upload tar files and csvs

.gitattributes

@@ -52,3 +52,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+*.csv filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,364 @@
+---
+pretty_name: AfriSpeech-200
+annotations_creators:
+- expert-generated
+language_creators:
+- crowdsourced
+- expert-generated
+language:
+- en
+license:
+- cc-by-nc-sa-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- automatic-speech-recognition
+task_ids: []
+dataset_info:
+  features:
+  - name: user_id
+    dtype: string
+  - name: path
+    dtype: string
+  - name: audio
+    dtype:
+      audio:
+        sampling_rate: 44100
+  - name: transcript
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 1722002133
+    num_examples: 58000
+  - name: dev
+    num_bytes: 86120227
+    num_examples: 3231
+  download_size: 1475540500
+  dataset_size: 1808122360
+  extra_gated_prompt: By clicking on “Access repository” below, you also agree to not attempt to determine the
+   identity of speakers in the Common Voice dataset.
+---
+
+# Dataset Card for AfriSpeech-200
+
+## Table of Contents
+- [Dataset Card for AfriSpeech-200](#dataset-card-for-afrispeech-200)
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Description](#dataset-description)
+    - [Dataset Summary](#dataset-summary)
+    - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+    - [Languages](#languages)
+  - [Dataset Structure](#dataset-structure)
+    - [Data Instances](#data-instances)
+    - [Data Fields](#data-fields)
+    - [Data Splits](#data-splits)
+  - [Dataset Creation](#dataset-creation)
+    - [Curation Rationale](#curation-rationale)
+    - [Source Data](#source-data)
+      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
+      - [Who are the source language producers?](#who-are-the-source-language-producers)
+    - [Annotations](#annotations)
+      - [Annotation process](#annotation-process)
+      - [Who are the annotators?](#who-are-the-annotators)
+    - [Personal and Sensitive Information](#personal-and-sensitive-information)
+  - [Considerations for Using the Data](#considerations-for-using-the-data)
+    - [Social Impact of Dataset](#social-impact-of-dataset)
+    - [Discussion of Biases](#discussion-of-biases)
+    - [Other Known Limitations](#other-known-limitations)
+  - [Additional Information](#additional-information)
+    - [Dataset Curators](#dataset-curators)
+    - [Licensing Information](#licensing-information)
+    - [Citation Information](#citation-information)
+    - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://github.com/intron-innovation/AfriSpeech-Dataset-Paper
+- **Repository:** https://github.com/intron-innovation/AfriSpeech-Dataset-Paper
+- **Paper:** [AfriSpeech-200: Pan-African accented speech dataset for clinical and general domain ASR](https://github.com/intron-innovation/AfriSpeech-Dataset-Paper)
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [Intron Innovation](mailto:intron@intron.io)
+
+### Dataset Summary
+
+AFRISPEECH-200 is a 200hr Pan-African speech corpus for clinical and general domain English accented ASR; a dataset with 120 African accents from 13 countries and 2,463 unique African speakers. 
+Our goal is to raise awareness for and advance Pan-African English ASR research, especially for the clinical domain. 
+
+### Supported Tasks and Leaderboards
+
+- Automatic Speech Recognition
+- Speech Synthesis (Text-to-Speech)
+
+### Languages
+
+English (Accented)
+
+## Dataset Structure
+
+### Data Instances
+
+A typical data point comprises the path to the audio file, called `path` and its transcription, called `sentence`. Some additional information about the speaker and the passage which contains the transcription is provided.
+
+```
+{'user_id': 'ABCDXYZ01',
+ 'path': '/home/admin/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/vivos/train/waves/VIVOSSPK01/VIVOSSPK01_R001.wav',
+ 'audio': {'path': '/home/admin/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/vivos/train/waves/VIVOSSPK01/VIVOSSPK01_R001.wav',
+		   'array': array([-0.00048828, -0.00018311, -0.00137329, ...,  0.00079346, 0.00091553,  0.00085449], dtype=float32),
+		   'sampling_rate': 16000},
+ 'transcirpt': 'The patient took the correct medication'}
+```
+
+### Data Fields
+
+- speaker_id: An id for which speaker (voice) made the recording
+
+- path: The path to the audio file
+
+- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
+
+- transcript: The sentence the user was prompted to speak
+
+### Data Splits
+
+The speech material has been subdivided into portions for train, dev, and test.
+
+Speech was recorded in a quiet environment with high quality microphone, speakers were asked to read one sentence at a time.
+
+- Total Number of Unique Speakers: 2,463
+- Female/Male/Other Ratio: 57.11/42.41/0.48
+- Data was first split on speakers. Speakers in Train/Dev/Test do not cross partitions
+
+|  | Train | Dev | Test |
+| ----------- | ----------- | ----------- | ----------- |
+| # Speakers | 1466 | 247 | 750 |
+| # Seconds | 624228.83 | 31447.09 | 67559.10 |
+| # Hours | 173.4 | 8.74 | 18.77 |
+| # Accents | 71 | 45 | 108 |
+| Avg secs/speaker | 425.81 | 127.32 | 90.08 |
+| Avg num clips/speaker | 39.56 | 13.08 | 8.46 |
+| Avg num speakers/accent | 20.65 | 5.49 | 6.94 |
+| Avg secs/accent | 8791.96 | 698.82 | 625.55 |
+| # clips general domain | 21682 | 1407 | 2723 |
+| # clips clinical domain | 36318 | 1824 | 3623 |
+
+## Dataset Creation
+
+### Curation Rationale
+
+Africa has a very low doctor-to-patient ratio. 
+At very busy clinics, doctors could see 30+ patients per day-- a heavy patient burden compared with 
+developed countries-- but productivity tools such as clinical automatic speech recognition 
+(ASR) are lacking for these overworked clinicians. However, clinical ASR is mature, even ubiquitous, 
+in developed nations, and clinician-reported performance of commercial clinical ASR systems 
+is generally satisfactory. Furthermore, the recent performance of general domain ASR is 
+approaching human accuracy. However, several gaps exist. Several publications have 
+highlighted racial bias with speech-to-text algorithms and performance on minority 
+accents lags significantly. To our knowledge, there is no publicly available research or 
+benchmark on accented African clinical ASR, and speech data is non-existent for the 
+majority of African accents. We release AfriSpeech, 200hrs of Pan-African speech, 
+67,577 clips from 2,463 unique speakers, across 120 indigenous accents from 13 countries for 
+clinical and general domain ASR, a benchmark test set, with publicly available pre-trained 
+models with SOTA performance on the AfriSpeech benchmark.
+
+### Source Data
+
+#### Country Stats
+
+| Country | Clips | Speakers | Duration (seconds) | Duration (hrs) |
+| ----------- | ----------- | ----------- | ----------- | ----------- |
+| NG | 45875 | 1979 | 512646.88 | 142.40 |
+| KE | 8304 | 137 | 75195.43 | 20.89 |
+| ZA | 7870 | 223 | 81688.11 | 22.69 |
+| GH | 2018 | 37 | 18581.13 | 5.16 |
+| BW | 1391 | 38 | 14249.01 | 3.96 |
+| UG | 1092 | 26 | 10420.42 | 2.89 |
+| RW | 469 | 9 | 5300.99 | 1.47 |
+| US | 219 | 5 | 1900.98 | 0.53 |
+| TR | 66 | 1 | 664.01 | 0.18 |
+| ZW | 63 | 3 | 635.11 | 0.18 |
+| MW | 60 | 1 | 554.61 | 0.15 |
+| TZ | 51 | 2 | 645.51 | 0.18 |
+| LS | 7 | 1 | 78.40 | 0.02 |
+
+#### Accent Stats
+
+|  Accent | Clips | Speakers | Duration (s) | Country | Splits | 
+| ----------- | ----------- | ----------- | ----------- | ----------- | ----------- |
+| yoruba | 15407 | 683 | 161587.55 | US,NG | train,test,dev |
+| igbo | 8677 | 374 | 93035.79 | US,NG,ZA | train,test,dev |
+| swahili | 6320 | 119 | 55932.82 | KE,TZ,ZA,UG | train,test,dev |
+| hausa | 5765 | 248 | 70878.67 | NG | train,test,dev |
+| ijaw | 2499 | 105 | 33178.9 | NG | train,test,dev |
+| afrikaans | 2048 | 33 | 20586.49 | ZA | train,test,dev |
+| idoma | 1877 | 72 | 20463.6 | NG | train,test,dev |
+| zulu | 1794 | 52 | 18216.97 | ZA,TR,LS | dev,train,test |
+| setswana | 1588 | 39 | 16553.22 | BW,ZA | dev,test,train |
+| twi | 1566 | 22 | 14340.12 | GH | test,train,dev |
+| isizulu | 1048 | 48 | 10376.09 | ZA | test,train,dev |
+| igala | 919 | 31 | 9854.72 | NG | train,test |
+| izon | 838 | 47 | 9602.53 | NG | train,dev,test |
+| kiswahili | 827 | 6 | 8988.26 | KE | train,test |
+| ebira | 757 | 42 | 7752.94 | NG | train,test,dev |
+| luganda | 722 | 22 | 6768.19 | UG,BW,KE | test,dev,train |
+| urhobo | 646 | 32 | 6685.12 | NG | train,dev,test |
+| nembe | 578 | 16 | 6644.72 | NG | train,test,dev |
+| ibibio | 570 | 39 | 6489.29 | NG | train,test,dev |
+| pidgin | 514 | 20 | 5871.57 | NG | test,train,dev |
+| luhya | 508 | 4 | 4497.02 | KE | train,test |
+| kinyarwanda | 469 | 9 | 5300.99 | RW | train,test,dev |
+| xhosa | 392 | 12 | 4604.84 | ZA | train,dev,test |
+| tswana | 387 | 18 | 4148.58 | ZA,BW | train,test,dev |
+| esan | 380 | 13 | 4162.63 | NG | train,test,dev |
+| alago | 363 | 8 | 3902.09 | NG | train,test |
+| tshivenda | 353 | 5 | 3264.77 | ZA | test,train |
+| fulani | 312 | 18 | 5084.32 | NG | test,train |
+| isoko | 298 | 16 | 4236.88 | NG | train,test,dev |
+| akan (fante) | 295 | 9 | 2848.54 | GH | train,dev,test |
+| ikwere | 293 | 14 | 3480.43 | NG | test,train,dev |
+| sepedi | 275 | 10 | 2751.68 | ZA | dev,test,train |
+| efik | 269 | 11 | 2559.32 | NG | test,train,dev |
+| edo | 237 | 12 | 1842.32 | NG | train,test,dev |
+| luo | 234 | 4 | 2052.25 | UG,KE | test,train,dev |
+| kikuyu | 229 | 4 | 1949.62 | KE | train,test,dev |
+| bekwarra | 218 | 3 | 2000.46 | NG | train,test |
+| isixhosa | 210 | 9 | 2100.28 | ZA | train,dev,test |
+| hausa/fulani | 202 | 3 | 2213.53 | NG | test,train |
+| epie | 202 | 6 | 2320.21 | NG | train,test |
+| isindebele | 198 | 2 | 1759.49 | ZA | train,test |
+| venda and xitsonga | 188 | 2 | 2603.75 | ZA | train,test |
+| sotho | 182 | 4 | 2082.21 | ZA | dev,test,train |
+| akan | 157 | 6 | 1392.47 | GH | test,train |
+| nupe | 156 | 9 | 1608.24 | NG | dev,train,test |
+| anaang | 153 | 8 | 1532.56 | NG | test,dev |
+| english | 151 | 11 | 2445.98 | NG | dev,test |
+| afemai | 142 | 2 | 1877.04 | NG | train,test |
+| shona | 138 | 8 | 1419.98 | ZA,ZW | test,train,dev |
+| eggon | 137 | 5 | 1833.77 | NG | test |
+| luganda and kiswahili | 134 | 1 | 1356.93 | UG | train |
+| ukwuani | 133 | 7 | 1269.02 | NG | test |
+| sesotho | 132 | 10 | 1397.16 | ZA | train,dev,test |
+| benin | 124 | 4 | 1457.48 | NG | train,test |
+| kagoma | 123 | 1 | 1781.04 | NG | train |
+| nasarawa eggon | 120 | 1 | 1039.99 | NG | train |
+| tiv | 120 | 14 | 1084.52 | NG | train,test,dev |
+| south african english | 119 | 2 | 1643.82 | ZA | train,test |
+| borana | 112 | 1 | 1090.71 | KE | train |
+| swahili ,luganda ,arabic | 109 | 1 | 929.46 | UG | train |
+| ogoni | 109 | 4 | 1629.7 | NG | train,test |
+| mada | 109 | 2 | 1786.26 | NG | test |
+| bette | 106 | 4 | 930.16 | NG | train,test |
+| berom | 105 | 4 | 1272.99 | NG | dev,test |
+| bini | 104 | 4 | 1499.75 | NG | test |
+| ngas | 102 | 3 | 1234.16 | NG | train,test |
+| etsako | 101 | 4 | 1074.53 | NG | train,test |
+| okrika | 100 | 3 | 1887.47 | NG | train,test |
+| venda | 99 | 2 | 938.14 | ZA | train,test |
+| siswati | 96 | 5 | 1367.45 | ZA | dev,train,test |
+| damara | 92 | 1 | 674.43 | NG | train |
+| yoruba, hausa | 89 | 5 | 928.98 | NG | test |
+| southern sotho | 89 | 1 | 889.73 | ZA | train |
+| kanuri | 86 | 7 | 1936.78 | NG | test,dev |
+| itsekiri | 82 | 3 | 778.47 | NG | test,dev |
+| ekpeye | 80 | 2 | 922.88 | NG | test |
+| mwaghavul | 78 | 2 | 738.02 | NG | test |
+| bajju | 72 | 2 | 758.16 | NG | test |
+| luo, swahili | 71 | 1 | 616.57 | KE | train |
+| dholuo | 70 | 1 | 669.07 | KE | train |
+| ekene | 68 | 1 | 839.31 | NG | test |
+| jaba | 65 | 2 | 540.66 | NG | test |
+| ika | 65 | 4 | 576.56 | NG | test,dev |
+| angas | 65 | 1 | 589.99 | NG | test |
+| ateso | 63 | 1 | 624.28 | UG | train |
+| brass | 62 | 2 | 900.04 | NG | test |
+| ikulu | 61 | 1 | 313.2 | NG | test |
+| eleme | 60 | 2 | 1207.92 | NG | test |
+| chichewa | 60 | 1 | 554.61 | MW | train |
+| oklo | 58 | 1 | 871.37 | NG | test |
+| meru | 58 | 2 | 865.07 | KE | train,test |
+| agatu | 55 | 1 | 369.11 | NG | test |
+| okirika | 54 | 1 | 792.65 | NG | test |
+| igarra | 54 | 1 | 562.12 | NG | test |
+| ijaw(nembe) | 54 | 2 | 537.56 | NG | test |
+| khana | 51 | 2 | 497.42 | NG | test |
+| ogbia | 51 | 4 | 461.15 | NG | test,dev |
+| gbagyi | 51 | 4 | 693.43 | NG | test |
+| portuguese | 50 | 1 | 525.02 | ZA | train |
+| delta | 49 | 2 | 425.76 | NG | test |
+| bassa | 49 | 1 | 646.13 | NG | test |
+| etche | 49 | 1 | 637.48 | NG | test |
+| kubi | 46 | 1 | 495.21 | NG | test |
+| jukun | 44 | 2 | 362.12 | NG | test |
+| igbo and yoruba | 43 | 2 | 466.98 | NG | test |
+| urobo | 43 | 3 | 573.14 | NG | test |
+| kalabari | 42 | 5 | 305.49 | NG | test |
+| ibani | 42 | 1 | 322.34 | NG | test |
+| obolo | 37 | 1 | 204.79 | NG | test |
+| idah | 34 | 1 | 533.5 | NG | test |
+| bassa-nge/nupe | 31 | 3 | 267.42 | NG | test,dev |
+| yala mbembe | 29 | 1 | 237.27 | NG | test |
+| eket | 28 | 1 | 238.85 | NG | test |
+| afo | 26 | 1 | 171.15 | NG | test |
+| ebiobo | 25 | 1 | 226.27 | NG | test |
+| nyandang | 25 | 1 | 230.41 | NG | test |
+| ishan | 23 | 1 | 194.12 | NG | test |
+| bagi | 20 | 1 | 284.54 | NG | test |
+| estako | 20 | 1 | 480.78 | NG | test |
+| gerawa | 13 | 1 | 342.15 | NG | test |
+
+#### Initial Data Collection and Normalization
+
+[Needs More Information]
+
+#### Who are the source language producers?
+
+[Needs More Information]
+
+### Annotations
+
+#### Annotation process
+
+[Needs More Information]
+
+#### Who are the annotators?
+
+[Needs More Information]
+
+### Personal and Sensitive Information
+
+The dataset consists of people who have donated their voice online.  You agree to not attempt to determine the identity of speakers in this dataset.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed] 
+
+### Other Known Limitations
+
+Dataset provided for research purposes only. Please check dataset license for additional information. 
+
+## Additional Information
+
+### Dataset Curators
+
+The dataset was initially prepared by Intron and refined for public release by CLAIR Lab.
+
+### Licensing Information
+
+Public Domain, Creative Commons Attribution NonCommercial ShareAlike v4.0 ([CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode))
+
+### Citation Information
+
+[More Information Needed] 
+
+### Contributions
+
+Thanks to [@tobiolatunji](https://github.com/tobiolatunji) for adding this dataset.

afrispeech-200.py

@@ -0,0 +1,160 @@
+# coding=utf-8
+# Copyright 2023 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.
+""" AfriSpeech-200 Dataset"""
+
+
+import csv
+import os
+
+import datasets
+from datasets.utils.py_utils import size_str
+from tqdm import tqdm
+
+
+_CITATION = """ TBD """
+
+_DESCRIPTION = """\
+AFRISPEECH-200 is a 200hr Pan-African speech corpus for clinical and general domain English accented ASR; 
+a dataset with 120 African accents from 13 countries and 2,463 unique African speakers. 
+Our goal is to raise awareness for and advance Pan-African English ASR research, 
+especially for the clinical domain. 
+"""
+
+_HOMEPAGE = "https://github.com/intron-innovation/AfriSpeech-Dataset-Paper"
+
+_LICENSE = "http://creativecommons.org/licenses/by-nc-sa/4.0/"
+
+
+# TODO: change "streaming" to "main" after merge!
+_BASE_URL = "https://huggingface.co/datasets/intron/afrispeech-200/main/"
+
+_AUDIO_URL = _BASE_URL + "audio/{split}/{split}_{shard_idx}.tar.gz"
+
+_TRANSCRIPT_URL = _BASE_URL + "transcripts/{split}.csv"
+
+_SHARDS = {
+    'train': 35,
+    'dev': 2,
+    'test': 4
+}
+
+
+class AfriSpeech(datasets.GeneratorBasedBuilder):
+    DEFAULT_WRITER_BATCH_SIZE = 1000
+    VERSION = datasets.Version("1.1.0")
+    DEFAULT_CONFIG_NAME = "all"
+
+    def _info(self):
+        description = _DESCRIPTION
+        features = datasets.Features(
+            {
+                "speaker_id": datasets.Value("string"),
+                "path": datasets.Value("string"),
+                "audio": datasets.features.Audio(sampling_rate=44_100),
+                "transcript": datasets.Value("string"),
+                "age_group": datasets.Value("string"),
+                "gender": datasets.Value("string"),
+                "accent": datasets.Value("string"),
+                "domain": datasets.Value("string"),
+                "country": datasets.Value("string"),
+                "duration": datasets.Value("float"),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=description,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+            version=self.VERSION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # If several configurations are possible (listed in BUILDER_CONFIGS),
+        # the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
+        # It can accept any type or nested list/dict and will give back the same structure
+        # with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder
+        # where they are extracted is returned instead of the archive
+
+        n_shards = _SHARDS
+
+        audio_urls = {}
+        splits = ("train", "dev")  # , "test"
+        for split in splits:
+            audio_urls[split] = [
+                _AUDIO_URL.format(split=split, shard_idx=i) for i in range(n_shards[split])
+            ]
+        archive_paths = dl_manager.download(audio_urls)
+        local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}
+
+        meta_urls = {split: _TRANSCRIPT_URL.format(split=split) for split in splits}
+        meta_paths = dl_manager.download_and_extract(meta_urls)
+
+        split_generators = []
+        split_names = {
+            "train": datasets.Split.TRAIN,
+            "dev": datasets.Split.VALIDATION,
+            # "test": datasets.Split.TEST,
+        }
+        for split in splits:
+            split_generators.append(
+                datasets.SplitGenerator(
+                    name=split_names.get(split, split),
+                    gen_kwargs={
+                        "local_extracted_archive_paths": local_extracted_archive_paths.get(split),
+                        "archives": [dl_manager.iter_archive(path) for path in archive_paths.get(split)],
+                        "meta_path": meta_paths[split],
+                    },
+                ),
+            )
+
+        return split_generators
+
+    def _generate_examples(self, local_extracted_archive_paths, archives, meta_path):
+        """Yields examples as (key, example) tuples."""
+        # This method handles input defined in _split_generators to yield (key, example) tuples
+        # from the dataset.
+        # The `key` is here for legacy reason (tfds) and is not important in itself.
+
+        data_fields = list(self._info().features.keys())
+        metadata = {}
+        with open(meta_path, "r", encoding="utf-8") as f:
+            reader = csv.DictReader(f)
+            for row in tqdm(reader, desc="Reading metadata..."):
+                row["speaker_id"] = row["user_ids"]
+                # if data is incomplete, fill with empty values
+                for field in data_fields:
+                    if field not in row:
+                        row[field] = ""
+                metadata[row["audio_paths"]] = row
+
+        for i, audio_archive in enumerate(archives):
+            for filename, file in audio_archive:
+                _, filename = os.path.split(filename)
+                if filename in metadata:
+                    result = dict(metadata[filename])
+                    # set the audio feature and the path to the extracted file
+                    path = os.path.join(local_extracted_archive_paths[i], filename) if local_extracted_archive_paths else filename
+                    result["audio"] = {"path": path, "bytes": file.read()}
+                    # set path to None if the audio file doesn't exist locally (i.e. in streaming mode)
+                    result["path"] = path if local_extracted_archive_paths else filename
+
+                    yield path, result

audio/dev/dev_0.tar.gz

@@ -0,0 +1,3 @@
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+oid sha256:ad5799fabc150c6489af97c80de393ad0d3f841dd64eb057d92fb0acf4972f78
+size 1444447165

audio/dev/dev_1.tar.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2dc5abbffcc23b45113167b208bd1a19c56fcc59fc298e5e220e47b236810d82
+size 1251443845

audio/test/test_0.tar.gz

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