Habibi-TTS ALG Production Optimization
Production-ready optimization scripts for the Habibi-TTS Algerian Arabic (ALG) specialized model.
Research Validation Summary
| Claim | Status | Evidence |
|---|
EPSS / get_epss_timesteps() | TRUE | Built into F5-TTS v1.1.20+ since May 2025. Auto-applies when steps ∈ {5,6,7,10,12,16} |
| Sway Sampling | TRUE | Native to F5-TTS, default sway_sampling_coef=-1.0 |
| F5R-TTS (29.5% WER) | UNCONFIRMED | Paper not publicly indexed. GRPO for TTS validated by DMOSpeech 2 (~10% WER improvement) |
| Triton/TensorRT in F5-TTS | FALSE | No off-the-shelf support. Triton runtime exists but requires manual setup |
| SGLang/vLLM for TTS | FALSE | Architecturally incompatible. F5-TTS is DiT+flow-matching, not autoregressive LLM |
| TGI maintenance mode | TRUE | Official HF docs confirm maintenance mode, recommend vLLM/SGLang for LLMs |
| FP8 on A10G | FALSE | A10G (Ampere/SM80) does NOT support FP8. Use BF16 + INT8 instead |
| Arabic diacritization | TRUE | Sadeed (Misraj/Sadeed) is SOTA for MSA. Algerian dialect needs dialect-aware preprocessing |
Scripts
01_epss_optimization.py
EPSS (Empirically Pruned Step Sampling) - 4x speedup with minimal quality loss.
- NFE=7 is the sweet spot (from arxiv:2505.19931)
- Built into F5-TTS, auto-applies when
use_epss=True
02_bf16_compile_optimization.py
BF16 inference + torch.compile for A10G.
- BF16: ~2x faster than FP32, zero quality loss
- torch.compile: ~20-30% additional speedup
- Combined: RTF ~0.016-0.018 on A10G
03_arabic_preprocessing.py
Algerian Arabic text preprocessing pipeline.
- Numeral normalization (Eastern/Western → Arabic words)
- French/Arabic code-switching handling
- Diacritization via Sadeed model
- Text caching for repeated phrases
04_streaming_server.py
FastAPI streaming TTS server.
- Sentence-level chunking
- Opus-encoded streaming output
- Sub-500ms time-to-first-audio
- Health and info endpoints
05_quantization.py
INT8 weight-only quantization for A10G.
- INT8 W8A16 (weights quantized, activations BF16)
- ~50% memory reduction vs BF16
- ~1.3-1.5x speedup (memory bandwidth bound)
Recommended Priority Stack
| Priority | Action | Expected RTF on A10G |
|---|
| 1 | EPSS NFE=7 | 0.030 |
| 2 | BF16 | 0.022 |
| 3 | torch.compile | 0.016-0.018 |
| 4 | Sentence streaming | Sub-500ms TTFA |
| 5 | INT8 quantization | 0.012-0.014 |
Hardware: A10G (24GB VRAM)
- Single A10G can run 2 model replicas with BF16
- ~30-50 concurrent short utterances
- Always use BF16 (native support, no quality loss)
- Do NOT use FP8 (A10G lacks FP8 tensor cores)
Model Info
- Base: SWivid/F5-TTS (F5TTS_v1_Base)
- Fine-tuned: SWivid/Habibi-TTS/Specialized/ALG
- Checkpoint: model_100000.safetensors
- Vocab: Specialized/ALG/vocab.txt
- License: Apache 2.0 (ALG dialect)
