whisper-tiny.en

Maintainer: openai

Total Score

80

Last updated 5/28/2024

🔍

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

Create account to get full access

or

If you already have an account, we'll log you in

Model overview

The whisper-tiny.en model is part of the Whisper family of pre-trained models for automatic speech recognition (ASR) and speech translation. Developed by OpenAI, the Whisper models demonstrate a strong ability to generalize to many datasets and domains without the need for fine-tuning.

The whisper-tiny.en model is the smallest English-only Whisper checkpoint, with 39M parameters. Compared to the larger whisper-small and whisper-medium models, the tiny model may have slightly lower accuracy but can be more efficiently deployed. All Whisper models leverage a Transformer-based encoder-decoder architecture trained on 680k hours of labeled speech data.

Model inputs and outputs

Inputs

  • Audio samples in various formats and sampling rates

Outputs

  • Transcribed text in the same language as the input audio
  • Optionally, the model can also output timestamps for the transcribed text

Capabilities

The whisper-tiny.en model exhibits robust performance on English speech recognition tasks, with the ability to handle a variety of accents, background noise, and technical language. It can also perform zero-shot translation, generating English transcripts from non-English audio.

What can I use it for?

The whisper-tiny.en model can be a useful tool for developers building speech-to-text applications, especially for English language transcription. While it may not be suitable for real-time use due to its size, the model's efficiency makes it well-suited for batch processing or offline transcription. Potential use cases include improving accessibility through automatic captioning, developing voice-based interfaces, and streamlining audio-to-text workflows.

Things to try

One interesting aspect of the Whisper models is their ability to handle long-form audio through a chunking algorithm. By breaking up the input audio into 30-second segments, the whisper-tiny.en model can be used to transcribe recordings of arbitrary length, making it suitable for transcribing podcasts, lectures, or other long-form content.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

Related Models

🖼️

whisper-tiny

openai

Total Score

199

The whisper-tiny model is a pre-trained artificial intelligence (AI) model for automatic speech recognition (ASR) and speech translation, created by OpenAI. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalize to many datasets and domains without the need for fine-tuning. The whisper-tiny model is the smallest of the Whisper checkpoints, with only 39 million parameters. It is available in both English-only and multilingual versions. Similar models include the whisper-large-v3, a general-purpose speech recognition model, the whisper model by OpenAI, the incredibly-fast-whisper model, and the whisperspeech-small model, which is an open-source text-to-speech system built by inverting Whisper. Model inputs and outputs Inputs Audio data, such as recordings of speech Outputs Transcribed text in the same language as the input audio (for speech recognition) Transcribed text in a different language than the input audio (for speech translation) Capabilities The whisper-tiny model can transcribe speech and translate speech to text in multiple languages, demonstrating strong generalization abilities without the need for fine-tuning. It can be used for a variety of applications, such as transcribing audio recordings, adding captions to videos, and enabling multilingual communication. What can I use it for? The whisper-tiny model can be used in various applications that require speech recognition or speech translation, such as: Transcribing lectures, interviews, or other audio recordings Adding captions or subtitles to videos Enabling real-time translation in video conferencing or other communication tools Developing voice-controlled interfaces for various devices and applications Things to try You can experiment with the whisper-tiny model by trying it on different types of audio data, such as recordings of speeches, interviews, or conversations in various languages. You can also explore how the model performs on audio with different levels of noise or quality, and compare its results to other speech recognition or translation models.

Read more

Updated Invalid Date

👀

whisper-small

openai

Total Score

156

The whisper-small model is part of the Whisper family of pre-trained models for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need for fine-tuning. The whisper-small model is a 244M parameter version of the Whisper model, available in both English-only and multilingual configurations. Compared to the smaller whisper-tiny model, the whisper-small offers improved performance at the cost of increased model size and complexity. Model inputs and outputs The Whisper models are designed to take audio samples as input and generate transcriptions or translations as output. The WhisperProcessor is used to preprocess the audio inputs into log-Mel spectrograms that the model can understand, and to postprocess the model outputs back into readable text. Inputs Audio samples**: The model accepts raw audio data as input, which the WhisperProcessor converts to log-Mel spectrograms. Outputs Transcriptions/Translations**: The model outputs sequences of text, which can represent either transcriptions (in the same language as the input audio) or translations (to a different language). Capabilities The whisper-small model demonstrates strong performance on a variety of speech recognition and translation tasks, particularly for English and a handful of other high-resource languages. It is robust to factors like accents, background noise, and technical vocabulary. The model can also perform zero-shot translation from multiple languages into English. What can I use it for? The whisper-small model could be useful for developers building speech-to-text applications, especially for English transcription. It may also be applicable for improving accessibility tools that require accurate speech recognition. While the model cannot be used for real-time transcription out of the box, its speed and size suggest that others may be able to build applications on top of it that allow for near-real-time speech recognition and translation. Things to try One interesting aspect of the Whisper models is their ability to perform both speech recognition and speech translation. By setting the appropriate "context tokens", you can control whether the model should transcribe the audio in the same language, or translate it to a different language. This allows for a wide range of potential applications, from captioning videos in multiple languages to building multilingual voice assistants.

Read more

Updated Invalid Date

🔮

whisper-medium

openai

Total Score

176

The whisper-medium model is a pre-trained speech recognition and translation model developed by OpenAI. It is part of the Whisper family of models, which demonstrate a strong ability to generalize to many datasets and domains without the need for fine-tuning. The whisper-medium model has 769 million parameters and is trained on either English-only or multilingual data. It can be used for both speech recognition, where it transcribes audio in the same language, and speech translation, where it transcribes audio to a different language. The Whisper models are available in a range of sizes, from the whisper-tiny with 39 million parameters to the whisper-large and whisper-large-v2 with 1.55 billion parameters. Model inputs and outputs Inputs Audio samples in various formats and sampling rates Outputs Transcriptions of the input audio, either in the same language (speech recognition) or a different language (speech translation) Optionally, the model can also output timestamps for the transcribed text Capabilities The Whisper models demonstrate strong performance on a variety of speech recognition and translation tasks, including handling accents, background noise, and technical language. They can be used in zero-shot translation, taking audio in one language and translating it to English without any fine-tuning. However, the models can also sometimes generate text that is not actually present in the audio input (known as "hallucination"), and their performance can vary across different languages and accents. What can I use it for? The whisper-medium model and the other Whisper models can be useful for developers and researchers working on improving accessibility tools, such as closed captioning or subtitle generation. The models' speed and accuracy suggest they could be used to build near-real-time speech recognition and translation applications. However, users should be aware of the models' limitations, particularly around potential biases and disparate performance across languages and accents. Things to try One interesting aspect of the Whisper models is their ability to handle audio of up to arbitrary length through a chunking algorithm. This allows the models to be used for long-form transcription, where the audio is split into smaller segments and then reassembled. Users can experiment with this functionality to see how it performs on their specific use cases. Additionally, the Whisper models can be fine-tuned on smaller, domain-specific datasets to improve their performance in particular areas. The blog post on fine-tuning Whisper provides a step-by-step guide on how to do this.

Read more

Updated Invalid Date

👀

whisper-base

openai

Total Score

165

The whisper-base model is a pre-trained model for automatic speech recognition (ASR) and speech translation developed by OpenAI. Trained on 680,000 hours of labelled data, the Whisper models demonstrate a strong ability to generalize to many datasets and domains without the need for fine-tuning. The model was proposed in the paper Robust Speech Recognition via Large-Scale Weak Supervision and is available on the Hugging Face Hub. The whisper-tiny, whisper-medium, and whisper-large models are similar checkpoints of varying model sizes, also from OpenAI. The smaller models are trained on either English-only or multilingual data, while the larger models are multilingual only. All of the pre-trained checkpoints can be accessed on the Hugging Face Hub. Model inputs and outputs Inputs Audio**: The model takes audio samples as input and converts them to log-Mel spectrograms to feed into the Transformer encoder. Task**: The model is informed of the task to perform (transcription or translation) by passing "context tokens" to the decoder. Language**: The model can be configured to transcribe or translate audio in a specific language by providing the corresponding language token. Outputs Transcription or Translation**: The model outputs a text sequence representing the transcription or translation of the input audio. Timestamps**: Optionally, the model can also output timestamps for the generated text. Capabilities The Whisper models exhibit improved robustness to accents, background noise, and technical language compared to many existing ASR systems. They also demonstrate strong zero-shot translation capabilities, allowing users to translate audio from multiple languages into English. The models perform unevenly across languages, with lower accuracy on low-resource or low-discoverability languages. They also tend to hallucinate text that is not actually spoken in the audio input, and can generate repetitive outputs, though these issues can be mitigated to some extent. What can I use it for? The primary intended users of the Whisper models are AI researchers studying model capabilities, biases, and limitations. However, the models can also be useful as an ASR solution for developers, especially for English speech recognition tasks. The models' transcription and translation capabilities may enable the development of accessibility tools, though they cannot currently be used for real-time applications out of the box. Others may be able to build applications on top of Whisper that allow for near-real-time speech recognition and translation. Things to try Users can explore fine-tuning the pre-trained Whisper models on specialized datasets to improve performance for particular languages or domains. The blog post on fine-tuning Whisper provides a step-by-step guide for this process. Experimenting with different chunking and batching strategies can also help unlock the full potential of the Whisper models for long-form transcription and translation tasks. The ASR Chunking blog post goes into more detail on these techniques.

Read more

Updated Invalid Date