Publishing Bambara-ASR-ALL version 1.0.1

README.md

@@ -3,7 +3,7 @@ language:
 - bm  # ISO 639-1 code for Bambara
 - fr  # ISO 639-1 code for French
 pretty_name: "Bambara-ASR-All Audio Dataset"
-version: "1.0.0"  # Explicit versioning
+version: "1.0.1" # Explicit versioning
 tags:
 - audio
 - transcription
@@ -24,13 +24,13 @@ language_creators:
 - crowdsourced  # If the data was annotated or recorded by a team
 source_datasets:
 - jeli-asr  
-- mali-pense
-- reading-tutor-data-collection
+- oza-mali-pense
+- rt-data-collection
 size_categories:
 - 10GB<
 - 10K<n<100K
 dataset_info:
-  audio_format: "wav"
+  audio_format: "arrow"
   features:
   - name: audio
     dtype: audio
@@ -40,7 +40,7 @@ dataset_info:
     dtype: string
   - name: french
     dtype: string
-  total_audio_files: 38769 
+  total_audio_files: 38769
   total_duration_hours: ~37
 
 configs:
@@ -50,9 +50,9 @@ configs:
   - split: train
     path: 
       - "jeli-asr-rmai/train/*"
-      - "bam-asr-oza/train/*/*"
+      - "bam-asr-oza/train/*"
       - "oza-mali-pense/train/*"
-      - "rt-data-collection/*"
+      - "rt-data-collection/train/*"
   - split: test
     path: 
       - "jeli-asr-rmai/test/*"
@@ -62,7 +62,7 @@ configs:
   - split: train
     path: 
       - "jeli-asr-rmai/train/*"
-      - "bam-asr-oza/train/*/*"
+      - "bam-asr-oza/train/*"
   - split: test
     path: 
       - "jeli-asr-rmai/test/*"
@@ -74,14 +74,17 @@ configs:
 - config_name: rt-data-collection
   data_files:
   - split: train
-    path: "rt-data-collection/*"
+    path: "rt-data-collection/train/*"
 
 description: |
-  This Dataset is a multilingual audio dataset containing audio samples in Bambara with semi-expert transcriptions and French translations. 
-  Each audio file is paired with its transcription in Bambara or its translation in French (available in manifest files). 
-  The dataset is designed for tasks like automatic speech recognition (ASR) and translation. Data come from all publicly available sources providing audios and aligned bambara transcription.
+  The **Bambara-ASR-All Audio Dataset** is a multilingual dataset containing audio samples in Bambara, accompanied by semi-expert transcriptions and French translations. 
+  The dataset includes various subsets: `jeli-asr`, `oza-mali-pense`, and `rt-data-collection`. Each audio file is aligned with Bambara transcriptions or French translations, making it suitable for tasks such as automatic speech recognition (ASR) and translation. 
+  Data sources include all publicly available collections of audio with Bambara transcriptions, organized for accessibility and usability.
+
 ---
 
+# Documentation WILL be updated soon
+
 # All Bambara ASR Dataset
 
 This dataset aims to gather all publicly available Bambara ASR datasets. It is primarily composed of the **Jeli-ASR** dataset (available at [RobotsMali/jeli-asr](https://huggingface.co/datasets/RobotsMali/jeli-asr)), along with the **Mali-Pense** data curated and published by Aboubacar Ouattara (available at [oza75/bambara-tts](https://huggingface.co/datasets/oza75/bambara-tts)). Additionally, it includes 1 hour of audio recently collected by the RobotsMali AI4D Lab, featuring children's voices reading some of RobotsMali GAIFE books. This dataset is desihgned for automatic speech recognition (ASR) task primarily.

bam-asr-all.py

@@ -1,195 +0,0 @@
-"""
-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"],
-            }

bam-asr-oza/dataset_dict.json

@@ -0,0 +1 @@
+{"splits": ["train", "test"]}

bam-asr-oza/test/data-00000-of-00001.arrow

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+size 129214536

bam-asr-oza/test/dataset_info.json

@@ -0,0 +1,51 @@
+{
+  "builder_name": "jeli-asr",
+  "citation": "@inproceedings{jeli_asr_2025,\n  title={Jeli-ASR Audio Dataset},\n  author={RobotsMali AI4D Lab},\n  year={2025},\n  publisher={Hugging Face}\n}\n",
+  "config_name": "bam-asr-oza",
+  "dataset_name": "jeli-asr",
+  "dataset_size": 5546475,
+  "description": "\nThe **Jeli-ASR** dataset is a multilingual Bambara-French audio dataset combining\nthe cleaned subset of Oza's Bambara-ASR with the Jeli-ASR-RMAI subset.\n",
+  "download_checksums": {},
+  "download_size": 0,
+  "features": {
+    "audio": {
+      "sampling_rate": 16000,
+      "_type": "Audio"
+    },
+    "duration": {
+      "dtype": "float32",
+      "_type": "Value"
+    },
+    "bam": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "french": {
+      "dtype": "string",
+      "_type": "Value"
+    }
+  },
+  "homepage": "https://huggingface.co/datasets/RobotsMali/jeli-asr",
+  "license": "CC-BY-4.0",
+  "size_in_bytes": 5546475,
+  "splits": {
+    "train": {
+      "name": "train",
+      "num_bytes": 5339693,
+      "num_examples": 30685,
+      "dataset_name": "jeli-asr"
+    },
+    "test": {
+      "name": "test",
+      "num_bytes": 206782,
+      "num_examples": 1243,
+      "dataset_name": "jeli-asr"
+    }
+  },
+  "version": {
+    "version_str": "1.0.1",
+    "major": 1,
+    "minor": 0,
+    "patch": 1
+  }
+}

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