Step-by-Step Guide to Building a Speech and Voice AI Assistant Using ASR with Llama 3.1

September 2, 2024
By
Vansh Garg
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Table of Contents

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Example H3
Example H4
Example H5
Example H6

In today's digital age, AI-powered voice assistants have become necessary for e-commerce businesses to improve customer interactions. These voice assistants are used to provide real-time, personalized responses, enabling companies to offer an interactive and better shopping experience. 

By adding advanced voice recognition technologies like Wav2Vec2 which are open source, these assistants can accurately interpret spoken language through which customers can interact more naturally and smoothly. This integration also streamlines the buying process, making it easier for customers to find what they need through simple voice commands.

In this article, we will guide you on how you can build an AI-powered voice assistant for e-commerce. We will be focusing on integrating Wav2Vec2 for enhanced user interaction, setting up necessary tools and integrating them with a better interface, showing how you can create your own responsive, voice-enabled AI-powered virtual assistant that can satisfy your customers' needs 24/7.

What Is Wav2Vec2

Wav2Vec2 is a state-of-the-art automatic speech recognition (ASR) model developed by Meta AI. Unlike traditional speech recognition systems that rely heavily on pre-defined features and manual labeling, Wav2Vec2 uses unsupervised learning to understand the structure of speech, making it more accurate and robust across different accents and languages.

The need for Wav2Vec2 arises from the increasing demand for voice-based applications where users prefer to interact using natural speech instead of typing. Using Wav2Vec2, our AI Assistant can transcribe audio input into text, helping the system understand and process spoken queries. This capability is essential for applications where users might be busy, have difficulty typing, or prefer the convenience of voice commands.

How to Use Wav2Vec2

We will first showcase the steps to use Wav2Vec2. After that, we will show how to build a Voice AI assistant in the e-commerce domain that uses RAG architecture to provide contextual outputs.

To get started, first sign up to E2E Cloud and launch a cloud GPU node. E2E Cloud offers the most price-performant cloud GPUs in the Indian market. You get blazing-fast performance, at a price point that’s far cheaper than AWS, GCP or Azure. Check the pricing page to learn more.

When launching the cloud GPU, make sure you add your public SSH key (id_rsa.pub). This will allow you to remote SSH into the node in the following way: 


$ ssh root@

Once you have logged in, create a user using adduser command, and add the user to the sudo-ers list using visudo. 


$ adduser username
$ visudo

You can now create a Python virtual environment. 


$ python3 -m venv .env
$ source .env/bin/activate

Then install the dependencies:


$ pip install torch transformers librosa
$ pip install jupyterlab

You can now install Jupyter Lab and then use that to build this example: 


$ pip install jupyterlab
$ jupyter lab 

Testing Wav2Vec2

Let’s see how Wav2Vec2 works: 


import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import librosa

Set Device as GPU.


device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

Initialize Model and processor. 


model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h").to(device)processor = Wav2Vec2
Processor.from_pretrained("facebook/wav2vec2-large-960h")

Select path of audio file and input values. 


audio, rate = librosa.load("output.wav", sr=16000)
input_values = processor(audio, return_tensors="pt", padding="longest", sampling_rate=16000).input_values.to(device)

Generate text output.


with torch.no_grad():
   logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
print(transcription[0])

What Is Parler TTS

Along with ASR, we will also use a TTS (text-to-speech) model. Parler TTS is an advanced, open-source text-to-speech (TTS) model developed to generate high-quality, natural-sounding speech with a high degree of control over various features such as gender, pitch, speaking style, and background noise. Leveraging the power of an auto-regressive transformer-based architecture, Parler TTS generates speech by creating audio tokens in a causal manner, allowing for real-time streaming of audio as it is produced. This reduces latency significantly, providing near-instantaneous audio output when using modern GPUs.

The model supports efficient attention mechanisms, like SDPA and Flash Attention 2, which optimize the generation speed by up to 1.4 times compared to traditional methods. Additionally, Parler TTS benefits from compilation techniques that can accelerate the model’s performance by up to 4.5 times. The flexibility of this model is enhanced by its ability to be fine-tuned using simple text prompts, enabling precise adjustments in speech attributes without requiring extensive retraining.

This model has been trained on extensive datasets, including over 10,500 hours of audio, making it capable of delivering high-fidelity speech synthesis suitable for diverse applications in AI-driven communication, virtual assistants, and content creation.

Let’s now use it to build a Voice AI assistant. 

Application Workflow

The diagram below explains our entire workflow. First, we use ASR to convert user queries into text. We then convert the query into embeddings and use it to perform a similarity search and then generate LLM responses. The responses will then be converted back to speech using a TTS model. 

Using this workflow, you can build Voice AI assistants in several sectors. 

‍

Prerequisites

Before running the code, make sure you have the following libraries installed:


! pip install sentence-transformers
! pip install qdrant_client langchain-community
! pip install gradio

You should also install Qdrant using a simple Docker command in the following way: 


$ docker pull qdrant/qdrant
$ docker run -p 6333:6333 -p 6334:6334 \
   -v $(pwd)/qdrant_storage:/qdrant/storage:z \
   qdrant/qdrant

Step 1: Loading and Processing Customer Data

We start by loading customer data from a CSV file and processing it to create separate customer profiles that will later be used to generate responses. The code for processing may vary according to the data provided.


import pandas as pd


file_path = 'data.csv'
df = pd.read_csv(file_path,encoding='ISO-8859-1')


def create_profile(customer_name, df):
   customer_data = df[df['CustomerName'] == customer_name]
   customer_info = df[df['CustomerName'] == customer_name].iloc[0]
   customer_id = customer_info['CustomerID']


   transactions = [
       f"On {row['InvoiceDate']} , {customer_name} bought {row['Quantity']} {row['Description']}  for price: {row['UnitPrice']} (Stock Code: {row['StockCode']}, InvoiceNo:{row['InvoiceNo']}) in {row['Country']}"
       for _, row in customer_data.iterrows()
   ]
   history_text = "\n".join(transactions)
   profile=f"Name={customer_name},Customer Id={customer_id}\n"+history_text
  
   return history_text


def create_all_profiles(df):
   profiles = []
   unique_names = df['CustomerName'].unique()
  
   for name in unique_names:
       profiles.append(create_profile(name, df))
  
   return profiles
chunks=create_all_profiles(df)

Step 2: Encoding the Chunks Using a Pre-trained Embedding Model

You can use a pre-trained model like sentence-transformers/all-mpnet-base-v2 for turning chunks into embeddings by using the sentence-transformers library:


model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2")
vectors = model.encode(chunks)

Step 3: Storing the Embeddings in Qdrant

Now, you can store these embeddings in a database like Qdrant, which can also be used for semantic searches. The choice of the vector database is yours. 


from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams


client = QdrantClient(":memory:")


client.recreate_collection(
   collection_name="customer-profiles",
   vectors_config=VectorParams(size=len(vectors[0]), distance=Distance.COSINE),
)
client.upload_collection(
   collection_name="customer-profiles",
   ids=[i for i in range(len(chunks))],
   vectors=vectors,
)

Step 4: Implementing the Context Generation Function

We will now create a function that will fetch the context based on the query vector. It will use a similarity search to find document chunks closest to the query:


def make_context(question):
 ques_vector = model.encode(question)
 result = client.query_points(
     collection_name="customer-profiles",
     query=ques_vector,
 )
 sim_ids = []
 for i in result.points:
   sim_ids.append(i.id)
 context = ""
 for i in sim_ids[0:5]:
   context+=chunks[i]
 return context

Step 5: Generating Responses Using LLM

We can now use Ollama to access open-source multilingual models like Mistral to generate meaningful responses based on context – in this case, a customer profile provided.

For that, first install Ollama.


$ curl -fsSL https://ollama.com/install.sh | sh
$ ollama pull mistral-nemo
$ ollama run mistral-nemo

Now, you can use it in your code.


import ollama
def respond(question):
   stream = ollama.chat(
       model="mistal-nemo”,
       messages=[{'role':'user','content': f'This is the question asked by user {question} and the context given is {make_context(question)} answer this question based on the context provided in 1 to 2 lines '}],
   )
   return stream['message']['content']

Step 6: Adding Voice Recognition Using Wav2Vec2

To add voice interaction, we integrate the Wav2Vec2 model, which will convert the user's speech input into text format which the voice AI assistant uses to process voice queries effectively.


import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import librosa

import logging
logging.getLogger("transformers.modeling_utils").setLevel(logging.ERROR)

device = "cuda:0" if torch.cuda.is_available() else "cpu"
stt_model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h").to(device)
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h")

def transcribe_audio(audio_file):
   if audio_file is None:
       return "No audio file provided."
   audio, rate = librosa.load(audio_file, sr=16000)
   input_values = processor(audio, return_tensors="pt", padding="longest", sampling_rate=16000).input_values.to(device)
  
   with torch.no_grad():
       logits = stt_model(input_values).logits
  
   predicted_ids = torch.argmax(logits, dim=-1)
   transcription = processor.batch_decode(predicted_ids)
  
   return transcription[0].lower()

Step 7: Implementing Text-to-Speech Functionality Using Parler TTS

For adding interaction, we can integrate the Parler text-to-speech (TTS) model, which will convert the generated text response into an audio format with a certain description provided.


import torch
from parler_tts import ParlerTTSForConditionalGeneration
from transformers import AutoTokenizer
import soundfile as sf
device = "cuda:0" if torch.cuda.is_available() else "cpu"

tts_model = ParlerTTSForConditionalGeneration.from_pretrained("parler-tts/parler-tts-mini-v1").to(device)
tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler-tts-mini-v1")

def text_to_speech_fun(prompt):
   description = "A male speaker ,with the speaker's voice sounding clear and very close up."
   input_ids = tokenizer(description, return_tensors="pt").input_ids.to(device)
   prompt_input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
   generation = tts_model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids)
   audio_arr = generation.cpu().numpy().squeeze()
   sf.write("parler_tts_out.wav", audio_arr, tts_model.config.sampling_rate)

Step 8: Combining All Functions

We combine all functions to receive and process an audio input and return an html response for bot response and audio output.


def generate_response_from_audio(audio):
  
   user_query = transcribe_audio(audio)
  
   bot_response = respond(user_query)
   text_to_speech_fun(bot_response)
  
   audio_file = "parler_tts_out.wav"
  
   html_response = f"""
   

      
       
Input:

       
{user_query}

       
Response:

       
{bot_response}

   

   """
  
   return html_response, audio_file

Step 9: Integrating with the Gradio Interface

Finally, we can use Gradio to create a simple web interface for users to interact with the voice AI assistant. This interface will allow users to speak their queries and receive text as well as audio responses.


import gradio as gr


with gr.Blocks() as demo:
   gr.Markdown(
       """
       

           
Voice AI Assistant

       

       """
   )


   gr.Markdown(
       """
       

           Hello! How may i help you
       

       """
   )


   gr.Markdown("
")


   audio_input = gr.Audio(type="filepath", label="Record your query")


   output = gr.HTML()
   audio_output = gr.Audio()


   audio_input.change(fn=generate_response_from_audio, inputs=audio_input, outputs=[output, audio_output])


demo.launch(share=True)

Output

‍

Conclusion

By following this guide, you can create a powerful Voice AI Assistant for e-commerce that can understand customer queries via audio input, retrieve relevant information from customer profiles, and respond with text as well as audio output. This project combines powerful tools like LangChain, Qdrant, Wav2Vec2, Parler(TTS), and Gradio to deliver a highly interactive and intelligent user experience.

Sign up to E2E Cloud today to start building a bi-directional voice AI chatbot for the e-commerce domain. You can also reach out to us at sales@e2enetworks.com to learn how to build data-sovereign AI on our MeitY-empanelled cloud platform, or for availing startup credits.

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A Complete Guide To Customer Acquisition For Startups

Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.

So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.

The problem with customer acquisition

As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

How can you create the ideal customer acquisition strategy for your business?

  • Define what your goals are

You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

  • MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
  • CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.  
  • CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
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All these metrics tell you how well you will be able to grow your business and revenue.

  • Identify your ideal customers

You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

  • Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels. 

  • Communicate with your customers

If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

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Reference Links

https://www.helpscout.com/customer-acquisition/

https://www.cloudways.com/blog/customer-acquisition-strategy-for-startups/

https://blog.hubspot.com/service/customer-acquisition

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Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era

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The Main Objective of the 3D Object Reconstruction

Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:

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State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

The technology used for this purpose needs to stick to the following parameters:

  • Input

Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

  • Output

The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

  • Network architecture used

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

  • Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

  • Practical applications and use cases

Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

  • 3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
  • It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
  • They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
  • It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
  • It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website. 

‍

Reference Links

https://tongtianta.site/paper/68922

https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods

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A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.

So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning   is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

  1. Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
  2. Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
  3. Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
  4. Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.  

In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.

Reference Links

https://analyticsindiamag.com/comprehensive-guide-to-deep-q-learning-for-data-science-enthusiasts/

https://medium.com/@jereminuerofficial/a-comprehensive-guide-to-deep-q-learning-8aeed632f52f

This is a decorative image for: GAUDI: A Neural Architect for Immersive 3D Scene Generation
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GAUDI: A Neural Architect for Immersive 3D Scene Generation

The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.

An introduction to GAUDI

The GAUDI 3D immersive technique founders named it after the famous architect Antoni Gaudi. This AI model takes the help of a camera pose decoder, which enables it to guess the possible camera angles of a scene. Hence, the decoder then makes it possible to predict the 3D canvas from almost every angle.

What does GAUDI do?

GAUDI can perform multiple functions –

  • The extensions of these generative models have a tremendous effect on ML and computer vision. Pragmatically, such models are highly useful. They are applied in model-based reinforcement learning and planning world models, SLAM is s, or 3D content creation.
  • Generative modelling for 3D objects has been used for generating scenes using graf, pigan, and gsn, which incorporate a GAN (Generative Adversarial Network). The generator codes radiance fields exclusively. Using the 3D space in the scene along with the camera pose generates the 3D image from that point. This point has a density scalar and RGB value for that specific point in 3D space. This can be done from a 2D camera view. It does this by imposing 3D datasets on those 2D shots. It isolates various objects and scenes and combines them to render a new scene altogether.
  • GAUDI also removes GANs pathologies like mode collapse and improved GAN.
  • GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

  • Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
  • This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
  • The scenes are thus synthesized by interpolation within the hidden space.
  • The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
  • A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.

‍

Reference Links

https://www.researchgate.net/publication/362323995_GAUDI_A_Neural_Architect_for_Immersive_3D_Scene_Generation

https://www.technology.org/2022/07/31/gaudi-a-neural-architect-for-immersive-3d-scene-generation/ 

https://www.patentlyapple.com/2022/08/apple-has-unveiled-gaudi-a-neural-architect-for-immersive-3d-scene-generation.html

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