Efficient Multilingual DistilWhisper for Speech Models

Alapfogalmak

The author proposes the DistilWhisper approach to bridge the performance gap in ASR for under-represented languages by utilizing lightweight modular ASR fine-tuning and knowledge distillation from a larger model. This dual approach effectively boosts ASR performance while maintaining robustness inherited from multitask and multilingual pre-training.

Kivonat

The DistilWhisper approach aims to enhance ASR performance for under-represented languages by combining lightweight modular fine-tuning and knowledge distillation. By introducing language-specific experts, the model demonstrates improved results in both in- and out-of-domain test sets, showcasing its effectiveness over standard fine-tuning methods. The architecture leverages conditional language-specific routing modules to optimize ASR fine-tuning and knowledge distillation, resulting in a more efficient adaptation approach with negligible parameter overhead at inference. Extensive experiments across diverse languages confirm the superiority of the DistilWhisper approach compared to traditional adaptation methods, highlighting its potential to improve ASR performance across low-resource languages.

Statisztikák

Whisper covers 99 languages.
Whisper-large-v2 is 2-3 times slower than whisper-small.
CLSR training uses gate budget b = 0.5.
KD employs JS divergence with temperature τ = 1.
Training all models use learning rate 10^-4 with linear decay.

Idézetek

"Our approach involves two key strategies: lightweight modular ASR fine-tuning of whisper-small using language-specific experts, and knowledge distillation from whisper-large-v2."
"Results demonstrate that our approach is more effective than standard fine-tuning or LoRA adapters."

Főbb Kivonatok

Multilingual DistilWhisper

by Thomas Palme... : arxiv.org 03-13-2024

https://arxiv.org/pdf/2311.01070.pdf

Mélyebb kérdések

How can the DistilWhisper approach be adapted for other types of multilingual models?

The DistilWhisper approach can be adapted for other types of multilingual models by incorporating language-specific experts and knowledge distillation techniques. Firstly, similar to how CLSR modules were added to Whisper-small in this approach, other multilingual models can integrate these modules to enhance performance across different languages. By introducing conditional language-specific routing mechanisms, the model can effectively leverage language-specific expertise while maintaining a shared representation for multilingual tasks.
Additionally, the use of knowledge distillation from a larger teacher model can be applied to various multilingual models. This involves transferring knowledge from a large pre-trained model into a smaller one through supervised learning methods like JS divergence loss. By leveraging insights and robustness from larger models, smaller ones can benefit from improved performance without significantly increasing computational costs.
Furthermore, adapting the gating mechanism introduced in DistilWhisper allows for efficient adaptation based on specific languages or tasks within a multi-task framework. This flexibility enables fine-tuning and optimization tailored to individual languages or domains within a diverse set covered by the model.

What are the potential limitations or drawbacks of relying on language-specific experts in speech recognition models?

While utilizing language-specific experts in speech recognition models offers several advantages such as improved performance on under-represented languages and enhanced adaptability across diverse linguistic contexts, there are also potential limitations and drawbacks associated with this approach:

Data Availability: Language-specific expert modules require sufficient training data per language to effectively learn representations and optimize performance. Limited availability of annotated data for certain languages may hinder the effectiveness of these modules.

Model Complexity: Integrating multiple language-specific components increases the complexity of the overall model architecture, potentially leading to higher computational costs during training and inference.

Generalization: Language-specific experts may excel at recognizing patterns unique to their designated languages but could struggle with generalizing across multiple languages or handling code-switching scenarios where multiple languages are used interchangeably within utterances.

Maintenance Overhead: Managing and updating individual components for each supported language adds maintenance overhead in terms of monitoring performance degradation over time, ensuring consistency across all components, and addressing issues related to linguistic variations.

Scalability Challenges: Scaling up systems with numerous language-specific modules may become challenging as more languages need to be incorporated into the system due to increased parameter sizes and computational requirements.

How might advancements in speech technology impact cross-cultural communication barriers?

Advancements in speech technology have significant implications for breaking down cross-cultural communication barriers by facilitating seamless interactions between individuals speaking different languages:

Real-Time Translation: Advanced speech recognition capabilities combined with translation algorithms enable real-time interpretation during conversations or meetings involving speakers communicating in different languages.

Improved Accessibility: Speech-to-text technologies make content more accessible by providing automated transcriptions that cater to individuals with hearing impairments or those who prefer written text over spoken communication.

Enhanced Multimodal Communication: Integration of speech technology with visual cues like sign-language recognition enhances multimodal communication accessibility among diverse communities.

4 .Cultural Preservation: Speech technologies aid in preserving endangered dialects or indigenous languages through documentation efforts that capture oral traditions accurately.
5 .Global Collaboration: By enabling effective communication regardless of linguistic differences, advanced speech technologies foster global collaboration opportunities among researchers,
professionals,and individuals worldwide.
These advancements contribute towards fostering inclusivity,democratizing access
to information,and promoting understanding among culturally diverse populations,
ultimately bridging gaps created by linguistic diversity aroundthe world