English (India) General Conversation Speech Dataset

The audio dataset consist of general conversations between native English people from India along with metadata and transcription.

Category

Unscripted General Conversations

Total Volume

90 Speech Hours

Last updated

July 2023

Number of participants

110

Get this Speech Dataset

Get Dataset Btn

About this Off-the-shelf Speech Dataset

About Gradiet Line

What’s Included

Welcome to the English Language General Conversation Speech Dataset, a comprehensive and diverse collection of voice data specifically curated to advance the development of English language speech recognition models, with a particular focus on Indian accents and dialects.


With high-quality audio recordings, detailed metadata, and accurate transcriptions, it empowers researchers and developers to enhance natural language processing, conversational AI, and Generative Voice AI algorithms. Moreover, it facilitates the creation of sophisticated voice assistants and voice bots tailored to the unique linguistic nuances found in the English language spoken in India.


Speech Data:

This training dataset comprises 100 hours of audio recordings covering a wide range of topics and scenarios, ensuring robustness and accuracy in speech technology applications. To achieve this, we collaborated with a diverse network of 110 native English speakers from different part of India. This collaborative effort guarantees a balanced representation of Indian accents, dialects, and demographics, reducing biases and promoting inclusivity.


Each audio recording captures the essence of spontaneous, unscripted conversations between two individuals, with an average duration ranging from 15 to 60 minutes. The speech data is available in WAV format, with stereo channel files having a bit depth of 16 bits and a sample rate of 8 kHz. The recording environment is generally quiet, without background noise and echo.


Metadata:

In addition to the audio recordings, our dataset provides comprehensive metadata for each participant. This metadata includes the participant's age, gender, country, state, and dialect. Furthermore, additional metadata such as recording device detail, topic of recording, bit depth, and sample rate will be provided.


The metadata serves as a valuable tool for understanding and characterizing the data, facilitating informed decision-making in the development of English language speech recognition models.


Transcription:

This dataset provides a manual verbatim transcription of each audio file to enhance your workflow efficiency. The transcriptions are available in JSON format. The transcriptions capture speaker-wise transcription with time-coded segmentation along with non-speech labels and tags.


Our goal is to expedite the deployment of English language conversational AI and NLP models by offering ready-to-use transcriptions, ultimately saving valuable time and resources in the development process.


Updates and Customization:

We understand the importance of collecting data in various environments to build robust ASR models. Therefore, our voice dataset is regularly updated with new audio data captured in diverse real-world conditions.


If you require a custom training dataset with specific environmental conditions such as in-car, busy street, restaurant, or any other scenario, we can accommodate your request. We can provide voice data with customized sample rates ranging from 8kHz to 48kHz, allowing you to fine-tune your models for different audio recording setups. Additionally, we can also customize the transcription following your specific guidelines and requirements, to further support your ASR development process.


License:

This audio dataset, created by FutureBeeAI, is now available for commercial use.


Conclusion:

Whether you are training or fine-tuning speech recognition models, advancing NLP algorithms, exploring generative voice AI, or building cutting-edge voice assistants and bots, our dataset serves as a reliable and valuable resource.


Use Cases

Use of speech data for Automatic Speech Recognition

ASR

Use of speech data in Conversational AI

Conversational AI

Use of speech data for Chatbot & voicebot creation

Chatbot

Use of speech data in Language Modeling

Language Modelling

Use of speech data in Text-into-speech

TTS

Speech data usecase in Speech Analytics

Speech Analytics

Dataset Sample(s)

Sample Line

ATTRIBUTES

Channel 1Channel 2Format
Female(22)Female(21)wav, json

TRANSCRIPTION

LABELSTARTENDCHANNELTRANSCRIPT
Speech3.2744.674Speaker 1Hello Futurebee.
Speech5.1746.573Speaker 2Hello Futurebee.
Noise5.5995.974--
Speech8.3499.774Speaker 1Hi <PII>Sushmita</PII>.
Speech10.29814.022Speaker 1Did you catch the cricket match yesterday. It was incredible.
Speech12.07312.624Speaker 2(())
Speech16.11719.643Speaker 2oh I missed it. What happened? Tell me all about it.
Speech20.77125.312Speaker 1Well it was a thrilling match between our home team and the rival.
Speech26.46927.893Speaker 1Our team battle
Speech28.17831.553Speaker 1first and set a challenging target of three hundred run.
Speech32.06339.137Speaker 1They started of really well with our opening batsman scoring quick run and building a solid foundation.
Speech42.37746.350Speaker 2That sounds promising. Did they manage to maintain the momentum
Speech46.75247.850Speaker 2through out the inning?
Speech49.19556.045Speaker 1Yes. They did initially. But the rival teams baller made a strong comeback in the middle over.
Speech56.69770.870Speaker 1Our batsman struggled a bit to rotate the strike and find boundary. However one of our middle order batsman played a spectacular inning, hitting some massive sixes and stabilizing the inning.
Speech74.88178.605Speaker 2That's great to hear. Did they eventually reach the target?
Speech79.85585.105Speaker 1They came close but unfortunately, they failed short by just ten runs.
Speech85.51392.063Speaker 1It was a nail biting finish with our team leading twelve run in the last over.
Speech93.049100.299Speaker 1Our lower order batsman fought hard but the rival teams baller balled a fantastic final over.
Speech100.924104.899Speaker 1(()) only one run and taking two crucial wicket.
Speech108.382109.533Speaker 2That must have
Speech109.831110.881Speaker 2been disappointing
Speech111.090112.992Speaker 2but it sounds like an intense match.
Speech113.390115.265Speaker 2How was the rivals team batting?
Speech116.605122.105Speaker 1The rival teams batsman started over aggressively scoring boundaries from the beginning.
Speech123.049127.924Speaker 1They maintain a good run rate and kept the required run rate under control.
Speech128.258132.036Speaker 1However our ballers made a strong comeback in the middle over
Speech132.479135.401Speaker 1taking some crucial wickets and building pressure.
Speech138.722141.169Speaker 2So it was a close contest till the end.
Speech142.056150.205Speaker 1Absolutely. The rival team leaded fifteen runs in the last over and it seem like they might chase (())
Speech150.431160.131Speaker 1But our baller balled an exceptional over taking two wickets and considering only four run. In the end our team won the match by ten run.
Noise162.074163.924--
Speech162.735168.336Speaker 2Wow. What a comeback. It sounds like a memorable match. I wish I could have seen it live.
Speech170.258173.383Speaker 1Definitely, it was one of those matches
Speech173.651176.377Speaker 1where the momentum shifted back and forth
Speech176.627182.929Speaker 1The atmosphere in the stadium was electric and the crowd was on their feet through out the match.
Speech183.442187.491Speaker 1You should definitely catch the highlight. they are worth watching.

TRANSCRIPTION

TIMETRANSCRIPT
3.274
4.674
Hello Futurebee.
5.174
6.573
Hello Futurebee.
5.599
5.974
-
8.349
9.774
Hi <PII>Sushmita</PII>.
10.298
14.022
Did you catch the cricket match yesterday. It was incredible.
12.073
12.624
(())
16.117
19.643
oh I missed it. What happened? Tell me all about it.
20.771
25.312
Well it was a thrilling match between our home team and the rival.
26.469
27.893
Our team battle
28.178
31.553
first and set a challenging target of three hundred run.
32.063
39.137
They started of really well with our opening batsman scoring quick run and building a solid foundation.
42.377
46.350
That sounds promising. Did they manage to maintain the momentum
46.752
47.850
through out the inning?
49.195
56.045
Yes. They did initially. But the rival teams baller made a strong comeback in the middle over.
56.697
70.870
Our batsman struggled a bit to rotate the strike and find boundary. However one of our middle order batsman played a spectacular inning, hitting some massive sixes and stabilizing the inning.
74.881
78.605
That's great to hear. Did they eventually reach the target?
79.855
85.105
They came close but unfortunately, they failed short by just ten runs.
85.513
92.063
It was a nail biting finish with our team leading twelve run in the last over.
93.049
100.299
Our lower order batsman fought hard but the rival teams baller balled a fantastic final over.
100.924
104.899
(()) only one run and taking two crucial wicket.
108.382
109.533
That must have
109.831
110.881
been disappointing
111.090
112.992
but it sounds like an intense match.
113.390
115.265
How was the rivals team batting?
116.605
122.105
The rival teams batsman started over aggressively scoring boundaries from the beginning.
123.049
127.924
They maintain a good run rate and kept the required run rate under control.
128.258
132.036
However our ballers made a strong comeback in the middle over
132.479
135.401
taking some crucial wickets and building pressure.
138.722
141.169
So it was a close contest till the end.
142.056
150.205
Absolutely. The rival team leaded fifteen runs in the last over and it seem like they might chase (())
150.431
160.131
But our baller balled an exceptional over taking two wickets and considering only four run. In the end our team won the match by ten run.
162.074
163.924
-
162.735
168.336
Wow. What a comeback. It sounds like a memorable match. I wish I could have seen it live.
170.258
173.383
Definitely, it was one of those matches
173.651
176.377
where the momentum shifted back and forth
176.627
182.929
The atmosphere in the stadium was electric and the crowd was on their feet through out the match.
183.442
187.491
You should definitely catch the highlight. they are worth watching.

Dataset Demographics

Details Headline

Language

English

Language code

en-In

Country

India

Accents

Chandigarh,...more

Gender Distribution

M:55, F:45

Age Group

18-70

Audio File Details

Details Headline

Environment

Silent, Noisy

Bit Depth

16 bit

Format

wav

Sample rate

8khz

Channel

Dual separate channel

Audio file duration

15-60 minutes

Download Sample Speech Dataset Now!

Explore Audio Data, Metadata and Transcription to get more clarity and hands on experience of this dataset.

Download Free Dataset

Audio Download Btn
Audio Promp Bg
Audio Promp Bg

Start your AI/ML model creation journey with FutureBeeAI!

Contact Us

Audio Arrow BtnAudio Arrow Btn Black
Audio Promp 2 Bg