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README.md

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+---
+ymi_version: 1.0
+language:
+  - en
+tags:
+  - speech
+  - pronunciation
+  - error-detection
+  - forced-alignment
+license: cc-by-nc-4.0
+pretty_name: "EPADB: English Pronunciation Assessment Dataset"
+size_categories:
+  - 1K<n<10K
+task_categories:
+  - automatic-speech-recognition
+  - audio-classification
+task_ids:
+  - speech-recognition-other
+  - audio-classification-other
+---
+
+# EPADB: English Pronunciation Assessment Dataset for Batched Diagnosis
+
+## Dataset Summary
+
+EPADB contains curated pronunciation assessment data collected from Spanish-speaking learners of English. Each utterance has manually aligned phone-level annotations from up to two expert annotators along with per-utterance global proficiency scores. Metadata links the aligned phones with MFA (Montreal Forced Aligner) timestamps, derived error classifications, and reference transcriptions. The corpus ships with a `train` and a `test` partition and includes speaker-wise waveform recordings resampled to 16 kHz.
+
+## Supported Tasks
+
+- **Pronunciation Assessment** – predict utterance-level global scores or speaker-level proficiency tiers.
+- **Phone-level Error Detection** – classify each phone as insertion, deletion, distortion, substitution, or correct.
+- **Alignment Analysis** – leverage MFA timings to study forced alignment quality or to refine pronunciation models.
+
+## Languages
+
+- L2 utterances: English
+- Speaker L1: Spanish
+
+## Dataset Structure
+
+### Data Instances
+
+Each JSON entry describes one utterance:
+
+- Phone sequences for MFA reference (`reference`) and annotators (`annot_1`, optional `annot_2`).
+- Phone-level labels (`label_1`, `label_2`) and derived `error_type` categories.
+- MFA start/end timestamps per phone (`start_mfa`, `end_mfa`).
+- Per-utterance global scores (`global_1`, `global_2`) and propagated speaker levels (`level_1`, `level_2`).
+- Speaker metadata (`speaker_id`, `gender`).
+- Audio metadata (`duration`, `sample_rate`, `wav_path`) plus the waveform itself.
+- Reference sentence transcription (`transcription`).
+
+### Data Fields
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `utt_id` | string | Unique utterance identifier (e.g., `spkr28_1`). |
+| `speaker_id` | string | Speaker identifier. |
+| `sentence_id` | string | Reference sentence ID (matches `reference_transcriptions.txt`). |
+| `phone_ids` | sequence[string] | Unique phone identifiers per utterance. |
+| `reference` | sequence[string] | MFA reference phones. |
+| `annot_1` | sequence[string] | Annotator 1 phones (`-` marks deletions). |
+| `annot_2` | sequence[string] | Annotator 3 phones when available, empty otherwise. |
+| `label_1` | sequence[string] | Annotator 1 phone labels (`"1"` correct, `"0"` incorrect). |
+| `label_2` | sequence[string] | Annotator 3 phone labels when present. |
+| `error_type` | sequence[string] | Derived categories: `correct`, `insertion`, `deletion`, `distortion`, `substitution`. |
+| `start_mfa` | sequence[float] | Phone start times (seconds). |
+| `end_mfa` | sequence[float] | Phone end times (seconds). |
+| `global_1` | float or null | Annotator 1 utterance-level score (1–4). |
+| `global_2` | float or null | Annotator 3 score when available. |
+| `level_1` | string or null | Speaker-level proficiency tier from annotator 1 ("A"/"B"). |
+| `level_2` | string or null | Speaker tier from annotator 3. |
+| `gender` | string or null | Speaker gender (`"M"`/`"F"`). |
+| `duration` | float | Utterance duration in seconds (after resampling to 16 kHz). |
+| `sample_rate` | int | Sample rate in Hz (16,000). |
+| `wav_path` | string | Waveform filename (`<utt_id>.wav`). |
+| `audio` | Audio | Automatically loaded waveform (16 kHz). |
+| `transcription` | string or null | Reference sentence text. |
+
+### Data Splits
+
+| Split | # Examples |
+|-------|------------|
+| train | 1,903 |
+| test  | 1,263 |
+
+### Notes
+
+- When annotator 3 did not label an utterance, related fields (`annot_2`, `label_2`, `global_2`, `level_2`) are absent or set to null.
+- Error types come from simple heuristics contrasting MFA reference phones with annotator 1 labels.
+- Waveforms were resampled to 16 kHz using `ffmpeg` during manifest generation.
+
+## Data Processing
+
+1. Forced alignments and annotations were merged to produce enriched CSV files per speaker/partition.
+2. `create_db.py` aggregates these into JSON manifests, adds error types, and resamples audio.
+3. Global scores are averaged per speaker to derive `level_*` tiers (`A` if mean ≥ 3, `B` otherwise).
+
+## Licensing
+
+- Audio and annotations: CC BY-NC 4.0 (non-commercial use allowed with attribution).
+- Please ensure any downstream usage complies with participant consent and institutional policies.
+
+## Citation
+
+```
+@article{vidal2019epadb,
+  title   = {EpaDB: a database for development of pronunciation assessment systems},
+  author  = {Vidal, Jazmin and Ferrer, Luciana and Brambilla, Leonardo},
+  journal = {Proc. Interspeech},
+  pages   = {589--593},
+  year    = {2019}
+}
+```
+
+## Usage
+
+Install dependencies and load the dataset:
+
+```python
+from datasets import load_dataset
+
+# Local usage before uploading:
+ds = load_dataset(
+    "epadb_dataset/epadb.py",
+    data_dir="/path/to/epadb",  # folder with train.json, test.json, WAV/
+    split="train",
+)
+print(ds)
+print(ds[0]["utt_id"], ds[0]["audio"]["sampling_rate"])  # 16000
+
+# After pushing to the Hugging Face Hub:
+# ds = load_dataset("JazminVidal/epadb", split="train")
+```
+
+## Acknowledgements
+
+We thank the learners and expert annotators who contributed to EPADB, as well as the speech processing community for tools such as MFA and ffmpeg used in the data preparation pipeline.