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"""HuggingFace loading script for the JamALT dataset.""" |
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import csv |
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from dataclasses import dataclass |
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import json |
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import os |
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from pathlib import Path |
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from typing import Optional |
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import datasets |
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_VERSION = "1.0.0" |
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_CITATION = """\ |
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@misc{cifka-2023-jam-alt, |
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author = {Ond\v{r}ej C\'ifka and |
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Constantinos Dimitriou and |
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{Cheng-i} Wang and |
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Hendrik Schreiber and |
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Luke Miner and |
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Fabian-Robert St\"oter}, |
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title = {{Jam-ALT}: A Formatting-Aware Lyrics Transcription Benchmark}, |
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eprint = {arXiv:2311.13987}, |
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year = 2023 |
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} |
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@inproceedings{durand-2023-contrastive, |
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author={Durand, Simon and Stoller, Daniel and Ewert, Sebastian}, |
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booktitle={2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
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title={Contrastive Learning-Based Audio to Lyrics Alignment for Multiple Languages}, |
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year={2023}, |
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pages={1-5}, |
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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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_DESCRIPTION = """\ |
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Jam-ALT: A formatting-aware lyrics transcription benchmark. |
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""" |
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_HOMEPAGE = "https://audioshake.github.io/jam-alt" |
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_METADATA_FILENAME = "metadata.csv" |
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_LANGUAGE_NAME_TO_CODE = { |
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"English": "en", |
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"French": "fr", |
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"German": "de", |
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"Spanish": "es", |
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} |
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@dataclass |
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class JamAltBuilderConfig(datasets.BuilderConfig): |
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language: Optional[str] = None |
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with_audio: bool = True |
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decode_audio: bool = True |
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sampling_rate: Optional[int] = None |
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mono: bool = True |
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class JamAltDataset(datasets.GeneratorBasedBuilder): |
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_DESCRIPTION |
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VERSION = datasets.Version(_VERSION) |
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BUILDER_CONFIG_CLASS = JamAltBuilderConfig |
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BUILDER_CONFIGS = [JamAltBuilderConfig("all")] + [ |
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JamAltBuilderConfig(lang, language=lang) |
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for lang in _LANGUAGE_NAME_TO_CODE.values() |
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] |
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DEFAULT_CONFIG_NAME = "all" |
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def _info(self): |
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feat_dict = { |
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"name": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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"language": datasets.Value("string"), |
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"license_type": datasets.Value("string"), |
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} |
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if self.config.with_audio: |
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feat_dict["audio"] = datasets.Audio( |
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decode=self.config.decode_audio, |
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sampling_rate=self.config.sampling_rate, |
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mono=self.config.mono, |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features(feat_dict), |
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supervised_keys=("audio", "text") if "audio" in feat_dict else None, |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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metadata_path = dl_manager.download(_METADATA_FILENAME) |
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audio_paths, text_paths, metadata = [], [], [] |
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with open(metadata_path, encoding="utf-8") as f: |
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for row in csv.DictReader(f): |
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if ( |
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self.config.language is None |
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or _LANGUAGE_NAME_TO_CODE[row["Language"]] == self.config.language |
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): |
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audio_paths.append("audio/" + row["Filepath"]) |
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text_paths.append( |
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"lyrics/" + os.path.splitext(row["Filepath"])[0] + ".txt" |
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) |
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metadata.append(row) |
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text_paths = dl_manager.download(text_paths) |
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audio_paths = ( |
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dl_manager.download(audio_paths) if self.config.with_audio else None |
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) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs=dict( |
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text_paths=text_paths, |
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audio_paths=audio_paths, |
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metadata=metadata, |
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), |
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), |
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] |
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def _generate_examples(self, text_paths, audio_paths, metadata): |
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if audio_paths is None: |
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audio_paths = [None] * len(text_paths) |
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for text_path, audio_path, meta in zip(text_paths, audio_paths, metadata): |
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name = os.path.splitext(meta["Filepath"])[0] |
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with open(text_path, encoding="utf-8") as text_f: |
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record = { |
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"name": name, |
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"text": text_f.read(), |
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"language": _LANGUAGE_NAME_TO_CODE[meta["Language"]], |
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"license_type": meta["LicenseType"], |
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} |
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if audio_path is not None: |
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record["audio"] = audio_path |
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yield name, record |
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