README.md

metadata

license: apache-2.0
language:
  - ar
library_name: coqui
pipeline_tag: text-to-speech
tags:
  - tts
  - text-to-speech
  - speech-synthesis
  - arabic
  - egyptian-arabic
  - xtts
  - voice-cloning
datasets:
  - KickItLikeShika/NileTTS
base_model: coqui/XTTS-v2

Nile-XTTS Model 🇪🇬

Paper: https://arxiv.org/abs/2602.15675

Nile-XTTS is a fine-tuned version of XTTS v2 optimized for Egyptian Arabic (اللهجة المصرية) text-to-speech synthesis with zero-shot voice cloning capabilities.

Model Description

This model was fine-tuned on the NileTTS dataset, comprising 38 hours of Egyptian Arabic speech across medical, sales, and general conversation domains.

Key Features

• Egyptian Arabic optimized: Trained specifically on Egyptian dialect, not MSA or Gulf Arabic
• Zero-shot voice cloning: Clone any voice with just a 6-second reference audio
• Improved intelligibility: 29.9% reduction in WER compared to base XTTS v2
• Better pronunciation: 49.4% reduction in CER for Egyptian Arabic

Performance

Metric	XTTS v2 (Baseline)	Nile-XTTS-v2 (Ours)	Improvement
WER	26.8%	18.8%	29.9%
CER	8.1%	4.1%	49.4%
Speaker Similarity	0.713	0.755	+5.9%

Usage

Interactive Demo

Installation

pip install TTS

Usage (Direct Model Loading)

import torch
import torchaudio
from TTS.tts.configs.xtts_config import XttsConfig
from TTS.tts.models.xtts import Xtts

# load config and model
config = XttsConfig()
config.load_json("config.json")

model = Xtts.init_from_config(config)
model.load_checkpoint(
    config,
    checkpoint_path="model.pth",
    vocab_path="vocab.json",
    use_deepspeed=False
)
model.cuda()
model.eval()

# get speaker latents from reference audio
gpt_cond_latent, speaker_embedding = model.get_conditioning_latents(
    audio_path="reference.wav",
    gpt_cond_len=6,
    max_ref_length=30,
    sound_norm_refs=False
)

# synth speech
out = model.inference(
    text="مرحبا، إزيك النهارده؟",
    language="ar",
    gpt_cond_latent=gpt_cond_latent,
    speaker_embedding=speaker_embedding,
    temperature=0.7,
)

# save output
torchaudio.save("output.wav", torch.tensor(out["wav"]).unsqueeze(0), 24000)

Training Details

• Base model: XTTS v2
• Training data: NileTTS dataset (38 hours, 2 speakers)
• Epochs: 8 (early stopping)
• Learning rate: 5e-6

Limitations

• Limited to 2 speaker voices in training data
• Optimized for Egyptian Arabic; may not perform as well on other Arabic dialects
• Zero-shot cloning quality depends on reference audio quality

Citation

If you use this model, please cite: [TO BE ADDED]

License

This model is released under the Apache 2.0 license, following the original XTTS v2 license.

Acknowledgements

• Coqui TTS for the XTTS v2 base model
• The NileTTS team for the dataset creation