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README.md

@@ -1,24 +1,114 @@
 ---
-dataset_info:
-  features:
-  - name: input_values
-    list: float32
-  - name: labels
-    list: int64
-  splits:
-  - name: train
-    num_bytes: 17470155
-    num_examples: 45
-  - name: test
-    num_bytes: 1941128
-    num_examples: 5
-  download_size: 13427696
-  dataset_size: 19411283
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+license: apache-2.0
+task_categories:
+  - automatic-speech-recognition
+language:
+  - en
+tags:
+  - phoneme-recognition
+  - arpabet
+  - pronunciation
+  - wav2vec2
+  - ctc
+  - speech
+pretty_name: LibriSpeech ARPAbet Phonemes
+size_categories:
+  - 10K<n<100K
 ---
+
+# LibriSpeech ARPAbet Processed Dataset
+
+Pre-processed dataset for training ARPAbet phoneme recognition models using CTC loss.
+
+## Dataset Description
+
+This dataset is derived from [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) (train-clean-100 split) with the following preprocessing:
+
+- **Audio**: Resampled to 16kHz, normalized using Wav2Vec2 feature extractor
+- **Labels**: Text transcriptions converted to ARPAbet phoneme sequences using CMU Pronouncing Dictionary
+- **Filtering**: Samples with out-of-vocabulary words are excluded
+
+### Features
+
+| Feature | Type | Description |
+|---------|------|-------------|
+| `input_values` | `Sequence[float]` | Normalized audio waveform (16kHz) |
+| `labels` | `Sequence[int]` | ARPAbet phoneme token IDs |
+
+### ARPAbet Vocabulary (72 tokens)
+
+The vocabulary includes:
+- **Special tokens (3)**: `<pad>`, `<unk>`, `|` (word boundary)
+- **Consonants (24)**: B, CH, D, DH, F, G, HH, JH, K, L, M, N, NG, P, R, S, SH, T, TH, V, W, Y, Z, ZH
+- **Vowels with stress markers (45)**: 15 base vowels x 3 stress levels (0, 1, 2)
+  - Example: AA0 (no stress), AA1 (primary), AA2 (secondary)
+
+### Splits
+
+| Split | Samples | Description |
+|-------|---------|-------------|
+| train | ~25,600 | Training data (90%) |
+| test  | ~2,850  | Evaluation data (10%) |
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("davidggphy/librispeech-arpabet-processed")
+
+# Access samples
+sample = dataset["train"][0]
+print(f"Audio shape: {len(sample['input_values'])}")
+print(f"Labels: {sample['labels']}")
+```
+
+### Training with Wav2Vec2
+
+```python
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+
+# Load model and processor
+model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base", vocab_size=72)
+processor = Wav2Vec2Processor.from_pretrained("davidggphy/wav2vec2-arpabet-phoneme")
+
+# The dataset is ready for CTC training
+# input_values: normalized audio
+# labels: phoneme token IDs
+```
+
+## Intended Use
+
+This dataset is designed for:
+- Training phoneme recognition models for English pronunciation assessment
+- Fine-tuning Wav2Vec2 for ARPAbet output
+- Research in automatic pronunciation evaluation
+
+## Source Data
+
+- **Audio**: LibriSpeech train-clean-100 (read English speech)
+- **Phoneme Dictionary**: [CMU Pronouncing Dictionary](https://github.com/cmusphinx/cmudict)
+
+## Limitations
+
+- Only covers words present in CMU Dictionary (~126k words)
+- Based on American English pronunciation
+- Does not include phonetic variations or connected speech phenomena
+
+## Citation
+
+If you use this dataset, please cite LibriSpeech:
+
+```bibtex
+@inproceedings{panayotov2015librispeech,
+  title={Librispeech: an ASR corpus based on public domain audio books},
+  author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
+  booktitle={ICASSP},
+  year={2015}
+}
+```
+
+## License
+
+Apache 2.0 (same as LibriSpeech)