OLMoASR: Open Models and Data for Training Robust Speech Recognition Models
OLMoASR achieves comparable speech recognition performance to Whisper using the OLMoASR-Mix dataset.
Key Findings
Methodology
The study constructs the OLMoASR-Pool dataset with 3 million hours of English audio and 17 million transcripts, using text heuristic filters to remove low-quality data, resulting in the OLMoASR-Mix dataset with 1 million hours of high-quality audio-transcript pairs. A series of OLMoASR models, ranging from 39M to 1.5B parameters, are trained on this dataset.
Key Results
- OLMoASR-medium.en achieves 12.8% and 11.0% WER for short and long-form recognition, comparable to Whisper-medium.en's 12.4% and 10.5%.
- Across 14 short-form and 7 long-form benchmarks, OLMoASR performs comparably to Whisper at all model scales.
- Data filtering strategies significantly improved model performance, reducing WER by 14.5% in short-form recognition.
Significance
By providing large-scale, high-quality open-source datasets and models, this study advances robust speech recognition research. The open-source nature of the datasets and models offers new starting points for academia and industry, addressing limitations in data quality and scale in existing speech recognition models.
Technical Contribution
The technical contribution lies in developing a new data filtering pipeline that significantly enhances speech recognition model performance, offering an open-source alternative comparable to Whisper. The study demonstrates the critical impact of data quality on model performance using the OLMoASR-Mix dataset.
Novelty
This study is the first to provide an open-source speech recognition model and dataset comparable to Whisper, emphasizing the importance of data filtering in enhancing model performance.
Limitations
- The performance of OLMoASR models in multilingual environments has not been validated.
- Legal and privacy issues of the dataset require further investigation.
Future Work
Future research could explore the application of OLMoASR in multilingual speech recognition and further optimize data filtering strategies to enhance model performance.
AI Executive Summary
Recent advances in speech recognition have been significant, yet large-scale, high-quality datasets remain scarce. The OLMoASR project addresses this gap by constructing the OLMoASR-Pool dataset, comprising 3 million hours of audio and 17 million transcripts. Researchers designed a set of text heuristic filters to remove low-quality or mis-transcribed data, resulting in the OLMoASR-Mix dataset with 1 million hours of high-quality audio-transcript pairs.
Based on OLMoASR-Mix, the research team trained a series of OLMoASR models ranging from 39M to 1.5B parameters. Across all model scales, OLMoASR performs comparably to OpenAI's Whisper in short and long-form speech recognition benchmarks, with OLMoASR-medium.en achieving 12.8% and 11.0% WER for short and long-form recognition, respectively, comparable to Whisper-medium.en's 12.4% and 10.5%.
The open-source datasets and models of the OLMoASR project provide new starting points for academia and industry, advancing robust speech recognition research. Future plans include exploring the application of OLMoASR in multilingual environments and further optimizing data filtering strategies to enhance model performance.
Deep Analysis
Background
Speech recognition technology has seen significant advancements recently, particularly in models trained on large-scale datasets. However, existing datasets often vary in quality, affecting model robustness and generalization. To address this, researchers developed the OLMoASR-Pool dataset, containing 3 million hours of English audio and 17 million transcripts.
Core Problem
Speech recognition models are highly sensitive to data quality and scale. Existing datasets often have low-quality or mis-transcribed data, impacting model performance and robustness. Developing a high-quality, large-scale dataset is crucial for improving speech recognition model performance.
Innovation
The core innovation of the OLMoASR project lies in its data filtering strategy. Researchers designed a set of text heuristic filters to remove low-quality or mis-transcribed data, resulting in the OLMoASR-Mix dataset with 1 million hours of high-quality audio-transcript pairs.
Methodology
- �� Constructed the OLMoASR-Pool dataset with 3 million hours of audio and 17 million transcripts.
- �� Designed text heuristic filters to remove low-quality or mis-transcribed data.
- �� Formed the OLMoASR-Mix dataset with 1 million hours of high-quality audio-transcript pairs.
- �� Trained a series of OLMoASR models based on OLMoASR-Mix.
Experiments
The experimental design includes training a series of models ranging from 39M to 1.5B parameters using the OLMoASR-Mix dataset and evaluating their performance across 14 short-form and 7 long-form benchmarks. Key hyperparameters include learning rate, batch size, and training steps.
Results
Experimental results show that OLMoASR models perform comparably to Whisper at all scales, with OLMoASR-medium.en achieving 12.8% and 11.0% WER for short and long-form recognition.
Applications
OLMoASR models can be applied in various speech recognition applications, such as voice assistants, speech translation, and transcription. The high-quality datasets and open-source models provide new research starting points.
Limitations & Outlook
While OLMoASR models perform well in English speech recognition, their performance in multilingual environments has not been validated. Additionally, legal and privacy issues of the dataset require further investigation.
Plain Language Accessible to non-experts
Imagine you're in a library with thousands of books, but some books have inaccurate content. OLMoASR is like a smart librarian who can quickly identify and select the books with accurate content. This way, OLMoASR helps speech recognition systems better understand and transcribe human language, just like a librarian helps readers find the books they need.
ELI14 Explained like you're 14
Hey there! Imagine you're playing a super cool game where your mission is to find the best treasures. OLMoASR is like a super smart guide that helps you filter out the less useful clues, leaving only the most valuable treasures. This way, you can complete your mission faster and score high! That's what OLMoASR does in speech recognition; it helps computers better understand what we're saying.
Glossary
OLMoASR
An open-source speech recognition model and dataset aimed at improving speech recognition robustness.
Used for training and evaluating speech recognition models.
Whisper
A speech recognition model developed by OpenAI, known for its high performance.
Serves as a performance benchmark for OLMoASR.
WER
Word Error Rate, used to evaluate the accuracy of speech recognition models.
Used to compare the performance of OLMoASR and Whisper.
Data Filtering
The process of removing low-quality or erroneous data to improve model performance.
Used in OLMoASR to enhance dataset quality.
Zero-shot Learning
The ability to make predictions on unseen samples.
Achieved by OLMoASR through high-quality datasets.
Open Questions Unanswered questions from this research
- 1 How to validate OLMoASR's performance in multilingual environments?
- 2 How to address the legal and privacy issues of the dataset?
Applications
Immediate Applications
Voice Assistants
OLMoASR can improve the recognition accuracy of voice assistants, providing a better user experience.
Long-term Vision
Multilingual Speech Recognition
By expanding OLMoASR's datasets and models, high-precision multilingual speech recognition can be achieved in the future.
Abstract
Improvements in training data scale and quality have led to significant advances, yet its influence in speech recognition remains underexplored. In this paper, we present a large-scale dataset, OLMoASR-Pool, and series of models, OLMoASR, to study and develop robust zero-shot speech recognition models. Beginning from OLMoASR-Pool, a collection of 3M hours of English audio and 17M transcripts, we design text heuristic filters to remove low-quality or mistranscribed data. Our curation pipeline produces a new dataset containing 1M hours of high-quality audio-transcript pairs, which we call OLMoASR-Mix. We use OLMoASR-Mix to train the OLMoASR-Mix suite of models, ranging from 39M (tiny.en) to 1.5B (large.en) parameters. Across all model scales, OLMoASR achieves comparable average performance to OpenAI's Whisper on short and long-form speech recognition benchmarks. Notably, OLMoASR-medium.en attains a 12.8\% and 11.0\% word error rate (WER) that is on par with Whisper's largest English-only model Whisper-medium.en's 12.4\% and 10.5\% WER for short and long-form recognition respectively (at equivalent parameter count). OLMoASR-Pool, OLMoASR models, and filtering, training and evaluation code will be made publicly available to further research on robust speech processing.