Update README.md

README.md

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 # AE29H_float32
 
-**Audio Embeddings ~29 hours** dataset contains **precomputed audio embeddings** designed for training **NanoWakeWord** models. The embeddings are intended to be used as **general-purpose negative training data**, meaning the audio does **not contain the target wake word or phrase**.
+**Audio Embeddings ~29 hours** dataset contains **precomputed audio embeddings** designed for **[Nanowakeword](https://github.com/arcosoph/nanowakeword)** framework. The embeddings are intended to be used as **general-purpose negative training data**, meaning the audio does **not contain the target wake word or phrase**.
 
-Unlike raw audio datasets, the files in this dataset contain **low-dimensional audio embeddings** extracted from audio clips using a pre-trained speech embedding model. These embeddings can be directly used as input features when training wake-word detection models with NanoWakeWord.
+Unlike raw audio datasets, the files in this dataset contain **low-dimensional audio embeddings** extracted from audio clips using a pre-trained [speech embedding](https://www.kaggle.com/models/google/speech-embedding) model. These embeddings can be directly used as input features when training wake-word detection models with NanoWakeWord.
 
 The goal of this dataset is to provide **diverse background audio representations** (speech, environmental noise, music, etc.) that help wake-word models learn to avoid false activations.
 
@@ -20,7 +20,7 @@ The goal of this dataset is to provide **diverse background audio representation
 
 # Dataset Source
 
-The embeddings were generated from a subset of the **ACAV100M** dataset.
+The embeddings were generated from a subset of the **[ACAV100M](https://acav100m.github.io/)** dataset.
 
 ACAV100M is a very large automatically curated audio-visual dataset created from millions of internet videos and designed for large-scale audio-visual learning. It contains diverse real-world audio such as speech, environmental sounds, music, and background noise.
 
@@ -43,8 +43,10 @@ For this dataset:
   **21,115**
 
 * **Feature dimensions:**
-  * **Embedding size:** 96
 
+  * **Temporal steps:** 16
+  * **Embedding size:** 96
+Each sample represents approximately **1.28 seconds of audio**, where each temporal step corresponds to **~80 ms**.
 
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