Transcription and Translation of Videos Using XLSR Wav2Vec2 and mBART

Core Concepts

This research focuses on training personalized ASR models for low-resource languages like Hindi using innovative methodologies, such as RVC and XLSR Wav2Vec2, to achieve accurate transcription and translation of video content.

Abstract

The research addresses the challenge of training personalized ASR models for low-resource languages like Hindi. By utilizing innovative methodologies like RVC and XLSR Wav2Vec2, the study demonstrates how just 14 minutes of custom audio can lead to accurate transcription and translation of video content. The integration of mBART further enhances the system's capabilities in delivering multilingual solutions for personalized voice transcription.
The methodology involves data augmentation using the Ozen toolkit, fine-tuning the XLSR Wav2Vec2 model, training the RVC model, and implementing speaker diarization using pyannote. The results show significant accuracy improvements with limited data, paving the way for streamlined pipelines in speech technology advancements.

Stats

English language data spans approximately 3,438 hours of audio.
Hindi language section comprises about 21 hours of recorded audio.
The KL Divergence Loss reaches 0.9292 after 200 epochs.
The model gave a training accuracy of 0.80 and WER of 0.53.

Quotes

"No problem in social media according to my salary." - Content Highlighting Personal Experience

Key Insights Distilled From

Transcription and translation of videos using fine-tuned XLSR Wav2Vec2 on custom dataset and mBART

by Aniket Tathe... at arxiv.org 03-04-2024

https://arxiv.org/pdf/2403.00212.pdf

Deeper Inquiries

How can personalized ASR models impact accessibility in multilingual video content

Personalized ASR models can significantly impact accessibility in multilingual video content by providing accurate transcription and translation services tailored to individual voices. These models, trained on custom datasets like the Common Voice 16.0 corpus created from personalized audio, enable more precise recognition of unique speech patterns and accents. By integrating technologies such as XLSR Wav2Vec2 and mBART, these models can transcribe and translate videos efficiently, aligning the translated text with the video timeline to deliver accessible solutions for multilingual audiences. This customization enhances user experience by ensuring accurate subtitles that reflect the nuances of each speaker's voice, thereby improving comprehension and inclusivity in diverse linguistic contexts.

What are the potential limitations of relying on limited datasets for training ASR models

Relying on limited datasets for training ASR models poses several potential limitations that can impact model performance and generalizability. One major limitation is the risk of overfitting due to insufficient data diversity, leading to reduced accuracy when processing unseen or varied inputs. Limited datasets may also result in biased or incomplete representations of language features, hindering the model's ability to capture complex linguistic structures effectively. Additionally, training on a small dataset may restrict the model's adaptability across different speakers or dialects within a language, limiting its utility in real-world applications where variability is common. To mitigate these limitations, researchers must explore data augmentation techniques or leverage transfer learning approaches to enhance model robustness despite data constraints.

How might advancements in speech technology influence cross-cultural communication

Advancements in speech technology have profound implications for cross-cultural communication by facilitating seamless interaction between individuals speaking different languages. Improved ASR models equipped with self-supervised learning mechanisms like XLSR Wav2Vec2 demonstrate enhanced capabilities in recognizing diverse linguistic patterns across multiple languages accurately. Such advancements not only bridge language barriers but also promote cultural exchange and understanding by enabling real-time translation services that preserve context and meaning during conversations or content consumption. As speech technology continues to evolve with multilingual support and personalized voice recognition features, it has the potential to foster greater collaboration, empathy, and mutual understanding among global communities through effective communication tools tailored to individual preferences and linguistic backgrounds.