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_expressivecorpus (≈ 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 reachestrain-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.
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 HFAudiofeature) +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-v3with expressive relabel-based prompt (drawn from the top-quality pool per source speaker)
Streaming (recommended for 3.3 TB)
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
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
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 seedsamelang_expressive.
Legacy detail (auto-generated by stage_audio_prompts_for_hf.py)
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_expressivecorpus (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 reachestrain-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.
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 HFAudiofeature) +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-v3with expressive relabel-based prompt (drawn from the top-quality pool per source speaker)
Streaming (recommended for 3.3 TB)
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
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
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 seedsamelang_expressive.