Update README.md

README.md

@@ -1,8 +1,4 @@
 ---
-license: cc-by-4.0
-task_categories:
-- text-to-speech
-- automatic-speech-recognition
 language:
 - af  
 - bag  
@@ -52,8 +48,12 @@ language:
 - ybb  
 - yor  
 - zul  
+license: cc-by-4.0
+task_categories:
+- text-to-speech
+- automatic-speech-recognition
 
-
+---
 
 # SOREVA
 
@@ -100,8 +100,9 @@ from datasets import load_dataset
 dataset = load_dataset("OlameMend/soreva", "all", split="test")
 
 ```
+### 1. Out-of-domain TTS & ASR model
 
-### 🎧 Getting Audio and Transcription 
+#🎧 Getting Audio and Transcription 
 
 You can easily access and listen to audio samples along with their transcriptions:
 
@@ -116,13 +117,13 @@ soreva = load_dataset("OlameMend/soreva", "ha_ng", split='test' , trust_remote_c
 audio_array = soreva[0]['audio']['array']        # audio data as numpy array
 sr = soreva[0]['audio']['sampling_rate']         # sampling rate
 
-# Print the corresponding transcription
+# Print the corresponding transcription or use it for TTS inference for evaluation
 print(soreva[0]['transcription'])
 
 # Play the audio in a Jupyter notebook
 Audio(audio_array, rate=sr)
 ```
-thele
+
 ## Dataset Structure
 
 We show detailed information the example configurations `ewo_cm` of the dataset.
@@ -131,11 +132,10 @@ All other configurations have the same structure.
 ### Data Instances
 
 **ewo_cm**
-- Size of downloaded dataset files: 1.47 GB
-- Size of the generated dataset: 1 MB
-- Total amount of disk used: 1.47 GB
+- Size of downloaded dataset files: 14 MB
+
 
-An example of a data instance of the config `af_za` looks as follows:
+An example of a data instance of the config `ewo_cm` looks as follows:
 
 ```
 {'path': '/home/mendo/.cache/huggingface/datasets/downloads/extracted/3f773a931d09d3c4f9e9a8643e93d191a30d36df95ae32eedbafb6a634135f98/cm_ewo_001.wav',
@@ -183,9 +183,11 @@ The data were collected by the Goethe-Institut and consist of 150 audio samples
 This dataset is meant to encourage the development of speech technology in a lot more languages of the world. One of the goal is to give equal access to technologies like speech recognition or speech translation to everyone, meaning better dubbing or better access to content from the internet (like podcasts, streaming or videos).
 
 ### Discussion of Biases
+For all languages, only male voice is represented
 
 
 ### Other Known Limitations
+Certains transcript only contain single word instead of complete sentence; others line of transcription has deux sentences(variance) for the same audio
 
 
 ## Additional Information