Datasets:

mozilla-foundation

/

common_voice_11_0

like 151

In general this looks very nice, happy with the overall structure. Leaving some quite specific points just so that we can polish it off!

• Under "How to Use", I would first explain that all dataset loading and pre-processing is done through the datasets library and pure python functions -> we straight away break out of the typical Kaldi/C++ norms (which is our big selling point!)
• Next line about the dataset being downloaded by load_dataset function is great 👍
• Would explain how splits relate to langs:

- The entire dataset (or a particular split) can be downloaded to your local drive by using the `load_dataset` function.
+ The entire dataset (or a particular split) can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. To download the Hindi split, simply specify the corresponding dataset config name:

• (nit!) Would avoid caps in variable names

- CV_11_hi_train = load_dataset("mozilla-foundation/common_voice_11_0", "hi", split="train")
+ cv_11 = load_dataset("mozilla-foundation/common_voice_11_0", "hi", split="train")

• "iterating" over the dataset might be confusing without explaining what this means - I understand you but a new-comer might not! Would go up one level of abstraction and just say we load individual samples at a time

- Loading a dataset in streaming mode allows one to iterate over the dataset without downloading it on disk.
+ Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk. Storage...

• Maybe we can link the audio datasets blog here? "To find out more about loading and preparing audio datasets, ..."
• Like the data loader section a lot! Think this is great for integrating into other libraries/training scripts
• Suggestion: let's make it specific to CV11!

- Train your own CTC or Seq2Seq Automatic Speech Recognition models with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition).
+ Train your own CTC or Seq2Seq Automatic Speech Recognition models on Common Voice 11 with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition).

Love it! The snippets for data loaders are awesome. Also like using not English language in examples - btw maybe clarify it before the snippet, smth like "To load a Hindi subset of CV ...".

+1 for @sanchit-gandhi idea to link an audio datasets blog (https://huggingface.co/blog/audio-datasets)

Thank You! @sanchit-gandhi & @polinaeterna - I incorporated both of your changes. If you're okay with it then please feel free to merge it. 🤗

You're a writer at heart @reach-vb ! Think here we can trim it down for the final version to keep it streamlined 🐬

- The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. No need to rely on decades old, hacky shell scripts and C/C++ pre-processing scripts anymore. The minimalistic API ensures that you can plug-and-play this dataset in your existing Machine Learning workflow, with just a few lines of code.
+ The `datasets` library allows you to load and pre-process your dataset in pure Python. The entire dataset (or a particular split) ...

Thank You! @sanchit-gandhi - committed the suggestion! :)

Hi @anton-l - Can I please request you to merge this PR? :)

Sorry! @anton-l , you can ignore this request. @polinaeterna will take care of the merging and she has couple of suggestions for improvement here.

@reach-vb just two suggestions from my side:

line 379:

- The entire dataset (or a particular split) can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. 
+ The dataset can be downloaded and prepared to your local drive in one call by using the `load_dataset` function. 

I'd better say like this because for now even when providing split as a parameter, load_dataset triggers download (but not prepare) of the full config data, this is the library limitation. I'd better not confuse users here, this is a known pretty annoying issue, see for example https://github.com/huggingface/datasets/issues/5243. (btw I think I have an idea of how can we mitigate this in the library itself). Also suggest to swap "to your local drive" and "in one call", just for clearness.

line 381:

- For example, to download the Hindi split, simply specify the corresponding language config name (i.e., "hi" for Hindi):
+ For example, to download the Hindi config, simply specify the corresponding language config name (i.e., "hi" for Hindi):

• not to confuse the two concepts - of splits and of configs.

and we can definitely merge then :)

Brilliant point @polinaeterna ! I just made the update. If you are okay with it then feel free to merge :)

great! thank you @reach-vb 💙