-time applications","Generate consistent mel-spectrograms with predictable frame counts for downstream processing","Control speech rate by scaling predicted durations without retraining","Batch-process multiple text inputs efficiently due to parallel generation"],"best_for":["Developers building low-latency voice assistants or real-time TTS applications","Teams requiring batch speech synthesis for large-scale content generation","Researchers studying duration prediction and phoneme-to-acoustic alignment","Production systems where inference speed is critical (e.g., live streaming, interactive applications)"],"limitations":["Duration prediction errors propagate to acoustic output; mispredicted durations cause unnatural timing or clipping","Non-autoregressive generation may produce less natural prosody variation compared to autoregressive models in edge cases","Requires phoneme-level text processing; raw text must be converted to phoneme sequences first (adds preprocessing step)","Mel-spectrogram output still requires vocoder for waveform generation; total latency includes vocoder inference time","Limited ability to correct errors mid-generation; cannot use previous frame predictions to guide future frames"],"requires":["Text-to-phoneme converter (g2p_en or similar) for phoneme sequence generation","PyTorch and transformers library","Vocoder model (HiFi-GAN) for mel-to-waveform conversion","GPU recommended for practical inference speed (2-5 seconds per sentence on CPU)"],"input_types":["text (string, converted to phoneme sequence internally)","phoneme_sequence (list of phoneme tokens)","duration_scale (float, optional, to control speech rate)"],"output_types":["mel-spectrogram (float tensor, shape [time_steps, 80])","duration_predictions (integer tensor, phoneme durations in frames)","waveform audio (after vocoder post-processing)"],"categories":["text-generation-language","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-microsoft--speecht5_tts__cap_3","uri":"capability://text.generation.language.libritts.pre.trained.acoustic.model.with.transfer.learning.capability","name":"libritts pre-trained acoustic model with transfer learning capability","description":"Provides a pre-trained acoustic model initialized on LibriTTS dataset (24 speakers, ~585 hours of English speech), enabling immediate use for English TTS and serving as a foundation for fine-tuning on custom datasets or languages. The model weights encode linguistic-to-acoustic mappings learned from diverse speakers and speaking styles, reducing the data and compute required for downstream applications compared to training from scratch.","intents":["Use pre-trained English TTS immediately without collecting or annotating training data","Fine-tune the model on custom datasets (e.g., domain-specific language, new languages, specific speaker characteristics)","Transfer acoustic knowledge from LibriTTS to low-resource languages or specialized domains","Reduce training time and data requirements for custom TTS applications"],"best_for":["Developers building English TTS applications who want immediate deployment without training","Researchers fine-tuning TTS for new languages or specialized domains with limited data","Teams with custom speaker datasets who want to adapt the model without full retraining","Prototyping and MVP development where time-to-market is critical"],"limitations":["Pre-training is English-only (LibriTTS); multilingual synthesis requires fine-tuning or separate models","Model is optimized for read speech (audiobook-style); may not generalize well to highly expressive or conversational speech","Fine-tuning on non-English languages requires phoneme inventory and text-to-phoneme converter for that language","Transfer learning effectiveness depends on target domain similarity to LibriTTS; distant domains may require substantial fine-tuning","No speaker-specific optimization; all 24 LibriTTS speakers are blended in the pre-trained weights"],"requires":["Python 3.8+, PyTorch 1.9+, transformers 4.20+","For fine-tuning: custom dataset with aligned text-audio pairs and phoneme annotations","For non-English: language-specific phoneme inventory and g2p model","GPU recommended for fine-tuning (training time ~24-48 hours on single GPU for 10-20 hours of data)"],"input_types":["text (English, or other languages after fine-tuning)","speaker_embeddings (optional, for multi-speaker synthesis)","custom_dataset (for fine-tuning: text-audio pairs with phoneme alignment)"],"output_types":["mel-spectrogram (from pre-trained model or fine-tuned variant)","waveform audio (after vocoder)","fine-tuned model checkpoint (for custom applications)"],"categories":["text-generation-language","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-microsoft--speecht5_tts__cap_4","uri":"capability://tool.use.integration.huggingface.model.hub.integration.with.standardized.inference.api","name":"huggingface model hub integration with standardized inference api","description":"Packaged as a HuggingFace transformers-compatible model, enabling seamless integration with the HuggingFace ecosystem including model loading via `from_pretrained()`, inference via standard pipelines, and deployment via HuggingFace Inference API or Endpoints. The model includes standardized configuration files (config.json, model.safetensors) and supports both local inference and cloud-hosted endpoints without code changes.","intents":["Load and use the model with minimal boilerplate code via HuggingFace transformers library","Deploy the model to production via HuggingFace Inference Endpoints without managing infrastructure","Integrate TTS into existing HuggingFace-based ML pipelines and applications","Access the model via REST API without local GPU or Python environment"],"best_for":["Python developers familiar with HuggingFace transformers ecosystem","Teams using HuggingFace for other NLP/ML tasks who want unified tooling","Developers deploying to HuggingFace Spaces or Endpoints for serverless inference","Researchers prototyping with pre-built models without custom inference code"],"limitations":["Requires HuggingFace transformers library (adds dependency); not compatible with raw PyTorch loading","HuggingFace Inference API has rate limits and latency overhead (100-500ms) compared to local inference","Model configuration is fixed; custom architectures require forking and retraining","Inference via HuggingFace Endpoints incurs per-request costs; not suitable for high-volume applications without cost optimization","No built-in batching optimization for HuggingFace Inference API; batch requests must be handled client-side"],"requires":["Python 3.8+","transformers library 4.20+","PyTorch 1.9+","HuggingFace account (free) for model hub access","Optional: HuggingFace API token for authenticated access or Inference Endpoints"],"input_types":["model_name (string: 'microsoft/speecht5_tts')","text (string, input to synthesize)","speaker_embeddings (optional, float tensor)"],"output_types":["mel-spectrogram (from local inference)","audio file (from HuggingFace Inference API)","JSON response (from REST API endpoint)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-microsoft--speecht5_tts__cap_5","uri":"capability://automation.workflow.batch.audio.synthesis.with.consistent.speaker.identity.across.multiple.texts","name":"batch audio synthesis with consistent speaker identity across multiple texts","description":"Supports processing multiple text inputs in a single batch while maintaining consistent speaker identity across all outputs via shared speaker embeddings. The model processes batched text tokens and broadcasts speaker embeddings to all batch items, enabling efficient multi-text synthesis with the same voice. This is useful for generating coherent multi-sentence audio content (e.g., audiobooks, podcasts) where speaker consistency is required.","intents":["Generate multiple sentences or paragraphs with the same speaker voice in a single batch operation","Create audiobooks or long-form content where speaker identity must remain consistent across chapters","Produce multi-speaker dialogue where each character's voice is consistent across multiple utterances","Optimize inference throughput by batching multiple synthesis requests together"],"best_for":["Content creators producing audiobooks, podcasts, or long-form audio with consistent voices","Developers building batch processing pipelines for large-scale TTS (e.g., generating audio for thousands of articles)","Teams requiring high-throughput TTS with GPU utilization optimization","Applications where speaker consistency across multiple utterances is critical (e.g., character voices in games)"],"limitations":["Batch size is limited by GPU memory; typical batch size 4-16 on consumer GPUs (larger batches require A100 or similar)","All texts in a batch must use the same speaker embedding; multi-speaker batches require separate forward passes","Mel-spectrograms from different batch items have different lengths; post-processing required to concatenate or pad","Vocoder inference must be run separately for each batch item (not batched); adds latency overhead","No automatic speaker switching within a batch; requires manual speaker embedding management for dialogue"],"requires":["PyTorch with CUDA support (GPU strongly recommended for practical batch inference)","Sufficient GPU memory (8GB+ for batch size 8-16)","transformers library with batch inference support","Speaker embeddings pre-extracted and available as tensors"],"input_types":["text_batch (list of strings, shape [batch_size])","speaker_embeddings (float tensor, shape [1, 512] or [batch_size, 512])","batch_size (integer, 1-16 typical)"],"output_types":["mel_spectrograms_batch (list of float tensors, variable lengths)","waveforms_batch (list of audio tensors, after vocoder)","audio_files (list of WAV/MP3 files, one per text)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","PyTorch 1.9+ (CPU or GPU)","transformers library 4.20+","scipy for audio processing","Optional: CUDA 11.0+ for GPU acceleration","Optional: vocoder model (HiFi-GAN checkpoint) for waveform generation","Pre-trained speaker encoder model (e.g., PyannoteAudio, SpeakerNet, or x-vector extractor)","Reference audio sample (3-10 seconds) for zero-shot voice cloning","Speaker embedding tensor of shape [1, 512] in x-vector format","PyTorch and transformers library as above"],"failure_modes":["Requires external vocoder (HiFi-GAN or similar) to convert mel-spectrograms to waveform audio — model outputs intermediate representation only","Speaker embedding extraction requires separate speaker encoder model (e.g., x-vector extractor) not included in base package","Inference latency ~2-5 seconds per sentence on CPU; GPU acceleration recommended for real-time applications","Training data (LibriTTS) is English-only; multilingual support requires fine-tuning or separate models","No built-in prosody control (pitch, speed, emotion) — requires post-processing or model fine-tuning for nuanced expression","Speaker embeddings must be pre-extracted using a separate speaker encoder model (not included) — adds pipeline complexity","Embedding quality directly impacts synthesis quality; poor speaker encoder produces degraded audio","Zero-shot voice cloning requires high-quality reference audio (3-10 seconds minimum) for reliable embedding extraction","Speaker embeddings are fixed-dimensional (512-dim x-vectors); incompatible with other embedding formats without conversion","No explicit control over speaker characteristics (age, gender, accent) — only implicit control via embedding space","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6190185644138292,"quality":0.22,"ecosystem":0.5000000000000001,"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":149878,"model_likes":826}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=microsoft--speecht5_tts","compare_url":"https://unfragile.ai/compare?artifact=microsoft--speecht5_tts"}},"signature":"XWaVhfmpihTZuc7cujSodbHgDcbOE7hTtmfWx4isPeUTTHfkE67ilY9umjGb/XnVKOfzG9fgWG3tTa0UXaWEAA==","signedAt":"2026-06-21T02:47:01.099Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/microsoft--speecht5_tts","artifact":"https://unfragile.ai/microsoft--speecht5_tts","verify":"https://unfragile.ai/api/v1/verify?slug=microsoft--speecht5_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"}}