Datasets:

VoiceHub
/

DACFlow-EN-10k-backup

---
license: apache-2.0
language:
- en
pretty_name: "DACFlow-EN-10k-backup"
task_categories:
- text-to-speech
tags:
- speech
- tts
- synthetic-speech
- dacvae
- latents
- repa
- backup
size_categories:
- 1M<n<10M
source_datasets:
- SynDataLab-EN/echo-clones-4m-en
viewer: false
---
# DACFlow-EN-10k-backup

**The complete working backup of the data behind DACFlow-EN-10k, an English text-to-speech model ([VoiceHub/DACFlow-EN-10k](https://huggingface.co/VoiceHub/DACFlow-EN-10k)).** It holds
everything needed to resume the training and the evaluations if the training machine is lost: the tokenized training data,
the REPA targets, the quality annotations, the manifests and every catalog, the held-out voices' audio, the evaluation sets,
the experiment data and the processing bookkeeping. Every file is checked against the Hub's own record of it.

> **Looking for the dataset?** Start with [VoiceHub/DACFlow-EN-10k-data](https://huggingface.co/datasets/VoiceHub/DACFlow-EN-10k-data): only the tokenized training data (the codec latents, their texts and the
> exact training list), with a guide to use it.
>
> **Want to listen to the model?** Its samples are on [VoiceHub/DACFlow-EN-10k](https://huggingface.co/VoiceHub/DACFlow-EN-10k).

| | |
|---|---|
| What this repo is | the project's complete working backup (disaster recovery, and the record of every input); not a dataset release |
| The dataset | [VoiceHub/DACFlow-EN-10k-data](https://huggingface.co/datasets/VoiceHub/DACFlow-EN-10k-data) (the tokenized training data only) |
| The model, its checkpoints and samples | [VoiceHub/DACFlow-EN-10k](https://huggingface.co/VoiceHub/DACFlow-EN-10k) |
| The main run's training state (to resume it) | [VoiceHub/mytts-en-base](https://huggingface.co/datasets/VoiceHub/mytts-en-base) (prefix `en-base-s1`); not here |
| Made from | [SynDataLab-EN/echo-clones-4m-en](https://huggingface.co/datasets/SynDataLab-EN/echo-clones-4m-en) (revision `aebc7c787c64`) |
| Status | **complete**, updated 2026-10-03 00:22:12 UTC |
| Size now | 36,896 files, 522.6 GB |
| Licence | apache-2.0 (see [Provenance and licence](#provenance-and-licence)) |
| Code | [kadirnar/dacvae-next](https://github.com/kadirnar/dacvae-next/tree/roadmap/en-echo) (private at the moment), Python package `mytts` |

## What is in it

What each part is and what it is needed for ([Layout](#layout) has one line per folder). The latents and the REPA targets
come at 25 frames per second, so frame *i* of one lines up with frame *i* of the other.

| part | in plain words | exact form | needed to | folder |
|---|---|---|---|---|
| **Codec latents** (what the model learns to generate) | a compressed form of each clip's audio, with its text and voice; the codec's decoder turns it back into sound | [Semantic-DACVAE](https://huggingface.co/Aratako/Semantic-DACVAE-Japanese) posterior mean, 25 Hz x 128 numbers, float16 (codec `Aratako/Semantic-DACVAE-Japanese@737fbad2a679f880`) | train (the same files are on [VoiceHub/DACFlow-EN-10k-data](https://huggingface.co/datasets/VoiceHub/DACFlow-EN-10k-data)) | `tts_en/latents/echo_en/` |
| **Catalogs** | the filtered clip lists a run trains on; `echo_en_v1` is the main run's | `<name>/catalog.parquet` + `version.json` | train | `tts_en/catalogs/` |
| **Latent statistics** | the frozen normalization of the latents and the latent of silence | `latent_stats.json`, `silence_latent.npy` | train, synthesize | `tts_en/` |
| **REPA targets** (a training aid) | what a large speech-understanding model hears in each frame; the TTS model is nudged to match it, which makes it learn faster | [w2v-BERT 2.0](https://huggingface.co/facebook/w2v-bert-2.0) layer 16, reduced from 1,024 to 128 numbers with PCA (`tts_en/ssl/pca.npz`), float16 | resume the training (its REPA loss) | `tts_en/ssl/` |
| **Quality annotations** | what a speech recognizer heard, where the silences are; used to drop bad clips | [Parakeet CTC 1.1B](https://huggingface.co/nvidia/parakeet-ctc-1.1b) transcript + CER / WER / NLL of every clip; Whisper-large-v3-turbo on a 2 % sample; silence at the edges, the longest pause and whether the clip hit EchoTTS's length cap | rebuild or audit a catalog | `tts_en/annotations/` |
| **Manifests** | the source row of every clip | one JSON line per clip | provenance | `tts_en/manifests/` |
| **Held-out voices** | 236 voices never trained on: their clips' latents, REPA targets, annotations and original audio | as above, plus `.wav` | evaluation only | `*/echo_en_heldout/`, `eval/en/echo_heldout_audio/` |
| **Evaluation sets** | the echo tests, Seed-TTS test-en, LibriSpeech(-PC), AMI, validation runs, the protect list | lists + audio | evaluation | `eval/en/` |
| **Experiments** | HuBERT REPA targets of the screening subset (EN-25 teacher screen) | as the REPA targets | not used by the main run | `tts_en_hubl*/` |
| **Bookkeeping** | the processing state and ledger; this backup's file list, status and symlinks | JSON / JSONL | resume the processing; verify and restore this backup | `tts_en/ingest/`, `backup/` |

## What it is not

- **Not the place to start.** To use the data, start with [VoiceHub/DACFlow-EN-10k-data](https://huggingface.co/datasets/VoiceHub/DACFlow-EN-10k-data): the tokenized training data alone, with a guide. This
  repo holds much more, because it must be able to bring back everything.
- **Not a place to listen.** Latents are numbers. The model's samples are on [VoiceHub/DACFlow-EN-10k](https://huggingface.co/VoiceHub/DACFlow-EN-10k). The only audio here is evaluation
  material: the held-out voices' clips
  ([`eval/en/echo_heldout_audio/`](https://huggingface.co/datasets/VoiceHub/DACFlow-EN-10k-backup/tree/main/eval/en/echo_heldout_audio)) and the evaluation sets.
- **Not the source dataset.** The original audio and texts are [SynDataLab-EN/echo-clones-4m-en](https://huggingface.co/datasets/SynDataLab-EN/echo-clones-4m-en); every clip here points back to its source row
  (`tts_en/manifests/`, and `source` / `id` in each `.meta.parquet`).
- **Not usable with another codec.** The latents belong to this exact codec version; the REPA targets to `tts_en/ssl/pca.npz`.
- **No real recordings in the training data.** All of the training speech is synthetic (EchoTTS voice clones). Only the
  evaluation sets under `eval/en/` hold real recordings (LibriSpeech, AMI, the Common Voice prompts of Seed-TTS-eval).

## Size and progress

| | now | when complete |
|---|---:|---:|
| Source files (parquet) processed | 7,967 / 7,967 (100.0 %) | 7,967 |
| Source clips read | 3,983,250 | about 3.98 M |
| Groups uploaded (16 source files each) | 499 | 498 |
| Held-out clips kept (236 voices, at most 60 each) | 14,160 | at most 14,160 |
| Files in this repo | 36,896 | - |
| Size | 522.6 GB | 522.6 GB |

The stream is complete; this page is written again whenever files are added.
Last update: 2026-10-03 00:22:12 UTC (--force).

## Layout

| folder | what is inside | used for | files | size |
|---|---|---|---:|---:|
| `tts_en/latents/echo_en/` | codec latents of every training clip: `shard-<tag>-NNNNNN.bin` (float16, 25 frames/s x 128 numbers), `.idx.json` (where each clip starts and how long it is), `.meta.parquet` (text, voice, duration, source row of each clip) | training | 1,497 | 250.3 GB |
| `tts_en/ssl/echo_en/` | REPA targets of every training clip: w2v-BERT 2.0 layer 16, PCA to 128 numbers, float16, one row per latent frame (`shard-<tag>-t0-NNNNNN.{bin,idx.json}`) | training | 998 | 249.8 GB |
| `tts_en/ssl/` | `pca.npz`: the PCA that turns w2v-BERT's 1,024 numbers into 128; `info.json` | training | 2 | 529.8 kB |
| `tts_en/annotations/asr2/echo_en/` | Parakeet CTC 1.1B transcript of every clip with CER / WER / NLL against its text (`part-<tag>.parquet`) | training (clip filter) | 499 | 457.4 MB |
| `tts_en/annotations/asr/echo_en/` | Whisper-large-v3-turbo transcript of a 2 % sample: a cross-check of the Parakeet scores | analysis | 499 | 15.2 MB |
| `tts_en/annotations/edges/echo_en/` | silence at the start and the end of every clip | training (clip edges) | 499 | 196.8 MB |
| `tts_en/annotations/silence/echo_en/` | longest pause inside every clip, the generation length cap and whether the clip reached it (EchoTTS failure clips) | training (clip filter) | 499 | 105.4 MB |
| `tts_en/manifests/` | one JSON line per clip: id, text, voice, licence and its source row `hf://datasets/<source>@<revision>/<file>#<row>` (held-out clips: their kept audio file) | provenance | 499 | 1.5 GB |
| `tts_en/catalogs/` | catalogs: the filtered clip lists a run trains on (`<name>/catalog.parquet` + `version.json`, build log, leak check) | training | 12 | 496.3 MB |
| `tts_en/` | `latent_stats.json` (the frozen latent normalization of the model), `splits.json`, file lists and held-out voice lists | training | 8 | 214.3 kB |
| `tts_en/ingest/` | the ingest's state (run spec, finished source files) and ledger (one line per group), as of the last upload | bookkeeping | 2 | 1.8 MB |
| `tts_en/manifests_local/` | the Tier-1 screening subset's manifest (local raw paths; provenance only) | bookkeeping | 1 | 12.9 MB |
| `tts_en/latents/echo_en_heldout/` | codec latents of the held-out voices' clips | **held out: evaluation only** | 111 | 946.5 MB |
| `tts_en/ssl/echo_en_heldout/` | REPA targets of the held-out voices' clips | **held out: evaluation only** | 74 | 944.3 MB |
| `tts_en/annotations/*/echo_en_heldout/` | the four annotations of the held-out clips (Whisper on every one) | **held out: evaluation only** | 148 | 5.0 MB |
| `eval/en/echo_heldout_audio/` | the original audio of up to 60 clips per held-out voice (`<voice>/<id>.wav`): the prompts and references of the echo tests | **held out: evaluation only** | 14,160 | 5.5 GB |
| `eval/en/echo_heldout/` | the echo tests of the held-out voices: echo-dev / echo-test lists, prompt draws, prompt pairs (with their prompt audio) | evaluation only | 914 | 542.5 MB |
| `eval/en/echo_heldout_ann/` | annotations and manifests of the kept held-out audio | evaluation only | 7 | 1.6 MB |
| `eval/en/seedtts_testset/` | Seed-TTS-eval test sets (`en`: Common Voice prompts; `zh`), CC BY 4.0 | evaluation only | 5,134 | 1.2 GB |
| `eval/en/LibriSpeech/` | LibriSpeech test-clean / dev-clean audio (the LibriSpeech-PC test), CC BY 4.0 | evaluation only | 5,512 | 727.8 MB |
| `eval/en/ami/` | AMI Meeting Corpus clips (`ihm` headset, `sdm` distant microphone), CC BY 4.0 | evaluation only | 996 | 336.5 MB |
| `eval/en/validation/` | validation gates: evaluation runs (scores and audio) of the real test audio and of its codec resynthesis; they check the evaluation pipeline | evaluation only | 4,732 | 1.8 GB |
| `eval/en/voice_emb/` | speaker embeddings of eval prompts and references | evaluation only | 8 | 83.2 MB |
| `eval/en/` | evaluation lists (`*.jsonl`: Seed-TTS test-en dev / test, LibriSpeech-PC, AMI) and the protect list (test sentences no training catalog may hold) | evaluation only | 18 | 15.1 MB |
| `tts_en_hubl18/` | HuBERT-large layer 18 REPA targets of the screening subset (EN-25 teacher screen) | experiment (not used by the main run) | 25 | 2.5 GB |
| `tts_en_hubl24/` | HuBERT-large layer 24 REPA targets of the screening subset (EN-25) | experiment (not used by the main run) | 25 | 2.5 GB |
| `tts_en_hubl24_fp32/` | the same, computed in fp32 (EN-25) | experiment (not used by the main run) | 17 | 2.5 GB |
| `backup/` | `manifest.jsonl` (every file: size, sha256, group), `status.json` (the numbers on this page), `symlinks.json` | bookkeeping | 3 | - |

`<tag>` = `g<time_ns>-<group>` names one ingest group (16 source files, about 8,000 clips). Every file of a group carries
its tag, so a group can be downloaded on its own. `echo_en` is the training source, `echo_en_heldout` the held-out voices.

## Held-out and evaluation-only parts

Do **not** train on these if you want results that compare with ours:

- **Held-out voices**: every `echo_en_heldout` folder and `eval/en/echo_heldout_audio/`. 236 of the 4,000 voices
  ([list](https://huggingface.co/datasets/VoiceHub/DACFlow-EN-10k-backup/blob/main/tts_en/heldout_speakers.txt)) never enter training; at most 60 clips of each are kept for the echo
  tests (EN-08).
- **Evaluation sets**: everything under `eval/en/`. Seed-TTS test-en, LibriSpeech(-PC) and AMI come from other corpora
  and keep their own licences.
- **Echo-unseen voices**: 177 more voices ([list](https://github.com/kadirnar/dacvae-next/blob/roadmap/en-echo/configs/eval/en/echo_unseen_speakers.txt), EN-56) are inside `echo_en`
  but no training catalog holds them (the catalogs already leave them out, and the trainer refuses a catalog that does not).

## Backup and restore (maintainers)

`scripts/hub_backup.py` uploads only what the ingest has committed, checks every file after its commit (size, plus the LFS
sha256 or, for small files, the git blob sha1) and, before every status update, that every file of `backup/manifest.jsonl`
is on the repo with that size and hash. The LFS sha256 is the Hub's record of the upload, not a re-read of the stored
bytes: `restore` hashes what it downloads.

```bash
python scripts/hub_backup.py restore --repo VoiceHub/DACFlow-EN-10k-backup --out /workspace/restore_check --sample-groups 3   # a verified sample
python scripts/hub_backup.py restore --repo VoiceHub/DACFlow-EN-10k-backup --all --out /workspace/data_restore              # everything, verified
# then, with the ingest and its job_guard stopped and any existing /workspace/data/<root> moved aside:
for r in tts_en tts_en_hubl18 tts_en_hubl24 tts_en_hubl24_fp32; do mv -T /workspace/data_restore/$r /workspace/data/$r; done
mkdir -p /workspace/data/eval && mv -T /workspace/data_restore/eval/en /workspace/data/eval/en
```

`restore` writes only into an empty directory (`--into-empty-only` / `--overwrite-existing` allow a non-empty one) and
always refuses the data root of a running ingest. The manifests and the ingest state record absolute paths under
`/workspace/data`, so they hold again after the swap, and the ingest resumes from `tts_en/ingest/echo_en.json`.
The main run's checkpoints are on [VoiceHub/DACFlow-EN-10k](https://huggingface.co/VoiceHub/DACFlow-EN-10k), its training state (optimizer included) in [VoiceHub/mytts-en-base](https://huggingface.co/datasets/VoiceHub/mytts-en-base) (prefix `en-base-s1`): neither is here.

## Use a part of it

For the training data alone, [VoiceHub/DACFlow-EN-10k-data](https://huggingface.co/datasets/VoiceHub/DACFlow-EN-10k-data) is simpler (same latents, with a numpy reader and a decoder snippet).

### Download

All of it is large (522.6 GB now). One group with its REPA targets:

```python
from huggingface_hub import snapshot_download
tag = "g1790656525369654176-00001"  # any group; backup/status.json lists them all
local = snapshot_download("VoiceHub/DACFlow-EN-10k-backup", repo_type="dataset", local_dir="DACFlow-EN-10k-backup",
                          allow_patterns=[f"tts_en/*/echo_en/*{tag}*", "tts_en/ssl/pca.npz", "tts_en/latent_stats.json"])
```

### With the project code

```python
from mytts.config import DataConfig
from mytts.data.catalog import CatalogFilters, build_catalog
from mytts.data.dataset import Catalog, TTSDataset
root = f"{local}/tts_en"
cat_dir, version = build_catalog(root, "my_subset", CatalogFilters(), num_workers=2, verbose=False)  # index what you downloaded
cat = Catalog(cat_dir, split=None)
ds = TTSDataset(cat, root, DataConfig(use_ssl=True), latent_dim=128, ssl_dim=(version.filters or {}).get("ssl_dim"), train=False)
item = ds[0]
print(cat.get(0, "text"), item["latent"].shape, item["ssl"].shape)  # [frames, 128] each
```

### The catalog: the exact training list

`tts_en/catalogs/<name>/catalog.parquet` has one row per clip that passed the quality filters: key, text, voice, length,
where its latents and REPA targets are (paths relative to `tts_en/`) and its split (`train` / `val`); `version.json` records
the filters and the hours. `echo_en_v1` is the catalog of the main run (here):
Parakeet CER <= 0.3 (rescored with the en-v2 text normalization), no long pause (> 6 s), no clip stuck at the length cap with
silence, the echo-unseen voices left out, 0.1 % held back as `val`. `en_t1` is the 88.8 h screening subset.
Catalogs here: `echo_en_v1`, `en_t1`, `en_t1_x56`. To train on exactly what the model saw, download all of `tts_en/`, then
`Catalog(f"{root}/catalogs/echo_en_v1", split="train")`.

## How it was made

1. **Stream** ([`scripts/ingest_stream.py`](https://github.com/kadirnar/dacvae-next/blob/roadmap/en-echo/scripts/ingest_stream.py), [EN-12, issue #36](https://github.com/kadirnar/dacvae-next/issues/36)): the source's
   7,967 parquet files (1.51 TB) are downloaded 16 at a time (one *group*), in a fixed shuffled order.
2. **Decode** each clip once. Clips of the 236 held-out voices go to `echo_en_heldout` (the first 60 of each voice,
   with their audio kept); all others to `echo_en`.
3. **Encode** on the GPU: the codec latents (48 kHz audio, exact fp32 encoder, stored as float16) and the w2v-BERT 2.0
   layer-16 features, projected to 128 numbers with a PCA fitted once (the REPA targets).
4. **Annotate**: Parakeet CTC transcript and CER / WER / NLL for every clip, Whisper on the held-out clips and a 2 %
   sample, silence at the clip edges, the longest pause and the length cap.
5. **Check and commit**: a group counts only when every output covers its clips (the coverage gate); then it gets its
   line in the ledger (`tts_en/ingest/`) and its downloaded source files are deleted.
6. **Back up** ([`scripts/hub_backup.py`](https://github.com/kadirnar/dacvae-next/blob/roadmap/en-echo/scripts/hub_backup.py)): after every 50 committed groups the new files come here,
   each checked against the Hub's own record of it (size and sha256).
7. **Curate** (after the stream, `scripts/recipes/en_m4.sh catalog`): the filters above make the training catalog `echo_en_v1`.
8. **Publish** ([`scripts/hub_data_release.py`](https://github.com/kadirnar/dacvae-next/blob/roadmap/en-echo/scripts/hub_data_release.py)): the training latents, the `echo_en_v1` catalog
   and the latent statistics go to the data page [VoiceHub/DACFlow-EN-10k-data](https://huggingface.co/datasets/VoiceHub/DACFlow-EN-10k-data).

## Provenance and licence

- **This dataset: apache-2.0** (owner decision, 2026-09-29).
- **Source**: [SynDataLab-EN/echo-clones-4m-en](https://huggingface.co/datasets/SynDataLab-EN/echo-clones-4m-en); its card states **apache-2.0**. Its speech was generated with EchoTTS
  ([jordand/echo-tts-base](https://huggingface.co/jordand/echo-tts-base), **cc-by-nc-sa-4.0** on its card) from the reference voices of
  [SynDataLab-EN-Refs/echo-ref-speakers-4k-en](https://huggingface.co/datasets/SynDataLab-EN-Refs/echo-ref-speakers-4k-en) and the texts of
  [SynDataLab-EN-Refs/echo-4m-text-en](https://huggingface.co/datasets/SynDataLab-EN-Refs/echo-4m-text-en) (neither card states a licence). Check these terms
  yourself before any commercial use.
- **Models used to make the features** (each under its own licence; none of their weights are in this repo):
  Aratako/Semantic-DACVAE-Japanese (MIT), facebook/w2v-bert-2.0 (MIT), nvidia/parakeet-ctc-1.1b (CC BY 4.0),
  openai/whisper-large-v3-turbo (MIT); facebook/hubert-large-ll60k (Apache-2.0) for the `tts_en_hubl*` roots only.
- **Evaluation audio** under `eval/en/` keeps its own licence and attribution: Seed-TTS-eval (CC BY 4.0, Common Voice
  prompts), LibriSpeech (CC BY 4.0), AMI Meeting Corpus (CC BY 4.0, University of Edinburgh / AMI consortium).

## Links

- The dataset (tokenized training data only): [VoiceHub/DACFlow-EN-10k-data](https://huggingface.co/datasets/VoiceHub/DACFlow-EN-10k-data)
- The model, its checkpoints and samples: [VoiceHub/DACFlow-EN-10k](https://huggingface.co/VoiceHub/DACFlow-EN-10k)
- The main run's training state (to resume it): [VoiceHub/mytts-en-base](https://huggingface.co/datasets/VoiceHub/mytts-en-base) (prefix `en-base-s1`)
- Source audio and texts: [SynDataLab-EN/echo-clones-4m-en](https://huggingface.co/datasets/SynDataLab-EN/echo-clones-4m-en)
- Code: [kadirnar/dacvae-next](https://github.com/kadirnar/dacvae-next/tree/roadmap/en-echo) (branch `roadmap/en-echo`): `scripts/ingest_stream.py`, `mytts/data/`,
  `scripts/hub_backup.py`, `scripts/hub_data_release.py`

	---
	license: apache-2.0
	language:
	- en
	pretty_name: "DACFlow-EN-10k-backup"
	task_categories:
	- text-to-speech
	tags:
	- speech
	- tts
	- synthetic-speech
	- dacvae
	- latents
	- repa
	- backup
	size_categories:
	- 1M<n<10M
	source_datasets:
	- SynDataLab-EN/echo-clones-4m-en
	viewer: false
	---

	# DACFlow-EN-10k-backup

	The complete working backup of the data behind DACFlow-EN-10k, an English text-to-speech model ([VoiceHub/DACFlow-EN-10k](https://huggingface.co/VoiceHub/DACFlow-EN-10k)). It holds
	everything needed to resume the training and the evaluations if the training machine is lost: the tokenized training data,
	the REPA targets, the quality annotations, the manifests and every catalog, the held-out voices' audio, the evaluation sets,
	the experiment data and the processing bookkeeping. Every file is checked against the Hub's own record of it.

	> Looking for the dataset? Start with [VoiceHub/DACFlow-EN-10k-data](https://huggingface.co/datasets/VoiceHub/DACFlow-EN-10k-data): only the tokenized training data (the codec latents, their texts and the
	> exact training list), with a guide to use it.
	>
	> Want to listen to the model? Its samples are on [VoiceHub/DACFlow-EN-10k](https://huggingface.co/VoiceHub/DACFlow-EN-10k).

	\| \| \|
	\|---\|---\|
	\| What this repo is \| the project's complete working backup (disaster recovery, and the record of every input); not a dataset release \|
	\| The dataset \| [VoiceHub/DACFlow-EN-10k-data](https://huggingface.co/datasets/VoiceHub/DACFlow-EN-10k-data) (the tokenized training data only) \|
	\| The model, its checkpoints and samples \| [VoiceHub/DACFlow-EN-10k](https://huggingface.co/VoiceHub/DACFlow-EN-10k) \|
	\| The main run's training state (to resume it) \| [VoiceHub/mytts-en-base](https://huggingface.co/datasets/VoiceHub/mytts-en-base) (prefix `en-base-s1`); not here \|
	\| Made from \| [SynDataLab-EN/echo-clones-4m-en](https://huggingface.co/datasets/SynDataLab-EN/echo-clones-4m-en) (revision `aebc7c787c64`) \|
	\| Status \| complete, updated 2026-10-03 00:22:12 UTC \|
	\| Size now \| 36,896 files, 522.6 GB \|
	\| Licence \| apache-2.0 (see [Provenance and licence](#provenance-and-licence)) \|
	\| Code \| [kadirnar/dacvae-next](https://github.com/kadirnar/dacvae-next/tree/roadmap/en-echo) (private at the moment), Python package `mytts` \|

	## What is in it

	What each part is and what it is needed for ([Layout](#layout) has one line per folder). The latents and the REPA targets
	come at 25 frames per second, so frame i of one lines up with frame i of the other.

	\| part \| in plain words \| exact form \| needed to \| folder \|
	\|---\|---\|---\|---\|---\|
	\| Codec latents (what the model learns to generate) \| a compressed form of each clip's audio, with its text and voice; the codec's decoder turns it back into sound \| [Semantic-DACVAE](https://huggingface.co/Aratako/Semantic-DACVAE-Japanese) posterior mean, 25 Hz x 128 numbers, float16 (codec `Aratako/Semantic-DACVAE-Japanese@737fbad2a679f880`) \| train (the same files are on [VoiceHub/DACFlow-EN-10k-data](https://huggingface.co/datasets/VoiceHub/DACFlow-EN-10k-data)) \| `tts_en/latents/echo_en/` \|
	\| Catalogs \| the filtered clip lists a run trains on; `echo_en_v1` is the main run's \| `<name>/catalog.parquet` + `version.json` \| train \| `tts_en/catalogs/` \|
	\| Latent statistics \| the frozen normalization of the latents and the latent of silence \| `latent_stats.json`, `silence_latent.npy` \| train, synthesize \| `tts_en/` \|
	\| REPA targets (a training aid) \| what a large speech-understanding model hears in each frame; the TTS model is nudged to match it, which makes it learn faster \| [w2v-BERT 2.0](https://huggingface.co/facebook/w2v-bert-2.0) layer 16, reduced from 1,024 to 128 numbers with PCA (`tts_en/ssl/pca.npz`), float16 \| resume the training (its REPA loss) \| `tts_en/ssl/` \|
	\| Quality annotations \| what a speech recognizer heard, where the silences are; used to drop bad clips \| [Parakeet CTC 1.1B](https://huggingface.co/nvidia/parakeet-ctc-1.1b) transcript + CER / WER / NLL of every clip; Whisper-large-v3-turbo on a 2 % sample; silence at the edges, the longest pause and whether the clip hit EchoTTS's length cap \| rebuild or audit a catalog \| `tts_en/annotations/` \|
	\| Manifests \| the source row of every clip \| one JSON line per clip \| provenance \| `tts_en/manifests/` \|
	\| Held-out voices \| 236 voices never trained on: their clips' latents, REPA targets, annotations and original audio \| as above, plus `.wav` \| evaluation only \| `*/echo_en_heldout/`, `eval/en/echo_heldout_audio/` \|
	\| Evaluation sets \| the echo tests, Seed-TTS test-en, LibriSpeech(-PC), AMI, validation runs, the protect list \| lists + audio \| evaluation \| `eval/en/` \|
	\| Experiments \| HuBERT REPA targets of the screening subset (EN-25 teacher screen) \| as the REPA targets \| not used by the main run \| `tts_en_hubl*/` \|
	\| Bookkeeping \| the processing state and ledger; this backup's file list, status and symlinks \| JSON / JSONL \| resume the processing; verify and restore this backup \| `tts_en/ingest/`, `backup/` \|

	## What it is not

	- Not the place to start. To use the data, start with [VoiceHub/DACFlow-EN-10k-data](https://huggingface.co/datasets/VoiceHub/DACFlow-EN-10k-data): the tokenized training data alone, with a guide. This
	repo holds much more, because it must be able to bring back everything.
	- Not a place to listen. Latents are numbers. The model's samples are on [VoiceHub/DACFlow-EN-10k](https://huggingface.co/VoiceHub/DACFlow-EN-10k). The only audio here is evaluation
	material: the held-out voices' clips
	([`eval/en/echo_heldout_audio/`](https://huggingface.co/datasets/VoiceHub/DACFlow-EN-10k-backup/tree/main/eval/en/echo_heldout_audio)) and the evaluation sets.
	- Not the source dataset. The original audio and texts are [SynDataLab-EN/echo-clones-4m-en](https://huggingface.co/datasets/SynDataLab-EN/echo-clones-4m-en); every clip here points back to its source row
	(`tts_en/manifests/`, and `source` / `id` in each `.meta.parquet`).
	- Not usable with another codec. The latents belong to this exact codec version; the REPA targets to `tts_en/ssl/pca.npz`.
	- No real recordings in the training data. All of the training speech is synthetic (EchoTTS voice clones). Only the
	evaluation sets under `eval/en/` hold real recordings (LibriSpeech, AMI, the Common Voice prompts of Seed-TTS-eval).

	## Size and progress

	\| \| now \| when complete \|
	\|---\|---:\|---:\|
	\| Source files (parquet) processed \| 7,967 / 7,967 (100.0 %) \| 7,967 \|
	\| Source clips read \| 3,983,250 \| about 3.98 M \|
	\| Groups uploaded (16 source files each) \| 499 \| 498 \|
	\| Held-out clips kept (236 voices, at most 60 each) \| 14,160 \| at most 14,160 \|
	\| Files in this repo \| 36,896 \| - \|
	\| Size \| 522.6 GB \| 522.6 GB \|

	The stream is complete; this page is written again whenever files are added.
	Last update: 2026-10-03 00:22:12 UTC (--force).

	## Layout

	\| folder \| what is inside \| used for \| files \| size \|
	\|---\|---\|---\|---:\|---:\|
	\| `tts_en/latents/echo_en/` \| codec latents of every training clip: `shard-<tag>-NNNNNN.bin` (float16, 25 frames/s x 128 numbers), `.idx.json` (where each clip starts and how long it is), `.meta.parquet` (text, voice, duration, source row of each clip) \| training \| 1,497 \| 250.3 GB \|
	\| `tts_en/ssl/echo_en/` \| REPA targets of every training clip: w2v-BERT 2.0 layer 16, PCA to 128 numbers, float16, one row per latent frame (`shard-<tag>-t0-NNNNNN.{bin,idx.json}`) \| training \| 998 \| 249.8 GB \|
	\| `tts_en/ssl/` \| `pca.npz`: the PCA that turns w2v-BERT's 1,024 numbers into 128; `info.json` \| training \| 2 \| 529.8 kB \|
	\| `tts_en/annotations/asr2/echo_en/` \| Parakeet CTC 1.1B transcript of every clip with CER / WER / NLL against its text (`part-<tag>.parquet`) \| training (clip filter) \| 499 \| 457.4 MB \|
	\| `tts_en/annotations/asr/echo_en/` \| Whisper-large-v3-turbo transcript of a 2 % sample: a cross-check of the Parakeet scores \| analysis \| 499 \| 15.2 MB \|
	\| `tts_en/annotations/edges/echo_en/` \| silence at the start and the end of every clip \| training (clip edges) \| 499 \| 196.8 MB \|
	\| `tts_en/annotations/silence/echo_en/` \| longest pause inside every clip, the generation length cap and whether the clip reached it (EchoTTS failure clips) \| training (clip filter) \| 499 \| 105.4 MB \|
	\| `tts_en/manifests/` \| one JSON line per clip: id, text, voice, licence and its source row `hf://datasets/<source>@<revision>/<file>#<row>` (held-out clips: their kept audio file) \| provenance \| 499 \| 1.5 GB \|
	\| `tts_en/catalogs/` \| catalogs: the filtered clip lists a run trains on (`<name>/catalog.parquet` + `version.json`, build log, leak check) \| training \| 12 \| 496.3 MB \|
	\| `tts_en/` \| `latent_stats.json` (the frozen latent normalization of the model), `splits.json`, file lists and held-out voice lists \| training \| 8 \| 214.3 kB \|
	\| `tts_en/ingest/` \| the ingest's state (run spec, finished source files) and ledger (one line per group), as of the last upload \| bookkeeping \| 2 \| 1.8 MB \|
	\| `tts_en/manifests_local/` \| the Tier-1 screening subset's manifest (local raw paths; provenance only) \| bookkeeping \| 1 \| 12.9 MB \|
	\| `tts_en/latents/echo_en_heldout/` \| codec latents of the held-out voices' clips \| held out: evaluation only \| 111 \| 946.5 MB \|
	\| `tts_en/ssl/echo_en_heldout/` \| REPA targets of the held-out voices' clips \| held out: evaluation only \| 74 \| 944.3 MB \|
	\| `tts_en/annotations//echo_en_heldout/` \| the four annotations of the held-out clips (Whisper on every one) \| held out: evaluation only* \| 148 \| 5.0 MB \|
	\| `eval/en/echo_heldout_audio/` \| the original audio of up to 60 clips per held-out voice (`<voice>/<id>.wav`): the prompts and references of the echo tests \| held out: evaluation only \| 14,160 \| 5.5 GB \|
	\| `eval/en/echo_heldout/` \| the echo tests of the held-out voices: echo-dev / echo-test lists, prompt draws, prompt pairs (with their prompt audio) \| evaluation only \| 914 \| 542.5 MB \|
	\| `eval/en/echo_heldout_ann/` \| annotations and manifests of the kept held-out audio \| evaluation only \| 7 \| 1.6 MB \|
	\| `eval/en/seedtts_testset/` \| Seed-TTS-eval test sets (`en`: Common Voice prompts; `zh`), CC BY 4.0 \| evaluation only \| 5,134 \| 1.2 GB \|
	\| `eval/en/LibriSpeech/` \| LibriSpeech test-clean / dev-clean audio (the LibriSpeech-PC test), CC BY 4.0 \| evaluation only \| 5,512 \| 727.8 MB \|
	\| `eval/en/ami/` \| AMI Meeting Corpus clips (`ihm` headset, `sdm` distant microphone), CC BY 4.0 \| evaluation only \| 996 \| 336.5 MB \|
	\| `eval/en/validation/` \| validation gates: evaluation runs (scores and audio) of the real test audio and of its codec resynthesis; they check the evaluation pipeline \| evaluation only \| 4,732 \| 1.8 GB \|
	\| `eval/en/voice_emb/` \| speaker embeddings of eval prompts and references \| evaluation only \| 8 \| 83.2 MB \|
	\| `eval/en/` \| evaluation lists (`*.jsonl`: Seed-TTS test-en dev / test, LibriSpeech-PC, AMI) and the protect list (test sentences no training catalog may hold) \| evaluation only \| 18 \| 15.1 MB \|
	\| `tts_en_hubl18/` \| HuBERT-large layer 18 REPA targets of the screening subset (EN-25 teacher screen) \| experiment (not used by the main run) \| 25 \| 2.5 GB \|
	\| `tts_en_hubl24/` \| HuBERT-large layer 24 REPA targets of the screening subset (EN-25) \| experiment (not used by the main run) \| 25 \| 2.5 GB \|
	\| `tts_en_hubl24_fp32/` \| the same, computed in fp32 (EN-25) \| experiment (not used by the main run) \| 17 \| 2.5 GB \|
	\| `backup/` \| `manifest.jsonl` (every file: size, sha256, group), `status.json` (the numbers on this page), `symlinks.json` \| bookkeeping \| 3 \| - \|

	`<tag>` = `g<time_ns>-<group>` names one ingest group (16 source files, about 8,000 clips). Every file of a group carries
	its tag, so a group can be downloaded on its own. `echo_en` is the training source, `echo_en_heldout` the held-out voices.

	## Held-out and evaluation-only parts

	Do not train on these if you want results that compare with ours:

	- Held-out voices: every `echo_en_heldout` folder and `eval/en/echo_heldout_audio/`. 236 of the 4,000 voices
	([list](https://huggingface.co/datasets/VoiceHub/DACFlow-EN-10k-backup/blob/main/tts_en/heldout_speakers.txt)) never enter training; at most 60 clips of each are kept for the echo
	tests (EN-08).
	- Evaluation sets: everything under `eval/en/`. Seed-TTS test-en, LibriSpeech(-PC) and AMI come from other corpora
	and keep their own licences.
	- Echo-unseen voices: 177 more voices ([list](https://github.com/kadirnar/dacvae-next/blob/roadmap/en-echo/configs/eval/en/echo_unseen_speakers.txt), EN-56) are inside `echo_en`
	but no training catalog holds them (the catalogs already leave them out, and the trainer refuses a catalog that does not).

	## Backup and restore (maintainers)

	`scripts/hub_backup.py` uploads only what the ingest has committed, checks every file after its commit (size, plus the LFS
	sha256 or, for small files, the git blob sha1) and, before every status update, that every file of `backup/manifest.jsonl`
	is on the repo with that size and hash. The LFS sha256 is the Hub's record of the upload, not a re-read of the stored
	bytes: `restore` hashes what it downloads.

	```bash
	python scripts/hub_backup.py restore --repo VoiceHub/DACFlow-EN-10k-backup --out /workspace/restore_check --sample-groups 3 # a verified sample
	python scripts/hub_backup.py restore --repo VoiceHub/DACFlow-EN-10k-backup --all --out /workspace/data_restore # everything, verified
	# then, with the ingest and its job_guard stopped and any existing /workspace/data/<root> moved aside:
	for r in tts_en tts_en_hubl18 tts_en_hubl24 tts_en_hubl24_fp32; do mv -T /workspace/data_restore/$r /workspace/data/$r; done
	mkdir -p /workspace/data/eval && mv -T /workspace/data_restore/eval/en /workspace/data/eval/en
	```

	`restore` writes only into an empty directory (`--into-empty-only` / `--overwrite-existing` allow a non-empty one) and
	always refuses the data root of a running ingest. The manifests and the ingest state record absolute paths under
	`/workspace/data`, so they hold again after the swap, and the ingest resumes from `tts_en/ingest/echo_en.json`.
	The main run's checkpoints are on [VoiceHub/DACFlow-EN-10k](https://huggingface.co/VoiceHub/DACFlow-EN-10k), its training state (optimizer included) in [VoiceHub/mytts-en-base](https://huggingface.co/datasets/VoiceHub/mytts-en-base) (prefix `en-base-s1`): neither is here.

	## Use a part of it

	For the training data alone, [VoiceHub/DACFlow-EN-10k-data](https://huggingface.co/datasets/VoiceHub/DACFlow-EN-10k-data) is simpler (same latents, with a numpy reader and a decoder snippet).

	### Download

	All of it is large (522.6 GB now). One group with its REPA targets:

	```python
	from huggingface_hub import snapshot_download

	tag = "g1790656525369654176-00001" # any group; backup/status.json lists them all
	local = snapshot_download("VoiceHub/DACFlow-EN-10k-backup", repo_type="dataset", local_dir="DACFlow-EN-10k-backup",
	allow_patterns=[f"tts_en//echo_en/{tag}*", "tts_en/ssl/pca.npz", "tts_en/latent_stats.json"])
	```

	### With the project code

	```python
	from mytts.config import DataConfig
	from mytts.data.catalog import CatalogFilters, build_catalog
	from mytts.data.dataset import Catalog, TTSDataset

	root = f"{local}/tts_en"
	cat_dir, version = build_catalog(root, "my_subset", CatalogFilters(), num_workers=2, verbose=False) # index what you downloaded
	cat = Catalog(cat_dir, split=None)
	ds = TTSDataset(cat, root, DataConfig(use_ssl=True), latent_dim=128, ssl_dim=(version.filters or {}).get("ssl_dim"), train=False)
	item = ds[0]
	print(cat.get(0, "text"), item["latent"].shape, item["ssl"].shape) # [frames, 128] each
	```

	### The catalog: the exact training list

	`tts_en/catalogs/<name>/catalog.parquet` has one row per clip that passed the quality filters: key, text, voice, length,
	where its latents and REPA targets are (paths relative to `tts_en/`) and its split (`train` / `val`); `version.json` records
	the filters and the hours. `echo_en_v1` is the catalog of the main run (here):
	Parakeet CER <= 0.3 (rescored with the en-v2 text normalization), no long pause (> 6 s), no clip stuck at the length cap with
	silence, the echo-unseen voices left out, 0.1 % held back as `val`. `en_t1` is the 88.8 h screening subset.
	Catalogs here: `echo_en_v1`, `en_t1`, `en_t1_x56`. To train on exactly what the model saw, download all of `tts_en/`, then
	`Catalog(f"{root}/catalogs/echo_en_v1", split="train")`.

	## How it was made

	1. Stream ([`scripts/ingest_stream.py`](https://github.com/kadirnar/dacvae-next/blob/roadmap/en-echo/scripts/ingest_stream.py), [EN-12, issue #36](https://github.com/kadirnar/dacvae-next/issues/36)): the source's
	7,967 parquet files (1.51 TB) are downloaded 16 at a time (one group), in a fixed shuffled order.
	2. Decode each clip once. Clips of the 236 held-out voices go to `echo_en_heldout` (the first 60 of each voice,
	with their audio kept); all others to `echo_en`.
	3. Encode on the GPU: the codec latents (48 kHz audio, exact fp32 encoder, stored as float16) and the w2v-BERT 2.0
	layer-16 features, projected to 128 numbers with a PCA fitted once (the REPA targets).
	4. Annotate: Parakeet CTC transcript and CER / WER / NLL for every clip, Whisper on the held-out clips and a 2 %
	sample, silence at the clip edges, the longest pause and the length cap.
	5. Check and commit: a group counts only when every output covers its clips (the coverage gate); then it gets its
	line in the ledger (`tts_en/ingest/`) and its downloaded source files are deleted.
	6. Back up ([`scripts/hub_backup.py`](https://github.com/kadirnar/dacvae-next/blob/roadmap/en-echo/scripts/hub_backup.py)): after every 50 committed groups the new files come here,
	each checked against the Hub's own record of it (size and sha256).
	7. Curate (after the stream, `scripts/recipes/en_m4.sh catalog`): the filters above make the training catalog `echo_en_v1`.
	8. Publish ([`scripts/hub_data_release.py`](https://github.com/kadirnar/dacvae-next/blob/roadmap/en-echo/scripts/hub_data_release.py)): the training latents, the `echo_en_v1` catalog
	and the latent statistics go to the data page [VoiceHub/DACFlow-EN-10k-data](https://huggingface.co/datasets/VoiceHub/DACFlow-EN-10k-data).

	## Provenance and licence

	- This dataset: apache-2.0 (owner decision, 2026-09-29).
	- Source: [SynDataLab-EN/echo-clones-4m-en](https://huggingface.co/datasets/SynDataLab-EN/echo-clones-4m-en); its card states apache-2.0. Its speech was generated with EchoTTS
	([jordand/echo-tts-base](https://huggingface.co/jordand/echo-tts-base), cc-by-nc-sa-4.0 on its card) from the reference voices of
	[SynDataLab-EN-Refs/echo-ref-speakers-4k-en](https://huggingface.co/datasets/SynDataLab-EN-Refs/echo-ref-speakers-4k-en) and the texts of
	[SynDataLab-EN-Refs/echo-4m-text-en](https://huggingface.co/datasets/SynDataLab-EN-Refs/echo-4m-text-en) (neither card states a licence). Check these terms
	yourself before any commercial use.
	- Models used to make the features (each under its own licence; none of their weights are in this repo):
	Aratako/Semantic-DACVAE-Japanese (MIT), facebook/w2v-bert-2.0 (MIT), nvidia/parakeet-ctc-1.1b (CC BY 4.0),
	openai/whisper-large-v3-turbo (MIT); facebook/hubert-large-ll60k (Apache-2.0) for the `tts_en_hubl*` roots only.
	- Evaluation audio under `eval/en/` keeps its own licence and attribution: Seed-TTS-eval (CC BY 4.0, Common Voice
	prompts), LibriSpeech (CC BY 4.0), AMI Meeting Corpus (CC BY 4.0, University of Edinburgh / AMI consortium).

	## Links

	- The dataset (tokenized training data only): [VoiceHub/DACFlow-EN-10k-data](https://huggingface.co/datasets/VoiceHub/DACFlow-EN-10k-data)
	- The model, its checkpoints and samples: [VoiceHub/DACFlow-EN-10k](https://huggingface.co/VoiceHub/DACFlow-EN-10k)
	- The main run's training state (to resume it): [VoiceHub/mytts-en-base](https://huggingface.co/datasets/VoiceHub/mytts-en-base) (prefix `en-base-s1`)
	- Source audio and texts: [SynDataLab-EN/echo-clones-4m-en](https://huggingface.co/datasets/SynDataLab-EN/echo-clones-4m-en)
	- Code: [kadirnar/dacvae-next](https://github.com/kadirnar/dacvae-next/tree/roadmap/en-echo) (branch `roadmap/en-echo`): `scripts/ingest_stream.py`, `mytts/data/`,
	`scripts/hub_backup.py`, `scripts/hub_data_release.py`