Conformer-2 is an advanced automatic speech recognition AI model developed as a successor to Conformer-1. It's designed with robust improvements for decoding proper nouns, alphanumerics, and exhibiting superior performance in noisy environments.

This has been achieved through intensive training on a large corpus of English audio data. An advantage of Conformer-2 is that it does not compromise on word error rate compared to Conformer-1, while providing enhanced user-oriented metrics.

Further improvements to Conformer-2, in comparison to its predecessor, were realized by augmenting the training data volume and increasing pseudo-label models.

Furthermore, with modifications to the inference pipeline, the latency period of Conformer-2 is reduced, thus expediting overall performance. Another critical step-up with Conformer-2 pertains to its innovative training technique that leverages model ensembling.

Instead of deriving labels solely from a single 'teacher', labels are generated in this model from multiple 'teachers', ensuring a more versatile and robust model.

This has the effect of reducing the impact of individual model failures. The development of Conformer-2 also involved an exploration into data and model parameter scaling, increasing the model size, and extending the training audio data.

These approaches were aimed at matching the underutilized potential identified by the 'Chinchilla' paper for large language models. With these updates, Conformer-2 provides faster response times than Conformer-1, bucking the trend of larger models being slower and more expensive.

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Initial release

July 20, 2023

Initial release of Conformer2.

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Pros and Cons

Pros

Trained on 1.1 million hours

Enhanced proper noun recognition

Improved alphanumeric recognition

Increased noise robustness

Utilizes model ensembling

Reduced processing times

Impressed user-oriented metrics

Ideal for speech-to-text transcriptions

Significant model size enhancements

Large language model optimized

Reduced inference latency period

Excellence in handling individual model failures

Robust results on real-world data

Improved speed over predecessor

Optimized serving infrastructure

31.7% alphanumeric improvement

6.8% proper noun error rate improvement

12.0% noise robustness improvement

Scaling up data and model parameters

Faster results delivery

Reduced variability

Improvements in transcribing numerical data

Enhanced noise handling abilities

Flexibility for continual experimentation

API parameters speech_threshold

Minimal API changes for users

Model can be tried in Playground

Optimized for most real use cases

Designed to reduce model's variance

Failure cases subdued by model ensembling

Enables faster overall performance

Delivers more readable transcripts

Large gains in Alphanumeric Transcription Accuracy

Shows reduced variance in character error rate

Improved performance in noisy environments

Training speed is 1.6x faster

Automatic rejection of low speech proportion files

Capable of handling wide distribution of data

Explores into multimodality and self-supervised learning

Integration with in-house hardware

Improved real-world applications

State-of-the-art speech recognition model

Reduced transcription time

Copes with robust noises

Capabilities in robustness improvement

Efficient model size scaling

Equipped for model/dataset scaling

Efficient model ensembling

View 43 more pros

Cons

Only trained on English

Potential bias from teachers

No multi-language support

Narrow training data focus

Dependent on ensembling technique

Problems with edge-case alphanumerics

May inconsistently handle noise

No small-scale application

Requires substantial computational power

In-house infrastructure dependency

View 5 more cons

Q&A

What is Conformer-2?

Conformer-2 is an advanced AI model designed for automatic speech recognition, developed as a successor to Conformer-1. It is particularly effective at recognizing proper nouns, alphanumerics, and is robust in noisy environments.

How is Conformer-2 different from its predecessor, Conformer-1?

Conformer-2 distinguishes itself from its predecessor, Conformer-1, through several key improvements. It has made significant advancements in decoding proper nouns, alphanumerics, and demonstrating superior performance in noisy situations. This has been achieved through extensive training on a vast quantity of English audio data. Moreover, it uses an enhanced training technique that employs model ensembling, generating labels from multiple strong 'teachers' instead of just one. This makes Conformer-2 more versatile and robust as it reduces the impact of individual model failures. Additionally, despite being a larger model, Conformer-2 offers faster response times compared to Conformer-1 due to optimizations in the inference pipeline.

What is the main function of Conformer-2?

The primary function of Conformer-2 is to provide automatic speech recognition. It transforms spoken data into text, making it an essential component for AI pipelines focusing on generative AI applications that use spoken data.

How much English audio data has Conformer-2 been trained on?

Conformer-2 has been trained on an extensive amount of 1.1 million hours of English audio data.

What enhancements does Conformer-2 provide in terms of speech recognition?

Conformer-2 offers enhanced recognition of proper nouns and alphanumerics. It also provides robustness to noise, thereby proving superior performance in real-world audio conditions which could be challenging.

What is model ensembling in the context of Conformer-2?

Model ensembling in the context of Conformer-2 is a training technique in which labels are generated from multiple strong 'teachers' rather than a single one, reducing variance and enhancing the model's performance when presented with unseen data during training.

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How does Conformer-2's speed compare with that of Conformer-1?

Despite its increased model size, Conformer-2 offers a significant improvement in 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.

What improvements does Conformer-2 offer in terms of user-oriented metrics?

Conformer-2 demonstrates significant enhancements in various user-oriented metrics. These include a 31.7% improvement on alphanumerics, a 6.8% improvement on the proper noun error rate, and a 12.0% improvement in noise robustness.

How does Conformer-2 perform in real-world applications?

In real-world applications, Conformer-2 shows immense capability. For instance, it achieves significantly lower error rates in recognizing proper nouns and alphanumeric data, which are often crucial in real-world use cases. Conformer-2 is also more robust to noise, making it well adapted to varied and potentially challenging audio conditions found in the real world.

What type of AI applications would benefit the most from Conformer-2?

AI applications focused on generative use of spoken data would benefit the most from Conformer-2. This model is ideal for generating accurate speech-to-text transcriptions, a crucial component for these types of AI applications.

Why does Conformer-2 use multiple 'teachers' for label generation?

Conformer-2 uses multiple 'teachers' for label generation to create a more robust and versatile model. This approach mitigates the influence of individual model failures, broadening the model's exposure to a wider distribution of behaviors.

How is the Conformer-2 training method innovative?

Conformer-2's training method is innovative because it uses model ensembling, generating labels from multiple teacher models instead of just one. This approach reduces variance and produces a model that is more robust when exposed to unseen data during training.

How does Conformer-2 handle noise?

Conformer-2 displays superior noise robustness due to its advanced training on a vast quantity of English audio data. It has achieved a 12.0% improvement in handling noisy environments.

How does Conformer-2 deal with the recognition of alphanumerics?

Conformer-2 shows a significant 31.7% improvement on alphanumerics. This means it can more accurately recognize and transcribe alphanumeric data which is essential, for example, in cases of credit card numbers or confirmation codes.

What are the improvements in Conformer-2 in terms of proper noun error rate?

There has been a 6.8% improvement in the proper noun error rate with Conformer-2, resulting in more consistent transcription of entities like names and making transcripts generally more readable.

Does the size increase in Conformer-2 affect its speed?

Despite the increase in model size, Conformer-2 does not compromise on speed. On the contrary, due to substantial improvements in the serving infrastructure, Conformer-2 is faster than its predecessor, offering up to 55% faster processing times for any duration of audio file.

What is the correlation between data scaling and Conformer-2's performance?

Data scaling, as highlighted in the DeepMind's Chinchilla paper, is an important factor for large language models like Conformer-2. The paper indicated the importance of sufficient training data for large language models. Conforming to these scaling laws, Conformer-2 has been trained on a substantial amount of data, resulting in a robust model with enhanced performance.

How does Conformer-2 contribute to the generation of AI applications utilizing spoken data?

By providing accurate speech-to-text transcriptions, Conformer-2 plays a vital role in the generation of AI applications that utilize spoken data. Its ability to robustly recognize proper nouns, alphanumerics, and handle noisy environments makes it valuable in AI pipelines that require high-quality transcriptions of spoken data.

How has Conformer-2 optimized its serving infrastructure for faster processing times?

Conformer-2 has significantly optimized its serving infrastructure to ensure faster processing times, achieving up to a 55% reduction in relative processing time across all audio file durations. This enables the accurate transcription of spoken data at a much higher speed compared to Conformer-1.

How has the development of Conformer-2 been influenced by the scaling laws in DeepMind's Chinchilla paper?

The development of Conformer-2 has been substantially influenced by the scaling laws proposed in DeepMind's Chinchilla paper. The paper emphasized the importance of ample training data for large language models. Adhering to these laws, Conformer-2 was trained on over a million hours of English audio data, leading to substantial improvements in performance.

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