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phost.py

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+import os
+from pathlib import Path
+from typing import Dict, List, Tuple
+from zipfile import ZipFile
+
+import datasets
+import yaml
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@inproceedings{PhoST,
+    title     = {{A High-Quality and Large-Scale Dataset for English-Vietnamese Speech Translation}},
+    author    = {Linh The Nguyen and Nguyen Luong Tran and Long Doan and Manh Luong and Dat Quoc Nguyen},
+    booktitle = {Proceedings of the 23rd Annual Conference of the International Speech Communication Association (INTERSPEECH)},
+    year      = {2022}
+}
+"""
+
+_DATASETNAME = "phost"
+
+_DESCRIPTION = """\
+PhoST is a high-quality and large-scale benchmark dataset for English-Vietnamese speech translation
+with 508 audio hours, consisting of 331K triplets of (sentence-lengthed audio, English source
+transcript sentence, Vietnamese target subtitle sentence).
+"""
+
+_HOMEPAGE = "https://github.com/VinAIResearch/PhoST"
+
+_LICENSE = Licenses.CC_BY_NC_ND_4_0.value
+
+_LOCAL = True
+
+_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION, Tasks.SPEECH_TO_TEXT_TRANSLATION, Tasks.MACHINE_TRANSLATION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+_LANGUAGES = ["eng", "vie"]
+
+
+def seacrowd_config_constructor(src_lang, tgt_lang, schema, version):
+    if src_lang == "" or tgt_lang == "":
+        raise ValueError(f"Invalid src_lang {src_lang} or tgt_lang {tgt_lang}")
+
+    if schema not in ["source", "seacrowd_sptext", "seacrowd_t2t"]:
+        raise ValueError(f"Invalid schema: {schema}")
+
+    return SEACrowdConfig(
+        name="phost_{src}_{tgt}_{schema}".format(src=src_lang, tgt=tgt_lang, schema=schema),
+        version=datasets.Version(version),
+        description="phost schema for {schema} from {src} to {tgt}".format(schema=schema, src=src_lang, tgt=tgt_lang),
+        schema=schema,
+        subset_id="phost_{src}_{tgt}".format(src=src_lang, tgt=tgt_lang),
+    )
+
+
+class Phost(datasets.GeneratorBasedBuilder):
+    """
+    PhoST is a high-quality and large-scale benchmark dataset for English-Vietnamese speech translation
+    with 508 audio hours, consisting of 331K triplets of (sentence-lengthed audio, English source
+    transcript sentence, Vietnamese target subtitle sentence).
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        seacrowd_config_constructor("en", "vi", "source", _SOURCE_VERSION),
+        seacrowd_config_constructor("en", "vi", "seacrowd_sptext", _SEACROWD_VERSION),
+        seacrowd_config_constructor("en", "vi", "seacrowd_t2t", _SEACROWD_VERSION),
+    ]
+
+    DEFAULT_CONFIG_NAME = "phost_en_vi_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "file": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=16_000),
+                    "en_text": datasets.Value("string"),
+                    "vi_text": datasets.Value("string"),
+                    "timing": datasets.Sequence(datasets.Value("string")),
+                }
+            )
+        elif self.config.schema == "seacrowd_sptext":
+            features = schemas.speech_text_features
+        elif self.config.schema == "seacrowd_t2t":
+            features = schemas.text2text_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+        if self.config.data_dir is None:
+            raise ValueError("This is a local dataset. Please pass the data_dir kwarg to load_dataset.")
+        else:
+            data_dir = self.config.data_dir
+
+            aud_path = os.path.join(data_dir, "audio_data")
+            if not os.path.exists(aud_path):
+                os.makedirs(aud_path)
+
+            # loading the temp.zip and creating a zip object
+            with ZipFile(os.path.join(data_dir, "train_audio.zip"), "r") as zObject:
+                for member in zObject.namelist():
+                    if not os.path.exists(os.path.join(aud_path, "train", member)) or not os.path.isfile(os.path.join(aud_path, "train", member)):
+                        zObject.extract(member, os.path.join(aud_path, "train"))
+
+            # dev audio files
+            with ZipFile(os.path.join(data_dir, "dev_audio.zip"), "r") as zObject:
+                for member in zObject.namelist():
+                    if not os.path.exists(os.path.join(aud_path, "dev", member)) or not os.path.isfile(os.path.join(aud_path, "dev", member)):
+                        zObject.extract(member, aud_path)
+            # test audio files
+            with ZipFile(os.path.join(data_dir, "test_audio.zip"), "r") as zObject:
+                for member in zObject.namelist():
+                    if not os.path.exists(os.path.join(aud_path, "test", member)) or not os.path.isfile(os.path.join(aud_path, "test", member)):
+                        zObject.extract(member, aud_path)
+            # text data
+            with ZipFile(os.path.join(data_dir, "text_data.zip"), "r") as zObject:
+                for member in zObject.namelist():
+                    if not os.path.exists(os.path.join(data_dir, member)) or not os.path.isfile(os.path.join(data_dir, member)):
+                        zObject.extract(member, data_dir)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": {"audio": os.path.join(aud_path, "train", "wav"), "text": os.path.join(data_dir, "text_data", "train")},
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": {"audio": os.path.join(aud_path, "test", "wav"), "text": os.path.join(data_dir, "text_data", "test")},
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": {"audio": os.path.join(aud_path, "dev", "wav"), "text": os.path.join(data_dir, "text_data", "dev")},
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        config_names_split = self.config.name.split("_")
+        src_lang = config_names_split[1]
+        tgt_lang = config_names_split[2]
+        track_ids = os.listdir(filepath["text"])
+        timing = []
+        en_sub = []
+        vi_sub = []
+        counter = 0
+        for key, track_id in enumerate(track_ids):
+            with open(os.path.join(filepath["text"], track_id, track_id + ".yaml")) as timing_file:
+                timing = yaml.safe_load(timing_file)
+            with open(os.path.join(filepath["text"], track_id, track_id + ".en")) as en_text:
+                en_sub = [line.strip() for line in en_text]
+            with open(
+                os.path.join(filepath["text"], track_id, track_id + ".vi"),
+            ) as vi_text:
+                vi_sub = [line.strip() for line in vi_text]
+
+            if self.config.schema == "source":
+                yield key, {"file": os.path.join(filepath["audio"], track_id + ".wav"), "audio": os.path.join(filepath["audio"], track_id + ".wav"), "en_text": " ".join(en_sub), "vi_text": " ".join(vi_sub), "timing": timing}
+
+            elif self.config.schema == "seacrowd_sptext":
+                if tgt_lang not in ["en", "vi"]:
+                    raise NotImplementedError(f"Target language '{tgt_lang}' is not defined.")
+
+                yield key, {
+                    "id": track_id,
+                    "path": os.path.join(filepath["audio"], track_id + ".wav"),
+                    "audio": os.path.join(filepath["audio"], track_id + ".wav"),
+                    "text": " ".join(en_sub) if tgt_lang == "en" else " ".join(vi_sub),
+                    "speaker_id": None,
+                    "metadata": {
+                        "speaker_age": None,
+                        "speaker_gender": None,
+                    },
+                }
+
+            elif self.config.schema == "seacrowd_t2t":
+                if src_lang not in ["en", "vi"]:
+                    raise NotImplementedError(f"Source language '{src_lang}' is not defined.")
+                if tgt_lang not in ["en", "vi"]:
+                    raise NotImplementedError(f"Target language '{tgt_lang}' is not defined.")
+                for en_line, vi_line in zip(en_sub, vi_sub):
+                    yield counter, {
+                        "id": f"{track_id}_{str(counter)}",
+                        "text_1": en_line if src_lang == "en" else vi_line,
+                        "text_2": en_line if tgt_lang == "en" else vi_line,
+                        "text_1_name": src_lang,
+                        "text_2_name": tgt_lang,
+                    }
+                    counter += 1
+            else:
+                raise NotImplementedError(f"Schema '{self.config.schema}' is not defined.")