README: samelang_expressive (in-progress, ≈3.3 TB) + samples_showcase landing

README.md

@@ -42,7 +42,7 @@ multi-lingual voice-clone models. Covers 4 target languages
 via sglang-omni.
 
 > **🚧 Upload in progress** — the main `samelang_expressive` corpus
-> (~4.78 M rows / **~3.3 TB**) is being pushed as parquet shards.
+> (≈ 4.78 M rows / **≈ 3.3 TB**) is being pushed as parquet shards.
 > Expect the shard count to grow over the next hours/days until each
 > language reaches `train-XXXXX-of-00240.parquet`.
 
@@ -59,7 +59,7 @@ ds = load_dataset("leeoxiang/multi_lingo_data", "samples_showcase", split="train
 print(ds[0]["text"], ds[0]["audio"]["sampling_rate"])
 ```
 
-## 📦 Main corpus — `samelang_expressive` (**~4.78 M rows, in progress**)
+## 📦 Main corpus — `samelang_expressive` (**≈ 4.78 M rows, in progress**)
 
 Same-language expansion: for every pseudo-reference (real Emilia speaker
 cross-cloned into the target language via `audio_prompts_expressive`), we
@@ -67,12 +67,12 @@ synthesize **1000 target-language texts**, giving
 `1200 speakers × 4 langs × 1000 texts ≈ 4.8 M` cloned wavs.
 
 - **Shards**: `data/samelang/<lang>/train-XXXXX-of-00240.parquet`
-  (each lang → 240 shards, ~5000 rows/shard)
+  (each lang → 240 shards, ≈ 5000 rows/shard)
 - **Row schema**: `audio` (struct{bytes, path}, auto-cast to HF `Audio`
   feature) + `text`, `lang`, `ref_id`, `speaker_id`, `text_id`,
   `duration`, `sample_rate`, `ref_audio_path`, `ref_text`, `engine`,
   `original_text`
-- **Total size (fully uploaded)**: **~3.3 TB** audio, ~4.78 M rows, ~11 k h
+- **Total size (fully uploaded)**: **≈ 3.3 TB** audio, ≈ 4.78 M rows, ≈ 11 k h
 - **Engine**: `sglang-omni-higgs-audio-v3` with expressive relabel-based
   prompt (drawn from the top-quality pool per source speaker)
 
@@ -129,7 +129,7 @@ multi-lingual voice-clone models. Covers 4 target languages
 via sglang-omni.
 
 > **🚧 Upload in progress** — the main `samelang_expressive` corpus
-> (~4.78 M rows / ~1.4 TB) is being pushed as parquet shards.
+> (~4.78 M rows / **~3.3 TB**) is being pushed as parquet shards.
 > Expect the shard count to grow over the next hours/days until each
 > language reaches `train-XXXXX-of-00240.parquet`.
 
@@ -154,16 +154,16 @@ synthesize **1000 target-language texts**, giving
 `1200 speakers × 4 langs × 1000 texts ≈ 4.8 M` cloned wavs.
 
 - **Shards**: `data/samelang/<lang>/train-XXXXX-of-00240.parquet`
-  (each lang → 240 shards, ~5000 rows/shard, ~1.5 GB with zstd)
+  (each lang → 240 shards, ~5000 rows/shard)
 - **Row schema**: `audio` (struct{bytes, path}, auto-cast to HF `Audio`
   feature) + `text`, `lang`, `ref_id`, `speaker_id`, `text_id`,
   `duration`, `sample_rate`, `ref_audio_path`, `ref_text`, `engine`,
   `original_text`
-- **Total size (fully uploaded)**: ~1.4 TB audio, ~4.78 M rows, ~11 k h
+- **Total size (fully uploaded)**: **~3.3 TB** audio, ~4.78 M rows, ~11 k h
 - **Engine**: `sglang-omni-higgs-audio-v3` with expressive relabel-based
   prompt (drawn from the top-quality pool per source speaker)
 
-### Streaming (recommended for 1.4 TB)
+### Streaming (recommended for 3.3 TB)
 
 ```python
 from datasets import load_dataset