15,287
Speech recognition
2023-07-20
Conformer2
18
Accurately transcribed spoken language.
Overview
Speech recognition#1 most recent
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Conformer-2 is an advanced AI model designed for automatic speech recognition. It has been trained on 1.1 million hours of English audio data, resulting in significant improvements over its predecessor, Conformer-1.
This model focuses on enhancing the recognition of proper nouns, alphanumerics, and noise robustness.The development of Conformer-2 was driven by the scaling laws proposed in DeepMind's Chinchilla paper, which highlighted the importance of sufficient training data for large language models.
Consequently, Conformer-2 has been trained on a substantial amount of data, utilizing 1.1 million hours of English audio.One notable feature of Conformer-2 is its adoption of model ensembling.
Instead of relying on predictions from a single teacher model, Conformer-2 generates labels from multiple strong teachers. This ensembling technique reduces variance and enhances the model's performance when faced with unseen data during training.Despite the increased model size, Conformer-2 offers improvements in terms of speed compared to Conformer-1.
The serving infrastructure has been optimized to ensure faster processing times, achieving up to a 55% reduction in relative processing duration across all audio file durations.In real-world applications, Conformer-2 demonstrates significant enhancements in various user-oriented metrics.
It achieves a 31.7% improvement on alphanumerics, a 6.8% improvement on proper noun error rate, and a 12.0% improvement in noise robustness. These improvements are a result of both increased training data and the use of an ensemble of models.The Conformer-2 model is ideal for generating accurate speech-to-text transcriptions, making it a valuable component for AI pipelines focused on generative AI applications that utilize spoken data.
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Conformer2 was manually vetted by our editorial team and was first featured on July 21st 2023.
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2 alternatives to Conformer2 for Speech recognition
Pros and Cons
Pros
Trained on 1.1 million hours
Focused on proper nouns
Improved noise robustness
Model ensembling technique
Better speed than predecessor
31.7% improvement on alphanumerics
6.8% improved proper noun accuracy
12.0% improvement in noise robustness
Accurate speech-to-text transcriptions
55% faster processing times
Optimized serving infrastructure
Improved results with ensemble
Strong proper noun recognition
Improves user-oriented metrics
Flexible API parameters
Speech threshold parameter
Available through API
Accessible Playground for testing
Free API token
Improved alphanumeric transcription
Reduced noise influences
30.7% reduced mean CER
Aligned with DeepMind's Chinchilla paper
Improved industry-use metrics
43% fewer noise errors
Improved on noise robustness
Training speed 1.6x faster
In-house GPU compute cluster
Enhances industry-friendly models
Fault-tolerant scaling cluster management
Training on own hardware
Cons
Only for English speech
No multilingual support
Cannot handle non-audio data
Lacks user-friendly interface
Not open-source
Specialized for speech transcription
Limited scalability
No offline usage
Dependent on large training data
Q&A
What is Conformer-2?
Conformer-2 is an advanced AI model designed specifically for automatic speech recognition. It has been trained on an extensive set of 1.1 million hours of English audio, focusing specifically on the improved recognition of proper nouns, alphanumerics, and noise robustness. Conformer-2 employs techniques like model ensembling for more robust and accurate predictions. It's also optimized for faster processing times, delivering up to a 55% reduction in relative processing duration across all audio file lengths.
How is Conformer-2 different from Conformer-1?
Conformer-2 differs from its predecessor, Conformer-1, in several aspects. First, it has been trained on a much larger dataset of 1.1 million hours of English audio, as opposed to the dataset size used for Conformer-1. Second, Conformer-2 employs model ensembling framework to generate more accurate and reliable labels, reducing variance and enhancing the model's performance when faced with unseen data during training. Furthermore, despite an increase in model size, Conformer-2 provides a significant enhancement in processing speed which is up to 55% faster than Conformer-1.
What is the dataset size that Conformer-2 is trained on?
Conformer-2 is trained on a remarkably extensive dataset comprising 1.1 million hours of English audio.
What improvements does Conformer-2 offer over Conformer-1?
Conformer-2 offers substantial improvements over Conformer-1 in a variety of ways. There is a 31.7% improvement in recognizing alphanumerics, an improvement of 6.8% on the proper noun error rate, and a 12.0% improvement in noise robustness. Additionally, despite being a larger model, Conformer-2 offers a much faster processing speed, achieving up to a 55% reduction in the relative processing duration across all audio file lengths.
What is model ensembling in the context of Conformer-2?
In the context of Conformer-2, model ensembling is a technique adopted to improve prediction accuracy and reliability. Instead of generating predictions from a single teacher model, Conformer-2 generates labels from multiple strong teacher models. This variance reduction technique contributes to a more robust model when faced with unseen data during training.
How does Conformer-2 enhance the recognition of proper nouns and alphanumerics?
Conformer-2 enhances the recognition of proper nouns and alphanumerics by using an advanced model developed using extensive training data and advanced techniques such as model ensembling. It performs notably better than Conformer-1, with a 31.7% improvement in alphanumeric recognition and a 6.8% improvement in proper noun error rate.
What is the speed improvement of Conformer-2 compared to Conformer-1?
Despite being a more complex model, Conformer-2 offers impressive improvements in speed over Conformer-1. The serving infrastructure for Conformer-2 has been profoundly optimized to ensure faster processing times, realizing up to a 55% reduction in relative processing duration across all audio file durations.
How does noise robustness improve in Conformer-2?
Conformer-2 showcases significant improvements in noise robustness, a critical element in speech recognition. The robustness to noise is enhanced by 12.0% compared to Conformer-1, making it even more suitable for real-world applications with naturally occurring variations and noise.
What are the real-world applications of Conformer-2?
Conformer-2 has wide-ranging real-world applications, predominantly in generating precise speech-to-text transcriptions. Its applications are valuable to AI systems dealing with large amounts of spoken data, as it provides high accuracy, speed, and noise robustness. This could include transcription services, voice assistants, call center analytics, and any AI system that needs to extract textual data from speech.
Is Conformer-2 available for use now?
Yes, Conformer-2 is available for use. It can be accessed through the API provided on the official website and incorporated into AI applications.
How does Conformer-2 handle alphanumeric data effectively?
Through extensive training on a wide data range, Conformer-2 has improved the accuracy of alphanumeric data recognition. In a comparison test with Conformer-1, Conformer-2 showcased a 30.7% relative reduction in the mean Character Error Rate (CER), decreasing the likelihood of transcribing numbers inaccurately. Such improvement can be particularly useful in applications requiring high numeric accuracy like credit card numbers or confirmation codes.
How well does Conformer-2 perform in terms of recognizing proper nouns?
Conformer-2 vastly enhances the recognition of proper nouns, with a substantial 6.8% improvement in the Proper Noun Error Rate (PPNER) compared to Conformer-1. It particularly focuses on the correct transcription of entities like names that contribute significantly to a transcript's overall readability and consistency.
What is the role of the new API parameter speech_threshold in Conformer-2?
The new API parameter 'speech_threshold' introduced with Conformer-2 allows users to set a threshold for the proportion of speech that must be present in an audio file for it to be processed. The API will automatically reject files that contain a proportion of speech lower than the set threshold, helping users control costs for files where transcription isn't required.
How is the performance of Conformer-2 under noisy conditions?
Under noisy conditions, Conformer-2 exhibits enhanced performance. It further pushes noise robustness acquired from Conformer-1. When tested on Librispeech-clean dataset with varying levels of added white noise, it showcased overall better noise resilience. At an equal signal-to-noise ratio, Conformer-2 improved upon Conformer-1 by 12.0%.
How can I integrate Conformer-2 into my own product?
To integrate Conformer-2 into your product, you can access it through the API provided on the official website. You can then follow the API documentation to understand how to use it within your specific use case. If help is required, you can contact the sales team for further guidance.
What tangible benefits will I see as a user when shifting from Conformer-1 to Conformer-2?
As a user, shifting from Conformer-1 to Conformer-2 will bring forth significant enhancements. These include a 31.7% improved accuracy in alphanumeric recognition, a 6.8% lowered proper noun error rate, and a 12.0% enhancement in noise robustness. Additionally, despite the larger model size, Conformer-2 offers much faster processing speed, providing results up to 55% faster than Conformer-1.
Can I access the Conformer-2 through the current API?
Yes, Conformer-2 is accessible through the current API. Current users of the API will automatically be switched to Conformer-2 without having to make any changes on their end.
How can I test Conformer-2?
You can test Conformer-2 through the Playground provided on the official website. It lets you upload a file or enter a YouTube link to see a transcription in just a few clicks. Alternatively, you can also sign up for a free API token and directly try out the API.
What kind of results can I expect with Conformer-2 in my AI pipeline?
With Conformer-2 in your AI pipeline, you can expect improved performance in various facets of speech recognition. This includes improved transcription of proper nouns and alphanumerics, enhanced noise robustness, and faster results owing to the optimized infrastructure. Such improvements can significantly impact applications like transcription services, call center analytics, and voice assistants.
What are the potential issues Conformer-2 can address in transcription?
Conformer-2 is designed to address several potential issues in transcription. With a significant 31.7% improvement on alphanumeric accuracy, it ensures important numerical data such as credit card numbers or confirmation codes are transcribed correctly. A 6.8% improvement in proper noun error rate ensures entities like names are accurately transcribed, making the transcripts more understandable. Furthermore, it is designed to be more robust to noise, ensuring high accuracy even under noisy conditions.
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