Today we are really excited to announce the launch of Voicegain Whisper, an optimized version of Open AI's Whisper Speech recognition/ASR model that runs on Voicegain managed cloud infrastructure and accessible using Voicegain APIs. Developers can use the same well-documented robust APIs and infrastructure that processes over 60 Million minutes of audio every month for leading enterprises like Samsung, Aetna and other innovative startups like Level.AI, Onvisource and DataOrb.
The Voicegain Whisper API is a robust and affordable batch Speech-to-Text API for developersa that are looking to integrate conversation transcripts with LLMs like GPT 3.5 and 4 (from Open AI) PaLM2 (from Google), Claude (from Anthropic), LLAMA 2 (Open Source from Meta), and their own private LLMs to power generative AI apps. Open AI open-sourced several versions of the Whisper models released. With today's release Voicegain supports Whisper-medium, Whisper-small and Whisper-base. Voicegain now supports transcription in over multiple languages that are supported by Whisper.
Here is a link to our product page
There are four main reasons for developers to use Voicegain Whisper over other offerings:
While developers can use Voicegain Whisper on our multi-tenant cloud offering, a big differentiator for Voicegain is our support for the Edge. The Voicegain platform has been architected and designed for single-tenant private cloud and datacenter deployment. In addition to the core deep-learning-based Speech-to-text model, our platform includes our REST API services, logging and monitoring systems, auto-scaling and offline task and queue management. Today the same APIs are enabling Voicegain to processes over 60 Million minutes a month. We can bring this practical real-world experience of running AI models at scale to our developer community.
Since the Voicegain platform is deployed on Kubernetes clusters, it is well suited for modern AI SaaS product companies and innovative enterprises that want to integrate with their private LLMs.
At Voicegain, we have optimized Whisper for higher throughput. As a result, we are able to offer access to the Whisper model at a price that is 40% lower than what Open AI offers.
Voicegain also offers critical features for contact centers and meetings. Our APIs support two-channel stereo audio - which is common in contact center recording systems. Word-level timestamps is another important feature that our API offers which is needed to map audio to text. There is another feature that we have for the Voicegain models - enhanced diarization models - which is a required feature for contact center and meeting use-cases - will soon be made available on Whisper.
We also offer premium support and uptime SLAs for our multi-tenant cloud offering. These APIs today process over 60 millions minutes of audio every month for our enterprise and startup customers.
OpenAI Whisper is an open-source automatic speech recognition (ASR) system trained on 680,000 hours of multilingual and multitask supervised data collected from the web. The architecture of the model is based on encoder-decoder transformers system and has shown significant performance improvement compared to previous models because it has been trained on various speech processing tasks, including multilingual speech recognition, speech translation, spoken language identification, and voice activity detection.
Learn more about Voicegain Whisper by clicking here. Any developer - whether a one person startup or a large enterprise - can access Voicegain Whisper model by signing up for a free developer account. We offer 15,000 mins of free credits when you sign up today.
There are two ways to test Voicegain Whisper. They are outlined here. If you would like more information or if you have any questions, please drop us an email support@voicegain.ai
We are super excited to announce the release of two new features with our Voicegain Transcribe app.
(i) Summarization powered by LLMs.
(ii) Single Sign On (Currently available for Voicegain Edge/On-Prem customers only)
Summarization of a transcript is extremely valuable for various types of audio content. Whether a user is transcribing a business meeting, a classroom lecture, a podcast or a web event, reviewing just the summary of the transcript is a big time-saver compared to having to read the entire transcript. With this release, every transcript generated by Voicegain Transcribe will be summarized accurately using powerful state-of-the-art LLMs.
In addition to the summary of the transcript, Voicegain also supports extraction of key items like Actions, Issues, Risks, and Dependencies.
For users of Voicegain Transcribe Cloud, the summarization is powered by ChatGPT (GPT 3.5 Turbo APIs). Essentially we submit the meeting transcript to ChatGPT and we ask it to summarize the meeting. We display and store the returned summary in Voicegain Transcribe.
For users of Voicegain Transcribe Edge/On-Prem, we offer an open-source LLM model that has been fine-tuned on meeting data. This fine-tuned LLM model gets deployed along with the entire Voicegain platform behind the customer's firewall (whether in a private cloud or datacenter).
With this new release, Voicegain Transcribe also supports the SSO feature using the OIDC protocol. Most popular Identity Management software solutions - like Okta, Ping Identity, Microsoft, Oracle, RSA etc support the OIDC protocol.
This feature is currently available only to Voicegain Edge/On-Prem customers and it will be made available very soon to Voicegain Cloud customers too.
Voicegain Transcribe is a privacy-first Meeting AI platform that can be deployed "fully behind" the firewall of a company/business. It is also available for access as a cloud service.
By signing up today, you will be signed up on our forever Free Plan - which makes you eligible for 120 mins of Meeting Transcription free every month . Once you are satisfied with our accuracy and our user experience, you can easily upgrade to Paid Plans or contact us for On-Premise/Virtual Private Cloud options.
If you have any questions, please email us at support@voicegain.ai
LLMs like ChatGPT and Bard are taking the world by storm! An LLM like ChatGPT is really good at both understanding language and acquiring knowledge of this content. The outcome of this is almost eerie and scary. Because once these LLMs acquire knowledge, they are able to answer very accurately questions that in the past seemed to require human judgement.
One big use-case for LLMs is in the analysis of business meetings - both internal (between employees) and external (e.g conversations with customers, vendors, etc).
In the past few years, companies have been primarily using multi-tenant Revenue/Sales Intelligence and Meeting AI SaaS offerings to transcribe business conversations and extract insights. With such multi-tenant offerings, transcription and natural language processing takes place on the Vendor cloud. Once the transcript is generated, NLU models offered by the Meeting AI vendor is used to extract insights. E.g, Revenue intelligence products like Gong extract questions and sales blockers in sales conversations. Most meeting AI assistants extract summaries and action items.
Essentially these NLU models - and many of these predate the LLMs - were able to summarize, extract topics, keywords and phrases. Enterprises did not mind using the cloud infrastructure of the vendor to store the transcripts as what this NLU could do seemed pretty harmless.
However the LLMs take this to a whole different level. Our team used Open AI Embeddings API to generate embeddings of our daily meeting transcripts that were conducted over a one-month period. We stored these embeddings in an open-source Vector database (our knowledge-base). During testing, for each user question, we generated embedding of the question and queried the vector database (i.e knowledge-base) to get related/similar embeddings.
Then we provided these related documents as context and the user question as a prompt to GPT 3.5 API so that it could generate the answer. We got really really good results.
We were able to get answers to the following questions
1. Provide a summary of the contract with <Largest Customer Name>.
2. What is the progress on <Key Initiative>?
3. Did the Company hire new employees?
4. Did the Company discuss any trade secrets?
5. What is the team's opinion on Mongodb Atlas vs Google Firestore?
6. What new products is the Company planning to develop?
7. Which Cloud provider is the Company using?
8. What is the progress on a key initiative?
9. Are employees happy working in the company?
10. Is the team fighting fires?
ChatGPT's responses to the above questions was amazingly and eerily accurate. For Question 4, it did indicate that it did not want to answer the question. And when it do not have adequate information (e.g. Question 9), it did indicate that in its response.
At Voicegain, we had always been a big proponents of why Voice AI needs to remain on the Edge. We had written about it in the past.
Meeting transcripts in any business is a veritable gold mine of information. Now with the power of LLMs, they can now be queried very easily to provide amazing insights. But if these transcripts are stored in another Vendor's cloud, it has the potential to expose very proprietary and confidential information of any business to 3rd parties.
Hence for businesses it is extremely critical that such transcripts are stored only in private infrastructure (behind the firewall). It is really important for Enterprise IT to make sure this happens in order to safeguard proprietary and confidential information.
If you are looking for such a solution, we can help. At Voicegain, we offer Voicegain Transcribe, an enterprise-ready solution for Meeting AI. With Voicegain Transcribe, the entire solution can deployed either in a datacenter (on bare-metal) or in a private cloud. You can read more about it here.
On March 1st 2023, Open AI announced that developers could access the Open AI Whisper Speech-to-Text model via easy-to-use REST APIs. OpenAI also released APIs to GPT3.5, the LLM behind the buzzy ChatGPT product. General availability of the next version of LLM - GPT 4 is expected in July 2023.
Since Open AI Whisper's initial release in October 2022, it has been a big draw for developers. A highly accurate open-source ASR is extremely compelling. OpenAI's Whisper has been trained on 680,000 hours of audio data which is much more than what most models are trained on. Here is a link to their github.
However the developer community looking to leverage Whisper faces three major limitations:
1. Infrastructure Costs: Running Whisper - especially the large and medium models - requires expensive memory-intensive GPU based compute options (see below).
2. In-house AI expertise: To use Open AI's Whisper model, a company has to invest in building an in-house ML engineering team that is able to operate, optimize and support Whisper in a production environment. While Whisper provides core features like Speech-to-Text, language identification, punctuation and formatting, there are still some missing AI features like speaker diarization and PII redaction that would need to be developed. In addition, companies would need to put in place a real-time NOC for ongoing support. Even a small scale 2-3 person developer team could be expensive to hire and maintain - unless the call volumes justify such an investment. This in-house team also needs to take full responsibility for the Cloud infrastructure related tasks like auto-scaling and log monitoring to ensure uptime.
3. Lack of support for real-time: Whisper is a batch speech-to-text model. For developers requiring streaming Speech-to-Text models, they need to evaluate other ASR/STT options.
By now taking over the responsibility of hosting this model and making it accessible via easy-to-use APIs, both Open AI and Voicegain addresses the first two limitations.
Aug 2023 Update: On Aug 5th 2023, Voicegain announced the release Voicegain Whisper, an optimized version of Open AI's Whisper using Voicegain APIs. Here is a link to the announcement. In addition to Voicegain Whisper, Voicegain also offer realtime/streaming Speech-to-Text and other features like two-channel/stereo support (required for call centers), speaker diarization and PII redaction. All of this is offered in Voicegain's PCI and SOC-2 compliant infrastructure.
This article highlights some of the key strengths and limitations of using Whisper - whether using Open AI's APIs, Voicegain APIs or hosting it on your own.
In our benchmark tests, OpenAI's Whisper models demonstrated high accuracy for a widely diverse range of audio datasets. Our ML engineers concluded that the Whisper models perform well on audio datasets ranging from meetings, podcasts, classroom lectures, YouTube videos and call center audio. We benchmarked Whisper-base, Whisper-small and Whisper-medium against some of the best ASR/Speech-to-Text engines in the market.
The median Word Error Rate (WER) for Whisper-medium was 11.46% for meeting audio and 17.7% for call center audio. This was indeed lower than the WERs of STT offerings of other large players like Microsoft Azure and Google. We did find that AWS Transcribe had a WER that is competitive with Whisper.
Here is an interesting observation - it is possible to exceed Whisper's recognition accuracy, however it would take building custom models. Custom models are models that are trained on our client's specific audio data. In fact for call center audio, our ML Engineers were able to demonstrate that our call-center specific Speech-to-text models were either equal to or even better than some of the Whisper models. This makes intuitive sense because call center audio is not readily available on the internet for Open AI to get access to.
Please contact us via email (support@voicegain.ai) if you would like to review and validate/test these accuracy benchmarks.
Whisper's pricing at $0.006/min ($0.36/hour) is much lower than the Speech-to-Text offerings of some of the other larger cloud players. This translates to a 75% discount to Google Speech-to-Text and AWS Transcribe (based on pricing as of the date of this post).
Aug 2023 Update: At the launch of Voicegain Whisper, Voicegain announced a list price at $0.0037/min ($0.225/hour). This price is 37.5% lower than Open AI's price and has been accomplished since we optimized the throughput of Whisper. To test it out, please sign up for a free developer account. Instructions are provided here.
What was also significant was Open AI announced the release of ChatGPT APIs with the release of Whisper APIs. Developers can combine the power of Whisper Speech-to-Text models with the GPT 3.5 and GPT 4.0 LLM (the underlying model that ChatGPT uses) to power very interesting conversational AI apps. However here is an important consideration - Using Whisper API with LLMs like ChatGPT works as long as the app only uses batch/pre-recorded audio (e.g analyzing recording of call center conversations for QA or Compliance or transcribe and mine Zoom meetings to recollect context). For developers looking to build Voice Bots or Speech IVRs, they would need a good real-time Speech-to-Text model.
As stated above, Open AI's Whisper does not support apps that require real-time/streaming transcription - this could be relevant to a wide variety of AI apps that target call center, education, legal and meetings use-case. In case you are looking for a streaming Speech-to-Text API provider, please feel free to contact us with the email address provided below
The throughput of Whisper models - both for the medium and large models - is relatively low. At Voicegain, our ML engineers have tested the throughput of Whisper models on several popular NVIDIA GPU-based compute instances available in public clouds (AWS, GCP, Microsoft Azure and Oracle Cloud). We also have real-life experience because we process over 10 million hours of audio annually. As a result, we have a strong understanding of what it takes to run a model like OpenAI's Whisper in a production environment.
We have found out that the infrastructure cost of running Whisper-medium in a cloud environment is in the range of $0.07 - $0.10/hour. You can contact us via email to get the in-depth assumptions and backup behind our cost model. An important factor to note is that in a single-tenant production environment the compute infrastructure cannot be run at a very high utilization. The peak throughput required to support real-life traffic can be several times (2-3x) the average throughput. Net-net, we determined that while developers would not have to pay for software licensing, the cloud infrastructure costs would still remain substantial.
In addition to this infrastructure cost the larger expense of running Whisper on the Edge (On-Premise + Private Cloud) is that it would require a dedicated back-end Engineering & Devops team that can chop the audio recording into segments that can be submitted to Whisper and perform the queue management. This team would need to also oversee all info-sec and compliance needs (e.g. running vulnerability scans, intrusion detection etc).
As of the publication of this post, Whisper does not have a multi-channel audio API. So if your application involves audio with multiple speakers, then Whisper's effective price-per-min = Number of channels * 0.006. For both meetings and call center use-cases, this pricing can become prohibitive.
This release of Whisper is missing some key features that developers would need. The three important features we noticed are Diarization (speaker separation), Time-stamps and PII Redaction.
Voicegain is working on releasing a Voicegain-Whisper Model over its APIs. With this developers can get benefits of Voicegain PCI/SOC-2 compliant infrastructure and advanced features like diarization, PII redaction, PCI compliance and time-stamps. To join the waitlist, please email us at sales@voicegain.ai
At Voicegain, we build deep-learning-based Speech-to-Text/ASR models that match or exceed the accuracy of STT models from the large players. For over 4 years now, startup and enterprise customers have used our APIs to build and launch successful products that process over 600 million minutes annually. We focus on developers that need high accuracy (achieved by training custom acoustic models) and deployment in private infrastructure at an affordable price. We provide an accuracy SLA where we guarantee that a custom model that is trained on your data will be as accurate if not more than most popular options including Open AI's Whisper.
We also have models that are trained specifically on call center audio. While Whisper is a worthy competitor (of course a much larger company with 100x our resources), as developers we welcome the innovation that Open AI is unleashing in this market. By adding ChatGPT APIs to our Speech-to-Text , we are planning to broaden our API offerings to developer community.
To sign up for a developer account on Voicegain with free credits, click here.
Like Voicegain Transcribe, there are other cloud-based Meeting AI and AI note-taking solutions that work with video meeting platforms like Zoom and Microsoft Teams. However they do not meet the requirements of privacy-sensitive enterprise customers in financial services, healthcare, manufacturing and high-tech and other industry verticals. Data privacy and control issues would mean that these customers would want to deploy an AI based meeting assistant in their private infrastructure behind their corporate firewall.
Voicegain Transcribe has been designed and developed for the On-Prem Datacenter or Virtual Private Cloud use-case. Voicegain has already deployed this at a large global Fortune 50 company, making it one of the first truly On-premise/private-cloud AI Meeting Assistant solutions in the market.
The key features of Voicegain Transcribe are:
Zoom Local Recordings are recordings of your meetings that are saved in your computer's hard disk on your file-system and not on Zoom's cloud. This feature ensures that confidential and privacy-sensitive recorded audio and video content is stored within the enterprise and is not accessible to Zoom.
Voicegain offers a Windows desktop app (App for Mac OS is on the roadmap) that accesses these Zoom recordings and submits it for transcription and NLU.
The other major advantage of Zoom Local Recordings is that Zoom supports recording of a separate audio track for each participant. This feature is not available in its Cloud recording as of yet (as of Feb 2023). Voicegain Transcribe with Zoom Local Recordings can hence assign speaker labels with 100% accuracy.
There are vendors that offer Meeting Assistants that join from the Cloud and record. However when this solution is picked, the Meeting Assistant has access only to a blended/merged mono audio file which includes audio of all the participants. So Meeting AI solution has to "diarize" the meeting audio - which is an inherently difficult problem to solve. Even state-of-the-art diarization/speaker separation models are only 83-85% accurate.
For any Meeting AI solution to extract meaningful insights, the accuracy of the underlying transcription is extremely important. If the Speech-to-Text is not accurate, then even the best NLU algorithm or the largest language model cannot deliver valuable and accurate analytics.
Voicegain can train the underlying Speech-to-Text to help accurately transcribe different accents, customer specific words and the specifiic acoustic environment.
Voicegain integrates with Enterprise SSO solutions using SAML. Voicegain also integrates with internal email systems to simplify user management tasks like sign-up, password reset and changes, adds and deletes.
All the meeting audio, transcripts and NLU-based analytics are stored in enterprise controlled NoSQL and SQL databases. Enterprises can either use in-house staff to maintain/administer these databases and storage or they can also use a managed database option like MongoDB Atlas or Managed PostgreSQL from a cloud provider like Azure, AWS or GCP
If you are looking for a Meeting AI solution that can be deployed fully behind your corporate firewall or in your own Private Cloud infrastructure, then Voicegain Transcribe is the perfect fit for your needs.
Have questions? We would love to hear from you. Send us an email -sales@voicegain.ai or support@voicegain.ai and we will be happy to offer more details.
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We are really excited to announce the launch of Zoom Meeting Assistant for Local Recordings. This is immediately available to all users of Voicegain Transcribe that have a Windows device. The Zoom Meeting Assistant can be installed on computers that have Windows 10 or Windows 11 as the OS.
What are local recordings? Zoom offers two ways to record a meeting - 1) Cloud Recording: Zoom users may save the recording of the meeting on Zoom's Cloud. 2) Local Recording - The meeting recording is saved locally on the Zoom user's computer. These recordings are saved in the default Zoom folder on the file system. Zoom processes the recording and makes it available in this folder a few minutes after the meeting is complete.
Below is a screenshot of how a Zoom user can initiate a local recording.
There are four big benefits of using Local Recordings
To use Voicegain Zoom Meeting Assistant, there are just two requirements
1. Users should first sign up for a Voicegain Transcribe account. Voicegain offers a free plan forever (up to 2 hours of transcription per month) and users can sign up using this link. You can learn more about Voicegain Transcribe here.
2. They should have a computer with Windows 10 or 11 as the OS.
This Windows App can be downloaded from the "Apps" page on Voicegain Transcribe. Once the app is installed, users will be able to access it on their Windows Taskbar (or Tray). All they need to do is to log into the Voicegain Transcribe App from the Meeting Assistant by entering their Transcribe user-id and password.
Once the Meeting Assistant App is logged into Voicegain Transcribe, it does two things
1. It constantly scans the Zoom folder for any new local recordings of Meetings. As soon as it finds such a recording, it submits/uploads it to Voicegain Transcribe for transcription, summarization and extraction of Key Items (Actions, Issues, Sales Blockers, Questions, Risks etc.)
2. It can also join any Zoom Meeting as the Users AI Assistant. Also this feature works whether the user is the Host of the Zoom Meeting or just a Participant . By joining the meeting, the Meeting Assistant is able to collect information on all the participants in the meeting.
While the current Meeting Assistant App works only for Windows users, Voicegain has native apps for Mac, Android and iPhone as part of its product roadmap.
Send us an email at support@voicegain.ai if you have any questions.
It has been another 6 months since we published our last speech recognition accuracy benchmark. Back then, the results were as follows (from most accurate to the least): Microsoft, then Amazon closely followed by Voicegain, then new Google latest_long and Google Enhanced last.
While the order has remained the same as the last benchmark, three companies - Amazon, Voicegain and Microsoft showed significant improvement.
Since the last benchmark, at Voicegain we invested in more training - mainly lectures - conducted over zoom and in a live setting. Training on this type of data resulted in a further increase in the accuracy of our model. We are actually in the middle of a further round of training with a focus on call center conversations.
As far as the other recognizers are concerned:
We have repeated the test using similar methodology as before: used 44 files from the Jason Kincaid data set and 20 files published by rev.ai and removed all files where none of the recognizers could achieve a Word Error Rate (WER) lower than 25%.
This time again only one file was that difficult. It was a bad quality phone interview (Byron Smith Interview 111416 - YouTube) with WER of 25.48%
We publish this since we want to ensure that any third party - any ASR Vendor, Developer or Analyst - to be able to reproduce these results.
You can see box-plots with the results above. The chart also reports the average and median Word Error Rate (WER)
Only 3 recognizers have improved in the last 6 months.
Detailed data from this benchmark indicates that Amazon is better than Voicegain on audio files with WER below the median and worse on audio files with accuracy above the median. Otherwise, AWS and Voicegain are very closely matched. However we have also run a client-specific benchmark where it was the other way around - Amazon as slightly better on audio files with WER above the median than Voicegain, but Voicegain was better on audio files with WER below the median. Net-net, it really depends on type of audio files, but overall, our results indicate that Voicegain is very close to AWS.
Let's look at the number of files on which each recognizer was the best one.
We now have done the same benchmark 5 times so we can draw charts showing how each of the recognizers has improved over the last 2 years and 3 months. (Note for Google the latest 2 results are from latest-long model, other Google results are from video enhanced.)
You can clearly see that Voicegain and Amazon started quite bit behind Google and Microsoft but have since caught up.
Google seems to have the longest development cycles with very little improvement since Sept. 2021 till about half a year ago. Microsoft, on the other hand, releases an improved recognizer every 6 months. Our improved releases are even more frequent than that.
As you can see, the field is very close and you get different results on different files (the average and median do not paint the whole picture). As always, we invite you to review our apps, sign-up and test our accuracy with your data.
When you have to select speech recognition/ASR software, there are other factors beyond out-of-the-box recognition accuracy. These factors are, for example:
1. Click here for instructions to access our live demo site.
2. If you are building a cool voice app and you are looking to test our APIs, click here to sign up for a developer account and receive $50 in free credits
3. If you want to take Voicegain as your own AI Transcription Assistant to meetings, click here.
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Voicegain helps developers build awesome voice enabled apps by providing them with the most accurate, affordable and accessible Speech-to-Text platform.
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