| | --- |
| | language: |
| | - en |
| | license: cc-by-4.0 |
| | configs: |
| | - config_name: default |
| | data_files: |
| | - split: emilia |
| | path: emilia/* |
| | - split: hifitts2 |
| | path: hifitts2/* |
| | splits: |
| | - name: emilia |
| | num_examples: 1693423 |
| | - name: hifitts2 |
| | num_examples: 1220574 |
| | pipeline_tag: text-to-speech |
| | tags: |
| | - voxtream |
| | - text-to-speech |
| | task_categories: |
| | - text-to-speech |
| | --- |
| | |
| | # Model Card for VoXtream training dataset |
| |
|
| | This repository contains a training dataset for [VoXtream](https://huggingface.co/herimor/voxtream) TTS model. |
| |
|
| | The dataset contains 9k hours: |
| |
|
| | - 4.5k hours sampled from [Emilia](https://huggingface.co/datasets/amphion/Emilia-Dataset) dataset. We applied additional diarization to remove multi-speaker utterances and discarded utterances with invalid automatic transcripts. We also used [NISQA](https://github.com/gabrielmittag/NISQA) model to remove low-quality utterances. |
| | - 4.5k hours sampled from [HiFiTTS2](https://huggingface.co/datasets/nvidia/hifitts-2) dataset (22 kHz subset). We selected only single-speaker utterances and filtered the dataset by the WER. |
| |
|
| | All utterances are 25-seconds long. For shorter audio clips we concatenated multiple utterances within the same speaker. Sampling rate: 24kHz. |
| |
|
| | ### Description |
| |
|
| | - **mimi_codes_16cb** - Tokens extracted by the [Mimi](https://huggingface.co/kyutai/mimi) audio codec (16 codebooks). |
| | - **phone_emb_indices** - Alignment of phoneme tokens to Mimi audio frames extracted by [MFA](https://montreal-forced-aligner.readthedocs.io). |
| | - **phone_tokens** - Phoneme tokens. |
| | - **sem_label_shifts** - Monotonic phoneme alignment labels. |
| | - **spk_templates** - Speaker templates for the first 3 seconds of audio extracted by [ReDimNet](https://github.com/IDRnD/redimnet) model. |
| |
|
| | ### Sources |
| |
|
| | - **Repository:** [repo](https://github.com/herimor/voxtream) |
| | - **Paper:** [paper](https://arxiv.org/pdf/2509.15969) |
| | - **Demo:** [demo](https://herimor.github.io/voxtream) |
| |
|
| | ## Get started |
| |
|
| | To download the dataset, use the following code: |
| |
|
| | ```bash |
| | from huggingface_hub import snapshot_download |
| | |
| | local_dir = snapshot_download('herimor/voxtream-train-9k', repo_type='dataset') |
| | ``` |
| |
|
| | Clone our [repo](https://github.com/herimor/voxtream) and follow the instructions in the README file. |
| |
|
| | ## Sample Usage |
| |
|
| | The following examples demonstrate how to use the VoXtream model (trained on this dataset) for output streaming and full streaming. |
| |
|
| |
|
| | ### Installation |
| | ```bash |
| | pip install voxtream |
| | ``` |
| |
|
| | ### Output streaming |
| | ```bash |
| | voxtream \ |
| | --prompt-audio assets/audio/male.wav \ |
| | --prompt-text "The liquor was first created as 'Brandy Milk', produced with milk, brandy and vanilla." \ |
| | --text "In general, however, some method is then needed to evaluate each approximation." \ |
| | --output "output_stream.wav" |
| | ``` |
| | * Note: Initial run may take some time to download model weights. |
| |
|
| | ### Full streaming |
| | ```bash |
| | voxtream \ |
| | --prompt-audio assets/audio/female.wav \ |
| | --prompt-text "Betty Cooper helps Archie with cleaning a store room, when Reggie attacks her." \ |
| | --text "Staff do not always do enough to prevent violence." \ |
| | --output "full_stream.wav" \ |
| | --full-stream |
| | ``` |
| |
|
| | ## Citation |
| |
|
| | ``` |
| | @article{torgashov2025voxtream, |
| | author = {Torgashov, Nikita and Henter, Gustav Eje and Skantze, Gabriel}, |
| | title = {Vo{X}tream: Full-Stream Text-to-Speech with Extremely Low Latency}, |
| | journal = {arXiv:2509.15969}, |
| | year = {2025} |
| | } |
| | ``` |
