up

README.md

@@ -959,16 +959,16 @@ crowdsourced voice recordings. There are 2,900 hours of speech represented in th
 
 The dataset contains the audio, transcriptions, and translations in the following languages, French, German, Dutch, Russian, Spanish, Italian, Turkish, Persian, Swedish, Mongolian, Chinese, Welsh, Catalan, Slovenian, Estonian, Indonesian, Arabic, Tamil, Portuguese, Latvian, and Japanese. 
 
-### How to use
+## How to use
 The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. 
 
-For example, to download the English-German config, simply specify the corresponding language config name (i.e., "en_de" for German):
+For example, to download the English-Turkish config, simply specify the corresponding language config name (i.e., "en_tr" for English to Turkish):
 ```python
 from datasets import load_dataset
 
-covost2 = load_dataset("covost2", "en_de", split="train")
+covost2 = load_dataset("covost2", "en_tr", data_dir="<path/to/manual/data>, split="train")
 ```
-Note: For a successful load, you'd first need to download the Common Voice 4.0 `en` split from the Hugging Face Hub. You can download it via `cv4 = load_dataset("mozilla-foundation/common_voice_4_0", "en", split="all")`.
+Note: For a successful load, you'd first need to download the Common Voice 4.0 `en` split from the Hugging Face Hub. You can download it via `cv4 = load_dataset("mozilla-foundation/common_voice_4_0", "en", split="all")`. Upon successful download pass the location of the CV 4.0 dataset in the `data_dir` argument of the CoVoST2 `load_dataset` call.
 
 *Bonus*: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets.
 
@@ -976,7 +976,7 @@ Note: For a successful load, you'd first need to download the Common Voice 4.0 `
 from datasets import load_dataset
 from torch.utils.data.sampler import BatchSampler, RandomSampler
 
-covost2 = load_dataset("covost2", "en_de", split="train")
+covost2 = load_dataset("covost2", "en_tr", data_dir="<path/to/manual/data>, split="train")
 batch_sampler = BatchSampler(RandomSampler(covost2), batch_size=32, drop_last=False)
 dataloader = DataLoader(covost2, batch_sampler=batch_sampler)
 ```