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Gujarati (India) Call Center Speech Dataset for Real Estate

The audio dataset comprises call center conversations for the Real Estate domain, featuring native Gujarati speakers from India. It includes speech data, detailed metadata and accurate transcriptions.

Category

Unscripted Call Center Conversations

Total Volume

30 Speech Hours

Last updated

Jun 2024

Number of participants

60

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About this Off-the-shelf Speech Dataset

About Gradiet Line

Introduction

Welcome to the Gujarati Call Center Speech Dataset for the Real Estate domain designed to enhance the development of call center speech recognition models specifically for the Real Estate industry. This dataset is meticulously curated to support advanced speech recognition, natural language processing, conversational AI, and generative voice AI algorithms.

Speech Data:

This training dataset comprises 30 Hours of call center audio recordings covering various topics and scenarios related to the Real Estate domain, designed to build robust and accurate customer service speech technology.

  • •Participant Diversity:
  • •
    Speakers: 60 expert native Gujarati speakers from the FutureBeeAI Community.
  • •
    Regions: Different regions of Gujarat, ensuring a balanced representation of Gujarati accents, dialects, and demographics.
  • •
    Participant Profile: Participants range from 18 to 70 years old, representing both males and females in a 60:40 ratio, respectively.
  • •Recording Details:
  • •
    Conversation Nature: Unscripted and spontaneous conversations between call center agents and customers.
  • •
    Call Duration: Average duration of 5 to 15 minutes per call.
  • •
    Formats: WAV format with stereo channels, a bit depth of 16 bits, and a sample rate of 8 and 16 kHz.
  • •
    Environment: Without background noise and without echo.

    • Topic Diversity

    This dataset offers a diverse range of conversation topics, call types, and outcomes, including both inbound and outbound calls with positive, neutral, and negative outcomes.

  • •Inbound Calls:
  • •Property Inquiry
  • •Rental Property Search & Availability
  • •Renovation Inquiries
  • •Property Features & Amenities Inquiry
  • •Investment Property Analysis & Advice
  • •Property History & Ownership Details, and many more
  • •Outbound Calls:
  • •New Property Listing Update
  • •Post Purchase Follow-ups
  • •Investment Opportunities & Property Recommendations
  • •Property Value Updates
  • •Customer Satisfaction Surveys, and many more

    • This extensive coverage ensures the dataset includes realistic call center scenarios, which is essential for developing effective customer support speech recognition models.

    Transcription

    To facilitate your workflow, the dataset includes manual verbatim transcriptions of each call center audio file in JSON format. These transcriptions feature:

  • •
    Speaker-wise Segmentation: Time-coded segments for both agents and customers.
  • •
    Non-Speech Labels: Tags and labels for non-speech elements.
  • •
    Word Error Rate: Word error rate is less than 5% thanks to the dual layer of QA.

    • These ready-to-use transcriptions accelerate the development of the Real Estate domain call center conversational AI and ASR models for the Gujarati language.

    Metadata

    The dataset provides comprehensive metadata for each conversation and participant:

  • •
    Participant Metadata: Unique identifier, age, gender, country, state, district, accent and dialect.
  • •
    Conversation Metadata: Domain, topic, call type, outcome/sentiment, bit depth, and sample rate.

    • This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of Gujarati call center speech recognition models.

    Usage and Applications

    This dataset can be used for various applications in the fields of speech recognition, natural language processing, and conversational AI, specifically tailored to the Real Estate domain. Potential use cases include:

  • •
    Speech Recognition Models: Training and fine-tuning speech recognition models for Gujarati.
  • •
    Speech Analytics Models: Building speech analytics models to extract insights, identify patterns, and glean valuable information from customer conversation, enables data-driven decision-making and process optimization within the Real Estate sector.
  • •
    Smart Assistants and Chatbots: Developing conversational agents and virtual assistants for customer service in the Real Estate industries.
  • •
    Sentiment Analysis: Analyzing customer sentiment and improving customer experience based on call center interactions.
  • •
    Generative AI: Training generative AI models capable of generating human-like responses, summaries, or content tailored to the Real Estate domain.

    • Secure and Ethical Collection

  • •Our proprietary data collection and transcription platform, “Yugo” was used throughout the process of this dataset creation.
  • •Throughout the data collection process, the data remained within our secure platform and did not leave our environment, ensuring data security and confidentiality.
  • •The data collection process adhered to strict ethical guidelines, ensuring the privacy and consent of all participants.
  • •It does not include any personally identifiable information about any participant, which makes the dataset safe to use.
  • •The dataset does not contain any copyrighted content.

    • Updates and Customization

    Understanding the importance of diverse environments for robust ASR models, our call center voice dataset is regularly updated with new audio data captured in various real-world conditions.

  • •Customization & Custom Collection Options:
  • •
    Environmental Conditions: Custom collection in specific environmental conditions upon request.
  • •
    Sample Rates: Customizable from 8kHz to 48kHz.
  • •
    Transcription Customization: Tailored to specific guidelines and requirements.

    • License

    This Real Estate domain call center audio dataset is created by FutureBeeAI and is available for commercial use.

    Use Cases

    Use of speech data in Conversational AI

    Call Center Conversational AI

    Use of speech data for Automatic Speech Recognition

    ASR

    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
    Male(30)Male(26)wav, json

    TRANSCRIPTION

    LABELSTARTENDCHANNELTRANSCRIPT
    Speech1.2741.825Speaker 1<lang:Foreign>Hello</lang:Foreign>
    Speech2.7503.725Speaker 2<lang:Foreign>Hello Freebee</lang:Foreign>
    Speech4.9247.948Speaker 1હા હા <lang:Foreign>Futurebee</lang:Foreign> <lang:Foreign>से</lang:Foreign> <lang:Foreign>Futurebee</lang:Foreign> થી વાત કરું છું હું.
    Speech8.3009.599Speaker 1<PII>અજીન્ક્ય</PII> છે મારું નામ
    Speech10.04811.698Speaker 1#આહ તમે <PII>સુભમ મહાજન</PII> ને
    Speech12.74915.022Speaker 2#આહ મેં <lang:Foreign>last time call</lang:Foreign> કર્યો હતો.
    Speech15.49619.823Speaker 1હા હા <lang:Foreign>sir</lang:Foreign> હા બોલો <lang:Foreign>sir</lang:Foreign> કેવી રીતે આજે હું તમારી સહાયતા કરી શકું છું. બોલો
    Speech17.34718.498Speaker 2ત્રણ દિવસ પેલા
    Speech21.12329.071Speaker 2હા તો [noise] મારે <lang:Foreign>actually investment</lang:Foreign> કરવું છે સો મારે <lang:Foreign>flats</lang:Foreign> ની <lang:Foreign>booking</lang:Foreign> કરવી છે. પુને માં
    Speech31.32135.710Speaker 1અચ્છા <lang:Foreign>flats</lang:Foreign> એટલે <lang:Foreign>sir</lang:Foreign> તમે એક <lang:Foreign>flat</lang:Foreign> તમારા <lang:Foreign>family</lang:Foreign> માટે લો છો કે કેવી રીતે છે?
    Speech36.68546.228Speaker 2હા એક મારા <lang:Foreign>father</lang:Foreign> ને <lang:Foreign>mother</lang:Foreign> એમના માટે લેવો છે. અને એક મારા માટે લેવો છે. તો <lang:Foreign>side by side</lang:Foreign> બે <lang:Foreign>property</lang:Foreign> હોય ને એવું અમને કરવાનું છે.
    Speech47.65251.350Speaker 1અચ્છા અચ્છા સરસ સરસ તો <lang:Foreign>sir</lang:Foreign> પુને માં તમે ક્યાં <lang:Foreign>area</lang:Foreign> માં વિચાર કરો છો?
    Speech52.37759.965Speaker 2#આહ મારે <lang:Foreign>job</lang:Foreign> હિંજેવાડી માં હોય તો તો <lang:Foreign>better</lang:Foreign> છે કે એન જે હિંજેવાડી <lang:Foreign>side</lang:Foreign> નહીં તો પીપંળી છીંછોડ <lang:Foreign>side</lang:Foreign>
    Speech61.34064.938Speaker 1અચ્છા આ બહુ <lang:Foreign>develop develop</lang:Foreign> થતો.
    Speech61.89066.489Speaker 2કેમ કે મારે વધારે <lang:Foreign>out of</lang:Foreign> મારે <lang:Foreign>out of city project</lang:Foreign> નથી લેવો છે.
    Speech66.78968.962Speaker 2<lang:Foreign>actual main city</lang:Foreign> માં જ લેવું છે તો
    Speech67.46267.989Speaker 1હા
    Speech69.63870.837Speaker 2એજ છે. <lang:Foreign>requirement</lang:Foreign>
    Speech71.51383.712Speaker 1અચ્છા અચ્છા હિજેવાળી પીપળી ચિનચોડ ખરાડી આ બધા <lang:Foreign>area develop</lang:Foreign> બી થાય છે સારા <lang:Foreign>area</lang:Foreign> છે તમારે સારું રહેશે. મને બે ચાર વિગત વધારે વધારે હું તમને <lang:Foreign>just information</lang:Foreign> લઉં છું તો તમને એ રીતના <lang:Foreign>suggestion</lang:Foreign> કરું.
    Speech83.91294.489Speaker 1તો મને બે વસ્તુ બતાવો <lang:Foreign>sir</lang:Foreign> એક કે તમે કેવા <lang:Foreign>type</lang:Foreign> ના ઘર જુઓ છો. <lang:Foreign>sir</lang:Foreign> તમે <lang:Foreign>two <initial>BHK</initial> three <initial>BHK</initial></lang:Foreign> <lang:Foreign>villa</lang:Foreign> કે <lang:Foreign>pent house</lang:Foreign> કે કયા<lang:Foreign>type</lang:Foreign>નું જોઈએ છે. <lang:Foreign>sir</lang:Foreign> તમને
    Speech95.739101.435Speaker 2 એક એક મારા <lang:Foreign>father mother</lang:Foreign> માટે જે <lang:Foreign>flat</lang:Foreign> લેવો છે. એ <lang:Foreign>two <initial>BHK</initial></lang:Foreign> હોય તો સારું અને
    Speech100.611101.159Speaker 1#હમ
    Speech101.775105.799Speaker 2 #હમ જે મારા માટે છે બીજો <lang:Foreign>flat</lang:Foreign> આપણે કરીશું <lang:Foreign>three <initial>BHK</initial></lang:Foreign> જોઈએ.
    Speech107.709108.983Speaker 1અચ્છા સરસ
    Speech109.325110.299Speaker 1#આહ
    Speech109.909110.730Speaker 2અને
    Speech111.049111.625Speaker 2ને
    Speech111.950117.724Speaker 2#આહ એટલે<lang:Foreign>area</lang:Foreign> તો સારો જોઈએ. જ પણ એમાં <lang:Foreign>facilities</lang:Foreign> પણ સારી જોઈએ. જે આપણે #આહ
    Speech120.328124.433Speaker 2<lang:Foreign>Security</lang:Foreign> ને એવું બધું <lang:Foreign>facilities</lang:Foreign> હોય <lang:Foreign>garden</lang:Foreign> કે <lang:Foreign>gym and all</lang:Foreign> એ વાળું
    Speech120.558121.507Speaker 1તો બધી
    Speech124.174125.150Speaker 1અચ્છા અચ્છા <lang:Foreign>sir</lang:Foreign>
    Speech124.700125.525Speaker 2<lang:Foreign>okay</lang:Foreign>
    Speech125.632135.348Speaker 1એ તો <lang:Foreign>gated security gated area</lang:Foreign> હોય <lang:Foreign>gated society</lang:Foreign> હોય મોટી એટલે એમાં આ બધું આજકાલ <lang:Foreign>included</lang:Foreign> જ આવે <lang:Foreign>sir</lang:Foreign> બધું તમારી <lang:Foreign>facilities</lang:Foreign> છે ને <lang:Foreign>swimming pull</lang:Foreign> થી લઈને <lang:Foreign>gym</lang:Foreign> થી લઈને <lang:Foreign>club house</lang:Foreign> થી લઈને
    Speech135.574141.925Speaker 1પાણી ને <lang:Foreign>power power back</lang:Foreign> ને આ બધું આવે જ છે અમારા બધા એમાં એ તમને વાંધો નઈ આવે.
    Speech142.199147.848Speaker 1#આહ અને <lang:Foreign>area specious area</lang:Foreign> હોય છે અમારા <lang:Foreign>Futurebee</lang:Foreign> ના બધા <lang:Foreign>project</lang:Foreign> છે ને બહુ <lang:Foreign>specious</lang:Foreign>
    Speech144.000144.675Speaker 2<lang:Foreign>okay</lang:Foreign>
    Speech148.816149.466Speaker 1હોય છે.
    Speech150.091156.413Speaker 1તો એ રીત ના બી <lang:Foreign>problem</lang:Foreign> નથી ને બધા #આહ ઘરોના ઘર માં <lang:Foreign>balcony</lang:Foreign> બી આવશે. તમને એક એક <lang:Foreign>room</lang:Foreign> માં
    Speech150.639151.288Speaker 2<lang:Foreign>okay</lang:Foreign>
    Speech156.562167.514Speaker 1અને તમને એક <lang:Foreign>storage area</lang:Foreign> બી આવશે અલગ થી એ બધું તો છે જ <lang:Foreign>sir</lang:Foreign> એનો વાંધો નથી. મને ખાલી એક જણાવશો બીજું <lang:Foreign>final</lang:Foreign> કે તમારું <lang:Foreign>sir budget</lang:Foreign> તમે કેટલાનું વિચારો છો.
    Speech170.735171.485Speaker 2#આહ
    Speech172.275176.074Speaker 2<lang:Foreign>approxi</lang:Foreign> (()) છે ને ત્રણ સાડા ત્રણ કરોડ સુધી
    Speech176.550177.900Speaker 2બન્ને <lang:Foreign>flat</lang:Foreign> ના મળીને
    Speech177.211177.763Speaker 1હા
    Speech179.187180.788Speaker 1બેવું <lang:Foreign>flat</lang:Foreign> મળીને ત્રણ
    Speech180.014181.590Speaker 2<lang:Foreign>including yes</lang:Foreign>

    TRANSCRIPTION

    TIMETRANSCRIPT
    1.274
    1.825    		<lang:Foreign>Hello</lang:Foreign>
    2.750
    3.725    		<lang:Foreign>Hello Freebee</lang:Foreign>
    4.924
    7.948    		હા હા <lang:Foreign>Futurebee</lang:Foreign> <lang:Foreign>से</lang:Foreign> <lang:Foreign>Futurebee</lang:Foreign> થી વાત કરું છું હું.
    8.300
    9.599    		<PII>અજીન્ક્ય</PII> છે મારું નામ
    10.048
    11.698    		#આહ તમે <PII>સુભમ મહાજન</PII> ને
    12.749
    15.022    		#આહ મેં <lang:Foreign>last time call</lang:Foreign> કર્યો હતો.
    15.496
    19.823    		હા હા <lang:Foreign>sir</lang:Foreign> હા બોલો <lang:Foreign>sir</lang:Foreign> કેવી રીતે આજે હું તમારી સહાયતા કરી શકું છું. બોલો
    17.347
    18.498    		ત્રણ દિવસ પેલા
    21.123
    29.071    		હા તો [noise] મારે <lang:Foreign>actually investment</lang:Foreign> કરવું છે સો મારે <lang:Foreign>flats</lang:Foreign> ની <lang:Foreign>booking</lang:Foreign> કરવી છે. પુને માં
    31.321
    35.710    		અચ્છા <lang:Foreign>flats</lang:Foreign> એટલે <lang:Foreign>sir</lang:Foreign> તમે એક <lang:Foreign>flat</lang:Foreign> તમારા <lang:Foreign>family</lang:Foreign> માટે લો છો કે કેવી રીતે છે?
    36.685
    46.228    		હા એક મારા <lang:Foreign>father</lang:Foreign> ને <lang:Foreign>mother</lang:Foreign> એમના માટે લેવો છે. અને એક મારા માટે લેવો છે. તો <lang:Foreign>side by side</lang:Foreign> બે <lang:Foreign>property</lang:Foreign> હોય ને એવું અમને કરવાનું છે.
    47.652
    51.350    		અચ્છા અચ્છા સરસ સરસ તો <lang:Foreign>sir</lang:Foreign> પુને માં તમે ક્યાં <lang:Foreign>area</lang:Foreign> માં વિચાર કરો છો?
    52.377
    59.965    		#આહ મારે <lang:Foreign>job</lang:Foreign> હિંજેવાડી માં હોય તો તો <lang:Foreign>better</lang:Foreign> છે કે એન જે હિંજેવાડી <lang:Foreign>side</lang:Foreign> નહીં તો પીપંળી છીંછોડ <lang:Foreign>side</lang:Foreign>
    61.340
    64.938    		અચ્છા આ બહુ <lang:Foreign>develop develop</lang:Foreign> થતો.
    61.890
    66.489    		કેમ કે મારે વધારે <lang:Foreign>out of</lang:Foreign> મારે <lang:Foreign>out of city project</lang:Foreign> નથી લેવો છે.
    66.789
    68.962    		<lang:Foreign>actual main city</lang:Foreign> માં જ લેવું છે તો
    67.462
    67.989    		હા
    69.638
    70.837    		એજ છે. <lang:Foreign>requirement</lang:Foreign>
    71.513
    83.712    		અચ્છા અચ્છા હિજેવાળી પીપળી ચિનચોડ ખરાડી આ બધા <lang:Foreign>area develop</lang:Foreign> બી થાય છે સારા <lang:Foreign>area</lang:Foreign> છે તમારે સારું રહેશે. મને બે ચાર વિગત વધારે વધારે હું તમને <lang:Foreign>just information</lang:Foreign> લઉં છું તો તમને એ રીતના <lang:Foreign>suggestion</lang:Foreign> કરું.
    83.912
    94.489    		તો મને બે વસ્તુ બતાવો <lang:Foreign>sir</lang:Foreign> એક કે તમે કેવા <lang:Foreign>type</lang:Foreign> ના ઘર જુઓ છો. <lang:Foreign>sir</lang:Foreign> તમે <lang:Foreign>two <initial>BHK</initial> three <initial>BHK</initial></lang:Foreign> <lang:Foreign>villa</lang:Foreign> કે <lang:Foreign>pent house</lang:Foreign> કે કયા<lang:Foreign>type</lang:Foreign>નું જોઈએ છે. <lang:Foreign>sir</lang:Foreign> તમને
    95.739
    101.435    		 એક એક મારા <lang:Foreign>father mother</lang:Foreign> માટે જે <lang:Foreign>flat</lang:Foreign> લેવો છે. એ <lang:Foreign>two <initial>BHK</initial></lang:Foreign> હોય તો સારું અને
    100.611
    101.159    		#હમ
    101.775
    105.799    		 #હમ જે મારા માટે છે બીજો <lang:Foreign>flat</lang:Foreign> આપણે કરીશું <lang:Foreign>three <initial>BHK</initial></lang:Foreign> જોઈએ.
    107.709
    108.983    		અચ્છા સરસ
    109.325
    110.299    		#આહ
    109.909
    110.730    		અને
    111.049
    111.625    		ને
    111.950
    117.724    		#આહ એટલે<lang:Foreign>area</lang:Foreign> તો સારો જોઈએ. જ પણ એમાં <lang:Foreign>facilities</lang:Foreign> પણ સારી જોઈએ. જે આપણે #આહ
    120.328
    124.433    		<lang:Foreign>Security</lang:Foreign> ને એવું બધું <lang:Foreign>facilities</lang:Foreign> હોય <lang:Foreign>garden</lang:Foreign> કે <lang:Foreign>gym and all</lang:Foreign> એ વાળું
    120.558
    121.507    		તો બધી
    124.174
    125.150    		અચ્છા અચ્છા <lang:Foreign>sir</lang:Foreign>
    124.700
    125.525    		<lang:Foreign>okay</lang:Foreign>
    125.632
    135.348    		એ તો <lang:Foreign>gated security gated area</lang:Foreign> હોય <lang:Foreign>gated society</lang:Foreign> હોય મોટી એટલે એમાં આ બધું આજકાલ <lang:Foreign>included</lang:Foreign> જ આવે <lang:Foreign>sir</lang:Foreign> બધું તમારી <lang:Foreign>facilities</lang:Foreign> છે ને <lang:Foreign>swimming pull</lang:Foreign> થી લઈને <lang:Foreign>gym</lang:Foreign> થી લઈને <lang:Foreign>club house</lang:Foreign> થી લઈને
    135.574
    141.925    		પાણી ને <lang:Foreign>power power back</lang:Foreign> ને આ બધું આવે જ છે અમારા બધા એમાં એ તમને વાંધો નઈ આવે.
    142.199
    147.848    		#આહ અને <lang:Foreign>area specious area</lang:Foreign> હોય છે અમારા <lang:Foreign>Futurebee</lang:Foreign> ના બધા <lang:Foreign>project</lang:Foreign> છે ને બહુ <lang:Foreign>specious</lang:Foreign>
    144.000
    144.675    		<lang:Foreign>okay</lang:Foreign>
    148.816
    149.466    		હોય છે.
    150.091
    156.413    		તો એ રીત ના બી <lang:Foreign>problem</lang:Foreign> નથી ને બધા #આહ ઘરોના ઘર માં <lang:Foreign>balcony</lang:Foreign> બી આવશે. તમને એક એક <lang:Foreign>room</lang:Foreign> માં
    150.639
    151.288    		<lang:Foreign>okay</lang:Foreign>
    156.562
    167.514    		અને તમને એક <lang:Foreign>storage area</lang:Foreign> બી આવશે અલગ થી એ બધું તો છે જ <lang:Foreign>sir</lang:Foreign> એનો વાંધો નથી. મને ખાલી એક જણાવશો બીજું <lang:Foreign>final</lang:Foreign> કે તમારું <lang:Foreign>sir budget</lang:Foreign> તમે કેટલાનું વિચારો છો.
    170.735
    171.485    		#આહ
    172.275
    176.074    		<lang:Foreign>approxi</lang:Foreign> (()) છે ને ત્રણ સાડા ત્રણ કરોડ સુધી
    176.550
    177.900    		બન્ને <lang:Foreign>flat</lang:Foreign> ના મળીને
    177.211
    177.763    		હા
    179.187
    180.788    		બેવું <lang:Foreign>flat</lang:Foreign> મળીને ત્રણ
    180.014
    181.590    		<lang:Foreign>including yes</lang:Foreign>

    Dataset Demographics

    Details Headline

    Language

    Gujarati

    Language code

    gu-in

    Country

    India

    Accents

    Kathiawari,...more

    Gender Distribution

    M:60, F:40

    Age Group

    18-70

    Audio File Details

    Details Headline

    Environment

    Silent, Noisy

    Bit Depth

    16 bit

    Format

    wav

    Sample rate

    8khz & 16khz

    Channel

    Stereo

    Audio file duration

    5-15 minutes

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