backup: snapshot (--force; 499 groups, 36896 files, 522.57 GB)

README.md

@@ -2,7 +2,7 @@
 license: apache-2.0
 language:
 - en
-pretty_name: "mytts-en-v1-data: training data of the mytts-en-v1 English TTS"
+pretty_name: "DACFlow-EN-10k-backup"
 task_categories:
 - text-to-speech
 tags:
@@ -12,6 +12,7 @@ tags:
 - dacvae
 - latents
 - repa
+- backup
 size_categories:
 - 1M<n<10M
 source_datasets:
@@ -19,46 +20,57 @@ source_datasets:
 viewer: false
 ---
 
-# mytts-en-v1-data
+# DACFlow-EN-10k-backup
 
-**The prepared training data of the English text-to-speech model [VoiceHub/mytts-en-v1](https://huggingface.co/VoiceHub/mytts-en-v1).** Every clip of [SynDataLab-EN/echo-clones-4m-en](https://huggingface.co/datasets/SynDataLab-EN/echo-clones-4m-en) (about 3.98 M synthetic
-English speech clips of 4,000 voices) turned into the compact numbers the model learns from, plus the checks used to pick
-the good clips.
+**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).
 
 | | |
 |---|---|
-| Model trained on it | [VoiceHub/mytts-en-v1](https://huggingface.co/VoiceHub/mytts-en-v1) |
+| 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 2026-10-02 18:07:11 UTC |
+| 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) |
+| 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
 
-For every clip the model needs three things. The first two come at 25 frames per second, so frame *i* of the latents lines
-up with frame *i* of the REPA targets:
-
-| feature | in plain words | exact form | folder |
-|---|---|---|---|
-| **Codec latents** (what the model learns to generate) | a compressed form of the audio; 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`) | `tts_en/latents/` |
-| **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 | `tts_en/ssl/` |
-| **Text and quality checks** | the sentence each clip says, what a speech recognizer heard, where the silences are; used to drop bad clips | the source text (`.meta.parquet`, manifests); [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 | `tts_en/annotations/` |
-
-Also here: the original audio of the 236 **held-out voices** and the English **evaluation sets** (both evaluation only,
-see [below](#held-out-and-evaluation-only-parts)).
+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 audio to listen to.** Latents are numbers. To hear the speech: play the source clips in
-  [the source's dataset viewer](https://huggingface.co/datasets/SynDataLab-EN/echo-clones-4m-en/viewer), listen to the model's samples on [VoiceHub/mytts-en-v1](https://huggingface.co/VoiceHub/mytts-en-v1), or decode
-  latents with the codec ([Hear a clip](#hear-a-clip)). The only audio here is the held-out voices' clips
-  ([`eval/en/echo_heldout_audio/`](https://huggingface.co/datasets/VoiceHub/mytts-en-v1-data/tree/main/eval/en/echo_heldout_audio): open a `.wav` to play it) and the
-  evaluation sets.
+- **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 the training list by itself.** It holds every clip. The clips a model trains on (after the quality filters) are
-  listed in a catalog under `tts_en/catalogs/` ([The catalog](#the-catalog-the-exact-training-list)).
 - **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).
@@ -75,7 +87,7 @@ see [below](#held-out-and-evaluation-only-parts)).
 | Size | 522.6 GB | 522.6 GB |
 
 The stream is complete; this page is written again whenever files are added.
-Last update: 2026-10-02 18:07:11 UTC (the stream is complete).
+Last update: 2026-10-03 00:22:12 UTC (--force).
 
 ## Layout
 
@@ -105,9 +117,9 @@ Last update: 2026-10-02 18:07:11 UTC (the stream is complete).
 | `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 v1) | 25 | 2.5 GB |
-| `tts_en_hubl24/` | HuBERT-large layer 24 REPA targets of the screening subset (EN-25) | experiment (not used by v1) | 25 | 2.5 GB |
-| `tts_en_hubl24_fp32/` | the same, computed in fp32 (EN-25) | experiment (not used by v1) | 17 | 2.5 GB |
+| `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
@@ -118,49 +130,50 @@ its tag, so a group can be downloaded on its own. `echo_en` is the training sour
 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/mytts-en-v1-data/blob/main/tts_en/heldout_speakers.txt)) never enter training; at most 60 clips of each are kept for the echo
+  ([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).
 
-## How to load
+## 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 is enough to try the data:
+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/mytts-en-v1-data", repo_type="dataset", local_dir="mytts-en-v1-data",
+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"])
 ```
 
-Everything: `allow_patterns=["tts_en/*"]` (the training data) or no filter, or the verified restore below.
-
-### Read one clip (numpy only)
-
-```python
-import json
-
-import numpy as np
-import pyarrow.parquet as pq
-
-shard = f"{local}/tts_en/latents/echo_en/shard-{tag}-000000"
-idx = json.load(open(shard + ".idx.json"))  # keys, offsets, n_frames, dim, dtype
-meta = pq.read_table(shard + ".meta.parquet").to_pylist()  # one row per clip: key, text, speaker, duration, ...
-lat = np.memmap(shard + ".bin", dtype=idx["dtype"], mode="r").reshape(-1, idx["dim"])
-i = 0
-z = np.asarray(lat[idx["offsets"][i] : idx["offsets"][i] + idx["n_frames"][i]], np.float32)  # [frames, 128], 25 frames per second
-print(meta[i]["text"], z.shape)
-```
-
-The REPA targets of the same clip: `tts_en/ssl/echo_en/shard-<tag>-t0-000000.{bin,idx.json}`, same key, same frames.
-
-### With the project code (`mytts`)
+### With the project code
 
 ```python
 from mytts.config import DataConfig
@@ -185,19 +198,6 @@ silence, the echo-unseen voices left out, 0.1 % held back as `val`. `en_t1` is t
 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")`.
 
-### Hear a clip
-
-```python
-import soundfile as sf
-import torch
-
-from mytts.codec import DACVAECodec  # needs pip install git+https://github.com/facebookresearch/dacvae
-
-codec = DACVAECodec.load("Aratako/Semantic-DACVAE-Japanese@737fbad2a679f880")  # the codec the latents were made with
-wav = codec.decode(torch.from_numpy(z)[None])[0].numpy()  # z from 'Read one clip'; 48 kHz
-sf.write("clip.wav", wav, 48000)
-```
-
 ## 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
@@ -210,17 +210,19 @@ sf.write("clip.wav", wav, 48000)
    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. **Upload** ([`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,
+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
-  [SynData-2/echo-ref-speakers-4k-en](https://huggingface.co/datasets/SynData-2/echo-ref-speakers-4k-en) and the texts of
-  [SynData-2/echo-4m-text-en](https://huggingface.co/datasets/SynData-2/echo-4m-text-en) (neither card states a licence). Check these terms
+  [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),
@@ -228,34 +230,11 @@ sf.write("clip.wav", wav, 48000)
 - **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).
 
-## Backup and restore (maintainers)
-
-This repo is also the project's backup of its data roots. `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/mytts-en-v1-data --out /workspace/restore_check --sample-groups 3   # a verified sample
-python scripts/hub_backup.py restore --repo VoiceHub/mytts-en-v1-data --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`.
-
 ## Links
 
-- Model: [VoiceHub/mytts-en-v1](https://huggingface.co/VoiceHub/mytts-en-v1)
+- 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`
-- The project's earlier pages (screening phase, kept as an archive; not needed to use this data):
-  [VoiceHub/mytts-en](https://huggingface.co/VoiceHub/mytts-en) (screening checkpoints),
-  [VoiceHub/mytts-en-samples](https://huggingface.co/datasets/VoiceHub/mytts-en-samples) (their samples),
-  [VoiceHub/mytts-en-ablations](https://huggingface.co/datasets/VoiceHub/mytts-en-ablations) (screening runs and results),
-  [VoiceHub/mytts-en-eval](https://huggingface.co/datasets/VoiceHub/mytts-en-eval) (evaluation lists),
-  [VoiceHub/mytts-en-base](https://huggingface.co/datasets/VoiceHub/mytts-en-base) (the main run's internal training state)
+  `scripts/hub_backup.py`, `scripts/hub_data_release.py`

backup/manifest.jsonl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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+oid sha256:f863876b30ce2030f3beb74c6d1d7b334633e36853e5931fa919af366cacf7f4
 size 16089242

backup/status.json

@@ -151,7 +151,7 @@
   "en_t1",
   "en_t1_x56"
  ],
- "code": "46e3e71",
+ "code": "f0909e4",
  "excludes": [
   "*.tmp",
   "*.lock",
@@ -708,21 +708,21 @@
  },
  "partial": false,
  "pass": {
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+  "commits": 0,
   "failures": 0,
   "lost_files": [],
   "n_lost_files": 0,
-  "reason": "the stream is complete",
+  "reason": "--force",
   "repaired_on_hub": 0,
-  "seconds": 163.7,
-  "upload_mb_per_s": 3.6,
-  "upload_s": 120.6,
-  "uploaded_bytes": 431190769,
-  "uploaded_files": 703
+  "seconds": 17.8,
+  "upload_mb_per_s": null,
+  "upload_s": 0.0,
+  "uploaded_bytes": 0,
+  "uploaded_files": 0
  },
- "reason": "the stream is complete",
- "renamed_from": "VoiceHub/mytts-en-data",
- "repo": "VoiceHub/mytts-en-v1-data",
+ "reason": "--force",
+ "renamed_from": "VoiceHub/mytts-en-v1-data",
+ "repo": "VoiceHub/DACFlow-EN-10k-backup",
  "repo_type": "dataset",
  "roots": {
   "eval/en": "/workspace/data/eval/en",
@@ -732,5 +732,5 @@
   "tts_en_hubl24_fp32": "/workspace/data/tts_en_hubl24_fp32"
  },
  "symlinks": 9,
- "updated": "2026-10-02T18:07:11Z"
+ "updated": "2026-10-03T00:22:12Z"
 }