audio_id
stringlengths
24
24
audio
audioduration (s)
0.95
9.74
speaker_id
stringclasses
150 values
gender
stringclasses
2 values
duration
float32
0.95
9.74
normalized_text
stringlengths
1
195
CHMC_F_43_20ABR1232_0002
F_43
female
3.669
suma memoria uno más memoria dos
CHMC_F_43_20ABR1232_0000
F_43
female
7.558
um bueno creo que es cuando uno tiene total derecho a expresar lo que siente
CHMC_F_43_20ABR1232_0003
F_43
female
1.37
y eso
CHMC_F_43_20ABR1232_0004
F_43
female
1.231
luego
CHMC_F_43_20ABR1232_0006
F_43
female
3.936
ciento cuarenta y dos menos memoria uno
CHMC_F_43_20ABR1232_0005
F_43
female
2.38
e ochenta y siete por doce
CHMC_F_43_20ABR1232_0001
F_43
female
4.563
o lo que piensas sin ser juzgado o criticado
CHMC_F_40_20ABR1113_0005
F_40
female
1.428
enciéndete
CHMC_F_40_20ABR1113_0000
F_40
female
2.136
que todo está como
CHMC_F_40_20ABR1113_0001
F_40
female
2.299
pues la conveniencia de los políticos
CHMC_F_40_20ABR1113_0003
F_40
female
1.184
que
CHMC_F_40_20ABR1113_0006
F_40
female
1.834
entre setenta y uno
CHMC_F_40_20ABR1113_0004
F_40
female
1.196
íjole
CHMC_F_40_20ABR1113_0002
F_40
female
3.727
y que pues la política la hacemos todos no nada más los políticos
CHMC_F_28_09MAY1414_0015
F_28
female
2.136
pues se ven también
CHMC_F_28_09MAY1414_0018
F_28
female
1.103
también se ven
CHMC_F_28_09MAY1414_0028
F_28
female
3.901
e como están maltratando físicamente a uno de ellos
CHMC_F_28_09MAY1414_0034
F_28
female
1.219
calentar un
CHMC_F_28_09MAY1414_0011
F_28
female
2.67
y por ejemplo se ve que está dándole la espalda
CHMC_F_28_09MAY1414_0021
F_28
female
1.196
este em
CHMC_F_28_09MAY1414_0038
F_28
female
1.881
las claras diferencias es que
CHMC_F_28_09MAY1414_0008
F_28
female
1.823
colorido tiene
CHMC_F_28_09MAY1414_0045
F_28
female
1.138
a okey
CHMC_F_28_09MAY1414_0006
F_28
female
1.544
y pues bueno es
CHMC_F_28_09MAY1414_0022
F_28
female
1.66
se ven también
CHMC_F_28_09MAY1414_0036
F_28
female
1.637
también se ven
CHMC_F_28_09MAY1414_0044
F_28
female
1.056
okey
CHMC_F_28_09MAY1414_0002
F_28
female
2.508
e pues trabajando arduamente
CHMC_F_28_09MAY1414_0031
F_28
female
3.762
pues igual e como amedrentándolo con un
CHMC_F_28_09MAY1414_0019
F_28
female
1.207
algunas armas no
CHMC_F_28_09MAY1414_0016
F_28
female
4.319
algunos esclavos o bueno los veo como esclavos haciendo labor
CHMC_F_28_09MAY1414_0029
F_28
female
1.231
piernas
CHMC_F_28_09MAY1414_0024
F_28
female
3.332
bueno con las cuales pues están e tratando de
CHMC_F_28_09MAY1414_0000
F_28
female
1.324
unas llamas
CHMC_F_28_09MAY1414_0032
F_28
female
1.718
con pues es
CHMC_F_28_09MAY1414_0043
F_28
female
1.091
CHMC_F_28_09MAY1414_0014
F_28
female
1.161
color verde
CHMC_F_28_09MAY1414_0003
F_28
female
2.055
hay indígenas que hay
CHMC_F_28_09MAY1414_0012
F_28
female
1.405
un un español
CHMC_F_28_09MAY1414_0009
F_28
female
2.171
diferentes intensidades de color
CHMC_F_28_09MAY1414_0013
F_28
female
2.043
un sombrero puesto
CHMC_F_28_09MAY1414_0033
F_28
female
4.377
con u bueno hay caldero al lado y el caldero loc e lo que está haciendo es
CHMC_F_28_09MAY1414_0001
F_28
female
1.997
e a gente que está
CHMC_F_28_09MAY1414_0020
F_28
female
1.231
espadas
CHMC_F_28_09MAY1414_0035
F_28
female
1.068
como un tizón
CHMC_F_28_09MAY1414_0007
F_28
female
1.335
es
CHMC_F_28_09MAY1414_0027
F_28
female
1.045
también se ve
CHMC_F_28_09MAY1414_0005
F_28
female
1.962
se ve una lucha de
CHMC_F_28_09MAY1414_0042
F_28
female
1.091
traen tapabarrabo
CHMC_F_28_09MAY1414_0025
F_28
female
1.393
intimidar
CHMC_F_28_09MAY1414_0010
F_28
female
1.184
con su hijo
CHMC_F_28_09MAY1414_0017
F_28
female
1.347
de grabado
CHMC_F_28_09MAY1414_0026
F_28
female
1.196
a los
CHMC_F_28_09MAY1414_0030
F_28
female
5.863
y al frente de de esea de ese indígena se ve también a un español que está
CHMC_F_28_09MAY1414_0039
F_28
female
2.078
e los indígenas no traen
CHMC_F_28_09MAY1414_0041
F_28
female
1.544
qué más
CHMC_F_28_09MAY1414_0004
F_28
female
1.834
em españoles
CHMC_F_28_09MAY1414_0037
F_28
female
1.3
en este cuadro
CHMC_F_28_09MAY1414_0040
F_28
female
3.251
pues algunos incluso portan una especie de armadura
CHMC_F_28_09MAY1414_0023
F_28
female
1.997
por ejemplo un caldero
CHMC_F_04_04MAY1604_0000
F_04
female
1.556
bueno
CHMC_F_04_04MAY1604_0001
F_04
female
2.043
un hombre con un caballo
CHMC_F_04_04MAY1604_0009
F_04
female
1.126
ya
CHMC_F_04_04MAY1604_0006
F_04
female
2.09
como parte de un bosque o
CHMC_F_04_04MAY1604_0007
F_04
female
1.602
CHMC_F_04_04MAY1604_0003
F_04
female
1.8
y mujeres este
CHMC_F_04_04MAY1604_0005
F_04
female
1.707
una canasta
CHMC_F_04_04MAY1604_0011
F_04
female
1.602
morada naranja
CHMC_F_04_04MAY1604_0010
F_04
female
1.474
roja verde
CHMC_F_04_04MAY1604_0002
F_04
female
1.637
esclavitud
CHMC_F_04_04MAY1604_0008
F_04
female
1.312
personas
CHMC_F_04_04MAY1604_0004
F_04
female
1.509
cargando
CHMC_F_73_30ABR1415_0009
F_73
female
1.562
ay es que está confuso
CHMC_F_73_30ABR1415_0006
F_73
female
1.347
está un pintor
CHMC_F_73_30ABR1415_0005
F_73
female
1.19
qué hacen
CHMC_F_73_30ABR1415_0002
F_73
female
2.159
algunos sacerdotes
CHMC_F_73_30ABR1415_0014
F_73
female
1.312
pinta a otra persona
CHMC_F_73_30ABR1415_0015
F_73
female
1.643
tiempo otra persona está
CHMC_F_73_30ABR1415_0020
F_73
female
2.02
y nada más están en un estudio
CHMC_F_73_30ABR1415_0021
F_73
female
3.193
es que no no sé inglés
CHMC_F_73_30ABR1415_0003
F_73
female
1.109
realmente qué
CHMC_F_73_30ABR1415_0012
F_73
female
1.178
qué es
CHMC_F_73_30ABR1415_0013
F_73
female
1.863
una persona que
CHMC_F_73_30ABR1415_0016
F_73
female
1.788
a no lo sé hay un
CHMC_F_73_30ABR1415_0004
F_73
female
1.811
um no lo sé podría ser
CHMC_F_73_30ABR1415_0000
F_73
female
4.214
pareciera que hay alguna clase de rito o de ritual
CHMC_F_73_30ABR1415_0019
F_73
female
3.831
que posiblemente le está no sé murmurando que haga algo malo
CHMC_F_73_30ABR1415_0010
F_73
female
2.368
otra persona que también está pintando
CHMC_F_73_30ABR1415_0007
F_73
female
1.689
pues
CHMC_F_73_30ABR1415_0017
F_73
female
2.647
señor con algunos instrumentos de dibujo
CHMC_F_73_30ABR1415_0008
F_73
female
3.019
y en teoría debería de estar haciendo un autorretrato
CHMC_F_73_30ABR1415_0018
F_73
female
2.014
y tiene un diablo en el hombro
CHMC_F_73_30ABR1415_0011
F_73
female
1.904
pero hay otros autorretratos
CHMC_F_73_30ABR1415_0001
F_73
female
2.171
animales gente
CHMC_F_34_09MAY1844_0037
F_34
female
1.207
como de lateral
CHMC_F_34_09MAY1844_0025
F_34
female
3.065
ya lo había dicho hay alguien el el mismo visto de perfil
CHMC_F_34_09MAY1844_0005
F_34
female
2.473
a también está de perfil lateral
CHMC_F_34_09MAY1844_0010
F_34
female
1.579
como la voz de la conciencia
CHMC_F_34_09MAY1844_0039
F_34
female
1.707
este con lentes
CHMC_F_34_09MAY1844_0030
F_34
female
1.231
qué más

Dataset Card for chm150_asr

Dataset Summary

The CHM150 is a corpus of microphone speech of mexican Spanish taken from 75 male speakers and 75 female speakers in a noise environment of a "quiet office" with a total duration of 1.63 hours.

Speakers were encouraged to respond between some pre selected open questions or they could also describe a particular painting showed to them in a computer monitor. By so, the speech is completely spontaneous and one can see it in the transcription file, that captures disfluencies and mispronunciations in an orthographic way.

The CHM150 Corpus was created at the "Laboratorio de Tecnologías del Habla" of the "Facultad de Ingeniería (FI)" in the "Universidad Nacional Autónoma de México (UNAM)" in 2012 by Carlos Daniel Hernández Mena, supervised by José Abel Herrera Camacho, head of Laboratory.

Example Usage

The CHM150 CORPUS contains only the train split:

from datasets import load_dataset
cm150_asr = load_dataset("carlosdanielhernandezmena/chm150_asr")

It is also valid to do:

from datasets import load_dataset
cm150_asr = load_dataset("carlosdanielhernandezmena/chm150_asr",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

{
  'audio_id': 'CHMC_F_43_20ABR1232_0002', 
  'audio': {
    'path': '/home/carlos/.cache/HuggingFace/datasets/downloads/extracted/eadb709611fa8f6fa88f7fa085738cf1e438d9a98d9a4c95314944f0730a8893/train/female/F20ABR1232/CHMC_F_43_20ABR1232_0002.flac', 
    'array': array([ 0.00067139,  0.00387573, -0.00784302, ..., -0.00485229,
        0.00497437, -0.00338745], dtype=float32), 
    'sampling_rate': 16000
  }, 
  'speaker_id': 'F_43', 
  'gender': 'female', 
  'duration': 3.6689999103546143, 
  'normalized_text': 'suma memoria uno más memoria dos'
}

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.
  • normalized_text (string) - normalized audio segment transcription

Data Splits

The corpus counts just with the train split which has a total of 2663 speech files from 75 male speakers and 75 female speakers with a total duration of 1 hour and 38 minutes.

Dataset Creation

Curation Rationale

The CHM150 is a corpus of microphone speech of mexican Spanish taken from 75 male speakers and 75 female speakers in a noise environment of a "quiet office" with a total duration of 1.63 hours.

Only the most "clean" utterances were selected to be part of the corpus. By "clean" one can understand that there is no background music, loud noises, or more than one people speaking at the same time.

The audio equipment utilized to create the corpus was modest, it consisted in:

The software utilized for recording was Audacity and then the audio was downsampled and normalized with SoX.

The main characteristics of the audio files are:

  • Encoding: Signed PCM
  • Sample Rate: 16000
  • Precision: 16 bit
  • Channels: 1 (mono)

Source Data

Initial Data Collection and Normalization

Speakers were encouraged to respond between some pre selected open questions or they could also describe a particular painting showed to them in a computer screen. By so, the speech is completely spontaneous and one can see it in the transcription file, that captures disfluencies and mispronunciations in an orthographic way.

Annotations

Annotation process

The annotation process is at follows:

    1. A whole session is manually segmented keeping just the portions containing good quality speech.
    1. The resulting speech files between 2 and 10 seconds are transcribed by the author.

Who are the annotators?

The CHM150 Corpus was collected and transcribed by Carlos Daniel Hernández Mena in 2012 as part of the objectives of his PhD studies.

Personal and Sensitive Information

The dataset could contain names (not full names) revealing the identity of some speakers. However, 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 and presents particular challenges, making it highly recommended for testing purposes.

Discussion of Biases

The dataset is gender balanced. It is comprised of 75 male speakers and 75 female speakers and the vocabulary is limited to the description of 5 different images.

Other Known Limitations

"CHM150 CORPUS" by Carlos Daniel Hernández Mena and Abel Herrera is licensed under a Creative Commons Attribution-ShareAlike 4.0 International (CC 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 and curated by Carlos Daniel Hernández Mena in 2012 as part of the objectives of his PhD studies. The corpus was published in 2016 at the Linguistic Data Consortium (LDC).

Licensing Information

CC-BY-SA-4.0

Citation Information

@misc{carlosmena2016chm150,
      title={CHM150 CORPUS: Audio and Transcripts of Mexican Spanish Spontaneous Speech.}, 
      ldc_catalog_no={LDC2016S04},
      DOI={https://doi.org/10.35111/ygn0-wm25},
      author={Hernandez Mena, Carlos Daniel and Herrera, Abel},
      journal={Linguistic Data Consortium, Philadelphia},
      year={2016},
      url={https://catalog.ldc.upenn.edu/LDC2016S04},
}

Contributions

This dataset card was created as part of the objectives of the 16th edition of the Severo Ochoa Mobility Program (PN039300 - Severo Ochoa 2021 - E&T).

Downloads last month
41