--- 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-.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: Anti-spoofing — bonafide score distribution 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 `