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.

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)

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 seed samelang_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_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.

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)

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 seed samelang_expressive.