Evaluating w2v-BERT-2.0 Speech Tokenizer for Speech Models

Overview

This project explores the integration of the W2v-BERT-2.0 tokenizer into existing speech-language models (SLMs) to enhance speech-to-speech translation capabilities. This approach aims to process speech directly, eliminating the need for converting speech to text first. Our focus is on leveraging the Conformer-based architecture of the W2v-BERT-2.0 tokenizer, extensively pre-trained on a multilingual corpus.

Team

• Ishfaq Bhat
• Abubakar Aliyu Badawi
• Celil Yilmaz
• Tayyab Tahir

Department of Seatech, University de Toulon, La Garde 83130, France

Project Aim

To assess the efficacy of the W2v-BERT-2.0 tokenizer integrated into an existing model, aimed at reproducing the open-source version of Google’s AudioLM. This integration is intended to improve the accuracy and efficiency of speech language processing directly, bypassing the intermediary step of text conversion.

Key Objectives

1. Process audio data into TSV files containing training and testing datasets.
2. Explore the architecture and functionalities of the W2v-BERT-2.0 tokenizer.
3. Extract high-quality features from audio files using the tokenizer.
4. Train the enhanced model on extensive datasets, evaluating its performance against established benchmarks.

Datasets

• Train-clean-100: 100 hours of high-quality, clean audio.
• Libri-Light Large: 60,000 hours of varied audio recordings, enhancing model robustness and adaptability.

Methodology

This section outlines the detailed steps and techniques used in our project to integrate and evaluate the W2v-BERT-2.0 tokenizer within speech-language models. Our methodology is designed to ensure rigorous testing and validation of the tokenizer's effectiveness in enhancing speech-to-speech translation capabilities.

Data Preparation

1. Audio Data Collection:

   • Source audio files from publicly available datasets: Train-clean-100 and Libri-Light Large.
   • Ensure audio samples cover a diverse range of accents, dialects, and speaking speeds to test the robustness of the model under varied conditions.

2. Audio File Processing:

   • Convert audio files to a uniform format (e.g., WAV) and sample rate.
   • Segment audio files into smaller clips to facilitate efficient processing and analysis.

3. Data Annotation:

   • Annotate audio clips with phonetic and linguistic labels as needed for training and testing the tokenizer.
   • Use automated tools where possible to speed up the annotation process while ensuring accuracy through manual checks.

Feature Extraction

1. Tokenizer Configuration:

   • Configure the W2v-BERT-2.0 tokenizer to process raw audio signals directly.
   • Adjust tokenizer settings to optimize for the specific characteristics of the speech data, such as phonetic detail and linguistic complexity.

2. Optimal Layer Identification:

   • Run preliminary tests to identify which layer(s) of the W2v-BERT-2.0 model provide the most useful features for speech processing tasks.
   • Analyze the output from different layers to determine their effectiveness in capturing relevant linguistic and phonetic features.

3. Feature Extraction Process:

   • Extract features using the selected optimal layer(s) of the tokenizer.
   • Store features in a structured format (e.g., TSV or JSON) that includes timestamps and metadata for later analysis.

Model Training and Evaluation

1. Model Configuration:

   • Set up the speech language model architecture, incorporating the W2v-BERT-2.0 tokenizer as the frontend processor.
   • Configure training parameters such as learning rate, batch size, and number of epochs based on preliminary tests.

2. Training Process:

   • Train the model on the "Train-clean-100" dataset for initial tuning and parameter optimization.
   • Scale up training to the "Libri-Light Large" dataset to evaluate performance under more challenging and diverse conditions.

3. Model Evaluation:

   • Evaluate the model using a set of metrics designed to assess syntactic understanding, phonetic accuracy, and generalization capabilities across different linguistic contexts.
   • Use benchmarks such as the sBLIMP (syntax Benchmark of Linguistic Minimal Pairs) to provide a standardized measure of performance.

4. Optimization and Tuning:

   • Adjust model parameters and training settings based on initial evaluation results to enhance performance.
   • Iteratively refine the model through additional training cycles, focusing on areas identified as needing improvement.

Results

Our initial results indicate promising improvements in syntactic understanding and generalization capabilities of the speech model. Further training and optimization are recommended to fully realize the potential of the W2v-BERT-2.0 integration.

Conclusion and Recommendations

While initial findings are encouraging, extended training periods and enhanced computational resources are suggested to further improve the model's performance.

References

Detailed references are included for further reading and verification of the methodologies.