Publishing Jeli-ASR version 1.0.1

README.md

@@ -3,14 +3,14 @@ language:
 - bm  # ISO 639-1 code for Bambara
 - fr  # ISO 639-1 code for French
 pretty_name: "Jeli-ASR Audio Dataset"
-version: "1.0.0"  # Explicit versioning
+version: "1.0.1"  # Explicit versioning
 tags:
 - audio
 - transcription
 - multilingual
 - Bambara
 - French
-license: "cc-by-4.0" 
+license: "cc-by-4.0"
 task_categories:
 - automatic-speech-recognition
 - text-to-speech
@@ -28,7 +28,7 @@ size_categories:
 - 10GB<
 - 10K<n<100K
 dataset_info:
-  audio_format: "wav"
+  audio_format: "arrow"
   features:
   - name: audio
     dtype: audio
@@ -38,38 +38,40 @@ dataset_info:
     dtype: string
   - name: french
     dtype: string
-  total_audio_files: 33643 
+  total_audio_files: 33643
   total_duration_hours: ~32
 
 configs:
 - config_name: jeli-asr-rmai
   data_files:
   - split: train
-    path: "jeli-asr-rmai/train/*"
+    path: "jeli-asr-rmai/train/data-*.arrow"
   - split: test
-    path: "jeli-asr-rmai/test/*"
+    path: "jeli-asr-rmai/test/data-*.arrow"
 - config_name: bam-asr-oza
   data_files:
   - split: train
-    path: "bam-asr-oza/train/*/*"
+    path: "bam-asr-oza/train/data-*.arrow"
   - split: test
-    path: "bam-asr-oza/test/*"
+    path: "bam-asr-oza/test/data-*.arrow"
 - config_name: jeli-asr
   default: true
   data_files:
   - split: train
     path: 
-      - "jeli-asr-rmai/train/*"
-      - "bam-asr-oza/train/*/*"
+      - "jeli-asr-rmai/train/data-*.arrow"
+      - "bam-asr-oza/train/data-*.arrow"
   - split: test
     path: 
-      - "jeli-asr-rmai/test/*"
-      - "bam-asr-oza/test/*"
+      - "jeli-asr-rmai/test/data-*.arrow"
+      - "bam-asr-oza/test/data-*.arrow"
 description: |
-  The **Jeli-ASR Audio 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), text-to-speech synthesis (TTS) and translation. Data was recorded in an organized setup in Mali with griots and semi-professionally transcribed, and translated into French.
+  The **Jeli-ASR Audio Dataset** is a multilingual dataset converted into the optimized Arrow format, 
+  ensuring fast access and compatibility with modern data workflows. It contains audio samples in Bambara 
+  with semi-expert transcriptions and French translations. Each subset of the dataset is organized by 
+  configuration (`jeli-asr-rmai`, `bam-asr-oza`, and `jeli-asr`) and further split into training and testing sets. 
+  The dataset is designed for tasks like automatic speech recognition (ASR), text-to-speech synthesis (TTS), 
+  and translation. Data was recorded in Mali with griots, then transcribed and translated into French.
 ---
 
 # Jeli-ASR Dataset
@@ -79,137 +81,29 @@ This repository contains the **Jeli-ASR** dataset, which is primarily a reviewed
 ## Important Notes
 
 1. Please note that this dataset is currently in development and is therefore not fixed. The structure, content, and availability of the dataset may change as improvements and updates are made.
-2. The Dataset viewer has been disabled for this dataset since it uses a [custom loading script](bam-asr-all.py). You can safely load this dataset and all its features as a HF dataset object though (see usage section)
 
 ---
 
-## **Key Changes in This Version**
+## **Key Changes in Version 1.0.1**
 
-### **1. Name Change**
+Jeli-ASR 1.0.1 introduces several updates and enhancements, focused entirely on the transcription side of the dataset. There have been no changes to the audio files since version 1.0.0. Below are the key updates:
 
-- The Dataset name was changed from `jeli-data-manifest` to `jeli-asr`.
+1. **Symbol Removal:**  
+   All non-vocabulary symbols deemed unnecessary for Automatic Speech Recognition (ASR) were removed, including:  
+   `[` `]` `(` `)` `«` `»` `°` `"` `<` `>`  
 
-### **2. Mono Channel Conversion**
+2. **Punctuation Removal:**  
+   Common punctuation marks were removed to streamline the dataset for ASR use cases. These include:  
+   `:` `,` `;` `.` `?` `!`  
+   The exception is the hyphen (`-`), which remains as it is used in both Bambara and French compound words. While this punctuation removal enhances ASR performance, the previous version with full punctuation may still be better suited for other applications. You can still reconstruct the previous version with the archives.
 
-- All stereo audio files have been converted to mono to ensure consistency across the dataset.
-  - This step was required only for the `jeli-asr-rmai` subset as `oza-bam-asr` was already consistent.
+3. **Bambara Normalization:**  
+   The transcription were normalized using the [Bambara Normalizer](https://pypi.org/project/bambara-normalizer/), a python package designed to normalize Bambara text for different NLP applications.
 
-### **3. Removal of Misaligned Samples**
+4. **Optimized Data Format:**  
+   This version introduces `.arrow` files for efficient data storage and retrieval and compatibility with HuggingFace tools.
 
-- More than 70% of the data in the previous version contained misaligned samples due to concatenation issues that kind of spread misalignment in the dataset.
-- A filtering process was applied using both **manual classification** and **trained classifiers**:
-  - A **subset** of the data was **manually classified** as **aligned** or **misaligned**.
-  - This subset was used to **train classifiers** (Logistic Regression and XGBoost) to label the remaining samples.
-    **Classifier Performance**:
-  - Best-performing model: **Logistic Regression**
-    - **Accuracy**: 0.84
-    - **F1-score (misaligned - class 0)**: 0.86
-    - **F1-score (aligned - class 1)**: 0.82
-      **Training Details**:
-  - **Balanced training set**: Positive samples (aligned) were supplemented using additional aligned samples from **Oza's Bambara-ASR** dataset.
-  - **Misaligned samples**: No additional samples were needed as they formed a majority.
-  - **Embedding processing**: Manually separated data has been represented as embeddings for training classifiers. The embeddings were obtained by inferring Wav2Vec and BERT, then concatenated for every example and labeled as either aligned or misaligned.
-
-Misaligned samples identified during classification were removed. That subset is currently undergoing further review and may be partially reintegrated in a future version of this dataset.
-
-### **4. Integration of Oza's Bambara-ASR Dataset**
-
-- This version integrates a clean subset from **Oza's Bambara-ASR** dataset making about 90% of the data.
-
-### **5. Lowercased Transcriptions**
-
-- All transcriptions and translations have been converted to lowercase for consistency.
-
-### **6. Silent/Empty File Filtering**
-
-- Silent or empty audio files  with inaudible content were removed.
-
----
-
-## **Directory Structure**
-
-```
-jeli-asr/
-|
-├── README.md
-├── metadata.csv
-├── manifests/
-│   ├── jeli-asr-rmai-test-manifest.json
-│   ├── jeli-asr-rmai-train-manifest.json
-│   ├── oza-bam-asr-test-manifest.json
-│   └── oza-bam-asr-train-manifest.json
-│   └── train-manifest.json # jeli-asr-rmai-train-manifest.json + oza-bam-asr-train-manifest.json
-│   └── test-manifest.json # jeli-asr-rmai-test-manifest.json + oza-bam-asr-test-manifest.json
-│
-├── scripts/
-│   ├── clean_tsv.py
-│   ├── convert_to_mono_channel.py
-│   ├── create_data_manifest.py
-│   ├── create_manifest_oza_bam_asr.py
-│   ├── filter_silent_and_inaudible.py
-│   └── lower_transcriptions_in_manifests.py
-│
-├── french-manifests/
-│   ├── jeli-asr-rmai-test-french-manifest.json
-│   ├── jeli-asr-rmai-train-french-manifest.json
-│   ├── oza-bam-asr-test-french-manifest.json
-│   └── oza-bam-asr-train-french-manifest.json
-│
-├── jeli-asr-rmai/
-│   ├── train/
-│   └── test/
-│
-├── bam-asr-oza/
-│   ├── train/
-│   └── test/
-```
-
-### **manifests Directory**
-This directory contains the manifest files used for training speech recognition (ASR) and text-to-speech (TTS) models. Those are JSON files:
-
-Each line in the manifest files is a JSON object with the following structure:
-```json
-{
-  "audio_filepath": "jeli-asr/bam-asr-oza/train/oza75-bam-asr-14.wav", 
-  "duration": 4.888, 
-  "text": "n'o tɛ n'a fɔra den o den ma ko yiriba, i b'a kɔlɔsi a bɛna kɛ mɔgɔjɛmɔgɔ ye don dɔ."
-}
-```
-- **audio_filepath**: The relative path to the corresponding audio file.
-- **duration**: The duration of the audio file in seconds.
-- **text**: The transcription of the audio in Bambara.
-
-### 3. **french-manifests/**
-This directory contains French equivalent manifest files for the dataset. The structure is similar to the `manifests/` directory but with French transcriptions
-
----
-
-## **Scripts Explanation**
-
-### 1. convert\_to\_mono\_channel.py
-
-- Converts stereo audio files to mono.
-- Ensures consistent audio channel dimensions.
-
-### 2. filter\_silent\_and\_inaudible.py
-
-- Filters out silent or inaudible audio files.
-
-### 3. lower\_transcriptions\_in\_manifests.py
-
-- Converts all text in the manifest files to lowercase for uniform formatting.
-
-### 4. clean\_tsv.py
-
-- Script to remove some of the most common issues in the .tsv transcription files created during the last revision work on the dataset in January 2023, such as unwanted characters (", <>), consecutive tabs (making some rows incositent) and spacing errors *(used to create jeli-data-manifest)*.
-
-### 5. create\_data\_manifest.py
-
-- A script used to create manifest files for training and testing. It re-samples the audio files published as the first version of Jeli-ASR dataset and generates the corresponding JSON manifest files *(used to create jeli-data-manifest)*.
-
-### 6. create\_manifest\_oza\_bam\_asr.py
-
-- Create manifest files for the oza75/bambara-asr clean subset .
+Let us know if you have feedback or additional use suggestions for the dataset by opening a discussion or a pull request. You can find a record or updates of the dataset in [VERSIONING.md](VERSIONING.md)
 
 ---
 
@@ -249,8 +143,7 @@ git clone --depth 1 https://huggingface.co/datasets/RobotsMali/jeli-asr
 from datasets import load_dataset
 
 # Load the dataset into Hugging Face Dataset object
-dataset = load_dataset("RobotsMali/jeli-asr", trust_remote_code=True)
-# Note: You can also download only a specific subset if you with 
+dataset = load_dataset("RobotsMali/jeli-asr")
 ```
 
 ### Finetuning Example in NeMo:

VERSIONING.md

@@ -0,0 +1,65 @@
+## **Key Changes in Version 1.0.1**
+
+Jeli-ASR 1.0.1 introduces several updates and enhancements, focused entirely on the transcription side of the dataset. There have been no changes to the audio files since version 1.0.0. Below are the key updates:
+
+1. **Symbol Removal:**  
+   All non-vocabulary symbols deemed unnecessary for Automatic Speech Recognition (ASR) were removed, including:  
+   `[` `]` `(` `)` `«` `»` `°` `"` `<` `>`  
+
+2. **Punctuation Removal:**  
+   Common punctuation marks were removed to streamline the dataset for ASR use cases. These include:  
+   `:` `,` `;` `.` `?` `!`  
+   The exception is the hyphen (`-`), which remains as it is used in both Bambara and French compound words. While this punctuation removal enhances ASR performance, the previous version with full punctuation may still be better suited for other applications. You can still reconstruct the previous version with the archives.
+
+3. **Bambara Normalization:**  
+   The transcription were normalized using the [Bambara Normalizer](https://pypi.org/project/bambara-normalizer/), a python package designed to normalize Bambara text for different NLP applications.
+
+4. **Optimized Data Format:**  
+   This version introduces `.arrow` files for efficient data storage and retrieval and compatibility with HuggingFace tools.
+
+These changes enhance the dataset's usability for ASR tasks while providing a cleaner transcription format. Let us know if you have feedback or additional use suggestions for the dataset by opening a discussion or a pull request.  
+
+---
+
+## **Key Changes in Version 1.0.0**
+
+### **1. Name Change**
+
+- The Dataset name was changed from `jeli-data-manifest` to `jeli-asr`.
+
+### **2. Mono Channel Conversion**
+
+- All stereo audio files have been converted to mono to ensure consistency across the dataset.
+  - This step was required only for the `jeli-asr-rmai` subset as `oza-bam-asr` was already consistent.
+
+### **3. Removal of Misaligned Samples**
+
+- More than 70% of the data in the previous version contained misaligned samples due to concatenation issues that kind of spread misalignment in the dataset.
+- A filtering process was applied using both **manual classification** and **trained classifiers**:
+  - A **subset** of the data was **manually classified** as **aligned** or **misaligned**.
+  - This subset was used to **train classifiers** (Logistic Regression and XGBoost) to label the remaining samples.
+    **Classifier Performance**:
+  - Best-performing model: **Logistic Regression**
+    - **Accuracy**: 0.84
+    - **F1-score (misaligned - class 0)**: 0.86
+    - **F1-score (aligned - class 1)**: 0.82
+      **Training Details**:
+  - **Balanced training set**: Positive samples (aligned) were supplemented using additional aligned samples from **Oza's Bambara-ASR** dataset.
+  - **Misaligned samples**: No additional samples were needed as they formed a majority.
+  - **Embedding processing**: Manually separated data has been represented as embeddings for training classifiers. The embeddings were obtained by inferring Wav2Vec and BERT, then concatenated for every example and labeled as either aligned or misaligned.
+
+Misaligned samples identified during classification were removed. That subset is currently undergoing further review and may be partially reintegrated in a future version of this dataset.
+
+### **4. Integration of Oza's Bambara-ASR Dataset**
+
+- This version integrates a clean subset from **Oza's Bambara-ASR** dataset making about 90% of the data.
+
+### **5. Lowercased Transcriptions**
+
+- All transcriptions and translations have been converted to lowercase for consistency.
+
+### **6. Silent/Empty File Filtering**
+
+- Silent or empty audio files  with inaudible content were removed.
+
+---

bam-asr-oza/dataset_dict.json

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

bam-asr-oza/test/{oza75-bam-asr-30700.wav → data-00000-of-00001.arrow}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0417260ef3f21c7c23a9d7472917f7595a8db11a8a771ca5df6a40b4c3297a44
-size 89422
+oid sha256:c4cda4de3902d1d196fc4335a79aa0208ba7e1a5e70ff62dbb31e7975320790a
+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
+  }
+}

bam-asr-oza/test/oza75-bam-asr-30704.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:df81318e3312e18baaaa8b97be4a0ec49e1d17bdafa34aa25ca2d267e1bff0e5
-size 96398

bam-asr-oza/test/oza75-bam-asr-30705.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:24864615af02212adbfc9e65ce987181040a9ef2d7960d01e5ae498a8048697a
-size 82284

bam-asr-oza/test/oza75-bam-asr-30706.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:74c51142b1d9b6152d78020c51caf76e8860a9d687d74539b90dbb26fc575687
-size 124844

bam-asr-oza/test/oza75-bam-asr-30707.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5cee908f0d5fc06dcdae2f435f6d69cf43338db2afba0d56ba6a0d6ce8935235
-size 68748

bam-asr-oza/test/oza75-bam-asr-30708.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2e7ec8caeca9d758cde96f3100231c317ba5ac097e526cf36ed18aa77ed5c291
-size 136044

bam-asr-oza/test/oza75-bam-asr-30709.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f2c78a3e8cb24066ac6964383675d71bc8808256a34fc7f338012e3565626a31
-size 48684

bam-asr-oza/test/oza75-bam-asr-30710.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4d11c3d06700f36fc804495763fac4973b994bfedac4c2b72c2175386accd48f
-size 35884

bam-asr-oza/test/oza75-bam-asr-30711.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2bfccb228e928bde7bc76d081d1f34568dc2d7dcc4162863430fb5174ae8d5d4
-size 49644

bam-asr-oza/test/oza75-bam-asr-30712.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2fa2f3d25bc3a4273b6df2fccf4cf0d5941b1f873170b31f395621ca77b4eaa6
-size 229804

bam-asr-oza/test/oza75-bam-asr-30713.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d84b8fdebaedf263b41a5335777e1fcdab98a8b775e9de948907e65ee176e237
-size 65836

bam-asr-oza/test/oza75-bam-asr-30714.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:af2304f17781dcdf810c88cfe121141980fca31cbc0e48fe42c0eab704aa4bec
-size 170734

bam-asr-oza/test/oza75-bam-asr-30715.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2579417015e3dff9ab0e39dbf8066ee5fa8db952a85292feee269f90dd52e67d
-size 61164

bam-asr-oza/test/oza75-bam-asr-30716.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d643c95bf97b059032c88aba58abdb09a6fd2011ec9deb91ac06b66b535291c0
-size 41294

bam-asr-oza/test/oza75-bam-asr-30717.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a17a10ea5a3e7e1a77e15510a3e6fc77cb78b09fd3ce026dc27e167c0d1046b9
-size 88844

bam-asr-oza/test/oza75-bam-asr-30718.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1217f629ca799e1b8fa75f48101324e50254ea8743e8ef2190d383e6aae9e306
-size 35244

bam-asr-oza/test/oza75-bam-asr-30719.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5474a6fd21d6e90fc2f0af3e0e6dc8dff806e631d04625f59748d8ef9402fc57
-size 118508

bam-asr-oza/test/oza75-bam-asr-30720.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a3b4294c41569deada5c9fc8f183569487c573c5b17d4c41ed0928548e0175ce
-size 124782

bam-asr-oza/test/oza75-bam-asr-30721.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e13d0fc7ba100655756c2a903ec385da657fdf6927783b3b74086f1ab15e462c
-size 108204

bam-asr-oza/test/oza75-bam-asr-30722.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f36a7058ff691b8cce6d85af9638f75eebebf3461c47b6a1e582da1ccea3bded
-size 223724

bam-asr-oza/test/oza75-bam-asr-30723.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3e9f5c6d1e9d80803327158ee37998674b988dc584229ccc5e8f56ad7472fe01
-size 61484

bam-asr-oza/test/oza75-bam-asr-30724.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f60f2de8c5c218c31093b3f19c972f769204a09e1222be0b2aefcf73936403ef
-size 264368

bam-asr-oza/test/oza75-bam-asr-30725.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a74d6dda80156093a964bfe22b9e565a97c7d1173806def19cbf27da5aa2b1d3
-size 42124

bam-asr-oza/test/oza75-bam-asr-30726.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:783b0cfa6da71d7642b21a47a380dd2510e624b99cc87b67e9cc95c62dc95a18
-size 34764

bam-asr-oza/test/oza75-bam-asr-30727.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:02be59f6b19af164303e27f4b2b28ccf66657c4077ae4228ed35a93cf673a915
-size 57614

bam-asr-oza/test/oza75-bam-asr-30728.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:fef5139a695e339f2512b9b41f9e3c946759a26c852991aefd26f052f6c7fdb3
-size 81900

bam-asr-oza/test/oza75-bam-asr-30729.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a3da8eb9c5ea3a8ece4c64b2a3a40e0683f281ff236df5472f915e43a8b0585e
-size 41100

bam-asr-oza/test/oza75-bam-asr-30730.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a6eabf1f885faa5b798eb024ac3df32a99da91c9d0124811a1fb0de349f4fa9e
-size 161900

bam-asr-oza/test/oza75-bam-asr-30731.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:34aeb05c9fbf098fa8c8b23aad72d37e4c62b2152e0042d6c4bc2caad2591646
-size 54124

bam-asr-oza/test/oza75-bam-asr-30732.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:718f43bf24543f80c96dd38e0adb97ae0a28bb0220484d8e8579aae797b6ce7a
-size 73964

bam-asr-oza/test/oza75-bam-asr-30733.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:600e4b2e9d1fd09f0b97eaec6bab80b339b462b42edbd42a6ef791f238936695
-size 221964

bam-asr-oza/test/oza75-bam-asr-30734.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7b0ea2a3c16ac0daa25606e19884bac00d3044662c61fd2b5609d7c1793bb985
-size 61804

bam-asr-oza/test/oza75-bam-asr-30735.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:653bd94b1c32161fa7366eef32cc6b6371db16caa2bc43c0173d7f7b974d1072
-size 212044

bam-asr-oza/test/oza75-bam-asr-30736.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1ef301a9ade3e57ef33e5ce61a8d7d376577d4cd5c2c214cb9dfc50e50786385
-size 66158

bam-asr-oza/test/oza75-bam-asr-30737.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:20128b4ad262f5d1fe8192e45bac094479f8c6352b97a29225d3727979cb5640
-size 53804

bam-asr-oza/test/oza75-bam-asr-30738.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e8aa2c10816b4b5741d2b55f0dd9b2acc794fbc5ecaceb264388a70662781286
-size 34414

bam-asr-oza/test/oza75-bam-asr-30739.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:438635f59c89fee8d524e38533446b0ebf4d07bba230c3aaf7a52b5ae08c5ced
-size 160684

bam-asr-oza/test/oza75-bam-asr-30740.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1d5d707e8c517562e38ab7b8b24ae98c34834ddfe014913f6a34c299aca92876
-size 78028

bam-asr-oza/test/oza75-bam-asr-30741.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1eb5b5273ee304ffaf94f6f50ee2f677a3d6ce7a47010701cc3b9021b63666ec
-size 104364

bam-asr-oza/test/oza75-bam-asr-30742.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2a250449696eb1718a823609a0d70c424e4b0b46d7b2a7aa3db81803927512d6
-size 39244

bam-asr-oza/test/oza75-bam-asr-30743.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d71926fb57f174ec1c4f22ecaa7e5d46b7321eac084b0eaea22608289eb2b125
-size 112940

bam-asr-oza/test/oza75-bam-asr-30744.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4e61c5616d836247a6b36f74ad9ff93da22335887ac8d0500ce1f62962c46365
-size 68844

bam-asr-oza/test/oza75-bam-asr-30745.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:21278d54dfafa4b08cf8c0b531adf0c21953c84339e200eb52b5423385ce7c20
-size 51566

bam-asr-oza/test/oza75-bam-asr-30746.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8ce98d66e926e1d4f729d3bfc30287d3d3f9db14b02c39a29b12746c085a9d3b
-size 47310

bam-asr-oza/test/oza75-bam-asr-30747.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:31b7d8e978e652a5c541684a6048e17d68b76cf6bf7ae0430a6f0a0787d2a9ed
-size 209968

bam-asr-oza/test/oza75-bam-asr-30748.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:371727e73cc5a2bb37a067e0c9f3283fa651e78263786b1cfa298ece09318334
-size 117484