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asr_ibsc.py

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+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import fsspec
+import pandas as pd
+from fsspec.callbacks import TqdmCallback
+
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import (SCHEMA_TO_FEATURES, TASK_TO_SCHEMA,
+                                      Licenses, Tasks)
+
+_CITATION = """\
+@inproceedings{Juan14,
+    Title = {Semi-supervised G2P bootstrapping and its application to ASR for a very under-resourced language: Iban},
+    Author = {Sarah Samson Juan and Laurent Besacier and Solange Rossato},
+    Booktitle = {Proceedings of Workshop for Spoken Language Technology for Under-resourced (SLTU)},
+    Year = {2014}}
+    Month = {May},
+
+@inproceedings{Juan2015,
+    Title = {Using Resources from a closely-Related language to develop ASR for a very under-resourced Language: A case study for Iban},
+    Author = {Sarah Samson Juan and Laurent Besacier and Benjamin Lecouteux and Mohamed Dyab},
+    Booktitle = {Proceedings of INTERSPEECH},
+    Year = {2015},
+    Month = {September}}
+    Address = {Dresden, Germany},
+"""
+
+_DATASETNAME = "asr_ibsc"
+
+_DESCRIPTION = """\
+This package contains Iban language text and speech suitable for Automatic
+Speech Recognition (ASR) experiments. In addition, transcribed speech, 2M tokens
+corpus crawled from an online newspaper site is provided. News data was provided
+by a local radio station in Sarawak, Malaysia.
+"""
+
+_HOMEPAGE = "https://github.com/sarahjuan/iban"
+
+_LANGUAGES = ["iba"]
+
+_LICENSE = Licenses.CC_BY_SA_3_0.value
+
+_LOCAL = False
+
+_URL = "https://github.com/sarahjuan/iban/tree/master/data"
+
+_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]
+_SEACROWD_SCHEMA = f"seacrowd_{TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]].lower()}"  # sptext
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class ASRIbanDataset(datasets.GeneratorBasedBuilder):
+    """Iban language text and speech suitable for ASR experiments"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=_DATASETNAME,
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_{_SEACROWD_SCHEMA}",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=_SEACROWD_SCHEMA,
+            subset_id=_DATASETNAME,
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "audio": datasets.Audio(sampling_rate=16_000),
+                    "transcription": datasets.Value("string"),
+                    "speaker_id": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == _SEACROWD_SCHEMA:
+            features = SCHEMA_TO_FEATURES[TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]]]  # speech_text_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."""
+        # prepare data directory
+        data_dir = Path.cwd() / "data" / "asr_ibsc"
+        data_dir.mkdir(parents=True, exist_ok=True)
+
+        # download data
+        # if rate limiting is an issue, pass github username and token
+        username = None
+        token = None
+        fs = fsspec.filesystem("github", org="sarahjuan", repo="iban", ref="master", username=username, token=token)
+        fs.clear_instance_cache()
+
+        # download annotation
+        print("Downloading annotation...")
+        fs.get(fs.ls("data/train/"), (data_dir / "train").as_posix(), recursive=True)
+        fs.get(fs.ls("data/test/"), (data_dir / "test").as_posix(), recursive=True)
+
+        # download audio files
+        print("Downloading audio files (~1GB). It may take several minutes...")
+        for idx, folder in enumerate(fs.ls("data/wav/")):
+            folder_name = folder.split("/")[-1]
+            pbar = TqdmCallback(tqdm_kwargs={"desc": f"-> {folder_name} [{idx+1:2d}/{len(fs.ls('data/wav/'))}]", "unit": "file"})
+            fs.get(fs.ls(f"data/wav/{folder_name}/"), (data_dir / "wav" / folder_name).as_posix(), recursive=True, callback=pbar)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_dir": data_dir,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "data_dir": data_dir,
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_dir: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        text_file = data_dir / split / f"{split}_text"
+        utt2spk_file = data_dir / split / f"{split}_utt2spk"
+        wav_scp_file = data_dir / split / f"{split}_wav.scp"
+
+        # load the data
+        text_df = pd.read_csv(text_file, sep="  ", header=None, names=["utt_id", "text"])
+        utt2spk_df = pd.read_csv(utt2spk_file, sep="\t", header=None, names=["utt_id", "speaker"])
+        wav_df = pd.read_csv(wav_scp_file, sep="\t", header=None, names=["utt_id", "wav_path"])
+        merged_df = pd.merge(text_df, utt2spk_df, on="utt_id")
+        merged_df = pd.merge(merged_df, wav_df, on="utt_id")
+
+        for _, row in merged_df.iterrows():
+            wav_file = data_dir / "wav" / row["speaker"] / row["wav_path"].split("/")[-1]
+
+            if self.config.schema == "source":
+                yield row["utt_id"], {
+                    "audio": str(wav_file.as_posix()),
+                    "transcription": row["text"],
+                    "speaker_id": row["speaker"],
+                }
+            elif self.config.schema == _SEACROWD_SCHEMA:
+                yield row["utt_id"], {
+                    "id": row["utt_id"],
+                    "path": str(wav_file),
+                    "audio": str(wav_file.as_posix()),
+                    "text": row["text"],
+                    "speaker_id": row["speaker"],
+                    "metadata": None,
+                }