whisper-wordtimestamps
Maintainer: hnesk
549
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| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | View on Arxiv |
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Model overview
The whisper-wordtimestamps model is a variant of OpenAI's Whisper model that exposes settings for word-level timestamps. Like the original Whisper, it is a general-purpose speech recognition model that can convert speech in audio to text. However, this model also provides the ability to extract word-level timestamps, which can be useful for applications that require precise timing information.
The whisper-wordtimestamps model is maintained by hnesk, and is inspired by the cog-whisper project. It builds upon the capabilities of other Whisper-based models, such as whisper, whisper-large-v3, and whisperx.
Model inputs and outputs
The whisper-wordtimestamps model accepts an audio file as input, along with various optional parameters to customize the transcription process. These include the Whisper model to use, the language spoken in the audio, temperature, and options related to word-level timestamps.
Inputs
- audio: The audio file to be transcribed
- model: The Whisper model to use, with "base" as the default
- language: The language spoken in the audio (or "None" to perform language detection)
- patience: The patience value to use in beam decoding
- temperature: The temperature to use for sampling
- initial_prompt: An optional initial prompt to provide for the first window
- suppress_tokens: A comma-separated list of token IDs to suppress during sampling
- word_timestamps: A boolean flag to enable/disable word-level timestamps
- logprob_threshold: The minimum average log probability to consider a transcription successful
- append_punctuations: Punctuation symbols to merge with the previous word when word_timestamps is True
- no_speech_threshold: The probability threshold for considering a segment as silence
- prepend_punctuations: Punctuation symbols to merge with the next word when word_timestamps is True
- condition_on_previous_text: Whether to provide the previous output as a prompt for the next window
- compression_ratio_threshold: The maximum compression ratio to consider a transcription successful
- temperature_increment_on_fallback: The temperature increment to use when falling back due to threshold failures
Outputs
- The transcribed text, with optional word-level timestamps
Capabilities
The whisper-wordtimestamps model can accurately transcribe speech in audio files, with the added capability of providing word-level timestamps. This can be useful for applications that require precise timing information, such as video subtitling, language learning, or meeting transcription.
What can I use it for?
The whisper-wordtimestamps model can be used in a variety of applications that require speech-to-text conversion, such as:
- Transcribing podcasts, lectures, or meetings
- Generating captions or subtitles for videos
- Providing language learning tools with synchronized audio and text
- Analyzing spoken content for market research or customer service applications
The word-level timestamps can also be used to create more advanced applications, such as:
- Aligning audio and text in multimedia content
- Improving the user experience of voice-based interfaces
- Developing tools for speech therapy or language assessment
Things to try
One interesting aspect of the whisper-wordtimestamps model is its ability to handle a wide range of audio conditions, from clear studio recordings to noisy field recordings. You can experiment with different audio files, languages, and model parameters to see how the model performs in different scenarios.
Additionally, you can explore the use of the word-level timestamps for various applications, such as synchronizing text with audio, analyzing speaking patterns, or creating interactive transcripts. By leveraging the additional timing information provided by this model, you can build more sophisticated and engaging applications.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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whisper-wordtimestamps is a fork of the Whisper Wordtimestamps project, which is built on top of OpenAI's Whisper model. This fork aims to enhance the functionality of the Whisper model by allowing it to handle large audio files. Whisper is an Automatic Speech Recognition (ASR) system that converts spoken language into written text. The main change in this fork is the addition of a tweak that allows the Whisper model to process large audio files, as proposed in a thread on the Replicate Python repository. Model inputs and outputs The whisper-wordtimestamps model takes in an audio file or URL and various optional parameters that allow you to customize the model's behavior, such as the Whisper model to use, the language spoken in the audio, and settings for word-level timestamps. Inputs audio**: The audio file to be transcribed audio_url**: The URL of the audio file to be transcribed model**: The Whisper model to use (default is "base") language**: The language spoken in the audio (or "None" to perform language detection) patience**: The patience value to use in beam decoding temperature**: The temperature to use for sampling initial_prompt**: Optional text to provide as a prompt for the first window suppress_tokens**: A comma-separated list of token IDs to suppress during sampling word_timestamps**: Whether to extract word-level timestamps logprob_threshold**: The average log probability threshold for decoding success no_speech_threshold**: The probability threshold for detecting no speech append_punctuations**: Punctuation symbols to merge with the previous word (if word_timestamps is true) prepend_punctuations**: Punctuation symbols to merge with the next word (if word_timestamps is true) condition_on_previous_text**: Whether to provide the previous output as a prompt for the next window compression_ratio_threshold**: The gzip compression ratio threshold for decoding success temperature_increment_on_fallback**: The temperature increment to use when falling back due to decoding failure Outputs The transcribed text, with optional word-level timestamps. Capabilities The whisper-wordtimestamps model can accurately transcribe speech in audio files and provide word-level timestamps for the transcribed text. This can be useful for applications like video subtitling, audio indexing, and speech analysis. What can I use it for? You can use whisper-wordtimestamps to transcribe audio files and generate word-level timestamps, which can be helpful for a variety of applications. For example, you could use it to: Generate captions or subtitles for videos Analyze the timing and pacing of speech in audio recordings Index audio content for better searchability and discoverability Improve the accessibility of audio-based content for people with hearing impairments Things to try To get the most out of whisper-wordtimestamps, you can experiment with the various input parameters to fine-tune the model's behavior for your specific use case. For example, you could try: Adjusting the patience and temperature parameters to improve the quality of the transcription Using the initial_prompt or condition_on_previous_text options to provide additional context to the model Exploring the append_punctuations and prepend_punctuations settings to customize how punctuation is handled in the word-level timestamps By tweaking these settings, you can optimize the model's performance and tailor it to your needs.
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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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