Datasets:
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Browse files- .gitignore +2 -0
- README.md +39 -0
- data/1_PearlRiver.zip +3 -0
- data/2_YoungChang.zip +3 -0
- data/3_Steinway-T.zip +3 -0
- data/4_Hsinghai.zip +3 -0
- data/5_Kawai.zip +3 -0
- data/6_Steinway.zip +3 -0
- data/7_Kawai-G.zip +3 -0
- data/8_Yamaha.zip +3 -0
- piano_sound_quality.py +131 -0
.gitignore
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rename.sh
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test.py
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README.md
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---
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license: mit
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---
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---
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license: mit
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---
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# Dataset Card for "george-chou/pianos_wav"
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## Requirements
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```
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python 3.8-3.10
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soundfile
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librosa
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```
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## Usage
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```
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from datasets import load_dataset
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data = load_dataset("george-chou/pianos_wav", split="train")
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labels = data.features['label'].names
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for item in data:
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print('audio info: ', item['audio'])
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print('label name: ' + labels[item['label']])
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```
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## Maintenance
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```
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git clone git@hf.co:datasets/george-chou/pianos_wav
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```
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## Cite
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```
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@dataset{zhaorui_liu_2021_5676893,
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author = {Zhaorui Liu and Zijin Li},
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title = {{Music Data Sharing Platform for Computational Musicology Research (CCMUSIC DATASET)}},
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month = nov,
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year = 2021,
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publisher = {Zenodo},
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version = {1.1},
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doi = {10.5281/zenodo.5676893},
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url = {https://doi.org/10.5281/zenodo.5676893}
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}
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```
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data/1_PearlRiver.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:ec84513e69aa983dd60665d6e355ab3417802c4968de03a59bc5fd4f9edffea2
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size 16001626
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data/2_YoungChang.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:91b180cc693177597cc3515e4956a22ab671bc3dd5de43df1bba588b725ebc30
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size 99240728
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data/3_Steinway-T.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:1c5b66552d1ec1f98b5580f7eb93fac0850bea9c740279c778a0a7f4a24e830e
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size 61769871
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data/4_Hsinghai.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:7ef4acb8695fe80fbf9910059da3352c0818c63b761f1c44ffe1fba3a2198227
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size 35421121
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data/5_Kawai.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:af45d21ce74a607a769fc8edeca82175415a42f70df19fbd0a14731e5efbbe6a
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size 38260629
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data/6_Steinway.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:a5b83de1f95d94b5f23b2636cdda1c5feb78fa576cf1e5f12827e48487c4b205
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size 35046320
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data/7_Kawai-G.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:8a51c08b58868d57a2aef708580507a63d16302133afa314472d72470fc25139
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size 19742382
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data/8_Yamaha.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:3a2ff74144de97c169edcacc77288fdc77fed2dc75a509899bb7b744be734332
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size 46875645
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piano_sound_quality.py
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import io
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import os
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import wave
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import zipfile
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import datasets
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import requests
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from datasets.tasks import AudioClassification
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# Once upload a new piano brand, please register its name here
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_NAMES = [
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"1_PearlRiver",
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"2_YoungChang",
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"3_Steinway-T",
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"4_Hsinghai",
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"5_Kawai",
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"6_Steinway",
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"7_Kawai-G",
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"8_Yamaha",
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]
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_DBNAME = os.path.basename(__file__).split('.')[0]
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_HOMEPAGE = "https://huggingface.co/datasets/ccmusic-database/" + _DBNAME
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_CITATION = """\
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@dataset{zhaorui_liu_2021_5676893,
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author = {Zhaorui Liu and Zijin Li},
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title = {{Music Data Sharing Platform for Computational Musicology Research (CCMUSIC DATASET)}},
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month = nov,
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year = 2021,
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publisher = {Zenodo},
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version = {1.1},
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doi = {10.5281/zenodo.5676893},
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url = {https://doi.org/10.5281/zenodo.5676893}
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}
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"""
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_DESCRIPTION = """\
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Piano-Sound-Quality-Database is a dataset of piano sound.
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It consists of 8 kinds of pianos including
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PearlRiver, YoungChang, Steinway-T, Hsinghai, Kawai, Steinway, Kawai-G, Yamaha.
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Data was annotated by students from the China Conservatory of Music (CCMUSIC) in Beijing
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and collected by George Chou.
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"""
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_URLS = {piano: _HOMEPAGE + "/resolve/main/data/" +
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piano + ".zip" for piano in _NAMES}
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_PITCHES = {"009": "A2", "010": "A2#/B2b", "011": "B2", "100": "C1", "101": "C1#/D1b", "102": "D1", "103": "D1#/E1b",
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"104": "E1", "105": "F1", "106": "F1#/G1b", "107": "G1", "108": "G1#/A1b", "109": "A1", "110": "A1#/B1b",
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"111": "B1", "200": "C", "201": "C#/Db", "202": "D", "203": "D#/Eb", "204": "E", "205": "F", "206": "F#/Gb",
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"207": "G", "208": "G#/Ab", "209": "A", "210": "A#/Bb", "211": "B", "300": "c", "301": "c#/db", "302": "d",
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"303": "d#/eb", "304": "e", "305": "f", "306": "f#/gb", "307": "g", "308": "g#/ab", "309": "a", "310": "a#/bb",
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"311": "b", "400": "c1", "401": "c1#/d1b", "402": "d1", "403": "d1#/e1b", "404": "e1", "405": "f1",
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"406": "f1#/g1b", "407": "g1", "408": "g1#/a1b", "409": "a1", "410": "a1#/b1b", "411": "b1", "500": "c2",
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"501": "c2#/d2b", "502": "d2", "503": "d2#/e2b", "504": "e2", "505": "f2", "506": "f2#/g2b", "507": "g2",
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"508": "g2#/a2b", "509": "a2", "510": "a2#/b2b", "511": "b2", "600": "c3", "601": "c3#/d3b", "602": "d3",
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"603": "d3#/e3b", "604": "e3", "605": "f3", "606": "f3#/g3b", "607": "g3", "608": "g3#/a3b", "609": "a3",
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"610": "a3#/b3b", "611": "b3", "700": "c4", "701": "c4#/d4b", "702": "d4", "703": "d4#/e4b", "704": "e4",
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"705": "f4", "706": "f4#/g4b", "707": "g4", "708": "g4#/a4b", "709": "a4", "710": "a4#/b4b", "711": "b4",
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"800": "c5"}
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class piano_sound_quality(datasets.GeneratorBasedBuilder):
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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"audio": datasets.Audio(sampling_rate=44_100),
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"label": datasets.features.ClassLabel(names=_NAMES),
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"pitch": datasets.Value("string"),
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"duration": datasets.Value("float64"),
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}
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),
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supervised_keys=("audio", "label"),
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homepage=_HOMEPAGE,
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license="mit",
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citation=_CITATION,
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task_templates=[
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AudioClassification(
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task="audio-classification",
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audio_column="audio",
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label_column="label",
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)
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],
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)
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def _get_wav_duration(self, file_bytes):
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with wave.open(io.BytesIO(file_bytes), 'r') as wav_file:
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frames = wav_file.getnframes()
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rate = wav_file.getframerate()
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duration = frames / float(rate)
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return round(duration, 3)
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def _read_zip(self, zip_url, wav_file_path):
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resp = requests.get(zip_url)
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with zipfile.ZipFile(io.BytesIO(resp.content)) as zip_file:
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with zip_file.open(wav_file_path) as file:
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file_data = file.read()
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return self._get_wav_duration(file_data)
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def _split_generators(self, dl_manager):
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data_files = dl_manager.download_and_extract(_URLS)
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split_generator = []
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for index in _URLS.keys():
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split_generator.append(
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datasets.SplitGenerator(
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name=index.replace('-', '_'),
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gen_kwargs={
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"files": dl_manager.iter_files([data_files[index]]),
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},
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)
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)
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return split_generator
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def _generate_examples(self, files):
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for i, path in enumerate(files):
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file_name = os.path.basename(path)
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if file_name.endswith(".wav"):
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yield i, {
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"audio": path,
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"label": os.path.basename(os.path.dirname(path)),
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"pitch": _PITCHES[file_name[1:4]],
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"duration": self._read_zip(path.split('::')[1], path.split('::')[0].split('//')[1]),
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}
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