Enrich model cards with statistics and tts use

README.md

@@ -24,6 +24,8 @@ source_datasets:
 - original
 task_categories:
 - automatic-speech-recognition
+- text-to-speech
+- text-to-audio
 ---
 
 # Dataset Card for MultiLingual LibriSpeech
@@ -71,6 +73,7 @@ MLS dataset is a large multilingual corpus suitable for speech research. The dat
 ### Supported Tasks and Leaderboards
 
 - `automatic-speech-recognition`, `speaker-identification`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at https://paperswithcode.com/dataset/multilingual-librispeech and ranks models based on their WER.
+- `text-to-speech`, `text-to-audio`: The dataset can also be used to train a model for Text-To-Speech (TTS).
 
 ### Languages
 
@@ -160,7 +163,7 @@ A typical data point comprises the path to the audio file, usually called `file`
 
 ### Data Splits
 
-|                             | Train | Train.9h | Train.1h  | Dev | Test |
+|           Number of samples                  | Train | Train.9h | Train.1h  | Dev | Test |
 | -----                       | ------ | ----- | ---- | ---- | ---- | 
 | german | 469942 | 2194 | 241 | 3469 | 3394 |
 | dutch | 374287 | 2153 | 234 | 3095 | 3075 |
@@ -170,8 +173,6 @@ A typical data point comprises the path to the audio file, usually called `file`
 | portuguese | 37533 | 2116 | 236 | 826 | 871 |
 | polish | 25043 | 2173 | 238 | 512 | 520 |
 
-
-
 ## Dataset Creation
 
 ### Curation Rationale
@@ -238,7 +239,48 @@ Public Domain, Creative Commons Attribution 4.0 International Public License ([C
 }
 ```
 
+
+### Data Statistics
+
+| Duration (h) | Train     | Dev   | Test  |
+|--------------|-----------|-------|-------|
+| English      | 44,659.74 | 15.75 | 15.55 |
+| German       | 1,966.51  | 14.28 | 14.29 |
+| Dutch        | 1,554.24  | 12.76 | 12.76 |
+| French       | 1,076.58  | 10.07 | 10.07 |
+| Spanish      | 917.68    | 9.99  | 10    |
+| Italian      | 247.38    | 5.18  | 5.27  |
+| Portuguese   | 160.96    | 3.64  | 3.74  |
+| Polish       | 103.65    | 2.08  | 2.14  |
+
+| # Speakers | Train |      | Dev |    | Test |    |
+|------------|-------|------|-----|----|------|----|
+|       Gender   | M     | F    | M   | F  | M    | F  |
+| English    | 2742  | 2748 | 21  | 21 | 21   | 21 |
+| German     | 81    | 95   | 15  | 15 | 15   | 15 |
+| Dutch      | 9     | 31   | 3   | 3  | 3    | 3  |
+| French     | 62    | 80   | 9   | 9  | 9    | 9  |
+| Spanish    | 36    | 50   | 10  | 10 | 10   | 10 |
+| Italian    | 22    | 43   | 5   | 5  | 5    | 5  |
+| Portuguese | 26    | 16   | 5   | 5  | 5    | 5  |
+| Polish     | 6     | 5    | 2   | 2  | 2    | 2  |
+
+| # Hours / Gender | Dev  |      | Test |      |
+|------------------|------|------|------|------|
+|       Gender   | M    | F    | M    | F    |
+| English          | 7.76 | 7.99 | 7.62 | 7.93 |
+| German           | 7.06 | 7.22 | 7    | 7.29 |
+| Dutch            | 6.44 | 6.32 | 6.72 | 6.04 |
+| French           | 5.13 | 4.94 | 5.04 | 5.02 |
+| Spanish          | 4.91 | 5.08 | 4.78 | 5.23 |
+| Italian          | 2.5  | 2.68 | 2.38 | 2.9  |
+| Portuguese       | 1.84 | 1.81 | 1.83 | 1.9  |
+| Polish           | 1.12 | 0.95 | 1.09 | 1.05 |
+
+
+
+
 ### Contributions
 
 Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) 
-and [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.
+and [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.