Update README.md

README.md

@@ -2,4 +2,67 @@
 
 https://groups.inf.ed.ac.uk/ami/corpus/
 
-To be filled!
+To be filled!
+
+**Note**: This dataset corresponds to the data-processing of [KALDI's AMI S5 recipe](https://github.com/kaldi-asr/kaldi/tree/master/egs/ami/s5).
+This means text is normalized and the audio data is chunked according to the scripts above!
+To make the user experience as simply as possible, we provide the already chunked data to the user here so that the following can be done:
+
+```python
+from datasets import load_dataset
+ds = load_dataset("edinburghcstr/ami", "ihm")
+
+print(ds)
+```
+gives:
+```
+DatasetDict({
+    train: Dataset({
+        features: ['segment_id', 'audio_id', 'text', 'audio', 'begin_time', 'end_time', 'microphone_id', 'speaker_id'],
+        num_rows: 108502
+    })
+    validation: Dataset({
+        features: ['segment_id', 'audio_id', 'text', 'audio', 'begin_time', 'end_time', 'microphone_id', 'speaker_id'],
+        num_rows: 13098
+    })
+    test: Dataset({
+        features: ['segment_id', 'audio_id', 'text', 'audio', 'begin_time', 'end_time', 'microphone_id', 'speaker_id'],
+        num_rows: 12643
+    })
+})
+```
+
+```py
+ds["train"][0]
+```
+
+automatically loads the audio into memory:
+
+```
+{'segment_id': 'EN2001a',
+ 'audio_id': 'AMI_EN2001a_H00_MEE068_0000557_0000594',
+ 'text': 'OKAY',
+ 'audio': {'path': '/home/patrick_huggingface_co/.cache/huggingface/datasets/downloads/extracted/2d75d5b3e8a91f44692e2973f08b4cac53698f92c2567bd43b41d19c313a5280/EN2001a/train_ami_en2001a_h00_mee068_0000557_0000594.wav',
+  'array': array([0.        , 0.        , 0.        , ..., 0.00033569, 0.00030518,
+         0.00030518], dtype=float32),
+  'sampling_rate': 16000},
+ 'begin_time': 5.570000171661377,
+ 'end_time': 5.940000057220459,
+ 'microphone_id': 'H00',
+ 'speaker_id': 'MEE068'}
+```
+
+
+The dataset was tested for correctness by fine-tuning a Wav2Vec2-Large model on it, more explicitly [the `wav2vec2-large-lv60` checkpoint](https://huggingface.co/facebook/wav2vec2-large-lv60).
+
+As can be seen in this experiments, training the model for less than 2 epochs gives
+
+*Result (WER)*:
+
+| "dev" | "eval" |
+|---|---|
+| 25.27 | 25.21 |
+
+as can be seen [here]( ).
+
+You can run [run.sh]( ) to reproduce the result.