Saudi Xtts v2

--- language: - ar - en license: cc-by-nc-4.0 library_name: coqui pipeline_tag: text-to-speech tags: - text-to-speech - tts - xtts - xtts-v2 - arabic - saudi-arabic - najdi-arabic - code-switching - voice-cloning base_model: - coqui/XTTS-v2 --- # Saudi XTTS-v2 A fine-tuned [XTTS-v2](https://huggingface.co/coqui/XTTS-v2) model for **Saudi Arabic / English code-switching** text-to-speech, with a single female speaker voice ("Hoda"). ## Model Details | Detail | Value | |---|---| | Base model | coqui/XTTS-v2 | | Fine-tuned on | ~50 000 synthetic Saudi Arabic sentences | | Speaker | Hoda (female, Saudi) | | Languages | Arabic (`ar`) + English (`en`) | | Sample rate | 24 000 Hz | | Training dialect | Najdi / Saudi colloquial Arabic (`ars`) | | Precision | fp16 | | Checkpoint step | 320 190 | ## Training Data The model was trained on ~50 000 synthetically generated sentences covering: - **Saudi dialect corpus** segments (TaghreedT/SDC, ~60%) - **Code-switching templates** mixing Saudi Arabic and English (~24%) - **Number verbalization** sentences — digits rendered in Saudi colloquial words (~16%) Audio was synthesized with the `lahgtna-omnivoice-v2` TTS system using the Najdi Arabic (`ars`) voice. ## Files | File | Description | |---|---| | `model.pth` | Fine-tuned GPT weights (upload this as the XTTS checkpoint) | | `config.json` | Training / inference configuration | | `vocab.json` | XTTS-v2 tokenizer vocabulary | | `dvae.pth` | Discrete VAE (from XTTS-v2 base, unchanged) | | `mel_stats.pth` | Mel spectrogram normalisation stats (from XTTS-v2 base, unchanged) | | `speakers_xtts.pth` | Speaker embedding library (from XTTS-v2 base) | | `reference_audios/hoda.wav` | Reference audio for voice cloning | | `references.json` | Speaker metadata | ## Quick Start ```python from TTS.api import TTS tts = TTS(model_path="Rabe3/saudi-xtts-v2", progress_bar=True) tts.tts_to_file( text="كيف الحال؟ وش قاعد تسوي اليوم؟", speaker_wav="reference_audios/hoda.wav", language="ar", file_path="output.wav", ) ``` Or using the XTTS model directly: ```python from TTS.tts.configs.xtts_config import XttsConfig from TTS.tts.models.xtts import Xtts config = XttsConfig() config.load_json("config.json") model = Xtts.init_from_config(config) model.load_checkpoint( config, checkpoint_dir=".", # directory containing model.pth, dvae.pth, etc. use_deepspeed=False, ) model.cu...