Super-squash branch 'main' using huggingface_hub

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.avro filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text

LICENSE.md

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+# YO-CPT-ru — License
+
+YO-CPT-ru is a *derived* dataset: beyond attribution, the authors impose no restrictions of their own —
+users are responsible for complying with the upstream licenses below.
+
+**Contribution of the authors.** The annotations and the compilation of the corpus are released under
+[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) — attribution required, no other limitation on
+use, including commercial use.
+
+**Upstream components.** The source audio comes from [YODAS2](https://huggingface.co/datasets/espnet/yodas2),
+collected exclusively from YouTube videos published under a Creative Commons license; the recordings remain
+the intellectual property of their original creators. The released audio and metadata are produced by the
+following components:
+
+| component | released output | license |
+|---|---|---|
+| YODAS2 (source recordings) | `audio` | CC BY 3.0 |
+| Silero VAD | — (segmentation only) | MIT |
+| VoxBlink2 ResNet34 | `local_spk_id` | not stated (trained on CC BY-NC-SA 4.0 data) |
+| DistillMOS | `mos_score` | MIT |
+| Whisper-large-v3-turbo (custom) | `text` (ensemble) | MIT |
+| GigaAM v3 RNNT | `text` (ensemble) | MIT |
+| Vosk | `text` (ensemble) | Apache-2.0 |
+| Spectra-0 | — (spoof filter only) | Apache-2.0 |
+| ClearVoice MossFormer2_SE_48K | `audio` (enhanced waveform) | Apache-2.0 |
+| wav2vec2-BERT (custom CTC) | `text_alignment` | MIT |
+| RUAccent turbo3.1 | stress marks in `text_denorm` / `text_alignment` | MIT |
+| voice-gender-classifier (ECAPA-TDNN) | `spk_desc.audio_desc.gender` | MIT |
+| OpenAI `gpt-4.1-mini` (API) | `text_denorm` | OpenAI ToS |
+| TalkNet-ASD | — (face selection only) | MIT |
+| LVFace | `global_spk_id` | code MIT; **weights: non-commercial research only** |
+| Qwen3.5-Flash (API) | `spk_desc.image_desc` | Alibaba Cloud API ToS |
+
+For questions about the dataset, or to request removal of your material (as a rights holder or as a person
+appearing in the recordings), contact us at **aleksei.gusev@ncspeech.org**.

README.md

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+---
+language:
+- ru
+license: other
+license_name: yo-cpt-ru
+license_link: LICENSE.md
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/*.parquet
+task_categories:
+- text-to-speech
+- automatic-speech-recognition
+- audio-classification
+pretty_name: YO-CPT-ru
+size_categories:
+- 1M<n<10M
+tags:
+- speech
+- tts
+- asr
+- speaker-verification
+- turn-detection
+- russian
+---
+
+# YO-CPT-ru
+
+**Y**ouTube-**O**riented dataset for **C**ontinual **P**re-**T**raining (Russian). A large, heavily
+quality-filtered corpus of Russian speech mined from YouTube (via [YODAS2](https://huggingface.co/datasets/espnet/yodas2))
+and processed into clean, single-speaker, TTS-grade utterances. Every utterance ships with an
+ensemble-verified transcription, a punctuated/denormalized and stress-marked text variant, word-level
+forced alignment, within- and cross-video speaker identities, an audio-quality (MOS) score, and a
+speaker persona built from the voice and, where available, the speaker's on-screen face.
+
+- **~1.63M utterances · ~6,052 hours**, single-speaker chunks, 24 kHz mono
+- **Primary use — continual pre-training (CPT) of TTS models**: a corpus scaled and filtered for the
+  large-scale pre-training stage of TTS training — TTS-grade audio (denoised, loudness-normalized,
+  edge-clean, force-aligned) with rich text and speaker conditioning.
+- **Also suitable for** **ASR** (ensemble-agreed transcripts), **Speaker Verification** (within/cross-video
+  speaker ids), and **Turn-Detection** (single-speaker segmentation + word/pause timings).
+
+Audio is embedded (HF `Audio` feature). Shards hold **10 source YODAS2 archives each**
+(`<part>-<bucket>.parquet`); `shard_map.tsv` maps every shard back to its source archives.
+
+## Pipeline
+
+Three stages take raw YouTube audio to the finished corpus:
+
+```text
+YODAS2  —  raw YouTube audio (Russian)   ·   ~43,610 hours
+   │
+   ▼
+STAGE 1 · ASR mining
+   VAD chunking (Silero) → speaker consistency (VoxBlink2) → quality MOS ≥ 3.0 (DistillMOS)
+   → ASR ensemble (Whisper-large-v3-turbo · GigaAM v3 RNNT · Vosk) → WER cross-check + ROVER
+   │
+   ▼  clean single-speaker utterances + ensemble-verified text   ·   9,135 h · 2.53M utts
+STAGE 2 · TTS processing
+   anti-spoofing (Spectra-0) → enhancement (ClearVoice MossFormer2) → loudness norm + clip guard
+   → edge check + recrop (re-ASR reconcile) → forced alignment (wav2vec2-BERT, + [pause])
+   │
+   ▼  TTS-grade audio + word/pause alignment   ·   6,052 h · 1.63M utts
+STAGE 3 · Metadata enrichment
+   stress (RUAccent) · denorm + punctuation (GPT-4.1-mini)
+   speaker persona:  face (TalkNet → LVFace) + face description (Qwen3.5-Flash) + voice (librosa + ECAPA gender)  →  spk_desc
+   │
+   ▼
+YO-CPT-ru  ·  1.63M utterances  ·  6,052 hours
+```
+
+### Stage 1 — ASR mining
+
+Turns full-length YouTube audio into clean, transcribed, single-speaker utterances.
+
+- **VAD chunking** — [Silero VAD](https://github.com/snakers4/silero-vad) segments each full-length video
+  into utterance-length pieces; the maximum segment length is *randomized* per file so cut points don't
+  land on a fixed grid, and over-long / over-short segments are dropped.
+- **Speaker consistency** — [VoxBlink2](https://github.com/VoxBlink2/ScriptsForVoxBlink2) (ResNet34)
+  speaker embeddings are computed over sliding windows, and a chunk is discarded entirely if it contains
+  speech from more than one speaker; the same check also drops non-speech (silence / noise) chunks.
+- **Audio quality** — [DistillMOS](https://github.com/microsoft/Distill-MOS) no-reference MOS estimation;
+  chunks below **MOS 3.0** (noisy, reverberant, low-fidelity) are discarded.
+- **ASR ensemble** — three deliberately *different* systems transcribe every chunk: a custom
+  [Whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) fine-tuned for ru
+  (attention encoder–decoder), [GigaAM v3 RNNT](https://github.com/salute-developers/GigaAM)
+  (transducer), and [Vosk](https://github.com/alphacep/vosk-api) (Kaldi-style). Architectural diversity
+  makes correlated errors unlikely, so agreement is a strong signal of correctness.
+- **Cross-validation + ROVER** — pairwise WER is computed between the three hypotheses; chunks without
+  close agreement between at least two systems are **dropped**. On tight agreement the transcription is
+  trusted as-is; on moderate disagreement the three hypotheses are aggregated by **ROVER** word-level
+  voting — where combining several recognizers is expected to improve the final transcription — yielding
+  high-precision transcripts.
+
+### Stage 2 — TTS processing
+
+Turns transcribed utterances into TTS-grade audio with word-level alignment.
+
+- **Anti-spoofing** — runs first in this stage, on the audio *before enhancement*, which could otherwise
+  mask synthesis artifacts. YouTube contains synthetic and TTS-generated speech, which must be filtered
+  out of a TTS corpus; a [Spectra-0](https://huggingface.co/MTUCI/spectra_0)
+  spoof detector scores each chunk and aggregates per video, dropping likely-synthetic speech so downstream
+  models learn from genuine human voice only.
+- **Speech enhancement** — [ClearVoice](https://github.com/modelscope/ClearerVoice-Studio)
+  `MossFormer2_SE_48K` denoises and dereverberates audio, removing background music and noise while
+  preserving the target speaker.
+- **Loudness normalization + clipping guard** — loudness is normalized to a consistent target level with
+  peak limiting across the corpus; chunks with too high a clipped-sample ratio are rejected outright
+  (enhancement cannot recover clipped audio).
+- **VAD edge check + recrop** — TTS is sensitive to boundaries, so speech must not run into the segment
+  edges. The source audio is often noisy, so the initial VAD boundaries are imprecise — after enhancement
+  they can be refined. A second VAD pass trims each clip to the nearest internal silence boundary (or
+  discards it if that's impossible), after which the recropped audio is **re-transcribed and reconciled**
+  with the original transcription to ensure that trimming has not altered the content.
+- **Forced alignment** — a custom [wav2vec2-BERT](https://huggingface.co/facebook/w2v-bert-2.0) CTC model
+  produces word-level start/end timings, inserting explicit `[pause]` tags at internal silences.
+  This provides the `text_alignment` required by TTS models for duration modeling.
+
+### Stage 3 — Metadata enrichment
+
+One stage, three branches: word stress, denormalized/punctuated text, and a speaker persona built from
+both the on-screen face and the voice signal.
+
+- **Stress** — [RUAccent](https://github.com/Den4ikAI/ruaccent) `turbo3.1` (omograph model + dictionary)
+  places Russian word-stress marks; the marks are carried into `text_denorm` and `text_alignment`.
+- **Denormalization + punctuation** — OpenAI `gpt-4.1-mini` restores casing/punctuation and denormalizes
+  the raw ASR text → `text_denorm`.
+- **Speaker persona** — a static per-speaker profile from face + voice. The identity design is based on
+  **voice locally, faces globally**: a voice reliably separates speakers *within* one video, while a face
+  is what re-identifies the *same person across different videos*.
+  - *Voice → local id* — [VoxBlink2](https://github.com/VoxBlink2/ScriptsForVoxBlink2) (ResNet34) embeddings cluster
+    the speakers **within each video** → `local_spk_id`.
+  - *Face → global id* — [TalkNet](https://github.com/TaoRuijie/TalkNet-ASD) selects the active-speaker
+    (actually-talking) face, [LVFace](https://github.com/bytedance/LVFace) embeds it, and these are
+    clustered **across videos** into a `global_face_id`, which becomes the speaker's cross-video
+    `global_spk_id` (`null` for audio-only speakers with no validated face).
+  - *Audio signal* — gender via an [ECAPA-TDNN voice classifier](https://huggingface.co/JaesungHuh/voice-gender-classifier);
+    [librosa](https://librosa.org/) features: median F0 → pitch, spectral centroid → brightness,
+    spectral flatness → breathiness, words/sec → rate.
+  - *Visual description* — head-and-shoulders crops of the speaker are described by **Qwen3.5-Flash**
+    (gender, age, nationality, appearance, style).
+  - *Fuse* — face demographics (if real **and** live) + voice traits → structured fields plus a natural
+    `spk_desc`.
+
+## Implementation details
+**Speech enhancement.** ClearVoice MossFormer2 was chosen over two alternatives on a benchmark subset
+sampled from the corpus. Automatic metrics: WER between Whisper transcripts of the original vs the
+enhanced clip, and speaker similarity via VoxBlink2 embeddings. The last two columns report manual
+listening evaluations — how much noise the model removes and how natural the enhanced audio sounds:
+
+| model | RTF ↓ | WER vs orig ↓ | speaker sim ↑ | denoising (manual) | output quality (manual) |
+|---|---|---|---|---|---|
+| **ClearVoice MossFormer2** (chosen) | **0.016** | **0.003** | 0.976 | good | good |
+| Resemble Enhance | 0.025 | 0.005 | 0.979 | average | good |
+| Sidon v0.1 | 0.024 | 0.016 | 0.724 | best | average |
+
+**Anti-spoofing.** Spectra-0 scores each chunk (higher = more likely genuine). The distribution has **two
+obvious centers** — a bonafide mode near +5 and a low-scoring mode near −6:
+
+<img src="figures/fig_spoof.png" alt="Anti-spoofing — bonafide score distribution" width="560">
+
+The drop threshold (**−2.0**) was chosen by manually auditioning chunks scoring in the −3 to −1 range;
+~2.3 % of chunks fall below it and are dropped.
+
+**Face validation.** Matching a face to a voice is error-prone: a frame may contain several faces
+(co-hosts, panels, audiences), and channels often show posters or static photos of someone other than the
+speaker. A face is therefore attached to a speaker only after passing every step of this funnel:
+
+- **137,082** — speakers total;
+- **121,268** — with a processable video clip;
+- **50,219** (41 %) — with a confidently talking on-screen face:
+  [TalkNet-ASD](https://github.com/TaoRuijie/TalkNet-ASD) active-speaker detection accepts a face only if
+  its lip motion is synchronized with the audio;
+- **48,085** — after face-embedding quality checks;
+- **45,845** (33 %) — after the realness check (drops cartoons, posters, static photos).
+
+Only these 45,845 local speakers get the face-derived fields (`image_desc`, `global_spk_id`); everyone else
+stays audio-only.
+
+**Speakers.** Two levels of annotation.
+
+**`local_spk_id`** — unique within a single video: **137,082** within-video speakers, obtained by
+agglomerative clustering (AHC, average linkage) of VoxBlink2 voice embeddings within each video. Because
+utterances containing more than one speaker were filtered out upstream (Stage 1), within-video speaker
+labelling is reliable.
+
+**`global_spk_id`** — the same person linked *across* videos. Matching speakers across a large number of
+videos is a much harder problem than clustering within one video, and doing it from voice alone would
+introduce a substantial error rate. We therefore link identities through the **face modality, and only when
+the speaking person's identity could be established on screen**: the 45,845 face-validated speakers
+collapse into **17,598 global identities**, of which **2,349 appear in at least 3 videos**.
+
+## Data format
+
+```python
+from datasets import load_dataset
+ds = load_dataset("NCSpeech/YO-CPT-ru", split="train", streaming=True)
+```
+
+A sample record:
+
+```json
+{
+  "utt_id": "o_FOkvR3oaM_0_469",
+  "text": "я бесконечно благодарен за тот опыт и возможность",
+  "text_denorm": "Я бескон+ечно благод+арен з+а тот +опыт и возм+ожность.",
+  "text_alignment": "[pause0.2](0.00,0.18) я(0.18,0.38) бескон[+]ечно(0.52,1.08) благод[+]арен(1.10,1.60) з[+]а(1.62,1.80) тот(1.82,2.10) [+]опыт(2.18,2.48) и(2.50,2.58) возм[+]ожность(2.60,3.18) [pause0.2](3.18,3.38)",
+  "duration": 4.89,
+  "local_spk_id": "o_FOkvR3oaM__spk4",
+  "global_spk_id": "spk_15827",
+  "lang": "ru",
+  "spk_desc": {
+    "audio_only": false,
+    "audio_desc": {
+      "gender": "male",
+      "f0_hz": 122.6,
+      "pitch": "low",
+      "rate": "very slow",
+      "brightness": "bright",
+      "breathiness": "slightly-breathy",
+      "text": "male voice: low pitch, bright, slightly-breathy, very slow pace; speaks Russian"
+    },
+    "image_desc": {
+      "gender": "male",
+      "age": "20s",
+      "nationality": "Slavic/Russian/European",
+      "appearance": "dark hair, dark eyes, rectangular glasses, light stubble/beard, headset",
+      "style": "casual black t-shirt, tech gear -> informal, tech-savvy persona",
+      "impression": "young tech reviewer or streamer",
+      "text": "Slavic/Russian/European male (20s). casual black t-shirt, tech gear -> informal, tech-savvy persona"
+    }
+  },
+  "mos_score": 3.456
+}
+```
+
+Each record has the following fields:
+
+| field | type | description |
+|---|---|---|
+| `audio` | Audio (24 kHz) | waveform (embedded WAV bytes) |
+| `utt_id` | string | utterance id `<video>_<channel>_<chunk>` |
+| `text` | string | normalized ASR transcription — lowercase, unpunctuated (ensemble/ROVER) |
+| `text_denorm` | string | denormalized + punctuated text with stress marks (GPT-4.1-mini + RUAccent) |
+| `text_alignment` | string | word-level forced alignment, `[pause]` tags at internal silences |
+| `duration` | float32 | duration, seconds |
+| `local_spk_id` | string | within-video speaker id (voice clustering) |
+| `global_spk_id` | string | cross-video identity from face clustering; `null` if audio-only |
+| `lang` | string | language (`ru`) |
+| `spk_desc` | string | speaker persona — JSON, see below |
+| `mos_score` | float32 | DistillMOS quality score |
+
+### `spk_desc` format
+
+| field | type | description |
+|---|---|---|
+| `audio_only` | bool | `true` → no validated face; persona is voice-only |
+| `audio_desc` | object | voice-derived (always present) |
+| `audio_desc.gender` | str | `male` / `female` (voice-based classifier) |
+| `audio_desc.f0_hz` | float | median fundamental frequency (Hz) |
+| `audio_desc.pitch` | str | `low` / `mid` / `high` |
+| `audio_desc.rate` | str | speaking rate — `slow` / `moderate` / `fast` |
+| `audio_desc.brightness` | str | spectral brightness (e.g. `dark` / `bright`) |
+| `audio_desc.breathiness` | str | voice quality (e.g. `clear/modal`, `breathy`) |
+| `audio_desc.text` | str | natural-language voice summary |
+| `image_desc` | object \| null | face-derived; `null` when `audio_only` |
+| `image_desc.gender` | str | apparent gender from the face |
+| `image_desc.age` | str | apparent age range (e.g. `20s`, `30-45`) |
+| `image_desc.nationality` | str | apparent nationality (from a shared multilingual prompt) |
+| `image_desc.appearance` | str | free-text appearance |
+| `image_desc.style` | str | free-text style / grooming |
+| `image_desc.impression` | str | free-text overall impression |
+| `image_desc.text` | str | natural-language face summary |
+
+Audio-only speakers carry `"audio_only": true` and `"image_desc": null`.
+
+## Bias, Risks, and Limitations
+
+- **Automatic annotation.** Every label is model-generated and not human-verified — human involvement was
+  limited to tuning the pipeline's hyperparameters (e.g. filter thresholds) — so minor errors are
+  expected: occasional inaccuracies in transcriptions, in text processing (punctuation, stress marks,
+  alignment timings), and in speaker metadata. A segment kept as single-speaker may still contain brief
+  intrusions of a second voice where one speaker clearly predominates.
+- **Visual persona is fragment-scoped.** The face-derived description (`spk_desc.image_desc`) is inferred
+  from a few frames of a *single* video clip. It reflects only how the person appears in that fragment and
+  may not represent them in general — appearance, styling, and context can differ across videos, and the
+  cross-video identity is based on the face, not on a full profile of the person.
+- **Persona attributes are apparent, not factual.** All demographic fields in `spk_desc` are automatic
+  estimates. They describe how the speaker sounds or looks in that fragment, can be wrong, and must not
+  be treated as facts about the person.
+- **Source representativeness.** The corpus mirrors the distribution of Russian-language YouTube content:
+  some speakers, accents, domains, and recording conditions are over- or under-represented, and recurring
+  presenters contribute disproportionately many utterances.
+- **Recovered source IDs.** Building the face/video modality required the original YouTube video IDs (to
+  fetch each clip), which YODAS2 does not expose directly — we recovered the source IDs for our own
+  processing; see [espnet/yodas2 discussion #2](https://huggingface.co/datasets/espnet/yodas2/discussions/2)
+  for context.
+
+## License
+
+YO-CPT-ru is a *derived* dataset: beyond attribution, the authors impose no restrictions of their own —
+users are responsible for complying with the upstream licenses below.
+
+**Contribution of the authors.** The annotations and the compilation of the corpus are released under
+[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) — attribution required, commercial use allowed.
+
+**Upstream components.** The source audio comes from [YODAS2](https://huggingface.co/datasets/espnet/yodas2),
+collected exclusively from YouTube videos published under a Creative Commons license; the recordings remain
+the intellectual property of their original creators (see [Contact](#contact) for removal requests). The
+released audio and metadata are produced by the following components:
+
+| component | released output | license |
+|---|---|---|
+| YODAS2 (source recordings) | `audio` | CC BY 3.0 |
+| Silero VAD | — (segmentation only) | MIT |
+| VoxBlink2 ResNet34 | `local_spk_id` | not stated (trained on CC BY-NC-SA 4.0 data) |
+| DistillMOS | `mos_score` | MIT |
+| Whisper-large-v3-turbo (custom) | `text` (ensemble) | MIT |
+| GigaAM v3 RNNT | `text` (ensemble) | MIT |
+| Vosk | `text` (ensemble) | Apache-2.0 |
+| Spectra-0 | — (spoof filter only) | Apache-2.0 |
+| ClearVoice MossFormer2_SE_48K | `audio` (enhanced waveform) | Apache-2.0 |
+| wav2vec2-BERT (custom CTC) | `text_alignment` | MIT |
+| RUAccent turbo3.1 | stress marks in `text_denorm` / `text_alignment` | MIT |
+| voice-gender-classifier (ECAPA-TDNN) | `spk_desc.audio_desc.gender` | MIT |
+| OpenAI `gpt-4.1-mini` (API) | `text_denorm` | OpenAI ToS |
+| TalkNet-ASD | — (face selection only) | MIT |
+| LVFace | `global_spk_id` | code MIT; **weights: non-commercial research only** |
+| Qwen3.5-Flash (API) | `spk_desc.image_desc` | Alibaba Cloud API ToS |
+
+## Contact
+
+For questions about the dataset, or to request removal of your material (as a rights holder or as a
+person appearing in the recordings), contact us at **aleksei.gusev@ncspeech.org**.
+
+## Citation
+
+If you use this dataset, please cite it as follows.
+
+```bibtex
+@misc{yocptru2026,
+  title        = {YO-CPT-ru: A YouTube-Oriented Russian Speech Corpus for Continual Pre-Training},
+  author       = {{NCSpeech team}},
+  year         = {2026},
+  howpublished = {Hugging Face Hub},
+  url          = {https://huggingface.co/datasets/NCSpeech/YO-CPT-ru}
+}
+```

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