Add dataset card with conventions and tag statistics

README.md

@@ -1,41 +1,156 @@
 ---
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: audio
-    dtype:
-      audio:
-        sampling_rate: 16000
-  - name: duration_in_s
-    dtype: float32
-  - name: split
-    dtype: string
-  - name: speaker
-    dtype: string
-  - name: verbatim_transcript
-    dtype: string
-  - name: intended_transcript
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 977547596
-    num_examples: 4458
-  - name: validation
-    num_bytes: 54758218
-    num_examples: 250
-  - name: test
-    num_bytes: 52181437
-    num_examples: 249
-  download_size: 1062612600
-  dataset_size: 1084487251
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
+language:
+- en
+license: apache-2.0
+task_categories:
+- automatic-speech-recognition
+pretty_name: Nyra Disfluency Speech English
+size_categories:
+- 1K<n<10K
 ---
+
+# Nyra Disfluency Speech English
+
+`nyrahealth/disfluency_speech_english` is an English speech dataset for evaluating **verbatim ASR**: models that should transcribe not only the intended words, but also fillers, cutoffs, repetitions, and sound events.
+
+This dataset is based on the [AMAAI Lab DisfluencySpeech dataset](https://huggingface.co/datasets/amaai-lab/DisfluencySpeech) and reformatted for verbatim-transcription benchmarking with paired:
+
+- `verbatim_transcript`: what the speaker actually said
+- `intended_transcript`: a cleaned version of what the speaker meant to say
+
+It is used by the [Nyra Verbatim Speech Benchmark](https://github.com/nyrahealth/nyra_verbatim_speech_benchmark), which evaluates verbatim ASR in detail and breaks errors down into fillers, sounds, cutoffs, repetitions, and intended-transcript failures.
+
+For the exact convention definitions used by the benchmark, see:
+
+- [Nyra Verbatim Speech Benchmark](https://github.com/nyrahealth/nyra_verbatim_speech_benchmark)
+- [Verbatim transcript conventions](https://github.com/nyrahealth/nyra_verbatim_speech_benchmark?tab=readme-ov-file#verbatim-transcript-conventions)
+
+## Source
+
+This release is derived from:
+
+- Dataset: [amaai-lab/DisfluencySpeech](https://huggingface.co/datasets/amaai-lab/DisfluencySpeech)
+- Paper: [DisfluencySpeech -- Single-Speaker Conversational Speech Dataset with Paralanguage](https://arxiv.org/abs/2406.08820)
+
+The original dataset provides annotated transcripts and several progressively cleaned transcript variants. This Nyra release converts the data into a format that is directly usable for verbatim-ASR evaluation with paired verbatim and intended references.
+
+## Dataset Structure
+
+The dataset contains `4,957` utterances and about `9.4` hours of audio.
+
+Splits:
+
+- `train`: `4,458`
+- `validation`: `250`
+- `test`: `249`
+
+Features:
+
+- `id`
+- `audio`
+- `duration_in_s`
+- `split`
+- `speaker`
+- `verbatim_transcript`
+- `intended_transcript`
+- `timings`
+- `verbatim_timings`
+
+## Transcription Conventions
+
+### Verbatim Transcript
+
+The `verbatim_transcript` follows a small set of explicit conventions so disfluencies and non-speech events can be evaluated consistently:
+
+- **Cutoffs** use `*`, for example `th*` or `w*`
+- **Fillers** are bracketed tags, primarily `[UH]` and `[UM]`
+- **Sound events** are also bracketed tags, for example `[laughter]`, `[breath]`, or `[cough]`
+- **Repetitions** are written as repeated words, not as separate tags
+- Spoken words are otherwise written as they were said
+
+Example:
+
+```text
+I mean we we [UH] should go on th* Thursday [laughter]
+```
+
+### Intended Transcript
+
+The `intended_transcript` is the cleaned target for intended ASR. It removes disfluent material while preserving the speaker's meaning, including fillers, sound tags, repeated restarts, and cutoff fragments.
+
+Example:
+
+```text
+verbatim: I mean we we [UH] should go on th* Thursday [laughter]
+intended: we should go on Thursday
+```
+
+This makes the dataset suitable for evaluating both:
+
+- verbatim transcription quality
+- intended transcription quality
+
+## Tag Analysis
+
+The counts below were computed over the full dataset from `verbatim_transcript`.
+
+Summary:
+
+- utterances: `4,957`
+- utterances with at least one bracketed tag or cutoff: `2,779` (`56.1%`)
+- total bracketed tags: `4,039`
+- total cutoff tokens: `582`
+
+### Fillers
+
+| Tag | Count | Per utterance |
+| --- | ---: | ---: |
+| `[UH]` | 2,568 | 51.81% |
+| `[UM]` | 504 | 10.17% |
+
+### Sound Tags
+
+| Tag | Count | Per utterance |
+| --- | ---: | ---: |
+| `[laughter]` | 714 | 14.40% |
+| `[breath]` | 105 | 2.12% |
+| `[lipsmack]` | 59 | 1.19% |
+| `[throatclearing]` | 55 | 1.11% |
+| `[sigh]` | 18 | 0.36% |
+| `[sniff]` | 12 | 0.24% |
+| `[cough]` | 4 | 0.08% |
+
+### Cutoffs
+
+| Marker | Count | Per utterance |
+| --- | ---: | ---: |
+| `*` cutoff tokens | 582 | 11.74% |
+
+These statistics are useful when interpreting benchmark results: fillers are common, laughter is the most frequent sound event, and cutoffs occur often enough to matter as a separate evaluation category.
+
+## Benchmark Usage
+
+This dataset is designed to be used with the [Nyra Verbatim Speech Benchmark](https://github.com/nyrahealth/nyra_verbatim_speech_benchmark).
+
+That benchmark:
+
+- derives gold disfluency labels automatically from the verbatim and intended transcript pair
+- computes transcript metrics such as `vWER` and `iWER`
+- computes event metrics for fillers, sounds, cutoffs, and repetitions
+- provides detailed error analysis for verbatim ASR models
+
+## Citation
+
+If you use this dataset, please cite the original DisfluencySpeech paper:
+
+```bibtex
+@misc{wang2024disfluencyspeechsinglespeakerconversational,
+  title={DisfluencySpeech -- Single-Speaker Conversational Speech Dataset with Paralanguage},
+  author={Kyra Wang and Dorien Herremans},
+  year={2024},
+  eprint={2406.08820},
+  archivePrefix={arXiv},
+  primaryClass={eess.AS},
+  url={https://arxiv.org/abs/2406.08820}
+}
+```