Add files using upload-large-folder tool

README.md

@@ -19,22 +19,22 @@ task_ids:
   - audio-classification-other
 ---
 
-# EPADB: English Pronunciation Assessment Dataset for Batched Diagnosis
+# EPADB: English Pronunciation by Argentinians
 
 ## 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.
+EpaDB is a speech database intended for research in pronunciation assessment at the phoneme level. The database includes audios from 50 Spanish speakers (25 males and 25 females) from Argentina reading phrases in English. Each speaker recorded 64 short phrases containing sounds hard to pronounce for this population adding up to ~3.5 hours of speech. Each utterance has phone-level annotations from up to two expert annotators along with per-utterance global proficiency scores. Additionally to MFA (Montreal Forced Aligner) timestamps, metadata includes derived error classifications, gender, and reference orthographic transcriptions. The database is organized in 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.
+- **Pronunciation Assessment** – predict utterance-level global scores or phone level accuracy scores
 - **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
+- Speaker L1: Spanish from Río de la Plata region Argentina
 
 ## Dataset Structure
 
@@ -42,13 +42,13 @@ EPADB contains curated pronunciation assessment data collected from Spanish-spea
 
 Each JSON entry describes one utterance:
 
-- Phone sequences for MFA reference (`reference`) and annotators (`annot_1`, optional `annot_2`).
+- Manually chose phoneme reference sequences (`reference`) and manual phonetic transcriptions (`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`).
+- Reference orthographic transcription (`transcription`).
 
 ### Data Fields
 
@@ -56,9 +56,9 @@ Each JSON entry describes one utterance:
 |-------|------|-------------|
 | `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`). |
+| `sentence_id` | string | Reference sentence ID. |
 | `phone_ids` | sequence[string] | Unique phone identifiers per utterance. |
-| `reference` | sequence[string] | MFA reference phones. |
+| `reference` | sequence[string] | 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). |
@@ -66,7 +66,7 @@ Each JSON entry describes one utterance:
 | `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_1` | float or null | Annotator 1 utterance-level score (1–5). |
 | `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. |
@@ -87,19 +87,14 @@ Each JSON entry describes one utterance:
 ### 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).
+- Error types come from simple heuristics contrasting the reference phones with annotator 1 labels.
+- Waveforms were resampled to 16 kHz using `ffmpeg` during data preparation.
+- 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
 
@@ -129,10 +124,8 @@ ds = load_dataset(
 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.
+The database is an effort of the Speech Lab at the Laboratorio de Inteligencia Artificial Aplicada from the Universidad de Buenos Aires and was partially funded by Google by a Google Latin America Reseach Award in 2018.