Datasets:
Download README.md from lgris/CVSS-X: direct link, hf CLI and curl.
- Browser
- Download file 36.1 kB
-
https://huggingface.co/datasets/lgris/CVSS-X/resolve/main/README.md
- Command line
-
hf download hf://datasets/lgris/CVSS-X/README.md
-
curl -L -o README.md https://huggingface.co/datasets/lgris/CVSS-X/resolve/main/README.md
configs:
- config_name: en-pt
data_files:
- split: train
path: data/en-pt/train/*/*.tar
- split: dev
path: data/en-pt/dev/*/*.tar
- split: test
path: data/en-pt/test/*/*.tar
- config_name: en-pt-canonical
data_files:
- split: train
path: data/en-pt/train/canonical/*.tar
- split: dev
path: data/en-pt/dev/canonical/*.tar
- split: test
path: data/en-pt/test/canonical/*.tar
- config_name: en-pt-timbre
data_files:
- split: train
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- split: dev
path: data/en-pt/dev/timbre/*.tar
- split: test
path: data/en-pt/test/timbre/*.tar
- config_name: en-es
data_files:
- split: train
path: data/en-es/train/*/*.tar
- split: dev
path: data/en-es/dev/*/*.tar
- split: test
path: data/en-es/test/*/*.tar
- config_name: en-es-canonical
data_files:
- split: train
path: data/en-es/train/canonical/*.tar
- split: dev
path: data/en-es/dev/canonical/*.tar
- split: test
path: data/en-es/test/canonical/*.tar
- config_name: en-es-timbre
data_files:
- split: train
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- split: dev
path: data/en-es/dev/timbre/*.tar
- split: test
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- config_name: en-fr
data_files:
- split: train
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- split: dev
path: data/en-fr/dev/*/*.tar
- split: test
path: data/en-fr/test/*/*.tar
- config_name: en-fr-canonical
data_files:
- split: train
path: data/en-fr/train/canonical/*.tar
- split: dev
path: data/en-fr/dev/canonical/*.tar
- split: test
path: data/en-fr/test/canonical/*.tar
- config_name: en-fr-timbre
data_files:
- split: train
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- split: dev
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- split: test
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- config_name: en-it
data_files:
- split: train
path: data/en-it/train/*/*.tar
- split: dev
path: data/en-it/dev/*/*.tar
- split: test
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- config_name: en-it-canonical
data_files:
- split: train
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- split: test
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data_files:
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data_files:
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- config_name: en-ca
data_files:
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- split: dev
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- split: test
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- config_name: en-ca-canonical
data_files:
- split: train
path: data/en-ca/train/canonical/*.tar
- split: dev
path: data/en-ca/dev/canonical/*.tar
- split: test
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data_files:
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- config_name: en-de
data_files:
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- split: test
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- config_name: en-de-canonical
data_files:
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data_files:
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- config_name: en-nl
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data_files:
- split: train
path: data/en-nl/train/canonical/*.tar
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path: data/en-nl/dev/canonical/*.tar
- split: test
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- config_name: en-nl-timbre
data_files:
- split: train
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- config_name: en-sv
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- config_name: en-sv-canonical
data_files:
- split: train
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- config_name: en-sv-timbre
data_files:
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- config_name: en-da
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data_files:
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- config_name: en-no
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- config_name: en-pl
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data_files:
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- config_name: en-uk
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- config_name: en-uk-canonical
data_files:
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data_files:
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- config_name: en-zh
data_files:
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data_files:
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- config_name: en-ja
data_files:
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- config_name: en-ja-canonical
data_files:
- split: train
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data_files:
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- config_name: en-ko
data_files:
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- config_name: en-ko-canonical
data_files:
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data_files:
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- config_name: en-fi
data_files:
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- config_name: en-fi-canonical
data_files:
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- config_name: en-fi-timbre
data_files:
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- config_name: en-hu
data_files:
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- config_name: en-hu-canonical
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data_files:
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- config_name: en-fa-canonical
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- config_name: en-vi
data_files:
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data_files:
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path: data/en-vi/dev/canonical/*.tar
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data_files:
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path: data/en-vi/dev/timbre/*.tar
- split: test
path: data/en-vi/test/timbre/*.tar
task_categories:
- audio-to-audio
- text-to-speech
- translation
language:
- en
- pt
- es
- fr
- it
- ro
- ca
- de
- nl
- sv
- da
- 'no'
- ru
- pl
- cs
- uk
- zh
- ja
- ko
- fi
- hu
- hi
- fa
- el
- he
- tr
- th
- id
- vi
license: cc-by-nc-4.0
tags:
- arxiv:2609.13413
- speech-to-speech-translation
- speech-translation
- common-voice
- cross-lingual-tts
- voice-cloning
size_categories:
- 10M<n<100M
CVSS-X: A Multilingual Speech-to-Speech Translation Corpus for 28 Languages
This repository contains the official, reproducible pipeline and evaluation suite for CVSS-X (CVSS-X), a massive synthetic speech-to-speech translation (S2ST) corpus that complements and extends the original CVSS dataset by reversing the translation direction (English → 28 target languages).
Table of Contents
- Overview
- Corpus Statistics
- Target Languages
- Dataset Generation Pipeline
- Quality Evaluation Results
- Repository Structure
- Quickstart & Reproduction
- Hugging Face Dataset Access
- Roadmap: Version 2 (In Development)
- License
- Citation
Overview
While the pioneering CVSS corpus enables translation from 21 source languages exclusively into English (many-to-one), CVSS-X provides one-to-many translation from English into 28 typologically diverse target languages across 12 language families.
Combined with CVSS, CVSS-X enables:
- Bidirectional speech-to-speech translation (English ↔ 28 languages).
- Multilingual translation between arbitrary language pairs using English as a pivot (X → EN → Y).
- Direct evaluation of cross-lingual voice preservation in speech-to-speech tasks.
CVSS-X is provided in two complementary variants:
- CVSS-X-C (Canonical): Target speech is synthesized using two fixed, high-clarity reference voices per language (one male, one female). Voice selection is conditioned on the source speaker's gender metadata (81.4% male, 18.6% female).
- CVSS-X-T (Timbre-Transferred): Preserves the speaker characteristics of the original English speaker in the target speech via zero-shot cross-lingual voice cloning with OmniVoice.
Corpus Statistics
| Metric | Value |
|---|---|
| Source Language | English (Common Voice 17) |
| Target Languages | 28 |
| Language Families | 12 (7 macro-families) |
| Nominal Samples per Language | 240,192 |
| Data Splits (per lang) | Train: 222,349 • Dev: 10,000 • Test: 7,843 |
| Total Parallel Speech Pairs | 6,725,176 (Canonical) + 6,724,326 (Timbre) = 13,449,502 audio files |
| CVSS-X-C Duration | ~6,730 hours (avg. 3.6s / utterance) |
| CVSS-X-T Duration | ~9,340 hours (avg. 5.0s / utterance) |
| Total Audio Duration | ~16,070 hours (8x larger than CVSS) |
Target Languages
CVSS-X spans 28 languages across 12 typological families:
| Family | N | Languages (ISO Codes) |
|---|---|---|
| Romance | 6 | Portuguese (pt), Spanish (es), French (fr), Italian (it), Romanian (ro), Catalan (ca) |
| Germanic | 5 | German (de), Dutch (nl), Swedish (sv), Danish (da), Norwegian (no) |
| Slavic | 4 | Russian (ru), Polish (pl), Czech (cs), Ukrainian (uk) |
| CJK | 3 | Chinese (zh), Japanese (ja), Korean (ko) |
| Uralic | 2 | Finnish (fi), Hungarian (hu) |
| Indo-Iranian | 2 | Hindi (hi), Persian (fa) |
| Other | 6 | Greek (el), Hebrew (he), Turkish (tr), Thai (th), Indonesian (id), Vietnamese (vi) |
Dataset Generation Pipeline
Common Voice 17 EN
(240K utterances)
│
▼
┌───────────────────────────┐
│ 1. Source Alignment │ ──► Aligns CV17 recordings with CVSS metadata
└─────────────┬─────────────┘ (train: 222,349 | dev: 10,000 | test: 7,843)
│
▼
┌───────────────────────────┐
│ 2. Text Translation │ ──► facebook/nllb-200-distilled-600M
└─────────────┬─────────────┘ (EN -> 28 target languages, ~17ms/sentence)
│
▼
┌───────────────────────────┐
│ 3. Speech Synthesis │ ──► k2-fsa/OmniVoice (zero-shot cross-lingual TTS)
└─────────────┬─────────────┘
│
┌───────┴───────────────────────┐
▼ ▼
CVSS-X-C (Canonical) CVSS-X-T (Timbre-Transferred)
2 fixed reference voices Cloned voice from English source
(~6,730 hours) (~9,340 hours)
- Source Data & Alignment: English recordings from Common Voice version 17 aligned via normalized text matching with the original CVSS corpus (recovering 91.0% of samples). The dev set is supplemented from the training split to reach exactly 10,000 samples.
- Text Translation: Transcripts translated using
facebook/nllb-200-distilled-600M, selected after extensive benchmarking across 7 translation models for its optimal quality/throughput trade-off. - Speech Synthesis: Generated with
k2-fsa/OmniVoice, a multilingual transformer TTS engine featuring zero-shot cross-lingual synthesis without transferring source accent artifacts.
Quality Evaluation Results
Evaluation performed on a stratified random sample of 200 utterances per language from the dev set (5,600 samples per variant, determined via statistical power analysis).
Evaluation by Language Family (CVSS-X)
| Family (N) | WER/CER (C) | WER/CER (T) | ASR-BLEU (C) | ASR-BLEU (T) | UTMOS (C) | UTMOS (T) |
|---|---|---|---|---|---|---|
| Romance (6) | 5.8 | 8.2 | 90.3 | 88.0 | 3.54 | 3.27 |
| Germanic (5) | 9.5 | 12.2 | 85.3 | 81.1 | 3.62 | 3.27 |
| Slavic (4) | 7.0 | 7.7 | 86.9 | 85.9 | 3.48 | 3.17 |
| CJK (3)* | 5.0 | 10.2 | 75.2 | 67.9 | 3.56 | 3.20 |
| Uralic (2) | 10.3 | 14.3 | 84.0 | 78.3 | 3.54 | 3.21 |
| Indo-Iranian (2) | 22.5 | 23.0 | 63.7 | 60.0 | 3.61 | 3.20 |
| Other (6) | 24.8 | 24.8 | 65.0 | 64.9 | 3.53 | 3.14 |
| Average | 12.1 | 14.1 | 82.4 | 79.4 | 3.55 | 3.21 |
*For unsegmented CJK languages (ZH, JA, KO), Character Error Rate (CER) and character-level BLEU are reported.
Overall Comparison with CVSS (Re-evaluated with Same Pipeline)
| Metric | CVSS-X-C | CVSS-X-T | CVSS-C | CVSS-T |
|---|---|---|---|---|
| UTMOS (1–5) | 3.55 | 3.21 | 4.43 | 3.61 |
| ASR-BLEU | 82.4 | 79.4 | 94.2 | 93.8 |
| WER/CER (%) | 12.1 | 14.1 | 3.5 | 4.0 |
| Speaker Similarity (ECAPA-TDNN) | -- | 0.607 | -- | -- |
Repository Structure
CVSS-X/
├── README.md # Dataset documentation and reproduction guide
├── LICENSE # CC-BY-NC 4.0 license
├── requirements.txt # Unified Python dependencies
├── setup_env.sh # One-stop environment setup script
├── .gitignore # Git ignore rules
│
├── metadata/ # Official Pre-computed Manifests
│ ├── manifest_train.json # 222,349 source utterances
│ ├── manifest_dev.json # 10,000 source utterances
│ └── manifest_test.json # 7,843 source utterances
│
├── config/
│ ├── languages.yaml # Complete metadata for all 28 target languages
│ └── pipeline.yaml # Default hyperparameters and paths
│
├── pipeline/ # Dataset Creation Pipeline (Clean & Modular)
│ ├── __init__.py
│ ├── 01_prepare_source.py # CV17 extraction, CVSS alignment, and split generation
│ ├── 02_translate.py # NLLB multilingual translation (EN -> 28 languages)
│ ├── 03_synthesize.py # OmniVoice TTS synthesis (Canonical & Timbre modes)
│ └── utils.py # Data schemas, manifest I/O, checkpoints, audio helpers
│
├── evaluation/ # Quality Evaluation Suite
│ ├── __init__.py
│ ├── sample_dev.py # Deterministic 200 utterances/lang dev sampling (seed=42)
│ ├── evaluate_asr_bleu.py # Whisper large-v3 ASR + SacreBLEU / chrF / CER / WER
│ ├── evaluate_utmos.py # Neural MOS speech naturalness (UTMOS)
│ ├── evaluate_speaker_sim.py # ECAPA-TDNN cross-lingual speaker similarity
│ ├── evaluate_cvss_baseline.py # Re-evaluation of original CVSS baseline
│ └── generate_tables.py # Summary report & LaTeX/Markdown table generator
│
├── scripts/ # Reproducible Bash Runners
│ ├── 01_run_data_prep.sh # Executes Step 1 (source data prep or load official)
│ ├── 02_run_translation.sh # Executes Step 2 (translation)
│ ├── 03_run_synthesis_canonical.sh # Executes Step 3 (canonical synthesis)
│ ├── 04_run_synthesis_timbre.sh # Executes Step 4 (timbre synthesis)
│ └── 05_run_evaluation.sh # Executes Step 5 (full evaluation suite)
│
└── assets/ # Reference voice templates
├── pt-male.wav
└── pt-female.wav
Quickstart & Reproduction
1. Environment Setup
Clone this repository and run the setup script:
git clone https://github.com/Ermisai/CVSS-X.git
cd CVSS-X
./setup_env.sh
conda activate cvss-x # or source .venv/bin/activate
2. Step 1: Source Preparation (Official vs. Custom)
You have two options to prepare the source data:
Option A: Use the Official Pre-computed Manifests (Recommended)
You can directly use the exact aligned splits included in this repository under metadata/:
# Export official manifests directly to data/manifests/
USE_OFFICIAL_MANIFESTS=true ./scripts/01_run_data_prep.sh
# Or optionally link your local Common Voice 17 audio directory:
USE_OFFICIAL_MANIFESTS=true CV17_DIR=/path/to/cv17 ./scripts/01_run_data_prep.sh
Option B: Recompute Alignment from Scratch
If you wish to re-execute text alignment from raw Common Voice v4 and v17:
CV4_DIR=/path/to/cv4 CV17_DIR=/path/to/cv17 ./scripts/01_run_data_prep.sh
This creates data/manifests/manifest_{train,dev,test}.json containing exactly 222,349 / 10,000 / 7,843 samples.
3. Step 2: Multilingual Translation
Translate English transcripts to all 28 target languages (or a specific subset):
# Translate all 28 languages:
./scripts/02_run_translation.sh
# Or translate specific languages (e.g., Portuguese and Spanish):
LANGS="pt,es" ./scripts/02_run_translation.sh
Checkpoints are saved automatically every 2,000 samples for seamless resume capability.
4. Step 3 & 4: Speech Synthesis
Generate speech using OmniVoice:
# 1. Canonical Variant (CVSS-X-C):
./scripts/03_run_synthesis_canonical.sh
# 2. Timbre-Transferred Variant (CVSS-X-T):
./scripts/04_run_synthesis_timbre.sh
Audio files are saved under data/synthesized/en-{lang}/{split}/{variant}/{sample_id}.wav at 24kHz.
5. Step 5: Evaluation Suite
Run the full evaluation pipeline (dev sampling, ASR-BLEU with Whisper large-v3, UTMOS, and ECAPA-TDNN):
./scripts/05_run_evaluation.sh
The resulting Markdown report and paper tables will be generated in evaluation/evaluation_report.md.
Hugging Face Dataset Access & WebDataset Streaming
The complete dataset (~2.7 TB across 28 language pairs, 13.4M+ audio files) is being mirrored to the Hugging Face Hub under the repository lgris/CVSS-X.
1. Standard Streaming via load_dataset
You can stream subsets directly per language pair without downloading the entire corpus:
from datasets import load_dataset
# Stream Portuguese canonical dev split
dataset = load_dataset("lgris/CVSS-X", "en-pt", split="dev", streaming=True)
sample = next(iter(dataset))
print(sample.keys())
2. Loading via WebDataset Format
Audio files are packaged into sequential .tar archives natively compatible with the Hugging Face Datasets WebDataset loader. You can stream shards directly:
from datasets import load_dataset
# Load WebDataset shards directly in streaming mode
dataset_wds = load_dataset(
"webdataset",
data_files={"train": "https://huggingface.co/datasets/lgris/CVSS-X/resolve/main/data/en-pt/train/*/*.tar"},
split="train",
streaming=True
)
sample_wds = next(iter(dataset_wds))
# WebDataset automatically decodes the audio feature
audio = sample_wds["wav"]
print(f"Sample key: {sample_wds['__key__']}, audio sampling rate: {audio['sampling_rate']}")
Roadmap: Version 2 & Downstream Models (In Development)
We are actively working on Version 2 (v2) of the dataset and downstream baseline models.
Key improvements and models currently planned or in progress:
- TAGARELA v2 Baseline Model Integration: Adapting and training discrete-token speech-to-speech translation baselines directly on CVSS-X using the TAGARELA v2 architecture (
freds0/TAGARELA_v2). - TranslateGemma-12B Integration: Replacing NLLB-200 with Google's TranslateGemma-12B to significantly improve translation fidelity, natural phrasing, and eliminate commercial restrictions by transitioning to an Apache 2.0 license.
- Common Voice 26 & Spontaneous Speech 4.0: Expanding from CV17 to Common Voice 26 and integrating Spontaneous Speech 4.0, offering far richer conversational speech, more realistic disfluencies, and greater speaker diversity.
License
The CVSS-X corpus and code are licensed as follows:
- Audio & Source Transcripts: Inherited from Mozilla Common Voice (released under CC0 1.0 Universal).
- Synthetic Translations & Speech Pairs: Distributed under Creative Commons Attribution-NonCommercial 4.0 International (CC-BY-NC 4.0) due to the underlying
facebook/nllb-200-distilled-600Mmodel license. - Codebase & Scripts: Released under the permissive MIT License.
See LICENSE for full legal text.
Citation
If you use CVSS-X in your research, please cite our paper:
@misc{gris2026cvssx,
title={{CVSS-X: A Multilingual Speech-to-Speech Translation Corpus for 28 Languages}},
author={Lucas Rafael Stefanel Gris and Alef Iury Siqueira Ferreira and Frederico Santos de Oliveira and Augusto Seben da Rosa and Alexandre Costa Ferro Filho and Arlindo Rodrigues Galv{\~a}o Filho and Anderson da Silva Soares},
year={2026},
eprint={2609.13413},
archivePrefix={arXiv},
primaryClass={cs.CL},
doi={10.48550/arXiv.2609.13413},
url={https://arxiv.org/abs/2609.13413}
}
Preprint available on arXiv: https://arxiv.org/abs/2609.13413