Add CrawlSinger-OS dataset card

README.md

@@ -1,3 +1,187 @@
 ---
+pretty_name: CrawlSinger-OS
 license: mit
+language:
+  - zh
+task_categories:
+  - text-to-speech
+  - text-to-audio
+  - automatic-speech-recognition
+size_categories:
+  - 100K<n<1M
+tags:
+  - audio
+  - music
+  - singing
+  - singing-voice-synthesis
+  - music-score
+  - arxiv:2607.27768
 ---
+
+# CrawlSinger-OS
+
+CrawlSinger-OS is a large-scale, open-source singing corpus constructed for
+score-native singing voice synthesis. It contains more than **2,300 hours** of
+processed singing data from multiple public song and singing collections, with
+a unified annotation scheme for lyrics, MIDI pitches, symbolic note values,
+lyric-to-note alignment, and global tempo.
+
+- [VocalRender paper](https://arxiv.org/abs/2607.27768)
+- [VocalRender code](https://github.com/pymaster17/VocalRender)
+- [VocalRender checkpoints](https://huggingface.co/pymaster/VocalRender)
+
+## Why CrawlSinger-OS
+
+Modern singing synthesizers benefit from large and diverse training corpora,
+but conventional SVS datasets are usually smaller than 100 hours. CrawlSinger-OS
+processes both synthetic and real-world music with the same pipeline and score
+representation, providing the scale and fine-grained lyric--note alignment
+needed to train VocalRender's autoregressive diffusion architecture.
+
+The core CrawlSinger-OS release contains:
+
+| Source | Processed hours | Segments | Type | Original annotation |
+| --- | ---: | ---: | --- | --- |
+| Muse (Chinese subset) | 2,013 | 601k | Synthetic songs | Time-aligned lyrics |
+| MuChin | 158 | 84k | Real songs | Time-aligned lyrics |
+| SongFormDB (Ext) | 93 | 48k | Real songs | None |
+| OpenSinger | 53 | 43k | Real singing | Lyrics |
+| **Total** | **2,317** | **776k** | Synthetic + real | Unified below |
+
+Synthetic and real subsets have different musical and semantic distributions.
+The paper therefore trains on them in two stages instead of treating them as
+interchangeable data.
+
+## Data construction
+
+The SingCrawl processing pipeline has four stages:
+
+1. **Vocal extraction.** Cascaded mel-RoFormer models remove accompaniment and
+   reverberation from song audio.
+2. **Slicing and lyric transcription.** Audio is divided into clips shorter
+   than 30 seconds. Depending on the source annotations, the pipeline uses a
+   direct ASR path, a candidate-lyrics context-biasing path, or a refined
+   timestamp path.
+3. **Forced alignment.** A retrained SOFA aligner with G2PW produces
+   fine-grained lyric timing. On held-out GTSinger and M4Singer subsets, the
+   aligner reaches a mean IoU of 0.84 and VlabelerEditRatio (50 ms) of 0.092.
+4. **Pitch transcription.** ROSVOT transcribes note pitches; realized pitch
+   durations are quantized into symbolic note values and a global BPM reference.
+
+## Repository contents
+
+The repository uses two storage layouts. `folder_based` sources keep per-song
+metadata inside their tar archives; `json_file` sources provide a central
+`annotations.json` plus audio archives.
+
+| Path | Role | Layout | Archive size |
+| --- | --- | --- | ---: |
+| `muse/` | CrawlSinger-OS core | folder-based, 3 shards | 51.40 GB |
+| `muchin/` | CrawlSinger-OS core | folder-based | 4.20 GB |
+| `songformdb/` | CrawlSinger-OS core | folder-based | 2.34 GB |
+| `opensinger/` | CrawlSinger-OS core | JSON + audio | 16.74 GB |
+| `m4singer/` | Additional public training corpus | JSON + audio | 11.33 GB |
+| `gtsinger/` | Additional public training corpus | JSON + audio | 19.36 GB |
+| `opencpop/` | Evaluation corpus | JSON + audio | 5.03 GB |
+| `manifest.json` | Layout, byte size, and SHA-256 for every shard | JSON | -- |
+
+The complete repository is approximately 110.4 GB. Select only the subsets
+needed for your experiment.
+
+## Annotation format
+
+JSON-based sources contain a list of entries such as:
+
+```json
+{
+  "item_name": "2002000039",
+  "word": ["你", "是", "我", "SP"],
+  "pitch": [50, 62, 60, 0],
+  "note": ["<NOTE_16>", "<NOTE_4>", "<NOTE_DOT_8>", "<NOTE_DOT_16>"],
+  "pitch2word": [0, 1, 2, 3],
+  "bpm": 81,
+  "wav_fn": "segments/wavs/2002000039.wav",
+  "word_dur": [0.17847, 0.84090, 0.51098, 0.25584],
+  "pitch_dur": [0.17847, 0.84090, 0.51098, 0.25584]
+}
+```
+
+| Field | Description |
+| --- | --- |
+| `word` | Lyric syllables; `AP`/`SP` denote non-lyric regions. |
+| `pitch` | MIDI pitch per note segment; `0` denotes a rest. |
+| `note` | Symbolic note-value token per note segment. |
+| `pitch2word` | Maps each note segment to its lyric index and supports melisma. |
+| `bpm` | Global tempo reference. |
+| `wav_fn` | Audio path relative to the extracted source directory. |
+| `word_dur`, `pitch_dur` | Optional realized durations for visualization and evaluation; they are not VocalRender conditioning fields. |
+
+## Download
+
+Install the Hugging Face CLI, then download only the desired source:
+
+```bash
+# Inspect the release manifest first
+hf download pymaster/CrawlSinger-OS manifest.json \
+    --repo-type dataset \
+    --local-dir data/CrawlSinger-OS
+
+# Example: download the processed OpenSinger subset
+hf download pymaster/CrawlSinger-OS \
+    --repo-type dataset \
+    --include "opensinger/*" \
+    --local-dir data/CrawlSinger-OS
+```
+
+Extract downloaded audio shards with `tar -xf`. Use the SHA-256 values in
+`manifest.json` to verify large downloads.
+
+## Relationship to VocalRender and VocalRender-Pro
+
+The two released models share the same score-native architecture: an
+interleaved lyric--note representation, continuous AudioVAE latents, and an
+autoregressive diffusion model that predicts acoustic patches and termination
+without an explicit duration predictor.
+
+| Model | Main training data | Training strategy | Main observed trade-off |
+| --- | --- | --- | --- |
+| **VocalRender** | CrawlSinger-OS (>2,300 h), with additional public singing corpora | 40k-step synthetic pretraining, then 20k-step real-data finetuning | Stronger subjective score following (MS-MOS 2.96). |
+| **VocalRender-Pro** | CrawlSinger (>5,600 h of in-house real singing) | 160k steps with a global batch size of 32,768 continuous tokens | Better intelligibility, speaker similarity, naturalness, and OOD robustness; MS-MOS 2.71. |
+
+On Opencpop, VocalRender-Pro reduces WER from 4.44 to 3.88 and improves speaker
+similarity from 0.922 to 0.929. On CrawlSinger-Eval, WER changes from 4.52 to
+4.45 and similarity from 0.919 to 0.926. The paper attributes these gains to
+the larger amount of real singing and broader singer coverage. Conversely,
+VocalRender's higher score-following rating may result from more reliable and
+precise score annotations in its finetuning subset.
+
+Only **CrawlSinger-OS and the accompanying public corpora** are distributed in
+this dataset repository. The in-house CrawlSinger training data used by
+VocalRender-Pro is not included.
+
+## Limitations
+
+- Most automatically produced scores are audio-centric transcriptions: they
+  describe realized ornaments and note splits rather than a composer's concise
+  intent-centric score.
+- Synthetic and real subsets have noticeably different musical, semantic, and
+  pitch distributions.
+- Automatically transcribed scores may contain chromatic fluctuations,
+  uncommon note values, or overly fragmented note sequences.
+- The release primarily targets Mandarin singing and can contain errors from
+  separation, ASR, forced alignment, and pitch transcription.
+
+## Citation
+
+```bibtex
+@article{chen2026vocalrender,
+  title   = {VocalRender: Score-Native Singing Voice Synthesis for Real-World Composition},
+  author  = {Chen, Yukun and Wang, Tianrui and Mu, Zhaoxi and Yang, Xinyu and Chng, EngSiong},
+  journal = {arXiv preprint arXiv:2607.27768},
+  year    = {2026},
+  url     = {https://arxiv.org/abs/2607.27768}
+}
+```
+
+Please also cite the original source datasets used by the subset(s) in your
+work.