Update README.md

README.md

@@ -1,36 +1,104 @@
----
-dataset_info:
-  features:
-  - name: speaker
-    dtype: string
-  - name: prompt_text
-    dtype: string
-  - name: chosen_text
-    dtype: string
-  - name: rejected_text
-    dtype: string
-  - name: prompt
-    dtype: audio
-  - name: chosen
-    dtype: audio
-  - name: rejected
-    dtype: audio
-  - name: auto_bleu2
-    dtype: float64
-  splits:
-  - name: validation
-    num_bytes: 12199479621.038
-    num_examples: 20006
-  - name: train
-    num_bytes: 28797300145.392
-    num_examples: 47928
-  download_size: 36106016770
-  dataset_size: 40996779766.43
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
----
+---
+dataset_info:
+  features:
+  - name: speaker
+    dtype: string
+  - name: prompt_text
+    dtype: string
+  - name: chosen_text
+    dtype: string
+  - name: rejected_text
+    dtype: string
+  - name: prompt
+    dtype: audio
+  - name: chosen
+    dtype: audio
+  - name: rejected
+    dtype: audio
+  - name: auto_bleu2
+    dtype: float64
+  splits:
+  - name: validation
+    num_bytes: 12199479621.038
+    num_examples: 20006
+  - name: train
+    num_bytes: 28797300145.392
+    num_examples: 47928
+  download_size: 36106016770
+  dataset_size: 40996779766.43
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
+license: mit
+task_categories:
+- audio-to-audio
+language:
+- en
+size_categories:
+- 10K<n<100K
+---
+
+# SpokenSwag
+We present here SpokenSwag as described in the paper ["_Slamming_: Training a Speech Language Model on One GPU in a Day"](link).
+This dataset is based on [allenai/swag](https://huggingface.co/datasets/allenai/swag) and synthetised with 4 speakers from [hexgrad/Kokoro-82M](https://huggingface.co/hexgrad/Kokoro-82M).
+We show that perfoming DPO over the dataset can really improve performance of Speech Language Models.
+We encourage you to also see the following resources, for further information:
+
+**Project Page:** https://pages.cs.huji.ac.il/adiyoss-lab/slamming/ \
+**Paper:** Soon! \
+**Code:** https://github.com/slp-rl/slam \
+
+
+If you use our dataset, please cite the paper as follows:
+```
+@article{maimon2024slamming,
+        soon!
+        }
+  
+```
+
+
+## Dataset Summary
+
+A dataset used for post-training spoken language models with DPO, which was showed to notably improve semantic abilities. 
+Specifically, the dataset is based on text only dataset [allenai/swag](https://huggingface.co/datasets/allenai/swag), and taking the correct 
+answer as the chosen contiuation and a random wrong answer as negative one. These were then synthesised using TTS by 
+[hexgrad/Kokoro-82M](https://huggingface.co/hexgrad/Kokoro-82M). We use 4 speakers - 2 male and 2 female. We generate both train and 
+validation splits from the original dataset.
+
+## Download
+
+#### Using 🤗 Datasets
+
+```python
+from datasets import load_dataset
+# entire dataset
+spoken_swag = load_dataset('slprl/SpokenSwag')
+```
+
+We refer you to the _Slam_ [codebase](https://github.com/slp-rl/slam) to see how you can train a SpeechLM with DPO over the dataset.
+
+## Data Fields
+
+The data has several fields:
+- `speaker`: One of the Kokoro voices - https://huggingface.co/hexgrad/Kokoro-82M/blob/main/VOICES.md
+- `prompt_text`: The text of the prompt recording.
+- `chosen_text`: The text of the chosen recording.
+- `rejected_text`: The text of the rejected recording.
+- `prompt`:   The prompt audio sample
+	- `array`:  array of audio samples
+	- `sample_rate`:  audio sampling rate
+	- `path`: path to the audio file saved location
+- `chosen`:   The chosen audio sample
+	- `array`:  array of audio samples
+	- `sample_rate`:  audio sampling rate
+	- `path`: path to the audio file saved location
+- `rejected`:   The rejected audio sample
+	- `array`:  array of audio samples
+	- `sample_rate`:  audio sampling rate
+	- `path`: path to the audio file saved location
+- `auto_bleu2`: The Auto-Bleu score with bi-grams, used to detect and filter repetetive samples