Clone from FatimahEmadEldin/habibi-tts-najdi-ft

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README.md

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+---
+language:
+  - ar
+license: cc-by-nc-sa-4.0
+library_name: f5-tts
+pipeline_tag: text-to-speech
+tags:
+  - text-to-speech
+  - tts
+  - arabic
+  - saudi-arabic
+  - najdi
+  - voice-cloning
+  - f5-tts
+  - habibi-tts
+  - dit
+  - flow-matching
+  - fine-tuned
+datasets:
+  - FatimahEmadEldin/History-Podcast
+  - FatimahEmadEldin/saudi_podcast_normalized
+  - FatimahEmadEldin/saudi-numerical-tts
+  - omarelshehy/mo5tlf_podcast
+  - omarelshehy/thamaneya_podcast
+base_model: SWivid/Habibi-TTS
+base_model_relation: finetune
+inference: false
+---
+
+# Habibi-TTS Najdi (Fine-Tuned)
+
+A voice-cloning text-to-speech model for **Najdi Arabic (Saudi Arabic)**,
+fully fine-tuned from [SWivid/Habibi-TTS](https://huggingface.co/SWivid/Habibi-TTS)'s
+SAU (Saudi) specialized checkpoint.
+
+Given 5–8 seconds of a reference voice saying something in Arabic, this model
+generates new arbitrary Arabic text in that same voice, with Najdi-dialect
+prosody and pronunciation.
+
+## Model Details
+
+### Architecture
+
+F5-TTS Diffusion Transformer (DiT), v1 Base configuration:
+
+| Parameter | Value |
+|---|---|
+| Model family | F5-TTS (flow-matching text-to-speech) |
+| Backbone | DiT (Diffusion Transformer) |
+| Embedding dim | 1024 |
+| Depth (layers) | 22 |
+| Attention heads | 16 |
+| FFN multiplier | 2 |
+| Text embedding dim | 512 |
+| Conv text encoder layers | 4 |
+| Total parameters | ~335M |
+| Tokenizer | Character-level (vocab size 2704) |
+| Vocoder | [charactr/vocos-mel-24khz](https://huggingface.co/charactr/vocos-mel-24khz) |
+| Mel features | 100 mels, 24kHz, hop 256, win 1024 |
+
+See [`config.json`](./config.json) for the full architecture spec.
+
+### Base model
+
+This is a **full fine-tune** (not LoRA) warm-started from
+`SWivid/Habibi-TTS/Specialized/SAU/model_200000.safetensors`. That base
+checkpoint was already trained on Arabic speech for 200,000 updates by SWivid;
+this fine-tune runs an additional ~last updates on Najdi-specific data
+on top of those weights. All ~335M parameters are trainable — no frozen layers,
+no adapters.
+
+### Why full fine-tuning, not LoRA?
+
+LoRA with low rank (r=8-16) on messy conditional generation tasks ends up
+averaging noise and signal together in its low-rank update, producing
+artifacts that weren't in either the base model or the training data. Full
+fine-tuning has the capacity to properly partition the data distribution —
+at the cost of not being able to toggle the adaptation on/off at inference.
+
+### Why flow matching / DiT for TTS?
+
+F5-TTS uses flow matching (a diffusion-family method) rather than
+autoregressive generation. Instead of generating audio token-by-token, it
+denoises a full mel-spectrogram in 32 ODE steps. The DiT transformer
+conditions on both the text and the masked reference audio — the same
+architecture used for text-to-image diffusion, adapted for audio. See the
+[original F5-TTS paper](https://arxiv.org/abs/2410.06885).
+
+## Training
+
+### Data
+
+Combined ~18.4 hours of Najdi and Saudi Arabic speech from five HuggingFace
+datasets, filtered to clips of 3–10 seconds in duration:
+
+| Dataset | Content | Notes |
+|---|---|---|
+| `FatimahEmadEldin/History-Podcast` | Arabic history podcast | Podcast audio + transcripts |
+| `FatimahEmadEldin/saudi_podcast_normalized` | Saudi podcast with normalized text | Podcast audio + transcripts |
+| `FatimahEmadEldin/saudi-numerical-tts` | Saudi numerical phrases | Clean TTS data, Najdi-filtered |
+| `omarelshehy/mo5tlf_podcast` | Mukhtalef podcast | Podcast audio + transcripts |
+| `omarelshehy/thamaneya_podcast` | Thmanyah podcast | Podcast audio + transcripts |
+
+**Total training clips:** ~13,614
+**Total audio duration:** ~18.4 hours
+**Sample rate:** resampled to 24 kHz mono
+
+### Hyperparameters
+
+| Parameter | Value |
+|---|---|
+| Mixed precision | bf16 |
+| Batch type | frame-packed |
+| Frames per GPU | 38,400 |
+| Max samples per batch | 64 |
+| Gradient accumulation steps | 2 |
+| Learning rate | 5e-5 |
+| Epochs | 20 |
+| Warmup updates | 300 |
+| Max grad norm | 1.0 |
+| Save interval | every 500 updates |
+| Total updates | ~2,180 |
+| Hardware | NVIDIA A100 80GB |
+
+See [`training_config.json`](./training_config.json) for the exact config used.
+
+## Usage
+
+### Quick start
+
+```python
+from huggingface_hub import hf_hub_download
+from f5_tts.model import DiT
+from f5_tts.infer.utils_infer import load_model, load_vocoder, preprocess_ref_audio_text
+from habibi_tts.infer.utils_infer import infer_process
+import torch, soundfile as sf
+
+# Download files
+ckpt_path  = hf_hub_download(repo_id="FatimahEmadEldin/habibi-tts-najdi-ft", filename="model_last.pt")
+vocab_path = hf_hub_download(repo_id="FatimahEmadEldin/habibi-tts-najdi-ft", filename="vocab.txt")
+
+# Load model
+V1_BASE_CFG = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
+model = load_model(DiT, V1_BASE_CFG, ckpt_path, vocab_file=vocab_path, device="cuda")
+model = model.to(torch.float32).eval()
+vocoder = load_vocoder()
+
+# Voice clone
+REF_AUDIO = "path/to/najdi_reference_clip.wav"   # 5-8s of clean Najdi speech
+REF_TEXT  = "exact transcript of that clip"
+ref_audio, ref_text = preprocess_ref_audio_text(REF_AUDIO, REF_TEXT)
+
+# Generate
+wave, sr, _ = infer_process(
+    ref_audio, ref_text,
+    "مرحبا، كيف حالك اليوم؟",    # text to speak
+    model, vocoder,
+    nfe_step=32, speed=1.0,
+)
+sf.write("output.wav", wave, sr)
+```
+
+Or use the [`inference.py`](./inference.py) script included in this repo.
+
+### Reference clip guidelines
+
+Quality of the generated output is dominated by quality of the reference clip.
+
+- **Duration:** 5–8 seconds. Less than 3s is too little prosody; more than 10s
+  costs VRAM with no quality gain.
+- **Clean audio:** no background music, no overlapping speakers, no heavy
+  room reverb. Clipped or noisy references produce clipped or noisy outputs.
+- **Single speaker, full phrases:** the reference should contain one person
+  speaking complete sentences, not isolated words.
+- **Accurate transcript:** REF_TEXT must be what's actually said in REF_AUDIO.
+  Even small drift here degrades the output.
+
+### Generation parameters
+
+- `nfe_step=32` — diffusion ODE steps. 32 is the quality/speed sweet spot;
+  raise to 64 for marginal quality gain (2x slower), lower to 16 for
+  faster iteration.
+- `speed=1.0` — speech speed multiplier. 0.8 for slower, 1.2 for faster.
+
+## Intended Use
+
+- Text-to-speech for Najdi Arabic content creation where you have a clean
+  reference clip of the target voice.
+- Research on Arabic dialect TTS.
+
+## Out-of-Scope / Limitations
+
+- **Not a multi-dialect model.** Fine-tuned on Najdi; will produce Najdi-flavored
+  output even for other Arabic dialect inputs. Use vanilla Habibi-TTS for
+  other dialects.
+- **Voice clone requires reference audio.** This is not a "read arbitrary
+  text in a fixed voice" model. Without a reference clip, there is no output.
+- **Training data includes podcast audio.** Some inherited background
+  characteristics (room tone, occasional distant music) may appear in
+  outputs, especially when reference clip is itself podcast-sourced.
+- **Non-commercial.** License is CC-BY-NC-SA-4.0 (inherited from the base
+  Habibi-TTS model). You may not use this for commercial purposes without
+  working out separate licensing with SWivid.
+- **No safeguards against voice cloning misuse.** Do not clone a person's
+  voice without their permission. Do not generate deceptive or impersonating
+  audio.
+
+## Evaluation
+
+No formal evaluation metrics are reported. Subjective quality improvement
+over vanilla Habibi-TTS SAU was observed for Najdi-accented prompts. A/B
+testing against the base model is recommended for your specific use case.
+
+## Files
+
+| File | Description |
+|---|---|
+| `model_last.pt` | Model weights for inference (step last) |
+| `model_last.pt` | Full training state (weights + optimizer) for resuming |
+| `vocab.txt` | Character-level tokenizer vocab (2704 tokens) |
+| `config.json` | Architecture hyperparameters |
+| `training_config.json` | Training hyperparameters used |
+| `inference.py` | Standalone inference script |
+
+## Citation
+
+If you use this model, please cite both this fine-tune and the base Habibi-TTS
+work:
+
+```bibtex
+@misc{habibi-tts-najdi-ft,
+  author = {FatimahEmadEldin},
+  title = {Habibi-TTS Najdi Fine-Tuned},
+  year = {2026},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/FatimahEmadEldin/habibi-tts-najdi-ft}},
+}
+
+@misc{habibi-tts,
+  author = {SWivid},
+  title = {Habibi-TTS},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/SWivid/Habibi-TTS}},
+}
+
+@article{f5tts,
+  title = {F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching},
+  author = {Chen, Yushen and others},
+  journal = {arXiv:2410.06885},
+  year = {2024},
+}
+```
+
+## Acknowledgments
+
+- [SWivid](https://huggingface.co/SWivid) for the Habibi-TTS SAU pretrained base
+- F5-TTS authors for the underlying architecture and training framework
+- `charactr/vocos-mel-24khz` for the neural vocoder
+- [`omarelshehy`](https://huggingface.co/omarelshehy) for the Thmanyah and
+  Mukhtalef podcast datasets

config.json

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+{
+  "model_type": "f5_tts_dit_v1_base",
+  "architecture": "F5-TTS (DiT variant)",
+  "dim": 1024,
+  "depth": 22,
+  "heads": 16,
+  "ff_mult": 2,
+  "text_dim": 512,
+  "conv_layers": 4,
+  "n_params_approx": "335M",
+  "mel_spec": {
+    "n_mels": 100,
+    "n_fft": 1024,
+    "hop_length": 256,
+    "win_length": 1024,
+    "sample_rate": 24000
+  },
+  "vocoder": "charactr/vocos-mel-24khz",
+  "tokenizer": "char",
+  "vocab_size": 2704
+}

inference.py

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+"""
+Standalone inference script for FatimahEmadEldin/habibi-tts-najdi-ft.
+
+Requirements:
+    pip install git+https://github.com/SWivid/F5-TTS.git
+    pip install git+https://github.com/SWivid/Habibi-TTS.git
+    pip install huggingface_hub safetensors soundfile
+
+Usage:
+    python inference.py \
+        --ref_audio path/to/reference.wav \
+        --ref_text "exact transcript of the reference clip" \
+        --text "النص المراد تحويله إلى صوت" \
+        --out output.wav
+"""
+import argparse
+import torch
+import soundfile as sf
+from huggingface_hub import hf_hub_download
+from f5_tts.model import DiT
+from f5_tts.infer.utils_infer import (
+    load_model, load_vocoder, preprocess_ref_audio_text,
+)
+from habibi_tts.infer.utils_infer import infer_process
+
+REPO_ID   = "FatimahEmadEldin/habibi-tts-najdi-ft"
+CKPT_FILE = "model_last.pt"
+VOCAB     = "vocab.txt"
+
+V1_BASE_CFG = dict(
+    dim=1024, depth=22, heads=16,
+    ff_mult=2, text_dim=512, conv_layers=4,
+)
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument("--ref_audio", required=True,
+                   help="Path to 5-8s clean reference WAV")
+    p.add_argument("--ref_text", required=True,
+                   help="Exact transcript of the reference clip")
+    p.add_argument("--text", required=True,
+                   help="Arabic text to synthesize")
+    p.add_argument("--out", default="output.wav",
+                   help="Output WAV path")
+    p.add_argument("--nfe_step", type=int, default=32,
+                   help="ODE solver steps (16=fast, 32=default, 64=quality)")
+    p.add_argument("--speed", type=float, default=1.0,
+                   help="Speech speed multiplier")
+    p.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu")
+    args = p.parse_args()
+
+    print(f"Downloading model from {REPO_ID}...")
+    ckpt_path  = hf_hub_download(repo_id=REPO_ID, filename=CKPT_FILE)
+    vocab_path = hf_hub_download(repo_id=REPO_ID, filename=VOCAB)
+
+    print(f"Loading model on {args.device}...")
+    model = load_model(DiT, V1_BASE_CFG, ckpt_path,
+                       vocab_file=vocab_path, device=args.device)
+    model = model.to(torch.float32).eval()
+    vocoder = load_vocoder()
+
+    print(f"Preparing reference: {args.ref_audio}")
+    ref_audio, ref_text = preprocess_ref_audio_text(args.ref_audio, args.ref_text)
+
+    print(f"Generating: {args.text}")
+    wave, sr, _ = infer_process(
+        ref_audio, ref_text, args.text,
+        model, vocoder,
+        nfe_step=args.nfe_step, speed=args.speed,
+    )
+
+    sf.write(args.out, wave, sr)
+    print(f"✅ Saved to {args.out}  ({len(wave)/sr:.1f}s at {sr} Hz)")
+
+
+if __name__ == "__main__":
+    main()

model_2000.safetensors

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training_config.json

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+{
+  "run_name": "najdi_full_ft_v3",
+  "pretrained_from": "SWivid/Habibi-TTS/Specialized/SAU (model_200000)",
+  "finetune_mode": "full (all parameters trainable, warm-started)",
+  "mixed_precision": "bf16",
+  "batch_size_type": "frame",
+  "batch_size_per_gpu_frames": 38400,
+  "max_samples_per_batch": 64,
+  "grad_accumulation_steps": 2,
+  "learning_rate": 5e-05,
+  "num_epochs": 20,
+  "num_warmup_updates": 300,
+  "max_grad_norm": 1.0,
+  "save_per_updates": 500,
+  "total_updates_approx": 2180,
+  "hardware": "NVIDIA A100 80GB",
+  "final_step": "last"
+}

vocab.txt

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