WhisperAPI

First month for free!Get startedWhisper APIpowered by Lemonfox.aiJust $0.17 / hourNew Larger AI ModelEasy-to-Use Whisper APISign Up for Whisper API for 30 Hours of Free Audio Transcription Today!Get StartedSimple integrationIntegrate our OpenAI-compatible API into your application within minutes and seamlessly scale to serve millions of users.Affordable APIThanks to our extensive scale and performance optimizations, our API is the most affordable solution on the market.Unbeatable valueFast and accurate transcription with speaker detection, translation, and supporting over 100 languages.Transcription API FeaturesBuild your application with our easy-to-use transcription API.Not a developer?Use Transcripo to convert speech to text for freeWhisper Large V3Latest Speech Recognition AIWhisper v3 is the latest and most precise speech recognition AI model that enables you to quickly transcribe audio from podcasts, videos, meetings, and more into text.Powerful FeaturesSpeaker Diarization, Translations and MoreOur API detects multiple speakers in audio files, supports over 100 languages, handles various file formats, and offers English translations or summaries using our other AI models.OpenAI compatible APIJust a Few Lines of CodeNo matter which programming language you use or which application you build, you can easily use our API to transcribe audio files into text. Our documentation and code examples will help you get started quickly.curl https://api.lemonfox.ai/v1/audio/transcriptions -H "Authorization: Bearer YOUR_API_KEY" -F file="https://output.lemonfox.ai/wikipedia_ai.mp3" -F language="english" -F speaker_labels=true -F response_format="json"Simple Affordable PricingTry it free for one month, including 30 hours of transcription. Just $0.17 per hour after that.Start Your Free TrialLoginSign UpImprint, Terms & PrivacyBack to topWhisper API BlogSpeech to Text Free ToolWhisper TranscriptionVideo to TextMP3 to TextWord Error Rate (WER)Create Your Own OpenAI Whisper Speech-to-Text APIHow to Test the Accuracy of a Transcription APIWhat is OpenAI WhisperSecurity Concerns When Using a Transcription APIWhat to Look For in a Transcription APIComparing Top Transcription APIsSpeed vs Accuracy Tradeoff for a Transcription APIShould You Use a Third-Party Transcription API?Understanding Pricing of Transcription APIsBuilding vs Buying a Transcription API: Pros and ConsCommon Use Cases for Transcription APIsGetting Started with a Transcription API: A Step-by-Step GuideAdvanced Features to Look for in a Transcription APIBest Practices for Implementing a Transcription APIDetermining the ROI of a Transcription APITop Speech-to-Text APIs in 2024VTT and SRT Files For Videos Using PythonWhat is ASRPython Speech-to-Text TutorialAccuracy Benchmarks of The Top Free Open Source Speech-to-Text OfferingsNote: WhisperAPI.com is in no way affiliated with OpenAI. Have questions? Email info@whisperapi.com. --- First month for free!Get startedWhisper APIpowered by Lemonfox.aiJust $0.17 / hourSpeech to Text Free ToolTranscribe 100+ languagesAll audio and video file formats supportedSpeaker diarization and translationSpeech-to-text has become more popular than ever, especially with the rise of Large Language Models (LLMs) and needed complementary speech-to-text (STT) capabilities. However, most tools are expensive and not as accurate as you'd like them to be. This free speech-to-text tool enables you to upload your audio files for free and get back high-quality transcriptions, powered by the OpenAI Whisper model.Drag audio file here or click to select fileTranscribe mp3, wav, and other files. It should not exceed 20mb.Need more features? Try Transcripo!Get 30 days of free transcriptions on our Starter Plan or a discount on any plan with code WHISPER.Free Speech-to-Text ConverterTry Our Speech to Text Online Free ToolUse the tool's drag-n-drop area above to get transcriptions of your audio files! While transcription speeds may vary, results can be as fast as 10x the audio length, meaning that a 10 minute audio file can be transcribed in as little as 1 minute. Additionally, Whisper API supports up to 96+ languages and most commonly-used audio file formats.MP3 to Text, WAV to Text, Diarization, Translation, and MoreIf you are looking for additional features to your transcription beyond just the file to text, WhisperAPI.com offers additional features such as diarization, enabling you to see who spoke which segments within a transcription, translation, enabling translation from dozens of languages to English, initial prompts, enabling you to provide spelling and conversation context to the API, and much, much more. These features are all available via our best-in-class speech-to-text API offering. Additionally, most major audio file formats are accepted by the API, including WAV and MP3.Free Transcription of Audio File Example using APIWhisper API, while not free forever, does offer generous free credits to new users. To take advantage of that free tier, simply sign up for an account and begin using the API. Below is a code snippet of how you can call the API with a free API Key you get from the free dashboard.curl https://api.lemonfox.ai/v1/audio/transcriptions -H "Authorization: Bearer YOUR_API_KEY" -F file="https://output.lemonfox.ai/wikipedia_ai.mp3" -F language="english" -F speaker_labels=true -F response_format="json"As you can see, utilizing this speech-to-text API is extremely easy. All it takes is an API key, which is free to get and an audio file to transcribe. Additionally, the API delivers next-level functionality out-of-the-box that many services charge a premium for. For this free offering, there is also no credit card required, as Whisper API believes that the speech-to-text service should speak for itself before requiring any commitments from its user.Free Speech to Text ConclusionThank you for using our speech to text free online tool for your audio transcription needs. We hope it met all your needs. If you liked this service and have a need for transcription services at scale using an API, we ask that you check out our speech-to-text API WhisperAPI.com to help support us continuing to offer a free speech to text upload system to the community. If you don't, no worries!Looking for a professional transcription tool?Check out Transcripo, the affordable, state-of-the-art transcription tool powered by an advanced AI model. Get started with Transcripo now.Learn More about TranscripoLoginSign UpImprint, Terms & PrivacyBack to topWhisper API BlogSpeech to Text Free ToolWhisper TranscriptionVideo to TextMP3 to TextWord Error Rate (WER)Create Your Own OpenAI Whisper Speech-to-Text APIHow to Test the Accuracy of a Transcription APIWhat is OpenAI WhisperSecurity Concerns When Using a Transcription APIWhat to Look For in a Transcription APIComparing Top Transcription APIsSpeed vs Accuracy Tradeoff for a Transcription APIShould You Use a Third-Party Transcription API?Understanding Pricing of Transcription APIsBuilding vs Buying a Transcription API: Pros and ConsCommon Use Cases for Transcription APIsGetting Started with a Transcription API: A Step-by-Step GuideAdvanced Features to Look for in a Transcription APIBest Practices for Implementing a Transcription APIDetermining the ROI of a Transcription APITop Speech-to-Text APIs in 2024VTT and SRT Files For Videos Using PythonWhat is ASRPython Speech-to-Text TutorialAccuracy Benchmarks of The Top Free Open Source Speech-to-Text OfferingsNote: WhisperAPI.com is in no way affiliated with OpenAI. Have questions? Email info@whisperapi.com. --- First month for free!Get startedWhisper APIpowered by Lemonfox.aiJust $0.17 / hourQuick & Easy Whisper TranscriptionTranscribe any audio or video file with WhisperAll audio and video file formats supportedTurn audio in 100+ languages into textEasily turn audio and video recordings into ready-to-use text with Whisper transcription, OpenAI's state-of-the-art speech-to-text model. Just drag-and-drop any audio or video file (podcasts, interviews, meetings, YouTube clips, etc.) and our secure cloud processors return an accurate transcript you can copy, search or download in moments. Get started for free with Whisper transcribe – no credit card, no signup, and no watermark.Drag audio file here or click to select fileTranscribe mp3, wav, and other files. It should not exceed 20mb.No limits or more features needed? Try Transcripo:Transcripo – Speech-to-Text ConverterHow Whisper Transcribe WorksUploadUpload your audio or video file to the tool. We support all audio and video file formats.TranscribeOur fast and secure online service will transcribe your file using the Whisper model.DownloadCopy or download the transcription as a text file: text, PDF, or SRT/VTT for video subtitles.Try Our Free Transcription ToolJust select your audio above and Whisper will deliver a clean transcript in as little as one-tenth of the playback time (a 10-minute file finishes in a few seconds). It recognises 96+ languages and works with virtually every popular audio format.Export, Speaker Labels, Timestamps, and MoreWe are supporting additional features that are not supported by Whisper by default. This includes speaker labels (also known as speaker diarization), timestamps, and file export. You may export the transcript as a text / PDF file and if you are working with a video, an SRT/VTT file for video subtitles. Check out the Transcripo tool to try these features.Summarize and Translate Your AudioOur built-in AI chat transforms a plain transcript into insights. You can converse with the text just as you would with a teammate—asking it to summarise key points, surface every mention of a budget item, or spin action items out of a brainstorming session, all in seconds. When you need to reach a global audience, a single click translates the entire transcript into any language. In short, chat-and-translate turns raw speech into multilingual insight with almost no effort.Looking for a professional transcription tool?Check out Transcripo, the affordable, state-of-the-art transcription tool powered by an advanced AI model. Get started with Transcripo now.Learn More about TranscripoLoginSign UpImprint, Terms & PrivacyBack to topWhisper API BlogSpeech to Text Free ToolWhisper TranscriptionVideo to TextMP3 to TextWord Error Rate (WER)Create Your Own OpenAI Whisper Speech-to-Text APIHow to Test the Accuracy of a Transcription APIWhat is OpenAI WhisperSecurity Concerns When Using a Transcription APIWhat to Look For in a Transcription APIComparing Top Transcription APIsSpeed vs Accuracy Tradeoff for a Transcription APIShould You Use a Third-Party Transcription API?Understanding Pricing of Transcription APIsBuilding vs Buying a Transcription API: Pros and ConsCommon Use Cases for Transcription APIsGetting Started with a Transcription API: A Step-by-Step GuideAdvanced Features to Look for in a Transcription APIBest Practices for Implementing a Transcription APIDetermining the ROI of a Transcription APITop Speech-to-Text APIs in 2024VTT and SRT Files For Videos Using PythonWhat is ASRPython Speech-to-Text TutorialAccuracy Benchmarks of The Top Free Open Source Speech-to-Text OfferingsNote: WhisperAPI.com is in no way affiliated with OpenAI. Have questions? Email info@whisperapi.com. --- First month for free!Get startedWhisper APIpowered by Lemonfox.aiJust $0.17 / hourWord Error Rate (WER)Word Error Rate (WER) is the calculation of how often a mistake was made in the audio transcription process. Word Error Rate is the most typical calculation for estimating the general accuracy for a speech-to-text service. This piece dives into the complexities of Word Error Rate, how it’s used in the ASR market today, and the future of WER.Why is Word Error Rate Important?WER is the closest to an apples-to-apples comparison of different speech-to-text softwares and APIs. By picking a neutral audio source as the input into each system, we can estimate how good each API is at audio transcription. While accuracy is just one of the inputs into evaluating a voice-to-text service, it is nonetheless one of the most important ones. By having an objective number attached to accuracy, a team can go into the evaluation process with clear key performance indicator (KPI) requirements for a speech-to-text API. Any API that meets that minimum threshold can be a contender for a production use case.How is Word Error Rate Calculated?WER = (S + D + I)/(S + D + C)Where:S is the number of substitutions (i.e. 'Dolly’ vs the actual text 'DALL·E’)D is the number of deletions (i.e. 'I speech-to-text’ vs the actual text 'I like speech-to-text’)I is the number of insertions (i.e. 'I really like speech-to-text’ vs the actual text 'I like speech-to-text’)C is the number of correctly predicted words.The accuracy of a given speech-to-text transcription is just 1-WER. So, if the word error rate is 20%, then the STT service was 80% accurate.Before a word error rate is calculated, there is typically a normalization process that removes a lot of punctuation, capitalizes beginnings of sentences, and standardizes things like numbers (i.e. 'thirteen’ and '13’), all to make sure that the S, D, C, and I calculations are as fair as possible.If you are looking to calculate WER on your own, there are a number of great services to do just that, including Jiwer.pip install jiwer from jiwer import wer reference = "speech to text" hypothesis = "Speak my text" error = wer(reference, hypothesis)What are the Shortcomings of Word Error Rate?Word Error Rate (WER) doesn't take into account the magnitude of the underlying errors. In the example above with dolly and DALL·E (an OpenAI image model), while that is technically a mistake, it is a much more understandable mistake than something like DALL·E being transcribed as elephant. It is nearly impossible to provide a closeness score for word errors, which is why WER should always be taken with a grain of salt.What Causes a Word Error?Similar Sounding WordsLike the example above, dolly and DALL·E can be mixed up. So too can to, two, and too. Most of the spellings of these words are situation-specific, which machine learning systems are not as good at adjusting for as a human would be, since their scope is much more limited to the sounds as opposed to the meaning of a phrase. Additionally, some words may be out of scope for a model. If a new word was invented, or industry-specific terms are used (such as DALL·E) that the model has never seen before, the chance of getting the words correct are much smaller.Tough to Decipher DialogueSometimes dialogue can be hard to understand. This can be caused by overlapping speakers, background noise, dialects, audio quality, and more. While humans are sometimes capable of dealing with these differences, systems trained on specific constraints can run into their limits.What is the Word Error Rate for Modern ASR Tools?If you're looking for accuracy benchmarks, this piece on free open source software accuracy benchmarks should help clarify. Whisper Large is one of the most accurate of the open source models, with Kaldi, Wav2vec 2.0, and SpeechBrain all as other great open source, accurate options.Additionally, here are some accuracy benchmarks for commercial offerings. Whisper Large is one of the most accurate commercial offerings, with Amazon Transcribe and Azure Speech-to-Text as other great offerings on the market.In general, offerings range with WER as low as 2% to as high as 40%.What is the Word Error Rate for Humans?This really depends! An often-cited figure is that humans have a WER of about 4%. It's hard to understand every word in an audio transcription, especially when context is unclear, or audio quality isn't the best. However, one study goes even deeper on human WER and finds WERs as high as 6.8%. To put that into perspective, on the same datasets, Whisper Large makes almost 3x as many mistakes at 17.6% WER. While modern ASR tools have come a long way, they aren't quite as good as humans.What are Typical Datasets for Testing Word Error Rate?There are many common datasets for Word Error Rate, including:Common VoiceCommon Voice is a wonderful Mozilla project for gathering transcripted speech. It allows anyone to either provide spoken word, or listen to existing spoken word and provide feedback on transcriptions to help with the ground-truth transcriptions.LibriSpeechLibriSpeech is 1,000 hours of audio book audio, carefully segmented by OpenSLR, an organization devoted to making publicly available speech and language datasets.FleursFleurs is a dataset that is great for many languages, as it has over 12 hours of audio per language and over 102 languages in the corpus.CHiME6CHiME6 is a dataset of over 20 dinner parties, testing the abilities of models to overcome noise, overlapping dialogue, and more.SwitchboardSwitchboard, originally released by Texas Instruments as part of a DARPA sponsorship, is now a dataset of over 2,400 two-sided telephone conversations across the US. The topics of the conversations were carefully picked to avoid any speaker speaking on a topic more than once and it was ensured that no speakers would speak together more than once.CallHomeCallHome was developed by Linguistic Data Consortium (LDC) as a series of 120 30-minute phone calls between native English speakers, primarily calls between close friends or family.As you can tell, depending on the use case of your transcription needs, any one of these comprehensive datasets could be good to utilize. If you are looking for speech in a crowded public space, then CHiME6 could be a great option. If you are looking for a diverse set of audio around the world, then Fleurs could be a great solution. It is wonderful to have benchmarks to compare speech-to-text offerings to be as informed as possible when choosing a solution for your needs.Are There Other Ways to Measure ASR Mistakes than WER?Yes, there are. Other metrics include:Match Error Rate (MER)This is the words that were incorrectly predicted or inserted, defined as:(S + D + I) / (S + D + C + I)Word Information Loss (WIL)This is the percentage of words that were incorrectly predicted, defined as:(1 - C/(S + D + C) * C/(C + S + I))Word Information Preserved (WIP)This is the percentage of correctly predicted words, defined as:C/(S + D + C) * C/(C + S + I)Character Error Rate (CER)This is the WER, but for the character level, defined with the same formula, just for characters as opposed to words.While each of these metrics can be helpful, WER is the most common metric of calculation in the market. Additionally, none of these other measurements materially tackle a lot of the underlying concerns with WER.Are ASR Word Error Rates Generally Improving?Yes, absolutely! With each passing year, speech-to-text, particularly with open source models, is only improving. As the community continues to share insights into best understanding and deriving insight from audio, speech-to-text and ASR technology in general should only improve. It is likely in the near future that call transcription by machines will be as good as that done by humans. With the rise of Large Language Models (LLMs) and Artificial Intelligence (AI) in general, the context of the speech can always be included, which further increases the chances of accuracy. Further, since LLMs can have a large corpus of training, it is possible these systems will have a wide variety of industry-specific terminology, to avoid the dreaded word similarity issue in today's speech-to-text offerings.ConclusionWord Error Rate is an important metric in evaluating speech-to-text offerings, since it provides a numerical evaluation that can be used to compare each offering against another. That being said, Word Error Rate is not necessarily perfect, as each ASR system performs better or worse depending on the variables of the underlying audio and each system can make mistakes in different ways. Overall, WER is the metric that is most likely to have continued influence in the industry, since it is objective and well-defined.Looking to add a transcription API to your workflow?Check out Whisper API, the affordable, state-of-the-art transcription API powered by groundbreaking work from OpenAI. Go to the Whisper API Homepage to learn more.Learn More about Whisper APILoginSign UpImprint, Terms & PrivacyBack to topWhisper API BlogSpeech to Text Free ToolWhisper TranscriptionVideo to TextMP3 to TextWord Error Rate (WER)Create Your Own OpenAI Whisper Speech-to-Text APIHow to Test the Accuracy of a Transcription APIWhat is OpenAI WhisperSecurity Concerns When Using a Transcription APIWhat to Look For in a Transcription APIComparing Top Transcription APIsSpeed vs Accuracy Tradeoff for a Transcription APIShould You Use a Third-Party Transcription API?Understanding Pricing of Transcription APIsBuilding vs Buying a Transcription API: Pros and ConsCommon Use Cases for Transcription APIsGetting Started with a Transcription API: A Step-by-Step GuideAdvanced Features to Look for in a Transcription APIBest Practices for Implementing a Transcription APIDetermining the ROI of a Transcription APITop Speech-to-Text APIs in 2024VTT and SRT Files For Videos Using PythonWhat is ASRPython Speech-to-Text TutorialAccuracy Benchmarks of The Top Free Open Source Speech-to-Text OfferingsNote: WhisperAPI.com is in no way affiliated with OpenAI. Have questions? Email info@whisperapi.com.