| --- |
| license: cc-by-nc-4.0 |
| language: |
| - en |
| - ja |
| - ko |
| - zh |
| task_categories: |
| - text-to-speech |
| - audio-to-audio |
| size_categories: |
| - 1M<n<10M |
| configs: |
| - config_name: samples_showcase |
| data_files: |
| - split: train |
| path: samples/samelang_higgs_expressive_0714_0902/samples.jsonl |
| - config_name: samelang_expressive |
| data_files: |
| - split: train |
| path: data/samelang/**/*.parquet |
| - config_name: references |
| data_files: |
| - split: train |
| path: references.jsonl |
| - config_name: audio_prompts |
| data_files: |
| - split: train |
| path: audio_prompts/metadata.csv |
| - config_name: audio_prompts_expressive |
| data_files: |
| - split: train |
| path: audio_prompts_expressive/metadata.csv |
| default_config_name: samples_showcase |
| --- |
| |
| # Multi-lingual TTS Data (leeoxiang/multi_lingo_data) |
|
|
| Large-scale cross-lingual + same-lingual TTS corpus for training expressive |
| multi-lingual voice-clone models. Covers 4 target languages |
| (**en / ja / ko / zh**), synthesized by `bosonai/higgs-tts-3-4b` |
| via sglang-omni. |
|
|
| > **🚧 Upload in progress** — the main `samelang_expressive` corpus |
| > (≈ 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`. |
| |
| ## 🎧 Listen first — `samples_showcase` |
|
|
| 40 randomly-picked cross-lingual sample pairs (10 per target language) |
| from the `samelang_expressive` corpus, each paired with the *reference |
| wav* used for voice cloning so you can A/B the timbre. This is what the |
| HF viewer above shows by default. |
|
|
| ```python |
| from datasets import load_dataset |
| 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**) |
| |
| Same-language expansion: for every pseudo-reference (real Emilia speaker |
| cross-cloned into the target language via `audio_prompts_expressive`), we |
| 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) |
| - **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 |
| - **Engine**: `sglang-omni-higgs-audio-v3` with expressive relabel-based |
| prompt (drawn from the top-quality pool per source speaker) |
|
|
| ### Streaming (recommended for 3.3 TB) |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset( |
| "leeoxiang/multi_lingo_data", |
| "samelang_expressive", |
| split="train", |
| streaming=True, |
| ) |
| row = next(iter(ds)) |
| print(row["lang"], row["text"][:40], row["audio"]["sampling_rate"]) |
| ``` |
|
|
| ### Single language |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset( |
| "leeoxiang/multi_lingo_data", |
| data_files="data/samelang/zh/train-*.parquet", |
| split="train", |
| streaming=True, |
| ) |
| ``` |
|
|
| ### Full download |
|
|
| ```bash |
| hf download leeoxiang/multi_lingo_data \ |
| --repo-type dataset \ |
| --include 'data/samelang/**' \ |
| --local-dir ./multi_lingo_data |
| ``` |
|
|
| ## Supporting configs |
|
|
| - **`references`** — 1200 curated Emilia mono-lingual real-speaker clips. |
| - **`audio_prompts`** — 14.4 K cross-lingual synth from Step 1 (each |
| real Emilia speaker cloned into 4 target langs × 3 texts). |
| - **`audio_prompts_expressive`** — same as above with expressive prompt |
| selection; the pseudo-refs used to seed `samelang_expressive`. |
| |
| <details><summary>Legacy detail (auto-generated by <code>stage_audio_prompts_for_hf.py</code>)</summary> |
| |
| # Multi-lingual TTS Data (leeoxiang/multi_lingo_data) |
| |
| Large-scale cross-lingual + same-lingual TTS corpus for training expressive |
| multi-lingual voice-clone models. Covers 4 target languages |
| (**en / ja / ko / zh**), synthesized by `bosonai/higgs-tts-3-4b` |
| via sglang-omni. |
| |
| > **🚧 Upload in progress** — the main `samelang_expressive` corpus |
| > (~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`. |
|
|
| ## 🎧 Listen first — `samples_showcase` |
| |
| 40 randomly-picked cross-lingual sample pairs (10 per target language) |
| from the `samelang_expressive` corpus, each paired with the *reference |
| wav* used for voice cloning so you can A/B the timbre. This is what the |
| HF viewer above shows by default. |
|
|
| ```python |
| from datasets import load_dataset |
| 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**) |
| |
| Same-language expansion: for every pseudo-reference (real Emilia speaker |
| cross-cloned into the target language via `audio_prompts_expressive`), we |
| 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) |
| - **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 |
| - **Engine**: `sglang-omni-higgs-audio-v3` with expressive relabel-based |
| prompt (drawn from the top-quality pool per source speaker) |
|
|
| ### Streaming (recommended for 3.3 TB) |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset( |
| "leeoxiang/multi_lingo_data", |
| "samelang_expressive", |
| split="train", |
| streaming=True, |
| ) |
| row = next(iter(ds)) |
| print(row["lang"], row["text"][:40], row["audio"]["sampling_rate"]) |
| ``` |
|
|
| ### Single language |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset( |
| "leeoxiang/multi_lingo_data", |
| data_files="data/samelang/zh/train-*.parquet", |
| split="train", |
| streaming=True, |
| ) |
| ``` |
|
|
| ### Full download |
|
|
| ```bash |
| hf download leeoxiang/multi_lingo_data \ |
| --repo-type dataset \ |
| --include 'data/samelang/**' \ |
| --local-dir ./multi_lingo_data |
| ``` |
|
|
| ## Supporting configs |
|
|
| - **`references`** — 1200 curated Emilia mono-lingual real-speaker clips. |
| - **`audio_prompts`** — 14.4 K cross-lingual synth from Step 1 (each |
| real Emilia speaker cloned into 4 target langs × 3 texts). |
| - **`audio_prompts_expressive`** — same as above with expressive prompt |
| selection; the pseudo-refs used to seed `samelang_expressive`. |
| |
| </details> |
| |
