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Gujarati (India) Call Center Speech Dataset for Retail & E-commerce

The audio dataset comprises call center conversations for the Retail & E-commerce 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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Speech training dataset for Retail and E-commerce in Gujarati (India)
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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 Retail domain designed to enhance the development of call center speech recognition models specifically for the Retail 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 Retail 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:
  • •Product Inquiry
  • •Return/Exchange Request
  • •Order Cancellation
  • •Refund Request
  • •Membership/Subscriptions Enquiry
  • •Order Cancellations, and many more
  • •Outbound Calls:
  • •Order Confirmation
  • •Cross-selling and Upselling
  • •Account Updates
  • •Loyalty Program offers
  • •Special Offers and Promotions
  • •Customer Verification, 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 Retail 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 Retail 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 Retail sector.
  • •
    Smart Assistants and Chatbots: Developing conversational agents and virtual assistants for customer service in the Retail 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 Retail 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 Retail 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
    Speech0.3531.020Speaker 1 <lang:Foreign>Hello</lang:Foreign>
    Speech1.8043.176Speaker 2<lang:Foreign>Hello futurebee</lang:Foreign>
    Speech6.21010.744Speaker 1<lang:Foreign>Yes sir</lang:Foreign> #આહ અજીન્ક્ય બોલું છું. <lang:Foreign>costumer service centre</lang:Foreign> થી <lang:Foreign>futurebee</lang:Foreign> ના
    Speech11.32014.353Speaker 1#આહ બોલો <lang:Foreign>sir</lang:Foreign> કેવી રીતે હું તમારી સહાયતા કરી શકું આજે.
    Speech15.20525.777Speaker 2#આહ ભાઈ મારો એક <lang:Foreign>order</lang:Foreign> મેં <lang:Foreign>place</lang:Foreign> કર્યો હતો. તમારા <lang:Foreign>app</lang:Foreign> પર એ અત્યારે <lang:Foreign>deliver</lang:Foreign> તો થઇ ગયો છે. <lang:Foreign>but</lang:Foreign> મારે એને <lang:Foreign>return</lang:Foreign> કરવું છે.
    Noise18.75019.300--
    Speech21.57521.937Speaker 1#હમ
    Speech22.68823.125Speaker 1#હમ
    Speech25.50026.100Speaker 1હા
    Speech29.52930.150Speaker 1અચ્છા
    Speech30.52633.254Speaker 1#આહ કેમ <lang:Foreign>return</lang:Foreign> કરો છો <lang:Foreign>sir any reason</lang:Foreign>?
    Speech31.92135.607Speaker 2#હમ એ <lang:Foreign>shirt</lang:Foreign> ની <lang:Foreign>quality</lang:Foreign> મને ખરાબ લાગી.
    Speech36.47037.500Speaker 2<lang:Foreign>Actual</lang:Foreign> માં
    Speech38.42641.098Speaker 2એ એટલી સારી નથી એટલે
    Speech39.50040.475Speaker 1અચ્છા
    Speech41.24748.854Speaker 2કોઈ પેરસે નહિ પાછું એ તો મારા માથા પર પડશે. એના કરતા મારે <lang:Foreign>return</lang:Foreign> કરાવું છે. ને એની જગ્યા એ જો બીજા સારા હોય તો હું એ <lang:Foreign>choose</lang:Foreign> કરી લઈશ.
    Speech51.86259.489Speaker 1તો <lang:Foreign>sir</lang:Foreign> તમે <lang:Foreign>return</lang:Foreign> કરવા માગો છો.કે <lang:Foreign>replace</lang:Foreign> કરવા માગો છો.એની જગ્યા એ તમે બીજું બી કોઈ <lang:Foreign>shirt</lang:Foreign> તમે <lang:Foreign>order</lang:Foreign> કરી શકો એટલા જ <lang:Foreign>value</lang:Foreign> નું, વધારે <lang:Foreign>price</lang:Foreign> થશે તો તમારે
    Speech59.64267.446Speaker 1થોડા <lang:Foreign>extra</lang:Foreign> જે હશે. એ તમે આપશો.અને ઓછા હશે. તો તમારા બાકી ના તમને <lang:Foreign>credit</lang:Foreign> થઇ જશે.તો <lang:Foreign>return</lang:Foreign> કરવા કરતા <lang:Foreign>replace</lang:Foreign> કરવું <lang:Foreign>option</lang:Foreign> સારું રેહશે. <lang:Foreign>sir</lang:Foreign>
    Speech68.60371.838Speaker 2હા તો <lang:Foreign>replace</lang:Foreign> કરી દઈએ. કેમ કે મારે બીજો <lang:Foreign>shirt</lang:Foreign> તો લેવો જ છે.
    Speech72.01574.387Speaker 2પણ <lang:Foreign>quality</lang:Foreign> મારે હ જોઈ લઉં.
    Speech73.44674.485Speaker 1અચ્છા અચ્છા
    Speech75.93681.328Speaker 1તો એક <lang:Foreign>minute</lang:Foreign> પહેલા હું તમારું જોઈ લઉં એકવાર. તમારું આજ <lang:Foreign>number</lang:Foreign> થી <lang:Foreign>login</lang:Foreign> છે અમારા <lang:Foreign>futurebee</lang:Foreign> માં?
    Speech81.37982.497Speaker 2હો હા
    Speech82.51785.340Speaker 1તો હું તમારું <lang:Foreign>details</lang:Foreign> જોઈ લઉં. કયું <lang:Foreign>order</lang:Foreign> છે? કેમ છે?
    Speech92.17197.858Speaker 1તો <lang:Foreign>sir</lang:Foreign> હું આ મેં તમારું આ ખોલ્યું છે. <lang:Foreign>account</lang:Foreign> અહિયાં હું તમારું <lang:Foreign>details</lang:Foreign> જોઈ શકું છું. તમારું નામ શુભમ મહાજન છો.
    Speech97.62298.407Speaker 2હા
    Speech99.956103.387Speaker 1હા તો અહિયાં તમે તમે <lang:Foreign>futurebee</lang:Foreign> ના <lang:Foreign>member sorry</lang:Foreign> તમે
    Speech103.575113.791Speaker 1ત્રણ વર્ષથી છો સારું #આહ આમાં તમે આ <lang:Foreign>last</lang:Foreign> જે આ તમે જે <lang:Foreign>last order</lang:Foreign> કહો છો એની વાત છે <lang:Foreign>sir</lang:Foreign> તમે જે આ <lang:Foreign>complete</lang:Foreign> કરો છો. એ <lang:Foreign>last order</lang:Foreign> માટે છે. તમારું અહિયાં તમે જે <lang:Foreign>floral</lang:Foreign>
    Speech110.771111.349Speaker 2હ
    Speech111.875113.144Speaker 2હા એ બે દિવસ
    Speech113.693115.653Speaker 2બે દિવસ પહેલા જ <lang:Foreign>received</lang:Foreign> થયો છે.
    Speech115.544116.269Speaker 1હા
    Speech118.244121.999Speaker 1અચ્છા હા હું જોઈ શકું છું. અહિયાં <lang:Foreign>floral floral print t-shirt</lang:Foreign>
    Speech121.936123.426Speaker 2હા હા
    Speech122.400123.073Speaker 1<lang:Foreign>black</lang:Foreign>
    Speech123.740124.857Speaker 1<lang:Foreign>size <initial>L</initial></lang:Foreign>
    Speech124.896125.563Speaker 2હા
    Speech125.814128.579Speaker 1અચ્છા તો તમે આ તમારી <lang:Foreign>replace</lang:Foreign> કરવાનું છે એમ?
    Speech128.010129.971Speaker 2હા એની <lang:Foreign>quality</lang:Foreign> બરાબર નથી.
    Speech129.049130.226Speaker 1(()) તો <lang:Foreign>sir</lang:Foreign> તમે
    Speech131.657135.932Speaker 1અચ્છા વાંધો નહીં <lang:Foreign>sir</lang:Foreign> #આહ <lang:Foreign>sorry</lang:Foreign> તમને આ <lang:Foreign>inconvenience</lang:Foreign> થયું?
    Speech136.049143.402Speaker 1<lang:Foreign>but sir</lang:Foreign> તમે છે ને આ બધી જે <lang:Foreign>complain</lang:Foreign> છે ને તમે <lang:Foreign>direct</lang:Foreign> પણ કરી શકો છો અમારું <lang:Foreign>app</lang:Foreign> છે. <lang:Foreign>sir</lang:Foreign> તમારી જોડે <lang:Foreign>futurebee app</lang:Foreign> છે.
    Speech143.767145.865Speaker 1કે તમે <lang:Foreign>website</lang:Foreign> પરથી <lang:Foreign>place</lang:Foreign> કર્યું છે. <lang:Foreign>order</lang:Foreign>
    Babble145.424145.774--
    Speech146.135148.531Speaker 2હવે આ <lang:Foreign>order</lang:Foreign> તો મેં <lang:Foreign>website</lang:Foreign>પરથી કર્યો હતો
    Speech150.649154.178Speaker 1તો <lang:Foreign>sir</lang:Foreign> તો <lang:Foreign>best</lang:Foreign>એ રહેશે તમે છે ને એક <lang:Foreign>futurebee</lang:Foreign> અમારું <lang:Foreign>app download</lang:Foreign> કરી લોને
    Speech154.293158.861Speaker 1તો એમાં તમને આ બધું બહુ <lang:Foreign>easily</lang:Foreign> તમારી રીતે કરી શકો <lang:Foreign>sir</lang:Foreign> <lang:Foreign>return</lang:Foreign> કરવાનું હોય
    Speech158.865165.492Speaker 1તમારું<lang:Foreign>order track</lang:Foreign> કરવાનું હોય <lang:Foreign>replace</lang:Foreign> કરવાનું હોય તમારું નું હોય તમારું<lang:Foreign>tracking</lang:Foreign>કરવાનું હોય<lang:Foreign>refund</lang:Foreign>થયું ના થયું.
    Speech165.649172.127Speaker 1ને બધા બી તમને બીજા <lang:Foreign>personal offers</lang:Foreign> તમને આવે એ <lang:Foreign>account</lang:Foreign> માં થશે તો <lang:Foreign>sir</lang:Foreign> તમે <lang:Foreign>app download</lang:Foreign> કરો તો બહુ સારું રહેશે.
    Speech172.951173.599Speaker 2અચ્છા
    Speech174.200175.382Speaker 2આ <lang:Foreign>complain</lang:Foreign> ને
    Speech175.029184.766Speaker 1હા બે જ <lang:Foreign>minute</lang:Foreign> લાગશે <lang:Foreign>sir</lang:Foreign> હમણાં હમણાં કરી લો તો હું તમને આ બધું અહીંયા તમને <lang:Foreign>guide</lang:Foreign> કરી દઉં વ્યવસ્થિત એટલે તમે <lang:Foreign>next time</lang:Foreign> કોઈક વાર તમને આવો <lang:Foreign>problem</lang:Foreign> થાય તો તો તમારે <lang:Foreign>wait</lang:Foreign> નહીં કરવો પડે <lang:Foreign>call</lang:Foreign> કરવાનો

    TRANSCRIPTION

    TIMETRANSCRIPT
    0.353
    1.020    		 <lang:Foreign>Hello</lang:Foreign>
    1.804
    3.176    		<lang:Foreign>Hello futurebee</lang:Foreign>
    6.210
    10.744    		<lang:Foreign>Yes sir</lang:Foreign> #આહ અજીન્ક્ય બોલું છું. <lang:Foreign>costumer service centre</lang:Foreign> થી <lang:Foreign>futurebee</lang:Foreign> ના
    11.320
    14.353    		#આહ બોલો <lang:Foreign>sir</lang:Foreign> કેવી રીતે હું તમારી સહાયતા કરી શકું આજે.
    15.205
    25.777    		#આહ ભાઈ મારો એક <lang:Foreign>order</lang:Foreign> મેં <lang:Foreign>place</lang:Foreign> કર્યો હતો. તમારા <lang:Foreign>app</lang:Foreign> પર એ અત્યારે <lang:Foreign>deliver</lang:Foreign> તો થઇ ગયો છે. <lang:Foreign>but</lang:Foreign> મારે એને <lang:Foreign>return</lang:Foreign> કરવું છે.
    18.750
    19.300    		-
    21.575
    21.937    		#હમ
    22.688
    23.125    		#હમ
    25.500
    26.100    		હા
    29.529
    30.150    		અચ્છા
    30.526
    33.254    		#આહ કેમ <lang:Foreign>return</lang:Foreign> કરો છો <lang:Foreign>sir any reason</lang:Foreign>?
    31.921
    35.607    		#હમ એ <lang:Foreign>shirt</lang:Foreign> ની <lang:Foreign>quality</lang:Foreign> મને ખરાબ લાગી.
    36.470
    37.500    		<lang:Foreign>Actual</lang:Foreign> માં
    38.426
    41.098    		એ એટલી સારી નથી એટલે
    39.500
    40.475    		અચ્છા
    41.247
    48.854    		કોઈ પેરસે નહિ પાછું એ તો મારા માથા પર પડશે. એના કરતા મારે <lang:Foreign>return</lang:Foreign> કરાવું છે. ને એની જગ્યા એ જો બીજા સારા હોય તો હું એ <lang:Foreign>choose</lang:Foreign> કરી લઈશ.
    51.862
    59.489    		તો <lang:Foreign>sir</lang:Foreign> તમે <lang:Foreign>return</lang:Foreign> કરવા માગો છો.કે <lang:Foreign>replace</lang:Foreign> કરવા માગો છો.એની જગ્યા એ તમે બીજું બી કોઈ <lang:Foreign>shirt</lang:Foreign> તમે <lang:Foreign>order</lang:Foreign> કરી શકો એટલા જ <lang:Foreign>value</lang:Foreign> નું, વધારે <lang:Foreign>price</lang:Foreign> થશે તો તમારે
    59.642
    67.446    		થોડા <lang:Foreign>extra</lang:Foreign> જે હશે. એ તમે આપશો.અને ઓછા હશે. તો તમારા બાકી ના તમને <lang:Foreign>credit</lang:Foreign> થઇ જશે.તો <lang:Foreign>return</lang:Foreign> કરવા કરતા <lang:Foreign>replace</lang:Foreign> કરવું <lang:Foreign>option</lang:Foreign> સારું રેહશે. <lang:Foreign>sir</lang:Foreign>
    68.603
    71.838    		હા તો <lang:Foreign>replace</lang:Foreign> કરી દઈએ. કેમ કે મારે બીજો <lang:Foreign>shirt</lang:Foreign> તો લેવો જ છે.
    72.015
    74.387    		પણ <lang:Foreign>quality</lang:Foreign> મારે હ જોઈ લઉં.
    73.446
    74.485    		અચ્છા અચ્છા
    75.936
    81.328    		તો એક <lang:Foreign>minute</lang:Foreign> પહેલા હું તમારું જોઈ લઉં એકવાર. તમારું આજ <lang:Foreign>number</lang:Foreign> થી <lang:Foreign>login</lang:Foreign> છે અમારા <lang:Foreign>futurebee</lang:Foreign> માં?
    81.379
    82.497    		હો હા
    82.517
    85.340    		તો હું તમારું <lang:Foreign>details</lang:Foreign> જોઈ લઉં. કયું <lang:Foreign>order</lang:Foreign> છે? કેમ છે?
    92.171
    97.858    		તો <lang:Foreign>sir</lang:Foreign> હું આ મેં તમારું આ ખોલ્યું છે. <lang:Foreign>account</lang:Foreign> અહિયાં હું તમારું <lang:Foreign>details</lang:Foreign> જોઈ શકું છું. તમારું નામ શુભમ મહાજન છો.
    97.622
    98.407    		હા
    99.956
    103.387    		હા તો અહિયાં તમે તમે <lang:Foreign>futurebee</lang:Foreign> ના <lang:Foreign>member sorry</lang:Foreign> તમે
    103.575
    113.791    		ત્રણ વર્ષથી છો સારું #આહ આમાં તમે આ <lang:Foreign>last</lang:Foreign> જે આ તમે જે <lang:Foreign>last order</lang:Foreign> કહો છો એની વાત છે <lang:Foreign>sir</lang:Foreign> તમે જે આ <lang:Foreign>complete</lang:Foreign> કરો છો. એ <lang:Foreign>last order</lang:Foreign> માટે છે. તમારું અહિયાં તમે જે <lang:Foreign>floral</lang:Foreign>
    110.771
    111.349    		હ
    111.875
    113.144    		હા એ બે દિવસ
    113.693
    115.653    		બે દિવસ પહેલા જ <lang:Foreign>received</lang:Foreign> થયો છે.
    115.544
    116.269    		હા
    118.244
    121.999    		અચ્છા હા હું જોઈ શકું છું. અહિયાં <lang:Foreign>floral floral print t-shirt</lang:Foreign>
    121.936
    123.426    		હા હા
    122.400
    123.073    		<lang:Foreign>black</lang:Foreign>
    123.740
    124.857    		<lang:Foreign>size <initial>L</initial></lang:Foreign>
    124.896
    125.563    		હા
    125.814
    128.579    		અચ્છા તો તમે આ તમારી <lang:Foreign>replace</lang:Foreign> કરવાનું છે એમ?
    128.010
    129.971    		હા એની <lang:Foreign>quality</lang:Foreign> બરાબર નથી.
    129.049
    130.226    		(()) તો <lang:Foreign>sir</lang:Foreign> તમે
    131.657
    135.932    		અચ્છા વાંધો નહીં <lang:Foreign>sir</lang:Foreign> #આહ <lang:Foreign>sorry</lang:Foreign> તમને આ <lang:Foreign>inconvenience</lang:Foreign> થયું?
    136.049
    143.402    		<lang:Foreign>but sir</lang:Foreign> તમે છે ને આ બધી જે <lang:Foreign>complain</lang:Foreign> છે ને તમે <lang:Foreign>direct</lang:Foreign> પણ કરી શકો છો અમારું <lang:Foreign>app</lang:Foreign> છે. <lang:Foreign>sir</lang:Foreign> તમારી જોડે <lang:Foreign>futurebee app</lang:Foreign> છે.
    143.767
    145.865    		કે તમે <lang:Foreign>website</lang:Foreign> પરથી <lang:Foreign>place</lang:Foreign> કર્યું છે. <lang:Foreign>order</lang:Foreign>
    145.424
    145.774    		-
    146.135
    148.531    		હવે આ <lang:Foreign>order</lang:Foreign> તો મેં <lang:Foreign>website</lang:Foreign>પરથી કર્યો હતો
    150.649
    154.178    		તો <lang:Foreign>sir</lang:Foreign> તો <lang:Foreign>best</lang:Foreign>એ રહેશે તમે છે ને એક <lang:Foreign>futurebee</lang:Foreign> અમારું <lang:Foreign>app download</lang:Foreign> કરી લોને
    154.293
    158.861    		તો એમાં તમને આ બધું બહુ <lang:Foreign>easily</lang:Foreign> તમારી રીતે કરી શકો <lang:Foreign>sir</lang:Foreign> <lang:Foreign>return</lang:Foreign> કરવાનું હોય
    158.865
    165.492    		તમારું<lang:Foreign>order track</lang:Foreign> કરવાનું હોય <lang:Foreign>replace</lang:Foreign> કરવાનું હોય તમારું નું હોય તમારું<lang:Foreign>tracking</lang:Foreign>કરવાનું હોય<lang:Foreign>refund</lang:Foreign>થયું ના થયું.
    165.649
    172.127    		ને બધા બી તમને બીજા <lang:Foreign>personal offers</lang:Foreign> તમને આવે એ <lang:Foreign>account</lang:Foreign> માં થશે તો <lang:Foreign>sir</lang:Foreign> તમે <lang:Foreign>app download</lang:Foreign> કરો તો બહુ સારું રહેશે.
    172.951
    173.599    		અચ્છા
    174.200
    175.382    		આ <lang:Foreign>complain</lang:Foreign> ને
    175.029
    184.766    		હા બે જ <lang:Foreign>minute</lang:Foreign> લાગશે <lang:Foreign>sir</lang:Foreign> હમણાં હમણાં કરી લો તો હું તમને આ બધું અહીંયા તમને <lang:Foreign>guide</lang:Foreign> કરી દઉં વ્યવસ્થિત એટલે તમે <lang:Foreign>next time</lang:Foreign> કોઈક વાર તમને આવો <lang:Foreign>problem</lang:Foreign> થાય તો તો તમારે <lang:Foreign>wait</lang:Foreign> નહીં કરવો પડે <lang:Foreign>call</lang:Foreign> કરવાનો

    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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