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Gujarati (India) Call Center Speech Dataset for Delivery & Logistics

The audio dataset comprises call center conversations for the Delivery & Logistics 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 Delivery and Logistics domain designed to enhance the development of call center speech recognition models specifically for the Delivery and Logistics 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 xscenarios related to the Delivery and Logistics 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:
  • •Order Tracking
  • •Delivery Complaint
  • •Undeliverable Address
  • •Delivery Method Selection
  • •Return Process Enquiry
  • •Order Modification, and many more
  • •Outbound Calls:
  • •Delivery Confirmation
  • •Delivery Subscription
  • •Incorrect Address
  • •Missed Delivery Attempt
  • •Delivery Feedback
  • •Out-of-Stock Notification
  • •Delivery Satisfaction Survey, 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 Delivery and Logistics 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 Delivery and Logistics 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 Delivery and Logistics sector.
  • •
    Smart Assistants and Chatbots: Developing conversational agents and virtual assistants for customer service in the Delivery and Logistics 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 Delivery and Logistics 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 Delivery and Logistics 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
    Speech0.5501.100Speaker 1<lang:Foreign>Hello</lang:Foreign>
    Speech3.4756.349Speaker 2 <lang:Foreign>Hello Futurbee Car Transport Services</lang:Foreign>
    Speech5.1155.716Speaker 1હા
    Speech7.77210.074Speaker 1હા હુ <lang:Foreign>Futurbee Delivery</lang:Foreign>બોલું છું
    Speech10.62514.897Speaker 1#હમ <lang:Foreign>Car Transport भी</lang:Foreign> છે એમા. બતાવો <lang:Foreign>sir</lang:Foreign> તમારી કેમ હુ શાહેતા કરી શકુ?
    Speech11.38111.782Speaker 2#અમ
    Speech16.18128.812Speaker 2#અમ અત્યારે મે મારે <lang:Foreign>Actually Sir Car</lang:Foreign> મોકલવી છે તો મે <lang:Foreign>Google</lang:Foreign> પર તમારા <lang:Foreign>Reviews</lang:Foreign> જોયા <lang:Foreign>Company</lang:Foreign> ના તો મને એમ લાગ્યુ કે એક વાર <lang:Foreign>Inquiry</lang:Foreign> કરી લેવાય એમ.
    Speech31.30344.078Speaker 1અચ્છા અચ્છા ના ના <lang:Foreign>Sir Google</lang:Foreign> તો છે જ અમે <lang:Foreign>But Actual</lang:Foreign> મા <lang:Foreign>भी</lang:Foreign> અમારુ સારૂ ચાલે છે ને <lang:Foreign>Customer</lang:Foreign> બોવ <lang:Foreign>Satisfied</lang:Foreign> હોય છે તો સારી જગ્યા એ <lang:Foreign>Call</lang:Foreign>છે બતાવો <lang:Foreign>Sir</lang:Foreign> કેવી રીતે હુ તમને <lang:Foreign>Help</lang:Foreign> કરુ? કઈ <lang:Foreign>Car</lang:Foreign> છે તમારી પેલા તો એ મને જણાવો?
    Speech43.43543.799Speaker 2#અમ
    Speech45.32648.950Speaker 2#ઉહ અમારુ #હમ <lang:Foreign>WagonR <initial>LXI</initial> Model</lang:Foreign> છે
    Speech50.05151.234Speaker 2બે હજાર દસ વાડુ.
    Speech50.50350.774Speaker 1#હમ
    Speech51.52652.125Speaker 1કેટલા
    Speech53.02656.576Speaker 1અચ્છા બે હજાર દસ હમડા <lang:Foreign>Car Fully Runing</lang:Foreign> છે કે કઈ તફલિક છે <lang:Foreign>Car</lang:Foreign> મા?
    Speech57.85959.898Speaker 2નઈ <lang:Foreign>Fully Running</lang:Foreign> છે
    Speech60.34861.512Speaker 2ને કોઈ
    Speech61.91463.225Speaker 2એને (())
    Speech63.72568.209Speaker 2(()) એટલે એને <lang:Foreign>Body</lang:Foreign> ને કઈ બધુ બરાબર છે કોઈ <lang:Foreign>Issue</lang:Foreign> નથી.
    Speech68.43372.459Speaker 1<lang:Foreign>Okay</lang:Foreign> ને <lang:Foreign>Petrol</lang:Foreign> પર છે <lang:Foreign>Sir</lang:Foreign> ગાડી કે <lang:Foreign><initial>CNG</initial></lang:Foreign> વગર આ <lang:Foreign>Diesel</lang:Foreign> વગર હાલે?
    Speech72.15773.956Speaker 2<lang:Foreign>Both Both</lang:Foreign> બન્ને વ છે.
    Speech75.28379.358Speaker 1અચ્છા સરસ તો તમારે <lang:Foreign>Sir</lang:Foreign> બિજુ એટલે ક્યાથી ક્યા મોકલવાની છે ગાડી આ?
    Speech80.40886.281Speaker 2#હમ <lang:Foreign>Actually</lang:Foreign> આમારે છે ને સુરત થી બેંગલોર સુધિ મોકલવાની છે.
    Speech89.07290.875Speaker 1અચ્છા સુરત થી બેંગલોર <lang:Foreign>Okay</lang:Foreign>
    Speech91.32695.772Speaker 1#હમ [noise] ને ક્યારે ક્યારે વિચાર છે તમારો <lang:Foreign>Move</lang:Foreign> કરવાનો?
    Speech97.79699.347Speaker 2#હમ આ <lang:Foreign>Month</lang:Foreign> ની
    Speech100.949102.072Speaker 2પંદર તારીખ પછી.
    Speech104.272106.421Speaker 1પંદર તારીખથી <lang:Foreign>Okay</lang:Foreign>
    Speech106.694108.750Speaker 2પંદર તારીખથી ને પચ્છી જો
    Speech109.176111.245Speaker 2<lang:Foreign>Next</lang:Foreign> દસ દિવસમા મડી જાતુ હોય તો.
    Speech113.171114.451Speaker 1<lang:Foreign>Okay Okay</lang:Foreign>
    Speech115.227123.245Speaker 1ઠીક છે અને <lang:Foreign>sir</lang:Foreign> તમારા ગાડી ના <lang:Foreign>Papers</lang:Foreign> તમારા જોડે <lang:Foreign>Ready</lang:Foreign> છે આપડે <lang:Foreign><initial>RC</initial>Book</lang:Foreign> છે જે બિજુ કાઈક છ ઈ <lang:Foreign>Polution Certificae Insurance</lang:Foreign>
    Speech120.968121.376Speaker 2હ
    Speech121.852126.694Speaker 2<lang:Foreign>Papers</lang:Foreign> છે બધા <lang:Foreign>papers Paper</lang:Foreign> છે બધા છે મારી જોડે પન.
    Speech126.918129.351Speaker 2ક્યા ક્યા લાગસે એક વાર મને કઈ દો તો હુ એની
    Speech129.777132.068Speaker 2અને <lang:Foreign>Original</lang:Foreign> જોસે કે <lang:Foreign>Zerox Copy</lang:Foreign>?
    Speech134.127139.682Speaker 1ના અમારે તો<lang:Foreign>Zerox Copy</lang:Foreign> ચાલી જશે એક છે તમારુ <lang:Foreign><initial>RC</initial> Book</lang:Foreign> છે ઈ તમારુ <lang:Foreign>Insurance</lang:Foreign> હસે ગાડી નુ ઈ
    Speech136.092136.667Speaker 2[noise] <lang:Foreign>Okay</lang:Foreign>
    Speech139.143139.838Speaker 2<lang:Foreign>Okay</lang:Foreign>
    Speech140.133144.751Speaker 1અને ત્રીજુ તમારુ <lang:Foreign><initial>PUC</initial> Certificate</lang:Foreign> હસે ને ગાડી નુ ઈ ત્યા ચલાવસો તો ત્યા લાગશે.
    Speech141.014141.491Speaker 2#હમ
    Speech144.217144.890Speaker 2#હમ
    Speech146.516147.101Speaker 2<lang:Foreign>Okay</lang:Foreign>
    Speech147.502149.376Speaker 2છે આ કાગડીયા તો છે મરી જોડે
    Speech149.827152.039Speaker 2ને મને એ કો પેલા કે?
    Speech153.116159.164Speaker 2કેટલા દિવસ મા ગાડી પોચસે ને <lang:Foreign>approximant Price</lang:Foreign> શુ રેસે એ મને થોડી જાણકારી આપશો?
    Speech159.965169.462Speaker 1અચ્છા તો સુરત થી નિકડતા બેંગ્લોર એક <lang:Foreign>Minute</lang:Foreign> હો હુ <lang:Foreign>Chek</lang:Foreign> કરી લવ <lang:Foreign>System</lang:Foreign> મા સુરત થી બેંગલોર <lang:Foreign>Distance</lang:Foreign> નઈ નઈ કભી પંદરશો કિલોમિટર એટલુ હોય <lang:Foreign>sir</lang:Foreign>.
    Speech170.139172.389Speaker 1તમારી દિવસમાં જાય છે.
    Speech173.330180.354Speaker 1તણસો કિલોમિટર ચલાવી અમે રોજ એના તો પાંચ દિવસ લગ્સે <lang:Foreign>sir</lang:Foreign> પોંચતા <lang:Foreign>Proxylate</lang:Foreign> કવ ચુ આમા પાંચ દિવસ મા એકાદ દિવસ ઉપર નિચે થાઈ સકે છે.

    TRANSCRIPTION

    TIMETRANSCRIPT
    0.550
    1.100    		<lang:Foreign>Hello</lang:Foreign>
    3.475
    6.349    		 <lang:Foreign>Hello Futurbee Car Transport Services</lang:Foreign>
    5.115
    5.716    		હા
    7.772
    10.074    		હા હુ <lang:Foreign>Futurbee Delivery</lang:Foreign>બોલું છું
    10.625
    14.897    		#હમ <lang:Foreign>Car Transport भी</lang:Foreign> છે એમા. બતાવો <lang:Foreign>sir</lang:Foreign> તમારી કેમ હુ શાહેતા કરી શકુ?
    11.381
    11.782    		#અમ
    16.181
    28.812    		#અમ અત્યારે મે મારે <lang:Foreign>Actually Sir Car</lang:Foreign> મોકલવી છે તો મે <lang:Foreign>Google</lang:Foreign> પર તમારા <lang:Foreign>Reviews</lang:Foreign> જોયા <lang:Foreign>Company</lang:Foreign> ના તો મને એમ લાગ્યુ કે એક વાર <lang:Foreign>Inquiry</lang:Foreign> કરી લેવાય એમ.
    31.303
    44.078    		અચ્છા અચ્છા ના ના <lang:Foreign>Sir Google</lang:Foreign> તો છે જ અમે <lang:Foreign>But Actual</lang:Foreign> મા <lang:Foreign>भी</lang:Foreign> અમારુ સારૂ ચાલે છે ને <lang:Foreign>Customer</lang:Foreign> બોવ <lang:Foreign>Satisfied</lang:Foreign> હોય છે તો સારી જગ્યા એ <lang:Foreign>Call</lang:Foreign>છે બતાવો <lang:Foreign>Sir</lang:Foreign> કેવી રીતે હુ તમને <lang:Foreign>Help</lang:Foreign> કરુ? કઈ <lang:Foreign>Car</lang:Foreign> છે તમારી પેલા તો એ મને જણાવો?
    43.435
    43.799    		#અમ
    45.326
    48.950    		#ઉહ અમારુ #હમ <lang:Foreign>WagonR <initial>LXI</initial> Model</lang:Foreign> છે
    50.051
    51.234    		બે હજાર દસ વાડુ.
    50.503
    50.774    		#હમ
    51.526
    52.125    		કેટલા
    53.026
    56.576    		અચ્છા બે હજાર દસ હમડા <lang:Foreign>Car Fully Runing</lang:Foreign> છે કે કઈ તફલિક છે <lang:Foreign>Car</lang:Foreign> મા?
    57.859
    59.898    		નઈ <lang:Foreign>Fully Running</lang:Foreign> છે
    60.348
    61.512    		ને કોઈ
    61.914
    63.225    		એને (())
    63.725
    68.209    		(()) એટલે એને <lang:Foreign>Body</lang:Foreign> ને કઈ બધુ બરાબર છે કોઈ <lang:Foreign>Issue</lang:Foreign> નથી.
    68.433
    72.459    		<lang:Foreign>Okay</lang:Foreign> ને <lang:Foreign>Petrol</lang:Foreign> પર છે <lang:Foreign>Sir</lang:Foreign> ગાડી કે <lang:Foreign><initial>CNG</initial></lang:Foreign> વગર આ <lang:Foreign>Diesel</lang:Foreign> વગર હાલે?
    72.157
    73.956    		<lang:Foreign>Both Both</lang:Foreign> બન્ને વ છે.
    75.283
    79.358    		અચ્છા સરસ તો તમારે <lang:Foreign>Sir</lang:Foreign> બિજુ એટલે ક્યાથી ક્યા મોકલવાની છે ગાડી આ?
    80.408
    86.281    		#હમ <lang:Foreign>Actually</lang:Foreign> આમારે છે ને સુરત થી બેંગલોર સુધિ મોકલવાની છે.
    89.072
    90.875    		અચ્છા સુરત થી બેંગલોર <lang:Foreign>Okay</lang:Foreign>
    91.326
    95.772    		#હમ [noise] ને ક્યારે ક્યારે વિચાર છે તમારો <lang:Foreign>Move</lang:Foreign> કરવાનો?
    97.796
    99.347    		#હમ આ <lang:Foreign>Month</lang:Foreign> ની
    100.949
    102.072    		પંદર તારીખ પછી.
    104.272
    106.421    		પંદર તારીખથી <lang:Foreign>Okay</lang:Foreign>
    106.694
    108.750    		પંદર તારીખથી ને પચ્છી જો
    109.176
    111.245    		<lang:Foreign>Next</lang:Foreign> દસ દિવસમા મડી જાતુ હોય તો.
    113.171
    114.451    		<lang:Foreign>Okay Okay</lang:Foreign>
    115.227
    123.245    		ઠીક છે અને <lang:Foreign>sir</lang:Foreign> તમારા ગાડી ના <lang:Foreign>Papers</lang:Foreign> તમારા જોડે <lang:Foreign>Ready</lang:Foreign> છે આપડે <lang:Foreign><initial>RC</initial>Book</lang:Foreign> છે જે બિજુ કાઈક છ ઈ <lang:Foreign>Polution Certificae Insurance</lang:Foreign>
    120.968
    121.376    		હ
    121.852
    126.694    		<lang:Foreign>Papers</lang:Foreign> છે બધા <lang:Foreign>papers Paper</lang:Foreign> છે બધા છે મારી જોડે પન.
    126.918
    129.351    		ક્યા ક્યા લાગસે એક વાર મને કઈ દો તો હુ એની
    129.777
    132.068    		અને <lang:Foreign>Original</lang:Foreign> જોસે કે <lang:Foreign>Zerox Copy</lang:Foreign>?
    134.127
    139.682    		ના અમારે તો<lang:Foreign>Zerox Copy</lang:Foreign> ચાલી જશે એક છે તમારુ <lang:Foreign><initial>RC</initial> Book</lang:Foreign> છે ઈ તમારુ <lang:Foreign>Insurance</lang:Foreign> હસે ગાડી નુ ઈ
    136.092
    136.667    		[noise] <lang:Foreign>Okay</lang:Foreign>
    139.143
    139.838    		<lang:Foreign>Okay</lang:Foreign>
    140.133
    144.751    		અને ત્રીજુ તમારુ <lang:Foreign><initial>PUC</initial> Certificate</lang:Foreign> હસે ને ગાડી નુ ઈ ત્યા ચલાવસો તો ત્યા લાગશે.
    141.014
    141.491    		#હમ
    144.217
    144.890    		#હમ
    146.516
    147.101    		<lang:Foreign>Okay</lang:Foreign>
    147.502
    149.376    		છે આ કાગડીયા તો છે મરી જોડે
    149.827
    152.039    		ને મને એ કો પેલા કે?
    153.116
    159.164    		કેટલા દિવસ મા ગાડી પોચસે ને <lang:Foreign>approximant Price</lang:Foreign> શુ રેસે એ મને થોડી જાણકારી આપશો?
    159.965
    169.462    		અચ્છા તો સુરત થી નિકડતા બેંગ્લોર એક <lang:Foreign>Minute</lang:Foreign> હો હુ <lang:Foreign>Chek</lang:Foreign> કરી લવ <lang:Foreign>System</lang:Foreign> મા સુરત થી બેંગલોર <lang:Foreign>Distance</lang:Foreign> નઈ નઈ કભી પંદરશો કિલોમિટર એટલુ હોય <lang:Foreign>sir</lang:Foreign>.
    170.139
    172.389    		તમારી દિવસમાં જાય છે.
    173.330
    180.354    		તણસો કિલોમિટર ચલાવી અમે રોજ એના તો પાંચ દિવસ લગ્સે <lang:Foreign>sir</lang:Foreign> પોંચતા <lang:Foreign>Proxylate</lang:Foreign> કવ ચુ આમા પાંચ દિવસ મા એકાદ દિવસ ઉપર નિચે થાઈ સકે છે.

    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

    Dual separate channel

    Audio file duration

    5-15 minutes

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