Building an AI-Powered ASR System for the BFSI Sector: A Step-by-Step Guide

The BFSI (Banking, Financial Services, and Insurance) industry, characterized by its reliance on precision, efficiency, and customer satisfaction, is undergoing a seismic shift. At the forefront of this transformation is Automatic Speech Recognition (ASR), a technology that is rapidly evolving the way financial institutions operate.

ASR, with its ability to accurately convert spoken language into text, offers a number of applications in the BFSI domain. From revolutionizing customer interactions to optimizing internal processes, the potential of ASR is immense.

By integrating ASR into their operations, financial institutions can:

• Elevate Customer Experience: ASR enables seamless interaction through voice-based channels like call centers and IVRs, providing customers with quicker resolutions and improved satisfaction.
• Streamline Operations: Automating tasks such as data entry, document processing, and fraud detection through ASR can significantly enhance operational efficiency and reduce costs.
• Strengthen Compliance: Voice recordings can be analyzed using ASR to ensure adherence to regulatory standards and mitigate risks.
• Expand Accessibility: ASR can break down language barriers and provide financial services to a wider audience, including individuals with disabilities.

The integration of ASR marks a pivotal moment for the BFSI sector, promising a future where technology and human interaction converge to deliver exceptional value.

In the following sections, we will explore how to build a robust ASR system tailored to the unique requirements of the BFSI (Banking, Financial Services, and Insurance) industry.

Facebook's Wav2Vec2: A Breakthrough in Speech Recognition

Wav2Vec 2.0 is a groundbreaking open-source self-supervised learning method for speech recognition developed by Facebook AI. It represents a significant leap forward in the field by learning powerful representations directly from raw audio data without relying heavily on transcribed speech.   

Unlike previous methods, Wav2Vec 2.0 learns to understand the structure of speech by masking portions of the audio and predicting the masked content. This approach allows the model to capture intricate speech patterns and generate highly informative latent representations.   

The model has demonstrated exceptional performance, especially in low-resource settings, where labeled data is scarce. Its ability to learn from vast amounts of unlabeled audio data makes it a versatile and powerful tool for speech recognition tasks across various domains, including the BFSI sector.   

By leveraging Wav2Vec 2.0, we can build robust ASR systems that can accurately transcribe speech, even in noisy environments or with diverse accents.

Let’s Play

Automatic Speech Recognition (ASR) is a complex process that involves transforming spoken language into text. It can be broken down into three fundamental stages:

1. Acoustic Feature Extraction

The initial step in ASR is to convert raw audio waveforms into meaningful representations that can be processed by machine learning models. This process is known as acoustic feature extraction. Key features like Mel-Frequency Cepstral Coefficients (MFCCs) or spectrograms are commonly extracted from the audio signal. These features capture essential characteristics of the speech signal, such as pitch, tone, and spectral information, which are crucial for accurate speech recognition.

2. Acoustic Model

Once the acoustic features are extracted, the next stage involves building a model to map these features to corresponding phonetic units or sub-word units. This model, often referred to as an acoustic model, is trained on large amounts of labeled speech data. It learns to associate specific acoustic patterns with particular sounds or words.

3. Language Model

The final stage involves transforming the sequence of phonetic units or sub-word units into actual words or sentences. This is where the language model comes into play. It incorporates knowledge of grammar, syntax, and semantics to generate the most probable word sequence based on the given acoustic information.

Leveraging Pre-trained Models

To accelerate the development of ASR systems, researchers and developers often utilize pre-trained models. These models, trained on massive datasets, provide a strong foundation for building accurate speech recognition systems.

Torchaudio, a PyTorch library, offers a convenient way to access pre-trained models and their associated information, such as expected sample rates and class labels. This simplifies the process of building ASR systems, allowing developers to focus on fine-tuning and customization of specific tasks.

By effectively combining these stages and leveraging pre-trained models, it's possible to develop robust and accurate ASR systems for various applications, including the BFSI sector.

Let's Dive into the Code

First, we'll import the necessary libraries for our ASR project.

import torch
import torchaudio
import IPython
import matplotlib.pyplot as plt
from torchaudio.utils import download_asset

Let’s download a sample .wav file and check it out. The same file can be found here: https://on.soundcloud.com/ntN6BgUWwjtRJmXY7.

SPEECH_FILE = download_asset("tutorial-assets/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav")


IPython.display.Audio(SPEECH_FILE)

We can then harness the power of Wav2Vec2.0 using the below code snippet:

bundle = torchaudio.pipelines.WAV2VEC2_ASR_BASE_960H
model = bundle.get_model().to(device)


print("Sample Rate:", bundle.sample_rate)
print("Labels:", bundle.get_labels())
print(model.__class__)

With our model ready, now we can peek into our audio file and extract acoustic features from it using the code below:

waveform, sample_rate = torchaudio.load(SPEECH_FILE)
waveform = waveform.to(device)


if sample_rate != bundle.sample_rate:
    waveform = torchaudio.functional.resample(waveform, sample_rate, bundle.sample_rate)

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with torch.inference_mode():
    features, _ = model.extract_features(waveform)

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Let’s check out the extracted features visually:

fig, ax = plt.subplots(len(features), 1, figsize=(16, 4.3 * len(features)))
for i, feats in enumerate(features):
    ax[i].imshow(feats[0].cpu(), interpolation="nearest")
    ax[i].set_title(f"Feature from transformer layer {i+1}")
    ax[i].set_xlabel("Feature dimension")
    ax[i].set_ylabel("Frame (time-axis)")
fig.tight_layout()

Once the acoustic features are extracted, the next step is to classify them into a set of categories.

The Wav2Vec2 model provides a method to perform the feature extraction and classification in one step.

with torch.inference_mode():
    emission, _ = model(waveform)

The outputs obtained will be in the form of logits instead of probabilities; we can visualize the same to understand the output better.

plt.imshow(emission[0].cpu().T, interpolation="nearest")
plt.title("Classification result")
plt.xlabel("Frame (time-axis)")
plt.ylabel("Class")
plt.tight_layout()
print("Class labels:", bundle.get_labels())

The output should be similar to this if everything works fine, where we can see that there are strong indications to certain labels across the timeline.

Once our model has processed the audio and generated a sequence of probability distributions for each time step, we need to convert these probabilities into actual words. This process is known as decoding.

Decoding is more complex than simple classification because it involves considering the context of surrounding words. For instance, consider the words "to" and "too". While they might sound similar, their meaning depends heavily on the surrounding words. A model needs to take this context into account to make accurate transcriptions.

Advanced decoding techniques, such as beam search or language models, incorporate this contextual information to improve accuracy. However, for simplicity, we'll focus on a basic approach called greedy decoding in this tutorial. Greedy decoding selects the most probable word at each time step without considering future possibilities. While it's a straightforward method, it often produces less accurate results compared to more sophisticated techniques.

We'll now implement the greedy decoding algorithm and apply it to our speech recognition task.

class GreedyCTCDecoder(torch.nn.Module):
    def __init__(self, labels, blank=0):
        super().__init__()
        self.labels = labels
        self.blank = blank


    def forward(self, emission: torch.Tensor):
        indices = torch.argmax(emission, dim=-1)
        indices = torch.unique_consecutive(indices, dim=-1)
        indices = [i for i in indices if i != self.blank]
        return "".join([self.labels[i] for i in indices])

Let’s stitch everything and transcribe the given audio file.

decoder = GreedyCTCDecoder(labels=bundle.get_labels())
transcript = decoder(emission[0])
print(transcript)

The final transcripted output for the file: https://on.soundcloud.com/ntN6BgUWwjtRJmXY7

I|HAD|THAT|CURIOSITY|BESIDE|ME|AT|THIS|MOMENT|

Hurray! We have successfully built our ASR model. Feel free to play around with different audio files to understand the full potential of the model.

Code

Access the code for this article here: https://colab.research.google.com/drive/1TcomYrVsJEbeO_wY1wl9wmkYHJtP8zqq?usp=sharing

Conclusion

The integration of Automatic Speech Recognition (ASR) in the BFSI sector marks a significant leap forward in enhancing customer experience, operational efficiency, and security. By leveraging advanced technologies like Wav2Vec 2.0, financial institutions can unlock the full potential of voice-driven interactions.

As the BFSI industry continues to evolve, the combination of ASR and cloud technology will be instrumental in driving innovation and delivering exceptional value to customers. By embracing these advancements, financial institutions can position themselves at the forefront of the industry and build a competitive advantage.