first

README.md

@@ -0,0 +1,9 @@
+---
+language: 
+- jav
+pretty_name: Jv Id Asr
+task_categories: 
+- speech-recognition
+tags: 
+- speech-recognition
+---

jv_id_asr_split.py

@@ -0,0 +1,194 @@
+# 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.
+
+import csv
+import os
+from pathlib import Path
+from typing import List
+import gdown
+import tempfile
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Tasks
+
+_CITATION = """\
+@inproceedings{kjartansson-etal-sltu2018,
+    title = {{Crowd-Sourced Speech Corpora for Javanese, Sundanese,  Sinhala, Nepali, and Bangladeshi Bengali}},
+    author = {Oddur Kjartansson and Supheakmungkol Sarin and Knot Pipatsrisawat and Martin Jansche and Linne Ha},
+    booktitle = {Proc. The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU)},
+    year  = {2018},
+    address = {Gurugram, India},
+    month = aug,
+    pages = {52--55},
+    URL   = {http://dx.doi.org/10.21437/SLTU.2018-11},
+  }
+"""
+
+_DATASETNAME = "jv_id_asr"
+
+_DESCRIPTION = """\
+This data set contains transcribed audio data for Javanese. The data set consists of wave files, and a TSV file.
+The file utt_spk_text.tsv contains a FileID, UserID and the transcription of audio in the file.
+The data set has been manually quality checked, but there might still be errors.
+This dataset was collected by Google in collaboration with Reykjavik University and Universitas Gadjah Mada in Indonesia.
+"""
+
+_HOMEPAGE = "http://openslr.org/35/"
+_LANGUAGES = ["jav"]
+_LOCAL = False
+
+_LICENSE = "Attribution-ShareAlike 4.0 International"
+
+_URLs = {
+    "jv_id_asr_train": "https://drive.google.com/file/d/1g1EsPWA1sibY5HMQlhhUVeLHTFLBevbH/view?usp=sharing",
+    "jv_id_asr_dev": "https://drive.google.com/file/d/1rrpCE2VZqFSjPu2diNnwzMmX2EG346dN/view?usp=sharing",
+    "jv_id_asr_test": "https://drive.google.com/file/d/1qqjKhEqWiTOQP5XbMeIVkef_aPi3ZNug/view?usp=sharing",
+}
+
+_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]  # example: [Tasks.TRANSLATION, Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+def download_from_gdrive(url, output_dir):
+    """Download a file from Google Drive and save it to the specified directory."""
+    file_id = url.split("/d/")[-1].split("/")[0]  # Extract FILE_ID from URL
+    gdrive_url = f"https://drive.google.com/uc?id={file_id}"
+    output_path = os.path.join(output_dir, f"{file_id}.zip")  # Save file
+    gdown.download(gdrive_url, output_path, quiet=False)
+    return output_path
+
+class JvIdASR(datasets.GeneratorBasedBuilder):
+    """Javanese ASR training data set containing ~185K utterances."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name="jv_id_asr_source",
+            version=SOURCE_VERSION,
+            description="jv_id_asr source schema",
+            schema="source",
+            subset_id="jv_id_asr",
+        ),
+        SEACrowdConfig(
+            name="jv_id_asr_seacrowd_sptext",
+            version=SEACROWD_VERSION,
+            description="jv_id_asr Nusantara schema",
+            schema="seacrowd_sptext",
+            subset_id="jv_id_asr",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "jv_id_asr_source"
+
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        def download_from_gdrive(url, name):
+            with tempfile.TemporaryDirectory() as temp_dir:
+                file_id = url.split("/d/")[-1].split("/")[0]
+                output_path = os.path.join(temp_dir, f"{name}.zip")
+                gdown.download(url, output_path, fuzzy=True)
+                extracted_path = dl_manager.extract(output_path)
+                return extracted_path
+
+        paths = {
+            "train": download_from_gdrive(_URLs["jv_id_asr_train"], 'asr_javanese_train'),
+            "dev": download_from_gdrive(_URLs["jv_id_asr_dev"], 'asr_javanese_dev'),
+            "test": download_from_gdrive(_URLs["jv_id_asr_test"], 'asr_javanese_test'),
+        }
+
+        return [
+            datasets.SplitGenerator(
+                name="phase_1_train",
+                gen_kwargs={"filepath": paths["train"], "split": "phase_1_train"},
+            ),
+            datasets.SplitGenerator(
+                name="phase_2_train",
+                gen_kwargs={"filepath": paths["train"], "split": "phase_2_train"},
+            ),
+            datasets.SplitGenerator(
+                name="phase_1_val",
+                gen_kwargs={"filepath": paths["dev"], "split": "phase_1_val"},
+            ),
+            datasets.SplitGenerator(
+                name="phase_2_val",
+                gen_kwargs={"filepath": paths["dev"], "split": "phase_2_val"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": paths["test"], "split": None},
+            ),
+        ]
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "speaker_id": datasets.Value("string"),
+                    "path": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=16_000),
+                    "text": datasets.Value("string"),
+                    "language": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == "seacrowd_sptext":
+            features = schemas.speech_text_features.copy()
+            features["language"] = datasets.Value("string")
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _generate_examples(self, filepath: str, split=None):
+        tsv_path = os.path.join(filepath, "asr_javanese", "utt_spk_text.tsv")
+        with open(tsv_path, "r") as f:
+            reader = list(csv.reader(f, delimiter="\t"))
+            total = len(reader)
+
+            if split == "phase_1_train" or split == "phase_1_val":
+                reader = reader[: total // 2]
+            elif split == "phase_2_train" or split == "phase_2_val":
+                reader = reader[total // 2 :]
+
+            for line in reader:
+                audio_id, sp_id, text = line[0], line[1], line[2]
+                wav_path = os.path.join(filepath, "asr_javanese", "data", "{}".format(audio_id[:2]), "{}.flac".format(audio_id))
+
+                if os.path.exists(wav_path):
+                    ex = {
+                        "id": audio_id,
+                        "speaker_id": sp_id,
+                        "path": wav_path,
+                        "audio": wav_path,
+                        "text": text,
+                        "language": "jv",
+                    }
+
+                    if self.config.schema == "seacrowd_sptext":
+                        ex["metadata"] = {"speaker_age": None, "speaker_gender": None}
+
+                    yield audio_id, ex
+        f.close()

requirements.txt

@@ -0,0 +1,2 @@
+seacrowd>=0.2.0
+gdown