Update README.md

README.md

@@ -15,7 +15,7 @@ dataset = load_dataset("amu-cai/CAMEO", config, split="test")
 ```
 
 Available configs:
-- `all` (default)
+- `all`
 - `CaFE`
 - `CREMA-D`
 - `EMNS`
@@ -33,13 +33,14 @@ Available configs:
 
 ## Supported Tasks
 
-- **Audio Classification**: Primarily designed for speech emotion recognition, each recording is tagged with one of six emotional states: anger, fear, happiness, sadness, surprise, and neutral. Additionally, each sample includes speaker ID and gender, enabling its use in various audio classification tasks.
+- **Audio Classification**: Primarily designed for speech emotion recognition, each recording is annotated with a label corresponding to an emotional state. Additionally, most samples include speaker identifier and gender, enabling its use in various audio classification tasks.
 
-- **Automatic Speech Recognition (ASR)**: With orthographic and normalized transcriptions for each recording, this dataset is a valuable resource for ASR tasks. The sentences are selected to cover a broad range of phonemes, particularly in the Polish language.
+- **Automatic Speech Recognition (ASR)**: With orthographic transcriptions for each recording, this dataset is a valuable resource for ASR tasks.
 
 - **Text-to-Speech (TTS)**: The dataset's emotional audio recordings, complemented by transcriptions, are beneficial for developing TTS systems that aim to produce emotionally expressive speech.
 
 ## Languages
+
 CAMEO contains audio and transcription in eight languages: Bengali, English, French, German, Italian, Polish, Russian, Spanish.
 
 ## Data Structure
@@ -67,20 +68,17 @@ CAMEO contains audio and transcription in eight languages: Bengali, English, Fre
 ```
 
 ### Data Fields
-The structure of a data instance is as follows:
-
-| Field          | Type   | Description                                                                 |
-|----------------|--------|-----------------------------------------------------------------------------|
-| `file_id`      | `str`  | A unique identifier of the audio sample.                                    |
-| `audio`        | `dict` | A dictionary containing the file path to the audio sample, the raw waveform as a one-dimensional NumPy array, and the sampling rate (16 kHz). |
-| `emotion`      | `str`  | A label indicating the expressed emotional state.                           |
-| `transcription`| `str`  | The orthographic transcription of the utterance.                            |
-| `speaker_id`   | `str`  | A unique identifier of the speaker.                                         |
-| `gender`       | `str`  | The gender of the speaker.                                                  |
-| `age`          | `str`  | The age of the speaker.                                                     |
-| `dataset`      | `str`  | The name of the dataset from which the sample was taken.                    |
-| `language`     | `str`  | The primary language spoken in the audio sample.                            |
-| `license`      | `str`  | The license under which the original dataset is distributed.                |
+
+- `file_id` (`str`): A unique identifier of the audio sample.
+- `audio` (`dict`): A dictionary containing the file path to the audio sample, the raw waveform as a one-dimensional NumPy array, and the sampling rate (16 kHz).
+- `emotion` (`str`): A label indicating the expressed emotional state.
+- `transcription` (`str`): The orthographic transcription of the utterance.
+- `speaker_id` (`str`): A unique identifier of the speaker.
+- `gender` (`str`): The gender of the speaker.
+- `age` (`str`): The age of the speaker.
+- `dataset` (`str`): The name of the dataset from which the sample was taken.
+- `language` (`str`): The primary language spoken in the audio sample.
+- `license` (`str`): The license under which the original dataset is distributed.
 
 ## Data Splits