Update README.md

README.md

@@ -5,20 +5,20 @@ license: mit
 # EDEN ASR Dataset
 A subset of this data was used to support the development of empathetic feedback modules in [EDEN](https://arxiv.org/abs/2406.17982) and [its prior work](https://arxiv.org/abs/2404.13764).
 
-To create data for testing various modules, we utilized audio clips of native Mandarin speakers conversing with a chatbot collected from an [English practice platform](https://dl.acm.org/doi/abs/10.1145/3491140.3528329?casa_token=ER-mfy0xauQAAAAA:FyDgmH0Y0ke7a6jpOnuycP1HRfeV1B5qaq5JWM5OV5dB9fLFL_vzVRUacZ4fUMRBDl71UeWMIA9Z). 
+The dataset contains audio clips of native Mandarin speakers. The speakers conversed with a chatbot hosted on an [English practice platform](https://dl.acm.org/doi/abs/10.1145/3491140.3528329?casa_token=ER-mfy0xauQAAAAA:FyDgmH0Y0ke7a6jpOnuycP1HRfeV1B5qaq5JWM5OV5dB9fLFL_vzVRUacZ4fUMRBDl71UeWMIA9Z). 
 3081 audio clips from 613 conversations and 163 users remained after filtering. 
 The filtering process removes audio clips containing only Mandarin, duplicates, and a subset of self-introductions from the users. 
-Each audio clip ranges from one second to two minutes. We did not collect demographic information for user identity protection.
+Each audio clip ranges from one second to two minutes. We did not collect demographic information to protect user identities.
 
-In our original work, we directly transcribed the speech with Whisper Medium. However, since the audio clips are accented speech, there exist instances of ASR error in these transcripts. We have **manually verified** the original transcripts to ensure they are high-quality.
+In our original work, we directly transcribed the speech with Whisper Medium. However, since the audio clips are accented speech, these transcripts have instances of ASR error. We have **manually verified** the original transcripts to ensure they are high-quality.
 
 ## Dataset Columns
-* **audio_url**: The URL to the audio clips; you can download them using wget to your local machines.
+* **audio_url**: The URL to the audio clips; you can download them using wget or urllib to your local machines (see the code snippet below).
 * **emotion_label**: We manually labeled a subset of the clips as **Neutral** (neutral emotions), **Negative** (the speaker is displaying negative emotions), or **Pauses** (there are a lot of pauses in the speech, potentially signaling language anxiety). The process is documented in [EDEN's prior work](https://arxiv.org/abs/2404.13764). When this field is empty, it means that the clip was not labeled.
 * **corrected_whisper_transcript**: The high-quality transcript we have verified; the person performing verification is a native English speaker.
 
 ## Intended Use
-This set of data can be used for Accented ASR research!
+Accented ASR research!
 
 ## Code Example
 
@@ -38,7 +38,7 @@ print(dataset[0]["corrected_whisper_transcript"])
 # Extract the emotion label
 print(dataset[0]["emotion_label"])
 
-# Check negative emotion audio clips
+# Check audio clips labeled as having negative emotions
 negative_emotion_clips = dataset.filter(lambda example: example["emotion_label"] == "Negative")
 print(len(negative_emotion_clips))
 print(negative_emotion_clips[0])