Datasets:
Add Parquet files (#2)
Browse files- Rename loader script (618096d8057a6aba10c1119e018c277cedb67715)
- Upload parquet/all (39c588fc7824d08c980a01c48d2502b5a461550a)
- Upload parquet/en (f779a4245bb273d4e4a82306fd481259bcf35e14)
- Upload parquet/es (107fa1fd88788f8e99b3ac1d567e0c5d968b4405)
- Upload parquet/de (53e3e9440d26e82ab8c7459fd06cf53088187865)
- Upload parquet/fr (25c746f96e62b118228bb59b593e14114e8b7dcf)
- Add push_to_hub.py (27752fa76f63e95191296428424aa339676a3200)
- Update README.md (5b65248c8862079c7d19f3765abbb945aa6f5755)
- Version 1.1.0 (db8d62101bfc3d833dcfb2a5fbebb33cd3110bb5)
README.md
CHANGED
@@ -16,6 +16,119 @@ tags:
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- transcription
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pretty_name: 'JamALT: A Readability-Aware Lyrics Transcription Benchmark'
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paperswithcode_id: jam-alt
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---
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# JamALT: A Readability-Aware Lyrics Transcription Benchmark
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@@ -42,17 +155,18 @@ See the [project website](https://audioshake.github.io/jam-alt/) for details.
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```python
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from datasets import load_dataset
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-
dataset = load_dataset("audioshake/jam-alt"
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```
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A subset is defined for each language (`en`, `fr`, `de`, `es`);
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for example, use `load_dataset("audioshake/jam-alt", "es")` to load only the Spanish songs.
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-
By default, the dataset comes with audio. To skip loading the audio, use `with_audio=False`.
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To control how the audio is decoded, cast the `audio` column using `dataset.cast_column("audio", datasets.Audio(...))`.
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Useful arguments to `datasets.Audio()` are:
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- `sampling_rate` and `mono=True` to control the sampling rate and number of channels.
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-
- `decode=False` to skip decoding the audio and just get the MP3 file paths.
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## Running the benchmark
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@@ -61,14 +175,14 @@ The evaluation is implemented in our [`alt-eval` package](https://github.com/aud
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from datasets import load_dataset
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from alt_eval import compute_metrics
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dataset = load_dataset("audioshake/jam-alt", revision="v1.
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# transcriptions: list[str]
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compute_metrics(dataset["text"], transcriptions, languages=dataset["language"])
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```
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For example, the following code can be used to evaluate Whisper:
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```python
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dataset = load_dataset("audioshake/jam-alt", revision="v1.
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dataset = dataset.cast_column("audio", datasets.Audio(decode=False)) # Get the raw audio file, let Whisper decode it
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model = whisper.load_model("tiny")
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]
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compute_metrics(dataset["text"], transcriptions, languages=dataset["language"])
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```
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-
Alternatively, if you already have transcriptions, you might prefer to skip loading the audio:
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```python
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-
dataset = load_dataset("audioshake/jam-alt", revision="v1.
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```
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## Citation
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@@ -108,4 +222,4 @@ When using the benchmark, please cite [our paper](https://www.arxiv.org/abs/2408
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address={Rhodes Island, Greece},
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doi={10.1109/ICASSP49357.2023.10096725}
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}
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-
```
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- transcription
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pretty_name: 'JamALT: A Readability-Aware Lyrics Transcription Benchmark'
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paperswithcode_id: jam-alt
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+
dataset_info:
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- config_name: all
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features:
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- name: name
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dtype: string
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- name: text
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dtype: string
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+
- name: language
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dtype: string
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- name: license_type
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dtype: string
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- name: audio
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dtype: audio
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splits:
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- name: test
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num_bytes: 409411912.0
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num_examples: 79
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download_size: 409150043
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dataset_size: 409411912.0
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- config_name: de
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features:
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- name: name
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dtype: string
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- name: text
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+
dtype: string
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+
- name: language
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+
dtype: string
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+
- name: license_type
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+
dtype: string
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+
- name: audio
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dtype: audio
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+
splits:
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+
- name: test
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+
num_bytes: 107962802.0
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+
num_examples: 20
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+
download_size: 107942102
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+
dataset_size: 107962802.0
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+
- config_name: en
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features:
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- name: name
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dtype: string
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- name: text
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dtype: string
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+
- name: language
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dtype: string
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- name: license_type
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dtype: string
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- name: audio
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dtype: audio
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splits:
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+
- name: test
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+
num_bytes: 105135091.0
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+
num_examples: 20
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+
download_size: 105041371
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dataset_size: 105135091.0
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- config_name: es
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features:
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- name: name
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dtype: string
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- name: text
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dtype: string
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+
- name: language
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dtype: string
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- name: license_type
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dtype: string
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- name: audio
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dtype: audio
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splits:
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- name: test
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num_bytes: 105024257.0
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num_examples: 20
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download_size: 104979012
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dataset_size: 105024257.0
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- config_name: fr
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features:
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- name: name
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dtype: string
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+
- name: text
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+
dtype: string
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+
- name: language
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+
dtype: string
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+
- name: license_type
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+
dtype: string
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+
- name: audio
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dtype: audio
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+
splits:
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+
- name: test
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+
num_bytes: 91289764.0
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+
num_examples: 19
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download_size: 91218543
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dataset_size: 91289764.0
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+
configs:
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- config_name: all
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data_files:
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- split: test
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path: parquet/all/test-*
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default: true
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- config_name: de
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data_files:
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- split: test
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path: parquet/de/test-*
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- config_name: en
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data_files:
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- split: test
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path: parquet/en/test-*
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- config_name: es
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data_files:
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- split: test
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path: parquet/es/test-*
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- config_name: fr
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data_files:
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- split: test
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path: parquet/fr/test-*
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---
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133 |
|
134 |
# JamALT: A Readability-Aware Lyrics Transcription Benchmark
|
|
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155 |
|
156 |
```python
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157 |
from datasets import load_dataset
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158 |
+
dataset = load_dataset("audioshake/jam-alt", split="test")
|
159 |
```
|
160 |
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161 |
A subset is defined for each language (`en`, `fr`, `de`, `es`);
|
162 |
for example, use `load_dataset("audioshake/jam-alt", "es")` to load only the Spanish songs.
|
163 |
|
|
|
164 |
To control how the audio is decoded, cast the `audio` column using `dataset.cast_column("audio", datasets.Audio(...))`.
|
165 |
Useful arguments to `datasets.Audio()` are:
|
166 |
- `sampling_rate` and `mono=True` to control the sampling rate and number of channels.
|
167 |
+
- `decode=False` to skip decoding the audio and just get the MP3 file paths and contents.
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168 |
+
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169 |
+
The `load_dataset` function also accepts a `columns` parameter, which can be useful for example if you want to skip downloading the audio (see the example below).
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## Running the benchmark
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172 |
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from datasets import load_dataset
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from alt_eval import compute_metrics
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dataset = load_dataset("audioshake/jam-alt", revision="v1.1.0", split="test")
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# transcriptions: list[str]
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compute_metrics(dataset["text"], transcriptions, languages=dataset["language"])
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```
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For example, the following code can be used to evaluate Whisper:
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```python
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dataset = load_dataset("audioshake/jam-alt", revision="v1.1.0", split="test")
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dataset = dataset.cast_column("audio", datasets.Audio(decode=False)) # Get the raw audio file, let Whisper decode it
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model = whisper.load_model("tiny")
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]
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compute_metrics(dataset["text"], transcriptions, languages=dataset["language"])
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```
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+
Alternatively, if you already have transcriptions, you might prefer to skip loading the `audio` column:
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```python
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dataset = load_dataset("audioshake/jam-alt", revision="v1.1.0", split="test", columns=["name", "text", "language", "license_type"])
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```
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## Citation
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address={Rhodes Island, Greece},
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doi={10.1109/ICASSP49357.2023.10096725}
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}
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```
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jam-alt.py → loader.py
RENAMED
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import datasets
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_VERSION = "1.
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_CITATION = """\
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import datasets
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_VERSION = "1.1.0"
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_CITATION = """\
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parquet/all/test-00000-of-00001.parquet
ADDED
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:ed027d2032652b082c79b63008eecea1dacce97400ce1d5cb0326e958302d6e1
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+
size 409150043
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parquet/de/test-00000-of-00001.parquet
ADDED
@@ -0,0 +1,3 @@
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+
version https://git-lfs.github.com/spec/v1
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+
oid sha256:d4fe32692f20daeb06b2b3c253b51dbd5bc4a4c6bb491a64fde821d263d95134
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+
size 107942102
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parquet/en/test-00000-of-00001.parquet
ADDED
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:6c8870334ca9ff4a5166940355169ee36a260725c782a23bd352948d38c83f70
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+
size 105041371
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parquet/es/test-00000-of-00001.parquet
ADDED
@@ -0,0 +1,3 @@
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+
version https://git-lfs.github.com/spec/v1
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+
oid sha256:96468681e62d4f69729c2d33890bfc17caf9718b4f56cd843ab1468a4c72c8a1
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+
size 104979012
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parquet/fr/test-00000-of-00001.parquet
ADDED
@@ -0,0 +1,3 @@
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+
version https://git-lfs.github.com/spec/v1
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2 |
+
oid sha256:935eb807ce731d44dfc904a1b8f2dd751b626e8c40774d56ee793b799ec987e0
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+
size 91218543
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push_to_hub.py
ADDED
@@ -0,0 +1,26 @@
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import argparse
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import datasets
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--repo", type=str, required=True)
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parser.add_argument("--revision", type=str, required=True)
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args = parser.parse_args()
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for config_name in ["all", "en", "es", "de", "fr"]:
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dataset = datasets.load_dataset(
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"./loader.py", config_name, trust_remote_code=True
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)
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dataset.push_to_hub(
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args.repo,
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config_name,
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set_default=(config_name == "all"),
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data_dir=f"parquet/{config_name}",
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commit_message=f"Upload parquet/{config_name}",
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revision=args.revision,
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)
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if __name__ == "__main__":
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main()
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