Trim README prose

README.md

@@ -15,12 +15,6 @@ tags:
   - voice-cloning
   - vietnamese-tts
   - tieng-viet
-# NOTE deliberately NO `datasets:` field. It is the only thing that populates
-# the Hub's cross-link, but the Hub renders it as "Models trained or fine-tuned
-# on <dataset>" — which for our own held-out benchmark reads as train/test
-# contamination. ZeroBench-TTS is EVALUATION data; every voice in it is held
-# out of training. The `model-index` block below states that correctly, and the
-# body links the benchmark in prose.
 metrics:
   - wer
 model-index:
@@ -141,29 +135,10 @@ for chunk in tts.synthesize_stream("Một đoạn văn bản dài hơn…", voic
 
 ## Benchmarks
 
-Measured on **[ZeroBench-TTS](https://huggingface.co/datasets/zeroweight-ai/ZeroBench-TTS)** —
-137 items, 59 held-out reference voices × 4 subsets — against
-[OmniVoice](https://huggingface.co/k2-fsa/OmniVoice) and the two public
-Vietnamese XTTS-v2 finetunes. 137/137 scored for every system, 0 empty
-generations. OmniVoice is given its optional `language="vi"` hint, which its
-model card recommends and which measurably helps it.
-
-**Scored by the benchmark, not by us.** ZeroTTS synthesizes the clips and hands
-them to `zerobench_eval`, the official scorer published inside the benchmark
-dataset repo. Nothing in this repo computes a metric.
-
-### Headline
+Measured on **[ZeroBench-TTS](https://huggingface.co/datasets/zeroweight-ai/ZeroBench-TTS)** 
 
 Every system reads **normalized text** — dates, numbers and acronyms already
-spoken out, from the benchmark's own curated reading. Every system gets exactly
-the same input, so the comparison is like-for-like.
-
-This is the condition a Vietnamese TTS system meets in production, where a text
-frontend runs ahead of the model. ZeroTTS ships one — `normalize_vi_text`,
-applied by default (see the [GitHub README](https://github.com/zeroweight-ai/ZeroTTS#usage)) — which reproduces the benchmark's
-reading on 34 of the 35 items that need normalization. Neither baseline ships a
-Vietnamese frontend at all, which is why the raw-text table below is so much
-harsher on them.
+spoken out, from the benchmark's own curated reading. 
 
 | | **ZeroTTS** | OmniVoice | XTTS-v2-vietnamse | viXTTS |
 |---|:-:|:-:|:-:|:-:|
@@ -206,60 +181,6 @@ with no Vietnamese text frontend faces.
 | `challenging` | acronyms, dates, %, currency | **1.75 %** | 4.46 % | 27.86 % | 31.85 % |
 | **overall** | | **1.03 %** | **4.13 %** | **16.42 %** | **18.40 %** |
 
-**Reading these fairly:**
-
-* **OmniVoice beats us on two things, and they are worth naming.** Its speaker
-  similarity is the best of the four (0.951 vs our 0.938), and on `code_switch`
-  it is roughly half our error rate (0.46 % vs 0.95 %). If cloning fidelity or
-  English-in-Vietnamese is your priority, it is a genuinely strong option — at
-  3.1 GB on a GPU.
-* **OmniVoice's overall figure is dominated by one subset.** `cross_lingual`
-  (foreign voice prompt, Vietnamese text) costs it 17.71 % raw against our
-  1.42 %, and it is language-dependent — German 0.00 %, Korean 0.13 %, Japanese
-  0.41 %. Excluding that subset it lands near 1.7 % raw. Both ASRs agree the
-  audio genuinely degrades there, so it is the model, not the scorer.
-* **Normalization is where the weakest systems gain most, and the order does not
-  change.** The XTTS tokenizers have no Vietnamese number expansion, so raw text
-  punishes them hard (`challenging` 27.86 %) and the normalized column is the
-  fairest comparison available — it improves XTTS 2.3× and viXTTS 2.1×, against
-  1.8× for us. What remains is the acoustic model.
-* **`vietnamese` barely moves for anyone** (0.16 % → 0.21 % for ZeroTTS). It has
-  no digits or acronyms, so there is nothing to normalize — which is the control
-  showing the other subsets' gains are real and not a scoring artifact.
-* **On `cross_lingual` our voice similarity is the weak spot** (0.911 vs
-  ~0.935 for the others): ZeroTTS carries a foreign speaker's timbre into
-  Vietnamese slightly less faithfully, while winning that subset's WER by 12×.
-* **The WER definition matters more than the WER.** ZeroBench scores every clip
-  with **two ASRs** (`whisper-large-v3` + `PhoWhisper-large`, min taken — neither
-  can judge Vietnamese code-switch TTS alone) against **every acceptable
-  reading** of the target text, so a system is never charged for an ASR's
-  choice between "31/12/2025" and "ba mươi mốt tháng mười hai". Its test suite
-  pins that in both directions: format differences must score 0, real
-  mispronunciations must still cost.
-* **Our remaining errors are published, not hidden.** Every item scoring above
-  0.00 is audited in [docs/BENCHMARKS.md](https://github.com/zeroweight-ai/ZeroTTS/blob/main/docs/BENCHMARKS.md). The two recurring
-  ones: a leading zero read aloud (`18/04` → "tháng **không** tư"), and the
-  letters `W` and `H` coming out wrong when an acronym has to be spelled —
-  `WHO` should be spelled out letter by letter, and instead comes out as
-  something like "Hall".
-
-Reproduce, or score your own system:
-
-```bash
-pip install "zerotts[eval]"
-SYNTH_FROM=text_normalized OUT_DIR=./eval/norm ./evaluation/run_benchmark.sh
-./evaluation/run_benchmark.sh                                   # raw text
-```
-
-Not using ZeroTTS? The scorer stands alone — bring wavs from any system:
-
-```bash
-huggingface-cli download zeroweight-ai/ZeroBench-TTS --repo-type dataset --local-dir ZeroBench-TTS
-cd ZeroBench-TTS && pip install -r zerobench_eval/requirements.txt
-python -m zerobench_eval manifest --out manifest.jsonl    # what to synthesize
-python -m zerobench_eval score --wav_dir my_wavs/ --name MyModel
-```
-
 ## Voices, and voice cloning
 
 A voice is a small array of speaker latents, `(1, n_voice_queries, d_model)`,
@@ -277,25 +198,6 @@ conditioning — no reference transcript, no audio prompt.
 Because a voice is just an array, latents obtained that way drop into
 `voices/<name>/voice.npz` and work with no code change.
 
-## Repository layout
-
-```
-config.json                          runtime config
-tokenizer.json                       BPE tokenizer
-null_voice_emb.npy                   learned unconditional voice prefix
-onnx/text_encoder.onnx               text → encoder states (once per utterance)
-onnx/prefix_step.onnx                global transformer step (once per frame)
-onnx/local_frame_decode.onnx         frame decode + sampling (once per frame)
-onnx/codec/                          MOSS-Audio-Tokenizer-Nano decoder (Apache-2.0)
-voices/<name>/voice.npz              speaker latents
-```
-
-fp32, not quantized: ~900 MB total. Two ONNX Runtime calls per audio frame;
-frames are produced at 12.5 Hz and decoded to 48 kHz.
-
-The model architecture, training code, and the ONNX export script are not
-published, and the voice encoder is not included.
-
 ## Intended use and limitations
 
 Built for **Vietnamese**. It handles English words embedded in Vietnamese text
@@ -328,7 +230,6 @@ no external runtime dependency; the encoder is not included. See
 ## License
 
 ZeroTTS weights and code: **MIT**.
-Bundled MOSS codec decoder: **Apache-2.0**.
 
 The ZeroBench-TTS *dataset* is CC-BY-NC-4.0 because it redistributes reference
 audio from VIVOS, viVoice, phoaudiobook and Emilia. That license applies to the