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Whisper is a general-purpose speech recognition model developed by OpenAI. This model can understand spoken language and instantly transcribe it into text, whether in English or another language.
The Whisper model has been trained on 680,000 hours of multilingual spoken audio from around the web. This means that Whisper is adaptable to different accents, languages, and talking speeds. Whisper can even transcribe when there is significant background noise.
Although Whisper is primarily used for English transcription, the model can also transcribe other spoken languages (e.g. Spanish, Italian) into English text. Whisper can also facilitate non-English transcription, whereby one spoken language is instantly transcribed into another non-English language.
Techopedia Explains Whisper
Whisper is a speech recognition model, which is not a new creation. These models have been around since the early 2010s, with two famous examples being Siri and Alexa. Both of these applications work similarly to Whisper, except they take the input (voice commands) and provide an audio-based output.
For speech recognition models to work accurately, they need to be trained on a large dataset so that they can understand words spoken in various tones, accents, and dialects. Training these models in this way also enables them to recognize patterns in the human language and make accurate predictions regarding which word might come next.
Whisper is disruptive because it is viewed as far more powerful than ‘legacy’ models like the aforementioned Siri and Alexa. Both of these models can struggle with loud background noises or complex sentences, yet Whisper is capable of overcoming these obstacles.
How Does Whisper Transcribe Voice to Text?
Whisper uses well-established practices to transcribe voice to text. These practices can be broken down into two distinct stages:
Encoding
Audio is received by Whisper and broken down into 30-second chunks. These chunks are then transformed into a log-Mel spectrum, which is a specific way of representing audio that computers can understand. Using a log-Mel spectrum means that unimportant audio elements (e.g. background noise) are ignored, allowing the model to focus on the critical aspects (e.g. speech).
The output from this process is then passed into an encoder. This encoder allows Whisper to understand the words being said in the audio clip being analyzed.
Decoding
During decoding, Whisper takes the data from the encoding process and uses a language model to ‘predict’ which words and phrases are being said. Using machine learning and statistical analysis, these ‘predictions’ are often highly accurate, resulting in effective transcribing.
Whisper also intermixes special ‘tokens’ that help identify the language being spoken. This is useful when completing multilingual speech transcription or if both the audio and resulting text output are in non-English languages.
Potential Applications for Whisper
At present, Whisper is still not as accurate as LibriSpeech, which is considered the benchmark in the speech recognition space. However, Whisper has been shown to produce 50% fewer errors than other models, making it a viable option for people worldwide.
Here are just a few of the possible use cases for Whisper:
- AI-Powered Assistants – As noted previously, assistants like Siri and Alexa already use speech recognition models similar to Whisper. In the future, there could theoretically be a Whisper-powered virtual assistant that is highly accurate at understanding different languages and accents.
- Transcription – Naturally, Whisper could completely revolutionize the transcription process. People will no longer need to listen and transcribe text manually since Whisper can automatically detect speech from meetings, interviews, and court settings.
- Customer Service – Customers could use voice commands to ask for help with specific tasks. Whisper could understand these commands and provide the support they need without requiring the customer to type text in a chatbot or help centre manually.
- Security – Whisper could be employed in a security setting, perhaps by using voice identification to provide (or prevent) access to a building.
- Health – Healthcare professionals could employ Whisper to detect changes in a patient’s voice or speech patterns. Many conditions, such as Parkinson’s, can impact someone’s voice – so Whisper could be an effective way to identify these issues early and enable more successful interventions.
As with any AI-powered model, such as ChatGPT, there are also legit concerns over the ethics of using Whisper. These concerns revolve around misuse, as someone could use Whisper to impersonate someone else.
Moreover, since Whisper is ‘listening’ to users and collecting data, there is always the worry regarding a data breach, which could result in identity theft.
