Robust Speech Recognition via Large-Scale Weak Supervision
Abstract
We study the capabilities of speech processing systems trained simply to predict large amounts of transcripts of audio on the internet. When scaled to 680,000 hours of multilingual and multitask supervision, the resulting models generalize well to standard benchmarks and are often competitive with prior fully supervised results but in a zero-shot transfer setting without the need for any fine-tuning. When compared to humans, the models approach their accuracy and robustness. We are releasing models and inference code to serve as a foundation for further work on robust speech processing.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of Whisper, a robust speech recognition system trained on a large-scale weakly supervised dataset, demonstrating competitive performance with minimal fine-tuning. This work significantly advances the field by showing the potential of leveraging vast amounts of diverse audio data for improving speech recognition systems, paving the way for future research in multilingual and multitask speech processing.
Methodology
The methodology presented in this paper is robust and innovative, focusing on large-scale weak supervision for speech recognition. The authors effectively leverage a massive dataset of 680,000 hours of multilingual and multitask audio, which is a significant step forward in the field. The approach of using a sequence-to-sequence Transformer model without requiring extensive fine-tuning is particularly noteworthy, as it simplifies the deployment of speech recognition systems. The authors also implement various filtering techniques to enhance the quality of transcripts, which is crucial given the noisy nature of the data sourced from the internet. Additionally, the multitask training format is well thought out, allowing the model to handle multiple speech processing tasks simultaneously.
Results & evaluation
The experimental evaluation is comprehensive, utilizing a variety of datasets to assess the performance of the Whisper models in a zero-shot setting. The results indicate that the models perform competitively against existing state-of-the-art systems, particularly in terms of robustness and generalization across different domains and languages. The authors provide detailed comparisons with human performance, which adds depth to their findings. However, the paper could benefit from more extensive ablation studies to isolate the effects of different components of their methodology.
Reproducibility
The paper includes sufficient details about the training process, model architecture, and data preprocessing steps, which aids in reproducibility. The authors also release their models and inference code, which is a positive aspect for the community. However, some hyperparameters and specific implementation details could be better documented to facilitate easier replication of results.
Limitations
One limitation noted is the potential for negative transfer when training on multiple tasks and languages, which the authors acknowledge. Additionally, the performance on lower-resource languages is still lacking, suggesting that further work is needed to improve recognition capabilities in these areas. The reliance on weakly supervised data also raises concerns about the inherent noise and quality of the training data, which could impact the model's performance.
Broader impact
The implications of this work are significant, particularly in making robust speech recognition systems more accessible and easier to deploy across various applications and languages. The ability to perform well in zero-shot settings without extensive fine-tuning could democratize access to advanced speech technologies, benefiting diverse industries such as customer service, content creation, and accessibility tools. The main contribution of this paper is the introduction of Whisper, a robust speech recognition system trained on a large-scale weakly supervised dataset, demonstrating competitive performance with minimal fine-tuning. This work significantly advances the field by showing the potential of leveraging vast amounts of diverse audio data for improving speech recognition systems, paving the way for future research in multilingual and multitask speech processing.
Curator note
Radford et al., OpenAI; 680k hours weak supervision; multilingual; became the standard open ASR system
Discussion
Talks & videos
Related later work
- Distil-Whisper 2023
- WhisperX (alignment) 2023
- Universal ASR integration in SALMONN, Qwen-Audio 2023
Other indexed descriptions from the archive
Robust Speech Recognition via Large-Scale Weak Supervision (Whisper)
Abstract
We study the capabilities of speech processing systems trained simply to predict large amounts of transcripts of audio on the internet. When scaled to 680,000 hours of multilingual and multitask supervision, the resulting models generalize well to standard benchmarks and are often competitive with prior fully supervised results but in a zero-shot transfer setting without the need for any fine-tuning. When compared to humans, the models approach their accuracy and robustness. We are releasing models and inference code to serve as a foundation for further work on robust speech processing.
AI research summary
The main contribution of this paper is the introduction of Whisper, a robust speech recognition system trained on a large-scale weakly supervised dataset, demonstrating competitive performance with minimal fine-tuning. This work significantly advances the field by showing the potential of leveraging vast amounts of diverse audio data for improving speech recognition systems, paving the way for future research in multilingual and multitask speech processing.
AI methodology analysis
The methodology presented in this paper is robust and innovative, focusing on large-scale weak supervision for speech recognition. The authors effectively leverage a massive dataset of 680,000 hours of multilingual and multitask audio, which is a significant step forward in the field. The approach of using a sequence-to-sequence Transformer model without requiring extensive fine-tuning is particularly noteworthy, as it simplifies the deployment of speech recognition systems. The authors also implement various filtering techniques to enhance the quality of transcripts, which is crucial given the noisy nature of the data sourced from the internet. Additionally, the multitask training format is well thought out, allowing the model to handle multiple speech processing tasks simultaneously.
AI results analysis
The experimental evaluation is comprehensive, utilizing a variety of datasets to assess the performance of the Whisper models in a zero-shot setting. The results indicate that the models perform competitively against existing state-of-the-art systems, particularly in terms of robustness and generalization across different domains and languages. The authors provide detailed comparisons with human performance, which adds depth to their findings. However, the paper could benefit from more extensive ablation studies to isolate the effects of different components of their methodology.
AI reproducibility analysis
The paper includes sufficient details about the training process, model architecture, and data preprocessing steps, which aids in reproducibility. The authors also release their models and inference code, which is a positive aspect for the community. However, some hyperparameters and specific implementation details could be better documented to facilitate easier replication of results.
AI limitations analysis
One limitation noted is the potential for negative transfer when training on multiple tasks and languages, which the authors acknowledge. Additionally, the performance on lower-resource languages is still lacking, suggesting that further work is needed to improve recognition capabilities in these areas. The reliance on weakly supervised data also raises concerns about the inherent noise and quality of the training data, which could impact the model's performance.
AI broader impact analysis
The implications of this work are significant, particularly in making robust speech recognition systems more accessible and easier to deploy across various applications and languages. The ability to perform well in zero-shot settings without extensive fine-tuning could democratize access to advanced speech technologies, benefiting diverse industries such as customer service, content creation, and accessibility tools. The main contribution of this paper is the introduction of Whisper, a robust speech recognition system trained on a large-scale weakly supervised dataset, demonstrating competitive performance with minimal fine-tuning. This work significantly advances the field by showing the potential of leveraging vast amounts of diverse audio data for improving speech recognition systems, paving the way for future research in multilingual and multitask speech processing.
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