Adding all the files for the first time including the README.

README.md

@@ -1,3 +1,203 @@
 ---
-license: cc-by-sa-4.0
+annotations_creators:
+- expert-generated
+language:
+- es
+language_creators:
+- other
+license:
+- cc-by-sa-4.0
+multilinguality:
+- monolingual
+pretty_name: 'CIEMPIESS FEM CORPUS: Audio and Transcripts of Female Speakers in Spanish.'
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+tags:
+- ciempiess
+- spanish
+- mexican spanish
+- ciempiess project
+- ciempiess-unam project
+task_categories:
+- automatic-speech-recognition
+task_ids: []
 ---
+
+# Dataset Card for ciempiess_fem
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#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)
+  - [Annotations](#annotations)
+  - [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:** [CIEMPIESS-UNAM Project](https://ciempiess.org/)
+- **Repository:** [CIEMPIESS FEM at LDC](https://catalog.ldc.upenn.edu/LDC2019S07)
+- **Point of Contact:** [Carlos Mena](mailto:carlos.mena@ciempiess.org)
+
+### Dataset Summary
+
+Since the publication of the [CIEMPIESS Corpus (LDC2015S07)](https://catalog.ldc.upenn.edu/LDC2015S07) in 2015 we have noticed that there is a lack of female speakers in the sources where we traditionally take audio to create new CIEMPIESS datasets. That is why we decided to create a corpus that helps to balance future gender unbalanced datasets.
+
+The CIEMPIESS FEM Corpus was created by recordings and human transcripts of 21 different women. 16 of these women are mexican. The other ones come from Latin American countries.
+
+The CIEMPIESS FEM Corpus is considered a CIEMPIESS dataset because it only contains audio from the same source of the first CIEMPIESS Corpus and it is "FEM", obviously because it only contains recordings of female speakers. This corpus is part of the [CIEMPIESS Experimentation](https://catalog.ldc.upenn.edu/LDC2019S07), which is a set of three different datasets, specifically CIEMPIESS COMPLEMENTARY, CIEMPIESS FEM and CIEMPIESS TEST.
+
+CIEMPIESS is the acronym for:
+
+"Corpus de Investigación en Español de México del Posgrado de Ingeniería Eléctrica y Servicio Social".
+
+### Example Usage
+The CIEMPIESS FEM contains only the train split:
+```python
+from datasets import load_dataset
+ciempiess_fem = load_dataset("ciempiess/ciempiess_fem")
+```
+It is also valid to do:
+```python
+from datasets import load_dataset
+ciempiess_fem = load_dataset("ciempiess/ciempiess_fem",split="train")
+```
+
+### Supported Tasks
+automatic-speech-recognition: The dataset can be used to test a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).
+
+### Languages
+The language of the corpus is Spanish with the accent of Central Mexico.
+
+## Dataset Structure
+
+### Data Instances
+```python
+{
+  'audio_id': 'CMPF_F_05_MEX_0387', 
+  'audio': {
+    'path': '/home/carlos/.cache/HuggingFace/datasets/downloads/extracted/8a3e27631315b39636ac51affc04585335f9699f9635269c49f7938936aa60b8/train/mexican/F_05/CMPF_F_05_MEX_0387.flac', 
+    'array': array([0.0090332 , 0.0151062 , 0.01257324, ..., 0.01861572, 0.01797485,
+       0.02017212], dtype=float32), 'sampling_rate': 16000
+  }, 
+  'speaker_id': 'F_05', 
+  'gender': 'female', 
+  'duration': 4.979000091552734, 
+  'country': 'Mexico', 
+  'normalized_text': 'entre dos o más personas pero eh tienen que darse de manera'
+}
+```
+
+### Data Fields
+* `audio_id` (string) - id of audio segment
+* `audio` (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).
+* `speaker_id` (string) - id of speaker
+* `gender` (string) - gender of speaker (male or female)
+* `duration` (float32) - duration of the audio file in seconds.
+* `country` (string) - accent of the speaker.
+* `normalized_text` (string) - normalized audio segment transcription.
+
+### Data Splits
+
+The corpus counts just with the train split which has a total of 8555 speech files from 53 female speakers and 34 male speakers with a total duration of 18 hours and 20 minutes.
+
+## Dataset Creation
+
+### Curation Rationale
+
+The CIEMPIESS FEM (CF) Corpus has the following characteristics:
+
+* The CF has a total of 6505 audio files of 21 different women. It has a total duration of 13 hours and 54 minutes.
+
+* Every audio file in the CF has a duration between 5 and 10 seconds approximately.
+
+* Data in CF is classified by speaker and also by country, so one can easily select audios from a particular set of speakers to do experiments.
+
+* Audio files in the CF and the first CIEMPIESS are all of the same type. In both, speakers talk about legal and lawyer issues. They also talk about things related to the [UNAM University](https://www.unam.mx/) and the [Facultad de Derecho de la UNAM](https://www.derecho.unam.mx/).
+
+* As in the first CIEMPIESS Corpus, transcriptions in the CF were made by humans.
+
+* Speakers in the CF are not present in any other CIEMPIESS dataset.
+
+* Audio files in the CF are distributed in a 16khz@16bit mono format.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The CIEMPIESS FEM is a Radio Corpus designed to train acoustic models of automatic speech recognition and it is made out of recordings of spontaneous conversations in Spanish between a radio moderator and his guests. Most of the speech in these conversations has the accent of Central Mexico.
+
+All the recordings that constitute the CIEMPIESS FEM come from [RADIO-IUS](https://www.derecho.unam.mx/cultura-juridica/radio.php), a radio station belonging to [UNAM](https://www.unam.mx/). Recordings were donated by Lic. Cesar Gabriel Alanis Merchand and Mtro. Ricardo Rojas Arevalo from the [Facultad de Derecho de la UNAM](https://www.derecho.unam.mx/) with the condition that they have to be used for academic and research purposes only.
+
+### Annotations
+
+#### Annotation process
+
+The annotation process is at follows:
+
+* 1. A whole podcast is manually segmented keeping just the portions containing good quality speech.
+* 2. A second pass os segmentation is performed; this time to separate speakers and put them in different folders.
+* 3. The resulting speech files between 5 and 10 seconds are transcribed by students from different departments (computing, engineering, linguistics). Most of them are native speakers but not with a particular training as transcribers.
+
+#### Who are the annotators?
+
+The CIEMPIESS FEM Corpus was created by the social service program ["Desarrollo de Tecnologías del Habla"](http://profesores.fi-b.unam.mx/carlos_mena/servicio.html) of the ["Facultad de Ingeniería"](https://www.ingenieria.unam.mx/) (FI) in the ["Universidad Nacional Autónoma de México"](https://www.unam.mx/) (UNAM) between 2016 and 2018 by Carlos Daniel Hernández Mena, head of the program.
+
+### Personal and Sensitive Information
+
+The dataset could contain names revealing the identity of some speakers; on the other side, the recordings come from publicly available podcasts, so, there is not a real intent of the participants to be anonymized. Anyway, you agree to not attempt to determine the identity of speakers in this dataset.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+This dataset is valuable because it contains spontaneous speech.
+
+### Discussion of Biases
+
+The dataset is not gender balanced. It is comprised of 6505 audio files of 21 different women and the vocabulary is limited to legal issues.
+
+### Other Known Limitations
+
+"CIEMPIESS FEM CORPUS" by Carlos Daniel Hernández Mena is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/) License with the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+
+### Dataset Curators
+
+The dataset was collected by students belonging to the social service program ["Desarrollo de Tecnologías del Habla"](http://profesores.fi-b.unam.mx/carlos_mena/servicio.html). It was curated by [Carlos Daniel Hernández Mena](https://huggingface.co/carlosdanielhernandezmena) in 2018.
+
+### Licensing Information
+[CC-BY-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/)
+
+### Citation Information
+```
+@misc{carlosmenaciempiessfem2019,
+      title={CIEMPIESS FEM CORPUS: Audio and Transcripts of Female Speakers in Spanish.}, 
+      ldc_catalog_no={LDC2019S07},
+      DOI={https://doi.org/10.35111/xdx5-n815},
+      author={Hernandez Mena, Carlos Daniel},
+      journal={Linguistic Data Consortium, Philadelphia},
+      year={2019},
+      url={https://catalog.ldc.upenn.edu/LDC2019S07},
+}
+```
+### Contributions
+
+The authors want to thank to Alejandro V. Mena, Elena Vera and Angélica Gutiérrez for their support to the social service program: "Desarrollo de Tecnologías del Habla." We also thank to the social service students for all the hard work.
+
+

ciempiess_fem.py

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+from collections import defaultdict
+import os
+import json
+import csv
+
+import datasets
+
+_NAME="ciempiess_fem"
+_VERSION="1.0.0"
+_AUDIO_EXTENSIONS=".flac"
+
+_DESCRIPTION = """
+The CIEMPIESS FEM Corpus was created by recordings and human transcripts of 21 different women. 16 of these women are mexican. The other ones come from Latin American countries.
+"""
+
+_CITATION = """
+@misc{carlosmenaciempiessfem2019,
+      title={CIEMPIESS FEM CORPUS: Audio and Transcripts of Female Speakers in Spanish.}, 
+      ldc_catalog_no={LDC2019S07},
+      DOI={https://doi.org/10.35111/xdx5-n815},
+      author={Hernandez Mena, Carlos Daniel},
+      journal={Linguistic Data Consortium, Philadelphia},
+      year={2019},
+      url={https://catalog.ldc.upenn.edu/LDC2019S07},
+}
+"""
+
+_HOMEPAGE = "https://catalog.ldc.upenn.edu/LDC2019S07"
+
+_LICENSE = "CC-BY-SA-4.0, See https://creativecommons.org/licenses/by-sa/4.0/"
+
+_BASE_DATA_DIR = "corpus/"
+_METADATA_TRAIN  =  os.path.join(_BASE_DATA_DIR,"files", "metadata_train.tsv")
+
+_TARS_TRAIN  = os.path.join(_BASE_DATA_DIR,"files", "tars_train.paths")
+
+class CiempiessFemConfig(datasets.BuilderConfig):
+    """BuilderConfig for CIEMPIESS FEM Corpus"""
+
+    def __init__(self, name, **kwargs):
+        name=_NAME
+        super().__init__(name=name, **kwargs)
+
+class CiempiessFem(datasets.GeneratorBasedBuilder):
+    """CIEMPIESS Fem Corpus"""
+
+    VERSION = datasets.Version(_VERSION)
+    BUILDER_CONFIGS = [
+        CiempiessFemConfig(
+            name=_NAME,
+            version=datasets.Version(_VERSION),
+        )
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "audio_id": datasets.Value("string"),
+                "audio": datasets.Audio(sampling_rate=16000),
+                "speaker_id": datasets.Value("string"),
+                "gender": datasets.Value("string"),
+                "duration": datasets.Value("float32"),
+                "country": datasets.Value("string"),
+                "normalized_text": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+
+        metadata_train=dl_manager.download_and_extract(_METADATA_TRAIN)
+        
+        tars_train=dl_manager.download_and_extract(_TARS_TRAIN)
+        
+        hash_tar_files=defaultdict(dict)
+
+        with open(tars_train,'r') as f:
+            hash_tar_files['train']=[path.replace('\n','') for path in f]
+
+        hash_meta_paths={"train":metadata_train}
+        audio_paths = dl_manager.download(hash_tar_files)
+        
+        splits=["train"]
+        local_extracted_audio_paths = (
+            dl_manager.extract(audio_paths) if not dl_manager.is_streaming else
+            {
+                split:[None] * len(audio_paths[split]) for split in splits
+            }
+        )                                                                                                            
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["train"]],
+                    "local_extracted_archives_paths": local_extracted_audio_paths["train"],
+                    "metadata_paths": hash_meta_paths["train"],
+                }
+            ),
+        ]
+
+    def _generate_examples(self, audio_archives, local_extracted_archives_paths, metadata_paths):
+
+        features = ["speaker_id","gender","duration","country","normalized_text"]
+        
+        with open(metadata_paths) as f:
+            metadata = {x["audio_id"]: x for x in csv.DictReader(f, delimiter="\t")}
+
+        for audio_archive, local_extracted_archive_path in zip(audio_archives, local_extracted_archives_paths):
+            for audio_filename, audio_file in audio_archive:
+                audio_id = audio_filename.split(os.sep)[-1].split(_AUDIO_EXTENSIONS)[0]
+                path = os.path.join(local_extracted_archive_path, audio_filename) if local_extracted_archive_path else audio_filename
+                                        
+                yield audio_id, {
+                    "audio_id": audio_id,
+                    **{feature: metadata[audio_id][feature] for feature in features},
+                    "audio": {"path": path, "bytes": audio_file.read()},
+                }

corpus/files/tars_train.paths

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+corpus/speech/train.tar.gz

corpus/speech/train.tar.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2777f2b901562bb763789ef1c0243def54cc360dbb95c1f94de2b6dd638d435f
+size 958382502