Add custom dataset loading script

bam-asr-all.py

@@ -0,0 +1,195 @@
+"""
+Copyright 2025 RobotsMali AI4D Lab.
+
+Licensed under the Creative Commons Attribution 4.0 International License (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+https://creativecommons.org/licenses/by/4.0/
+
+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 datasets
+from datasets import Split, SplitGenerator
+
+# -----------------------
+# 1. Basic meta-infos
+# -----------------------
+_CITATION = """\
+@inproceedings{bam_asr_all_2025,
+  title={Bam-ASR-All Audio Dataset},
+  author={RobotsMali AI4D Lab},
+  year={2025},
+  publisher={Hugging Face}
+}
+"""
+
+_DESCRIPTION = """
+The **Bam-ASR-All** dataset is a combined Bambara speech dataset featuring multiple subsets:
+- Oza-Mali-Pense
+- Jeli-ASR
+- RT-Data-Collection
+All subsets contain audio samples in Bambara along with transcriptions and (potentially) 
+French translations.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/RobotsMali/bam-asr-all"
+_LICENSE = "CC-BY-4.0"
+_VERSION = datasets.Version("1.0.0")
+
+# NOTE: No trailing slash here
+_BASE_URL = "https://huggingface.co/datasets/RobotsMali/bam-asr-all/resolve/main"
+
+# -----------------------
+# 2. Config + Builder
+# -----------------------
+class BamASRAllConfig(datasets.BuilderConfig):
+    """BuilderConfig for different subsets of Bam-ASR-All dataset."""
+
+class BamASRAll(datasets.GeneratorBasedBuilder):
+    """
+    This class defines how to load and parse the Bam-ASR-All dataset 
+    from metadata.csv + audio files on the Hub.
+    """
+
+    # 2a. Define your subsets (configs)
+    BUILDER_CONFIGS = [
+        BamASRAllConfig(
+            name="oza-mali-pense",
+            version=_VERSION,
+            description="Load only the Oza-Mali-Pense subset (files under oza-mali-pense/).",
+        ),
+        BamASRAllConfig(
+            name="jeli-asr",
+            version=_VERSION,
+            description="Load only the Jeli-ASR subset (files under jeli-asr/).",
+        ),
+        BamASRAllConfig(
+            name="rt-data-collection",
+            version=_VERSION,
+            description="Load only the RT-Data-Collection subset (files under rt-data-collection/).",
+        ),
+        # The "combined" option for everything can also be done
+        BamASRAllConfig(
+            name="bam-asr-all",  # The dataset's default name
+            version=_VERSION,
+            description="Combine oza-mali-pense, jeli-asr, and rt-data-collection (all rows).",
+        ),
+    ]
+
+    # 2b. Default subset name if none specified
+    DEFAULT_CONFIG_NAME = "bam-asr-all"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "audio": datasets.Audio(sampling_rate=16_000),
+                    "duration": datasets.Value("float32"),
+                    "bam": datasets.Value("string"),
+                    "french": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    # -----------------------
+    # 3. Splits
+    # -----------------------
+    def _split_generators(self, dl_manager):
+        """
+        1) Download 'metadata.csv' from the Hub by specifying its raw URL.
+        2) We'll then yield two splits (TRAIN, TEST) by reading that CSV 
+           and filtering rows by '/train/' or '/test/' in file paths.
+        """
+        metadata_url = f"{_BASE_URL}/metadata.csv"
+        local_metadata_path = dl_manager.download(metadata_url)
+
+        return [
+            SplitGenerator(
+                name=Split.TRAIN,
+                gen_kwargs={
+                    "metadata_path": local_metadata_path,
+                    "split": "train",
+                    "dl_manager": dl_manager,
+                },
+            ),
+            SplitGenerator(
+                name=Split.TEST,
+                gen_kwargs={
+                    "metadata_path": local_metadata_path,
+                    "split": "test",
+                    "dl_manager": dl_manager,
+                },
+            ),
+        ]
+
+    # -----------------------
+    # 4. Generate examples
+    # -----------------------
+    def _generate_examples(self, metadata_path, split, dl_manager):
+        """
+        Read metadata.csv row-by-row, filter by:
+          - the config name (oza-mali-pense, jeli-asr, rt-data-collection, or all)
+          - 'train' vs 'test' in file path
+        Then download each audio file from the Hub, yield local path + metadata.
+        """
+        audios_to_download = []
+        metadata_dict = {}
+
+        with open(metadata_path, "r", encoding="utf-8") as f:
+            reader = csv.DictReader(f)
+            for idx, row in enumerate(reader):
+                file_path = row["file_name"]  # e.g. "jeli-asr/train/.../some.wav"
+
+                # Filter by config name
+                if self.config.name == "oza-mali-pense":
+                    if "oza-mali-pense/" not in file_path:
+                        continue
+                elif self.config.name == "jeli-asr":
+                    if "jeli-asr/" not in file_path:
+                        continue
+                elif self.config.name == "rt-data-collection":
+                    if "rt-data-collection/" not in file_path:
+                        continue
+                elif self.config.name == "bam-asr-all":
+                    # Keep all rows
+                    pass
+
+                # Filter by split (train/test)
+                if split == "train" and "/train/" not in file_path:
+                    continue
+                if split == "test" and "/test/" not in file_path:
+                    continue
+
+                # Build the raw URL for this audio file
+                audio_url = f"{_BASE_URL}/{file_path}"
+                audios_to_download.append(audio_url)
+
+                # Store minimal metadata in a dictionary
+                metadata_dict[audio_url] = {
+                    "duration": float(row["duration"]),
+                    "bam": row["bam"],
+                    "french": row["french"],
+                }
+
+        # Download the audios. dl_manager returns the local paths in the cache.
+        local_audio_paths = dl_manager.download(audios_to_download)
+        for idx, audio_url in enumerate(audios_to_download):
+            local_audio_path = local_audio_paths[idx]
+            yield idx, {
+                "audio": local_audio_path,  # local path for datasets.Audio
+                "duration": metadata_dict[audio_url]["duration"],
+                "bam": metadata_dict[audio_url]["bam"],
+                "french": metadata_dict[audio_url]["french"],
+            }