Datasets:
Add dev/test split tag to sample_id and audio.path
Browse files- README.md +11 -11
- data/acr/dev.parquet +2 -2
- data/ccr/dev.parquet +2 -2
README.md
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Blind **development** subset of the VMC 2026 Track 1 (URGENT 2026 Speech Quality Assessment) data. Each row provides a `sample_id` and audio only -- no labels, no system identifiers, no metadata. Use this set to develop and validate your predictor; the held-out evaluation set lives at [`urgent-challenge/vmc2026-track1-test`](https://huggingface.co/datasets/urgent-challenge/vmc2026-track1-test).
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The data is organized into two configs corresponding to two subjective evaluation paradigms: absolute rating (`acr`) and pairwise comparison (`ccr`). The `sample_id` values are namespaced strings such as `vmc2026-track1-acr_489` and `vmc2026-track1-ccr_7233`, so a participant can submit one mixed prediction file and the organizer can split it by the `acr` / `ccr` segment.
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The dev and test sets are constructed from disjoint base utterances and disjoint speakers (no base noisy clip and no speaker appears in both); for CCR this means no preference-pair audio appears in both sets. Splits are balanced per language at roughly 20% / 80%.
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> Note: `sample_id`
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## `acr` -- Absolute Category Rating
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| Column | Type | Description |
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|--------|------|-------------|
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| `sample_id` | string | Namespaced identifier such as `vmc2026-track1-acr_489` |
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| `audio` | Audio | Speech audio (FLAC, mono); inner `path` is `<sample_id>.flac` |
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| `sample_rate` | int | Sample rate in Hz |
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| `duration` | float | Duration in seconds |
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| Column | Type | Description |
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|--------|------|-------------|
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| `sample_id` | string | Namespaced identifier such as `vmc2026-track1-ccr_7233` |
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| `audio_a` | Audio | Speech audio from system A (FLAC, mono); inner `path` is `<sample_id>_a.flac` |
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| `audio_b` | Audio | Speech audio from system B (FLAC, mono); inner `path` is `<sample_id>_b.flac` |
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| `sample_rate` | int | Sample rate in Hz |
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Submit one space-delimited, headerless `predictions.scp` file with one prediction per line:
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```text
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vmc2026-track1-acr_489 3.42
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vmc2026-track1-ccr_7233 -0.15
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```
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The file should contain exactly 1,008 ACR rows and 2,520 CCR rows -- one prediction per `sample_id` in this dataset. ACR scores must lie in [1, 5]; CCR scores in [-3, +3]. Preference accuracy for CCR is derived from the sign of the predicted CMOS.
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```python
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>>> acr[0]
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{
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'sample_id': 'vmc2026-track1-acr_489',
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'audio': {
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'path': 'vmc2026-track1-acr_489.flac',
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'array': array([-0.00039673, -0.00054932, -0.00045776, -0.00048828, ...]),
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'sampling_rate': 32000,
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},
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>>> ccr[0]
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{
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'sample_id': 'vmc2026-track1-ccr_7233',
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'audio_a': {
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'path': 'vmc2026-track1-ccr_7233_a.flac',
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'array': array([0.00054932, 0.00079346, 0.00079346, 0.00076294, ...]),
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'sampling_rate': 16000,
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},
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'audio_b': {
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'path': 'vmc2026-track1-ccr_7233_b.flac',
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'array': array([0.03192139, 0.06243896, 0.07189941, 0.08477783, ...]),
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'sampling_rate': 16000,
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},
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Blind **development** subset of the VMC 2026 Track 1 (URGENT 2026 Speech Quality Assessment) data. Each row provides a `sample_id` and audio only -- no labels, no system identifiers, no metadata. Use this set to develop and validate your predictor; the held-out evaluation set lives at [`urgent-challenge/vmc2026-track1-test`](https://huggingface.co/datasets/urgent-challenge/vmc2026-track1-test).
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The data is organized into two configs corresponding to two subjective evaluation paradigms: absolute rating (`acr`) and pairwise comparison (`ccr`). The `sample_id` values are namespaced strings such as `vmc2026-track1-dev-acr_489` and `vmc2026-track1-dev-ccr_7233`. The namespace encodes track / split / task: `vmc2026-track1-{dev|test}-{acr|ccr}_{index}`, so a participant can submit one mixed prediction file and the organizer can split it by the `acr` / `ccr` segment, while the `dev` / `test` segment makes the IDs disjoint from the held-out evaluation set.
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The dev and test sets are constructed from disjoint base utterances and disjoint speakers (no base noisy clip and no speaker appears in both); for CCR this means no preference-pair audio appears in both sets. Splits are balanced per language at roughly 20% / 80%.
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> Note: `sample_id` indices are drawn from a single 0..N-1 numbering shared with the test set, so the IDs in this dev set are not contiguous (e.g. `vmc2026-track1-dev-acr_28`, `vmc2026-track1-dev-acr_29`, `vmc2026-track1-dev-acr_30`, `vmc2026-track1-dev-acr_31`, `vmc2026-track1-dev-acr_52`, ...).
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## `acr` -- Absolute Category Rating
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| Column | Type | Description |
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|--------|------|-------------|
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| `sample_id` | string | Namespaced identifier such as `vmc2026-track1-dev-acr_489` |
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| `audio` | Audio | Speech audio (FLAC, mono); inner `path` is `<sample_id>.flac` |
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| `sample_rate` | int | Sample rate in Hz |
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| `duration` | float | Duration in seconds |
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| Column | Type | Description |
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|--------|------|-------------|
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| `sample_id` | string | Namespaced identifier such as `vmc2026-track1-dev-ccr_7233` |
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| `audio_a` | Audio | Speech audio from system A (FLAC, mono); inner `path` is `<sample_id>_a.flac` |
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| `audio_b` | Audio | Speech audio from system B (FLAC, mono); inner `path` is `<sample_id>_b.flac` |
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| `sample_rate` | int | Sample rate in Hz |
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Submit one space-delimited, headerless `predictions.scp` file with one prediction per line:
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```text
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vmc2026-track1-dev-acr_489 3.42
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vmc2026-track1-dev-ccr_7233 -0.15
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```
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The file should contain exactly 1,008 ACR rows and 2,520 CCR rows -- one prediction per `sample_id` in this dataset. ACR scores must lie in [1, 5]; CCR scores in [-3, +3]. Preference accuracy for CCR is derived from the sign of the predicted CMOS.
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```python
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>>> acr[0]
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{
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'sample_id': 'vmc2026-track1-dev-acr_489',
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'audio': {
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'path': 'vmc2026-track1-dev-acr_489.flac',
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'array': array([-0.00039673, -0.00054932, -0.00045776, -0.00048828, ...]),
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'sampling_rate': 32000,
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},
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>>> ccr[0]
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{
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'sample_id': 'vmc2026-track1-dev-ccr_7233',
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'audio_a': {
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'path': 'vmc2026-track1-dev-ccr_7233_a.flac',
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'array': array([0.00054932, 0.00079346, 0.00079346, 0.00076294, ...]),
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'sampling_rate': 16000,
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},
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'audio_b': {
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'path': 'vmc2026-track1-dev-ccr_7233_b.flac',
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'array': array([0.03192139, 0.06243896, 0.07189941, 0.08477783, ...]),
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'sampling_rate': 16000,
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},
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data/acr/dev.parquet
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version https://git-lfs.github.com/spec/v1
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size
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version https://git-lfs.github.com/spec/v1
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size 111843319
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data/ccr/dev.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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size 462571134
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