Hnesk

whisper-wordtimestamps

hnesk

548

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.

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