Rev, as a leader in human transcription of English, has amassed the highest quality English speech recognition dataset in the world. The research team at Rev has used this corpus to develop extremely accurate speech recognition and speech diarization models, currently available through the rev.ai API.

These models are accessible under a non-commercial license. For information on usage-based or all-inclusive commercial licenses, please contact us at licensing@rev.com. We are releasing both a full production pipeline for developers as well as pared-down research models for experimentation. Rev hopes that these releases will spur research and innovation in the fast-moving domain of voice technology. The speech recognition models released today outperform all existing open source speech recognition models across a variety of long-form speech recognition domains.

The released models, as well as usage instructions, can be found on github and HuggingFace. 

Shaping the Future of Speech Technology

This release, which we are calling Reverb, encompasses two separate models: an automatic speech recognition (ASR) model in the WeNet framework and a speech diarization model in the Pyannote framework. For researchers, we provide simple scripts for combining ASR and diarization output into a single diarized transcript. For developers, we provide a full pipeline that handles both ASR and diarization in a production environment. Additionally, we are releasing an int8 quantized version of the ASR model within the developer pipeline (“Reverb Turbo”) for applications that are particularly sensitive to speed and/or memory usage. 

Reverb ASR was trained on 200,000 hours of English speech, all expertly transcribed by humans — the largest corpus of human transcribed audio ever used to train an open-source model. The quality of this data has produced the world’s most accurate English automatic speech recognition (ASR) system, using an efficient model architecture that can be run on either CPU or GPU. 

Additionally, this model provides user control over the level of verbatimicity of the output transcript, making it ideal for both clean, readable transcription and use-cases like audio editing that require transcription of every spoken word including hesitations and re-wordings. Users can specify fully verbatim, fully non-verbatim, or anywhere in between for their transcription output. 

For diarization, Rev used the high-performance pyannote.audio library to fine-tune existing models on 26,000 hours of expertly labeled data, significantly improving their performance. Reverb diarization v1 uses the pyannote3.0 architecture, while Reverb diarization v2 uses WavLM instead of SincNet features. 

Training with the Largest Human-Transcribed Corpus

Preparing and Processing ASR Training Data

Rev’s ASR dataset is made up of long-form, multi-speaker audio featuring a wide range of domains, accents and recording conditions. This corpus contains audio transcribed in two different styles: verbatim and non-verbatim. 

Verbatim transcripts include all speech sounds in the audio (including false starts, filler words, and laughter), while non-verbatim transcripts have been lightly edited for readability. Training on both of these transcription styles is what enables the style control feature of the Reverb ASR model.

To prepare our data for training, we employ a joint normalization and forced-alignment process, which allows us to simultaneously filter out poorly-aligned data and get the best possible timings for segmenting the remaining audio into shorter training segments. During the segmentation process, we include multi-speaker segments, so that the resulting ASR model is able to effectively recognize speech across speaker switches. 

The processed ASR training corpus comprises 120,000 hours of speech with verbatim transcription labels and 80,000 hours with non-verbatim labels.

A Closer Look at Reverb’s ASR Model Architecture

Reverb ASR was trained using a modified version of the WeNet toolkit and uses a joint CTC/attention architecture. The encoder has 18 conformer layers and the bidirectional attention decoder has 6 transformer layers, 3 in each direction. In total, the model has approximately 600M parameters. 

One important modification available in Rev’s WeNet release is the use of the language-specific layer mechanism. While this technique was originally developed to give control over the output language of multilingual models, Reverb ASR uses these extra weights for control over the verbatimicity of the output. These layers are added to the first and last blocks of both the encoder and decoder. 

The joint CTC/attention architecture enables experimentation with a variety of inference modes, including: greedy CTC decoding, CTC prefix beam search (with or without attention rescoring), attention decoding, and joint CTC/attention decoding. The joint decoding available in Rev’s Wenet is a slightly modified version of the time synchronous joint decoding implementation from ESPnet. 

The production pipeline uses WFST-based beam search with a simple unigram language model on top of the encoder outputs, followed by attention rescoring. This pipeline also implements parallel processing and overlap decoding at multiple levels to achieve the best possible turn-around time without introducing errors at the chunk boundaries. While the research model outputs unformatted text, the production pipeline includes a post-processing system for generating fully formatted output. 

Setting New Benchmarking Standards for ASR Accuracy

Unlike many ASR providers, Rev primarily uses long-form speech recognition corpora for benchmarking. We use each model to produce a transcript of an entire audio file, then use fstalign to align and score the complete transcript. We report micro-average WER across all of the reference words in a given test suite. As part of our model release, we have included our scoring scripts so that anyone can replicate our work, benchmark other models, or experiment with new long-form test suites. 

Here, we’ve benchmarked Reverb ASR against the best performing open-source models currently available: OpenAI’s Whisper large-v3 and NVIDIA’s Canary-1B. Note that both of these models have significantly more parameters than Reverb ASR. 

For these models and Rev’s research model, we use simple chunking with no overlap - 30s chunks for Whisper and Canary, and 20s chunks for Reverb. The Reverb research results use CTC prefix beam search with attention rescoring. We used Canary through Hugging Face and used the WhisperX implementation of Whisper. For both Whisper and Canary, we use NeMo to normalize the model outputs before scoring.

For long-form ASR, we’ve used three corpora: Rev16 (podcasts), Earnings21 (earnings calls from US-based companies), and Earnings22 (earnings calls from global companies).
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¹ Description from https://cdn.openai.com/papers/whisper.pdf, Appendix A.2: “We use a subset of 16 files from the 30 podcast episodes in Rev.AI’s Podcast Transcription Benchmark, after finding that there are multiple cases where a significant portion of the audio and the labels did not match, mostly on the parts introducing the sponsors. We selected 16 episodes that do not have this error, whose file numbers are: 3, 4, 9, 10, 11, 14, 17, 18, 20, 21, 23, 24, 26, 27, 29, 32.”

For Rev16, we have produced both verbatim and non-verbatim human transcripts. For all Reverb models, we run in verbatim mode for evaluation with the verbatim reference and non-verbatim mode for evaluation with the non-verbatim reference.

We have also used GigaSpeech for a more traditional benchmark. We ran Reverb ASR in verbatim mode and used the HuggingFace Open ASR Leaderboard evaluation scripts. 

Overall, Reverb ASR significantly outperforms the competition on long-form ASR test suites. Rev’s models are particularly strong on the Earnings22 test suite, which contains mainly speech from non-native speakers of English. We see a small WER degradation from the use of the Turbo model, but a much larger gap between the production pipeline and research model - demonstrating the importance of engineering a complete system for long-form speech recognition. 

On the Gigaspeech test suite, Rev’s research model is worse than other open-source models. The average segment length of this corpus is 5.7 seconds; these short segments are not a good match for the design of Rev’s model. These results demonstrate that despite its strong performance on long-form tests, Rev is not the best candidate for short-form recognition applications like voice search. 

Customizing Verbatimicity Levels in Reverb ASR

Rev has the only AI transcription API and model that allows user control over the verbatimicity of the output. The developer pipeline offers a verbatim mode that transcribes all spoken content and a non-verbatim mode that removes unnecessary phrases to improve readability. The output of the research model can be controlled with a verbatimicity parameter that can be anywhere between zero and one. 

The Rev team has found that halfway between verbatim and non-verbatim produces a reader-preferred style for captioning - capturing all content while reducing some hesitations and stutters to make captions fit better on screen.

Real-life example:

Audio file: POD1000000032_S0000058.wav

‍Reverb verbatim transcription:
and and if you if you try and understand which ones there are you it's it's a it's a long list

‍Reverb non-verbatim transcription:
‍and if you try and understand which ones there are it's a long list

Reverb half-verbatim transcription:
‍and if you if you try and understand which ones there are you it's a long list

Diarization Innovations And Its Impact

Optimizing Data for ASR and Diarization Models

Rev’s diarization training data comes from the same diverse corpus as the ASR training data. However, annotation for diarization is particularly challenging, because of the need for precise timings specifically at speaker switches and the difficulties of handling overlapped speech. As a result, only a subset of the ASR training data is usable for diarization. The total corpus used for diarization is 26,000 hours.

Enhancing Diarization Precision with WavLM Technology

The Reverb diarization models were developed using the pyannote.audio library. Reverb diarization v1 is identical to pyannote3.0 in terms of architecture but it is fine-tuned on Rev’s transcriptions for 17 epochs. Training took 4 days on a single A100 GPU. The network has 2 LSTM layers with hidden size of 256, totaling approximately 2.2M parameters. 

Our most precise diarization model - Reverb diarization v2 - uses WavLM instead of the SincNet features in the pyannote3.0 basic model. 

Diarization Benchmarks and Performance

While DER is a valuable metric for assessing the technical performance of a diarization model in isolation, WDER (Word Diarization Error Rate) is more crucial in the context of ASR because it reflects the combined effectiveness of both the diarization and ASR components in producing accurate, speaker-attributed text. In practical applications where the accuracy of both “who spoke” and “what was spoken” is essential, WDER provides a more meaningful and relevant measure for evaluating system performance and guiding improvements. For this reason we only report WDER metrics.

We show results for two test-suites, earnings21 and rev16. 

Driving Innovation in Speech Technology

We are excited to release the state-of-the-art Reverb ASR and diarization models to the public. We hope that these releases will spur research and innovation in the fast-moving domain of voice technology. To get started, visit https://github.com/revdotcom/reverb for research models or https://github.com/revdotcom/revai for the complete developer solution. For our self-hosted solution, visit: https://github.com/revdotcom/reverb-self-hosted. Schedule a demo today to learn more about the Rev.ai API or email licensing@rev.com for Reverb commercial licensing. 

Rev would like to extend our sincere thanks to Nishchal Bhandari, Danny Chen, Miguel Del Rio, Natalie Delworth, Miguel Jette, Quinn McNamara, Corey Miller, Jan Profant, Nan Qin, Martin Ratajczak, Jean-Philippe Robichaud, Ondrej Novotny, Jenny Drexler Fox, and Lee Harris for their invaluable contributions to making this release possible.
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