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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>
|