Update README.md

README.md

@@ -32,6 +32,16 @@ tags:
 task_categories:
 - automatic-speech-recognition
 task_ids: []
+extra_gated_prompt: |- 
+    Three of the ESC datasets have specific terms of usage that must be agreed to before using the data. 
+    To do so, fill in the access forms on the specific datasets' pages:
+      * Common Voice: https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0
+      * GigaSpeech: https://huggingface.co/datasets/speechcolab/gigaspeech
+      * SPGISpeech: https://huggingface.co/datasets/kensho/spgispeech
+extra_gated_fields:
+ I hereby confirm that I have registered on the original Common Voice page and agree to not attempt to determine the identity of speakers in the Common Voice dataset: checkbox
+ I hereby confirm that I have accepted the terms of usages on GigaSpeech page: checkbox
+ I hereby confirm that I have accepted the terms of usages on SPGISpeech page: checkbox
 ---
 
 All eight of datasets in ESC can be downloaded and prepared in just a single line of code through the Hugging Face Datasets library:
@@ -48,15 +58,8 @@ librispeech = load_dataset("esc-benchmark/esc-datasets", "librispeech", split="t
 
 -  `split="train"`: the split. Set this to one of train/validation/test to generate a specific split. Omit the `split` argument to generate all splits for a dataset.
 
-The datasets are fully prepared, such that the audio and transcription files can be used directly in training/evaluation scripts.
 
-For three datasets of the benchmark: GigaSpeech, SPGISpeech, and Librispeech we provide different configurations.
-You can load a specific configuration by passing it to the `"subconfig"` parameter: 
-
-```python
-librispeech = load_dataset("esc-benchmark/esc-datasets", "spgispeech", subconfig="s", split="train")
-```
-If you omit this parameter, the default configuration will be downloaded.
+The datasets are full prepared, such that the audio and transcription files can be used directly in training/evaluation scripts.
 
 
 ## Dataset Information
@@ -100,7 +103,13 @@ Note that when accessing the audio column: `dataset[0]["audio"]` the audio file
 #### Transcriptions
 The transcriptions corresponding to each audio file are provided in their 'error corrected' format. No transcription pre-processing is applied to the text, only necessary 'error correction' steps such as removing junk tokens (_<unk>_) or converting symbolic punctuation to spelled out form (_<comma>_  to  _,_). As such, no further preparation of the transcriptions is required to be used in training/evaluation scripts.
 
-Transcriptions are provided for training and validation splits. The transcriptions are **not** provided for the test splits. The ESC benchmark requires you to generate predictions for the test sets and upload them to https://huggingface.co/spaces/esc-benchmark/esc for scoring!
+Transcriptions are provided for training and validation splits. The transcriptions are **not** provided for the test splits. The ESC benchmark requires you to generate predictions for the test sets and upload them to https://huggingface.co/spaces/esc-benchmark/esc for scoring.
+
+### Access
+All eight of the datasets in ESC are accessible and licensing is freely available. Three of the ESC datasets have specific terms of usage that must be agreed to before using the data. To do so, fill in the access forms on the specific datasets' pages:
+* Common Voice: https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0
+* GigaSpeech: https://huggingface.co/datasets/speechcolab/gigaspeech
+* SPGISpeech: https://huggingface.co/datasets/kensho/spgispeech
 
 ## LibriSpeech
 
@@ -110,8 +119,6 @@ Example Usage:
 
 ```python
 librispeech = load_dataset("esc-benchmark/esc-datasets", "librispeech")
-librispeech = load_dataset("esc-benchmark/esc-datasets", "librispeech", split="train")
-librispeech = load_dataset("esc-benchmark/esc-datasets", "librispeech", split="validation.clean")
 ```
 
 Train/validation splits:
@@ -123,22 +130,22 @@ Test splits:
 - `test.clean`
 - `test.other`
 
-Also available are subsets of the train split:
+Also available are subsets of the train split, which can be accessed by setting the `subconfig` argument:
+```python
+librispeech = load_dataset("esc-benchmark/esc-datasets", "librispeech", subconfig="clean.100")
+```
+
 - `clean.100`: 100 hours of training data from the 'clean' subset
 - `clean.360`: 360 hours of training data from the 'clean' subset
 - `other.500`: 500 hours of training data from the 'other' subset
 
-```python
-librispeech = load_dataset("esc-benchmark/esc-datasets", "librispeech", subconfig="clean.100", split="train")
-```
-
 ## Common Voice
 Common Voice is a series of crowd-sourced open-licensed speech datasets where speakers record text from Wikipedia in various languages. The English subset of contains approximately 1,400 hours of audio data from speakers of various nationalities, accents and different recording conditions. It is licensed under CC0-1.0.
 
 Example usage:
 
 ```python
-common_voice = load_dataset("esc-benchmark/esc-datasets", "common_voice")
+common_voice = load_dataset("esc-benchmark/esc-datasets", "common_voice", use_auth_token=True)
 ```
 
 Training/validation splits:
@@ -164,7 +171,6 @@ Training/validation splits:
 Test splits:
 - `test`
 
-
 ## TED-LIUM
 TED-LIUM consists of English-language TED Talk conference videos covering a range of different cultural, political, and academic topics. It contains approximately 450 hours of transcribed speech data. It is licensed under CC-BY-NC-ND 3.0.
 
@@ -187,52 +193,49 @@ GigaSpeech is a multi-domain English speech recognition corpus created from audi
 Example usage:
 
 ```python
-gigaspeech = load_dataset("esc-benchmark/esc-datasets", "gigaspeech")
+gigaspeech = load_dataset("esc-benchmark/esc-datasets", "gigaspeech", use_auth_token=True)
 ```
 
 Training/validation splits:
-- `train` (`l` subset)
+- `train` (`l` subset of training data (2,500 h))
 - `validation`
 
 Test splits:
 - `test`
 
-Also available are subsets of the train split:
+Also available are subsets of the train split, which can be accessed by setting the `subconfig` argument:
+```python
+gigaspeech = load_dataset("esc-benchmark/esc-datasets", "spgispeech", subconfig="xs", use_auth_token=True)
+```
 - `xs`: extra-small subset of training data (10 h)
 - `s`: small subset of training data (250 h)
 - `m`: medium subset of training data (1,000 h)
 - `xl`: extra-large subset of training data (10,000 h)
 
-You can load GigaSpeech with a specific train subset using `subconfig` parameter:
-```python
-gigaspeech = load_dataset("esc-benchmark/esc-datasets", "gigaspeech", subconfig="xs")
-```
-
-
 ## SPGISpeech
 SPGISpeech consists of company earnings calls that have been manually transcribed by S&P Global, Inc according to a professional style guide. We provide the large train set (5,000 hours) and the standard validation and test splits. It is licensed under a Kensho user agreement.
 
+Loading the dataset requires authorization.
+
 Example usage:
 
 ```python
-spgispeech = load_dataset("esc-benchmark/esc-datasets", "spgispeech")
+spgispeech = load_dataset("esc-benchmark/esc-datasets", "spgispeech", use_auth_token=True)
 ```
 
 Training/validation splits:
-- `train` (`l` subset)
+- `train` (`l` subset of training data (~5,000 h))
 - `validation`
 
 Test splits:
 - `test`
 
-Also available are subsets of the train split:
-- `s`: small subset of training data (~200 h)
-- `m`: medium subset of training data (~1,000 h)
-
-You can load GigaSpeech with a specific train subset using `subconfig` parameter:
+Also available are subsets of the train split, which can be accessed by setting the `subconfig` argument:
 ```python
-gigaspeech = load_dataset("esc-benchmark/esc-datasets", "spgispeech", subconfig="s")
+spgispeech = load_dataset("esc-benchmark/esc-datasets", "spgispeech", subconfig="s", use_auth_token=True)
 ```
+- `s`: small subset of training data (~200 h)
+- `m`: medium subset of training data (~1,000 h)
 
 
 ## Earnings-22