whisper
Maintainer: openai
15.8K
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Model overview
Whisper is a general-purpose speech recognition model developed by OpenAI. It is capable of converting speech in audio to text, with the ability to translate the text to English if desired. Whisper is based on a large Transformer model trained on a diverse dataset of multilingual and multitask speech recognition data. This allows the model to handle a wide range of accents, background noises, and languages. Similar models like whisper-large-v3, incredibly-fast-whisper, and whisper-diarization offer various optimizations and additional features built on top of the core Whisper model.
Model inputs and outputs
Whisper takes an audio file as input and outputs a text transcription. The model can also translate the transcription to English if desired. The input audio can be in various formats, and the model supports a range of parameters to fine-tune the transcription, such as temperature, patience, and language.
Inputs
- Audio: The audio file to be transcribed
- Model: The specific version of the
Whispermodel to use, currently onlylarge-v3is supported - Language: The language spoken in the audio, or
Noneto perform language detection - Translate: A boolean flag to translate the transcription to English
- Transcription: The format for the transcription output, such as "plain text"
- Initial Prompt: An optional initial text prompt to provide to the model
- Suppress Tokens: A list of token IDs to suppress during sampling
- Logprob Threshold: The minimum average log probability threshold for a successful transcription
- No Speech Threshold: The threshold for considering a segment as silence
- Condition on Previous Text: Whether to provide the previous output as a prompt for the next window
- Compression Ratio Threshold: The maximum compression ratio threshold for a successful transcription
- Temperature Increment on Fallback: The temperature increase when the decoding fails to meet the specified thresholds
Outputs
- Transcription: The text transcription of the input audio
- Language: The detected language of the audio (if
languageinput isNone) - Tokens: The token IDs corresponding to the transcription
- Timestamp: The start and end timestamps for each word in the transcription
- Confidence: The confidence score for each word in the transcription
Capabilities
Whisper is a powerful speech recognition model that can handle a wide range of accents, background noises, and languages. The model is capable of accurately transcribing audio and optionally translating the transcription to English. This makes Whisper useful for a variety of applications, such as real-time captioning, meeting transcription, and audio-to-text conversion.
What can I use it for?
Whisper can be used in various applications that require speech-to-text conversion, such as:
- Captioning and Subtitling: Automatically generate captions or subtitles for videos, improving accessibility for viewers.
- Meeting Transcription: Transcribe audio recordings of meetings, interviews, or conferences for easy review and sharing.
- Podcast Transcription: Convert audio podcasts to text, making the content more searchable and accessible.
- Language Translation: Transcribe audio in one language and translate the text to another, enabling cross-language communication.
- Voice Interfaces: Integrate
Whisperinto voice-controlled applications, such as virtual assistants or smart home devices.
Things to try
One interesting aspect of Whisper is its ability to handle a wide range of languages and accents. You can experiment with the model's performance on audio samples in different languages or with various background noises to see how it handles different real-world scenarios. Additionally, you can explore the impact of the different input parameters, such as temperature, patience, and language detection, on the transcription quality and accuracy.
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-subtitles
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The whisper-subtitles model is a variation of OpenAI's Whisper, a general-purpose speech recognition model. Like the original Whisper model, this model is capable of transcribing speech in audio files, with support for multiple languages. The key difference is that whisper-subtitles is specifically designed to generate subtitles in either SRT or VTT format, making it a convenient tool for creating captions or subtitles for audio and video content. Model inputs and outputs The whisper-subtitles model takes two main inputs: audio_path**: the path to the audio file to be transcribed model_name**: the name of the Whisper model to use, with options like tiny, base, small, medium, and large The model outputs a JSON object containing the transcribed text, with timestamps for each subtitle segment. This output can be easily converted to SRT or VTT subtitle formats. Inputs audio_path**: The path to the audio file to be transcribed model_name**: The name of the Whisper model to use, such as tiny, base, small, medium, or large format**: The subtitle format to generate, either srt or vtt Outputs text**: The transcribed text segments**: A list of dictionaries, each containing the start and end times (in seconds) and the transcribed text for a subtitle segment Capabilities The whisper-subtitles model inherits the powerful speech recognition capabilities of the original Whisper model, including support for multilingual speech, language identification, and speech translation. By generating subtitles in standardized formats like SRT and VTT, this model makes it easier to incorporate high-quality transcriptions into video and audio content. What can I use it for? The whisper-subtitles model can be useful for a variety of applications that require generating subtitles or captions for audio and video content. This could include: Automatically adding subtitles to YouTube videos, podcasts, or other multimedia content Improving accessibility by providing captions for hearing-impaired viewers Enabling multilingual content by generating subtitles in different languages Streamlining the video production process by automating the subtitle generation task Things to try One interesting aspect of the whisper-subtitles model is its ability to handle a wide range of audio file formats and quality levels. Try experimenting with different types of audio, such as low-quality recordings, noisy environments, or accented speech, to see how the model performs. You can also compare the output of the various Whisper model sizes to find the best balance of accuracy and speed for your specific use case.
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whisper
6
Whisper is a state-of-the-art speech recognition model developed by OpenAI. It is capable of transcribing audio into text with high accuracy, making it a valuable tool for a variety of applications. The model is implemented as a Cog model by the maintainer soykertje, allowing it to be easily integrated into various projects. Similar models like Whisper, Whisper Diarization, Whisper Large v3, WhisperSpeech Small, and WhisperX Spanish offer different variations and capabilities, catering to diverse speech recognition needs. Model inputs and outputs The Whisper model takes an audio file as input and generates a text transcription of the speech. The model also supports additional options, such as language specification, translation, and adjusting parameters like temperature and patience for the decoding process. Inputs Audio**: The audio file to be transcribed Model**: The specific Whisper model to use Language**: The language spoken in the audio Translate**: Whether to translate the text to English Transcription**: The format for the transcription (e.g., plain text) Temperature**: The temperature to use for sampling Patience**: The patience value to use in beam decoding Suppress Tokens**: A comma-separated list of token IDs to suppress during sampling Word Timestamps**: Whether to include word-level timestamps in the transcription Logprob Threshold**: The threshold for the average log probability to consider the decoding as successful No Speech Threshold**: The threshold for the probability of the token to consider the segment as silence Condition On Previous Text**: Whether to provide the previous output as a prompt for the next window Compression Ratio Threshold**: The threshold for the gzip compression ratio to consider the decoding as successful Temperature Increment On Fallback**: The temperature increase when falling back due to the above thresholds Outputs The transcribed text, with optional formatting and additional information such as word-level timestamps. Capabilities Whisper is a powerful speech recognition model that can accurately transcribe a wide range of audio content, including interviews, lectures, and spontaneous conversations. The model's ability to handle various accents, background noise, and speaker variations makes it a versatile tool for a variety of applications. What can I use it for? The Whisper model can be utilized in a range of applications, such as: Automated transcription of audio recordings for content creators, journalists, or researchers Real-time captioning for video conferencing or live events Voice-to-text conversion for accessibility purposes or hands-free interaction Language translation services, where the transcribed text can be further translated Developing voice-controlled interfaces or intelligent assistants Things to try Experimenting with the various input parameters of the Whisper model can help fine-tune the transcription quality for specific use cases. For example, adjusting the temperature and patience values can influence the model's sampling behavior, leading to more fluent or more conservative transcriptions. Additionally, leveraging the word-level timestamps can enable synchronized subtitles or captions in multimedia applications.
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whisper-large-v3
3
The whisper-large-v3 model is a general-purpose speech recognition model developed by OpenAI. It is a large Transformer-based model trained on a diverse dataset of audio data, allowing it to perform multilingual speech recognition, speech translation, and language identification. The model is highly capable and can transcribe speech across a wide range of languages, although its performance varies based on the specific language. Similar models like incredibly-fast-whisper, whisper-diarization, and whisperx-a40-large offer various optimizations and additional features built on top of the base whisper-large-v3 model. Model inputs and outputs The whisper-large-v3 model takes in audio files and can perform speech recognition, transcription, and translation tasks. It supports a wide range of input audio formats, including common formats like FLAC, MP3, and WAV. The model can identify the source language of the audio and optionally translate the transcribed text into English. Inputs Filepath**: Path to the audio file to transcribe Language**: The source language of the audio, if known (e.g., "English", "French") Translate**: Whether to translate the transcribed text to English Outputs The transcribed text from the input audio file Capabilities The whisper-large-v3 model is a highly capable speech recognition model that can handle a diverse range of audio data. It demonstrates strong performance across many languages, with the ability to identify the source language and optionally translate the transcribed text to English. The model can also perform tasks like speaker diarization and generating word-level timestamps, as showcased by similar models like whisper-diarization and whisperx-a40-large. What can I use it for? The whisper-large-v3 model can be used for a variety of applications that involve transcribing speech, such as live captioning, audio-to-text conversion, and language learning. It can be particularly useful for transcribing multilingual audio, as it can identify the source language and provide accurate transcriptions. Additionally, the model's ability to translate the transcribed text to English opens up opportunities for cross-lingual communication and accessibility. Things to try One interesting aspect of the whisper-large-v3 model is its ability to handle a wide range of audio data, from high-quality studio recordings to low-quality field recordings. You can experiment with different types of audio input and observe how the model's performance varies. Additionally, you can try using the model's language identification capabilities to transcribe audio in unfamiliar languages and explore its translation functionality to bridge language barriers.
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whisper
50
whisper is a large, general-purpose speech recognition model developed by OpenAI. It is trained on a diverse dataset of audio and can perform a variety of speech-related tasks, including multilingual speech recognition, speech translation, and spoken language identification. The whisper model is available in different sizes, with the larger models offering better accuracy at the cost of increased memory and compute requirements. The maintainer, cjwbw, has also created several similar models, such as stable-diffusion-2-1-unclip, anything-v3-better-vae, and dreamshaper, that explore different approaches to image generation and manipulation. Model inputs and outputs The whisper model is a sequence-to-sequence model that takes audio as input and produces a text transcript as output. It can handle a variety of audio formats, including FLAC, MP3, and WAV files. The model can also be used to perform speech translation, where the input audio is in one language and the output text is in another language. Inputs audio**: The audio file to be transcribed, in a supported format such as FLAC, MP3, or WAV. model**: The size of the whisper model to use, with options ranging from tiny to large. language**: The language spoken in the audio, or None to perform language detection. translate**: A boolean flag to indicate whether the output should be translated to English. Outputs transcription**: The text transcript of the input audio, in the specified format (e.g., plain text). Capabilities The whisper model is capable of performing high-quality speech recognition across a wide range of languages, including less common languages. It can also handle various accents and speaking styles, making it a versatile tool for transcribing diverse audio content. The model's ability to perform speech translation is particularly useful for applications where users need to consume content in a language they don't understand. What can I use it for? The whisper model can be used in a variety of applications, such as: Transcribing audio recordings for content creation, research, or accessibility purposes. Translating speech-based content, such as videos or podcasts, into multiple languages. Integrating speech recognition and translation capabilities into chatbots, virtual assistants, or other conversational interfaces. Automating the captioning or subtitling of video content. Things to try One interesting aspect of the whisper model is its ability to detect the language spoken in the audio, even if it's not provided as an input. This can be useful for applications where the language is unknown or variable, such as transcribing multilingual conversations. Additionally, the model's performance can be fine-tuned by adjusting parameters like temperature, patience, and suppressed tokens, which can help improve accuracy for specific use cases.
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