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README.md

@@ -30,127 +30,6 @@ tags:
 - audio
 - classification
 - extended
-dataset_info:
-- config_name: enrichment_only
-  features:
-  - name: label_string
-    dtype: string
-  - name: probability
-    dtype: float64
-  - name: probability_vector
-    sequence: float32
-  - name: prediction
-    dtype: int64
-  - name: prediction_string
-    dtype: string
-  - name: embedding_reduced
-    sequence: float32
-  splits:
-  - name: train
-    num_bytes: 8763867
-    num_examples: 51093
-  - name: validation
-    num_bytes: 1165942
-    num_examples: 6799
-  - name: test
-    num_bytes: 528408
-    num_examples: 3081
-  download_size: 12246039
-  dataset_size: 10458217
-- config_name: raw_and_enrichment_combined
-  features:
-  - name: file
-    dtype: string
-  - name: audio
-    dtype:
-      audio:
-        sampling_rate: 16000
-  - name: label
-    dtype:
-      class_label:
-        names:
-          '0': 'yes'
-          '1': 'no'
-          '2': up
-          '3': down
-          '4': left
-          '5': right
-          '6': 'on'
-          '7': 'off'
-          '8': stop
-          '9': go
-          '10': zero
-          '11': one
-          '12': two
-          '13': three
-          '14': four
-          '15': five
-          '16': six
-          '17': seven
-          '18': eight
-          '19': nine
-          '20': bed
-          '21': bird
-          '22': cat
-          '23': dog
-          '24': happy
-          '25': house
-          '26': marvin
-          '27': sheila
-          '28': tree
-          '29': wow
-          '30': _silence_
-  - name: is_unknown
-    dtype: bool
-  - name: speaker_id
-    dtype: string
-  - name: utterance_id
-    dtype: int8
-  - name: logits
-    sequence: float64
-  - name: embedding
-    sequence: float32
-  - name: label_string
-    dtype: string
-  - name: probability
-    dtype: float64
-  - name: probability_vector
-    sequence: float32
-  - name: prediction
-    dtype: int64
-  - name: prediction_string
-    dtype: string
-  - name: embedding_reduced
-    sequence: float32
-  splits:
-  - name: train
-    num_bytes: 1803565876.375
-    num_examples: 51093
-  - name: validation
-    num_bytes: 240795605.125
-    num_examples: 6799
-  - name: test
-    num_bytes: 109673146.875
-    num_examples: 3081
-  download_size: 2107299942
-  dataset_size: 2154034628.375
-configs:
-- config_name: enrichment_only
-  data_files:
-  - split: train
-    path: enrichment_only/train-*
-  - split: validation
-    path: enrichment_only/validation-*
-  - split: test
-    path: enrichment_only/test-*
-- config_name: raw_and_enrichment_combined
-  data_files:
-  - split: train
-    path: raw_and_enrichment_combined/train-*
-  - split: validation
-    path: raw_and_enrichment_combined/validation-*
-  - split: test
-    path: raw_and_enrichment_combined/test-*
 ---
 
 # Dataset Card for SpeechCommands
@@ -178,6 +57,8 @@ At [Renumics](https://renumics.com/?hf-dataset-card=speech-commands-enriched) we
 
 ### Explore the Dataset
 
+There are two configurations of the dataset: **Enrichment only** provides the enrichments calculated by Renumics using the MIT AST transformer, while **raw_and_enrichment_combined** provides a concatenated dataset of the original speech commands and the enrichment.  
+
 The enrichments allow you to quickly gain insights into the dataset. The open source data curation tool [Renumics Spotlight](https://github.com/Renumics/spotlight) enables that with just a few lines of code:
 
 Install datasets and Spotlight via [pip](https://packaging.python.org/en/latest/key_projects/#pip):
@@ -190,14 +71,32 @@ Install datasets and Spotlight via [pip](https://packaging.python.org/en/latest/
 Load the dataset from huggingface in your notebook and start exploring with a simple view:
 
 ```python
-from renumics import spotlight
 import datasets
+from renumics import spotlight
+
+from renumics.spotlight.layouts import debug_classification
 
-dataset = datasets.load_dataset("renumics/speech_commands_enriched", "v0.01")
-spotlight.show(dataset, port=8000, dtype={"file": spotlight.Audio})
+dataset = datasets.load_dataset("renumics/speech_commands_enrichment_only", "raw_and_enrichment_combined")
+joined_dataset = datasets.concatenate_datasets([dataset["train"], dataset["validation"], dataset["test"]])
+layout = debug_classification(label='label_string', prediction='prediction', embedding='embedding_reduced', 
+                                    features=["label", "prediction", "probability"], inspect={'audio': spotlight.Audio})
+dtypes = {
+    "audio": spotlight.Audio,
+    "embedding_reduced": spotlight.Embedding
+}
+spotlight.show(
+    joined_dataset,
+    dtype=dtypes,
+    layout= layout
+)
 ```
 You can use the UI to interactively configure the view on the data. Depending on the concrete tasks (e.g. model comparison, debugging, outlier detection) you might want to leverage different enrichments and metadata.
 
+As a plug and play option, you can check out the Huggingface space: [Huggingface Space for speech enrichment](https://huggingface.co/spaces/renumics/speech_commands_enrichment_space)
+
+Alternatively, you can run the notebook exploration.ipynb locally.
+
+
 
 ### SpeechCommands Dataset