| { |
| "_name_or_path": "LuisChary/LatinX-TTS", |
| "model_type": "latinx_tts", |
| "architectures": [ |
| "LatinXTTS" |
| ], |
| "library_name": "latinx-inference", |
| "pipeline_tag": "text-to-speech", |
| "task": "text-to-speech", |
| "license": "cc-by-nc-4.0", |
| "paper": { |
| "title": "LatinX: Aligning a Multilingual TTS Model with Direct Preference Optimization", |
| "arxiv": "2509.05863" |
| }, |
| "authors": [ |
| "Luis Felipe Chary", |
| "Miguel Arjona Ramirez" |
| ], |
| "description": "LatinX is a multilingual, voice-preserving text-to-speech model component designed for cascaded speech-to-speech translation research.", |
| "supported_languages": [ |
| "pt", |
| "en", |
| "es", |
| "fr", |
| "it", |
| "ro" |
| ], |
| "language_names": { |
| "pt": "Portuguese", |
| "en": "English", |
| "es": "Spanish", |
| "fr": "French", |
| "it": "Italian", |
| "ro": "Romanian" |
| }, |
| "audio": { |
| "sampling_rate": 16000, |
| "channels": 1, |
| "format": "waveform" |
| }, |
| "inference": { |
| "repository": "https://github.com/luischary/latinx-inference", |
| "entrypoint": "scripts/run_inference.py", |
| "recommended_usage": "Use the accompanying latinx-inference repository rather than loading this repository directly with a generic Transformers pipeline.", |
| "supports_reference_audio_conditioning": true, |
| "recommended_mode": "reference_audio_conditioning" |
| }, |
| "components": { |
| "text_normalization": { |
| "description": "Language-aware text normalization, including numeric value expansion in the inference repository." |
| }, |
| "g2p": { |
| "description": "Grapheme-to-phoneme conversion module.", |
| "checkpoint": "g2p.pt" |
| }, |
| "phoneme_tokenizer": { |
| "description": "Phoneme-level tokenization used before acoustic token generation." |
| }, |
| "acoustic_model": { |
| "description": "Autoregressive decoder-only acoustic-token generator.", |
| "checkpoint": "ar_decoder.pt", |
| "parameter_count": "approximately 210M", |
| "context_length": 8192 |
| }, |
| "acoustic_codec": { |
| "description": "VQ acoustic autoencoder used to decode generated acoustic tokens.", |
| "checkpoint": "vq_autoencoder.pt", |
| "mel_bins": 80, |
| "temporal_downsampling": 4, |
| "codebook_size": 4096, |
| "nominal_bitrate_kbps": 7.5 |
| }, |
| "vocoder": { |
| "description": "HiFi-GAN waveform synthesizer.", |
| "checkpoint": "hifi.pt" |
| } |
| }, |
| "checkpoint_files": [ |
| "g2p.pt", |
| "ar_decoder.pt", |
| "vq_autoencoder.pt", |
| "hifi.pt" |
| ], |
| "training": { |
| "alignment_method": "Direct Preference Optimization", |
| "training_code_included": false |
| }, |
| "limitations": { |
| "long_form_generation": false, |
| "requires_sentence_chunking_for_long_text": true, |
| "unconditioned_generation_may_be_less_stable": true, |
| "pronunciation_and_prosody_may_vary_by_language_speaker_and_domain": true |
| }, |
| "responsible_use": { |
| "requires_consent_for_reference_voice": true, |
| "synthetic_speech_disclosure_recommended": true, |
| "disallowed_uses": [ |
| "impersonation", |
| "deceptive voice cloning", |
| "non-consensual voice cloning", |
| "fraud", |
| "harassment", |
| "misinformation", |
| "commercial use" |
| ] |
| } |
| } |
