Upload config.json

config.json

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+{
+    "_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"
+        ]
+    }
+}