whisperx-a40-large

Maintainer: victor-upmeet

Total Score

9

Last updated 6/21/2024
AI model preview image
PropertyValue
Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkView on Arxiv

Create account to get full access

or

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

Model overview

The whisperx-a40-large model is an accelerated version of the popular Whisper automatic speech recognition (ASR) model. Developed by Victor Upmeet, it provides fast transcription with word-level timestamps and speaker diarization. This model builds upon the capabilities of Whisper, which was originally created by OpenAI, and incorporates optimizations from the WhisperX project for improved performance.

Similar models like whisperx, incredibly-fast-whisper, and whisperx-video-transcribe also leverage the Whisper architecture with various levels of optimization and additional features.

Model inputs and outputs

The whisperx-a40-large model takes an audio file as input and outputs a transcript with word-level timestamps and, optionally, speaker diarization. The model can automatically detect the language of the audio, or the language can be specified manually.

Inputs

  • Audio File: The audio file to be transcribed.
  • Language: The ISO code of the language spoken in the audio. If not specified, the model will attempt to detect the language.
  • Diarization: A boolean flag to enable speaker diarization, which assigns speaker ID labels to the transcript.
  • Alignment: A boolean flag to align the Whisper output for accurate word-level timestamps.
  • Batch Size: The number of audio chunks to process in parallel for improved performance.

Outputs

  • Detected Language: The language detected in the audio, if not specified manually.
  • Segments: The transcribed text, with word-level timestamps and speaker IDs (if diarization is enabled).

Capabilities

The whisperx-a40-large model excels at transcribing long-form audio with high accuracy and speed. It can handle a wide range of audio content, from interviews and lectures to podcasts and meetings. The model's ability to provide word-level timestamps and speaker diarization makes it particularly useful for applications that require detailed transcripts, such as video captioning, meeting minutes, and content indexing.

What can I use it for?

The whisperx-a40-large model can be used in a variety of applications that involve speech-to-text conversion, including:

  • Automated transcription of audio and video content
  • Real-time captioning for live events or webinars
  • Generating meeting minutes or notes from recordings
  • Indexing and searching audio/video archives
  • Powering voice interfaces and chatbots

As an accelerated version of the Whisper model, the whisperx-a40-large can be particularly useful for processing large audio files or handling high-volume transcription workloads.

Things to try

One interesting aspect of the whisperx-a40-large model is its ability to perform speaker diarization, which can be useful for analyzing multi-speaker audio recordings. Try experimenting with the diarization feature to see how it can help identify and separate different speakers in your audio content.

Additionally, the model's language detection capabilities can be useful for transcribing multilingual audio or content with code-switching between languages. Test the model's performance on a variety of audio sources to see how it handles different accents, background noise, and speaking styles.



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

AI model preview image

whisperx

victor-upmeet

Total Score

176

whisperx is a speech transcription model developed by researchers at Upmeet. It builds upon OpenAI's Whisper model, adding features like accelerated transcription, word-level timestamps, and speaker diarization. Unlike the original Whisper, whisperx supports batching for faster processing of long-form audio. It also offers several model variants optimized for different hardware setups, including the victor-upmeet/whisperx-a40-large and victor-upmeet/whisperx-a100-80gb models. Model inputs and outputs whisperx takes an audio file as input and generates a transcript with word-level timestamps and optional speaker diarization. It can handle a variety of audio formats and supports language detection and automatic transcription of multiple languages. Inputs Audio File**: The audio file to be transcribed Language**: The ISO code of the language spoken in the audio (optional, can be automatically detected) VAD Onset/Offset**: Parameters for voice activity detection Diarization**: Whether to assign speaker ID labels Alignment**: Whether to align the transcript to get accurate word-level timestamps Speaker Limits**: Minimum and maximum number of speakers for diarization Outputs Detected Language**: The ISO code of the detected language Segments**: The transcribed text, with word-level timestamps and optional speaker IDs Capabilities whisperx provides fast and accurate speech transcription, with the ability to generate word-level timestamps and identify multiple speakers. It outperforms the original Whisper model in terms of transcription speed and timestamp accuracy, making it well-suited for use cases such as video captioning, podcast transcription, and meeting notes generation. What can I use it for? whisperx can be used in a variety of applications that require accurate speech-to-text conversion, such as: Video Captioning**: Generate captions for videos with precise timing and speaker identification. Podcast Transcription**: Automatically transcribe podcasts and audio recordings with timestamps and diarization. Meeting Notes**: Transcribe meetings and discussions, with the ability to attribute statements to individual speakers. Voice Interfaces**: Integrate whisperx into voice-based applications and services for improved accuracy and responsiveness. Things to try Consider experimenting with different model variants of whisperx to find the best fit for your hardware and use case. The victor-upmeet/whisperx model is a good starting point, but the victor-upmeet/whisperx-a40-large and victor-upmeet/whisperx-a100-80gb models may be more suitable if you encounter memory issues when dealing with long audio files or when performing alignment and diarization.

Read more

Updated Invalid Date

AI model preview image

whisperx

daanelson

Total Score

39

whisperx is a Cog implementation of the WhisperX library, which adds batch processing on top of the popular Whisper speech recognition model. This allows for very fast audio transcription compared to the original Whisper model. whisperx is developed and maintained by daanelson. Similar models include whisperx-victor-upmeet, which provides accelerated transcription, word-level timestamps, and diarization with the Whisper large-v3 model, and whisper-diarization-thomasmol, which offers fast audio transcription, speaker diarization, and word-level timestamps. Model inputs and outputs whisperx takes an audio file as input, along with optional parameters to control the batch size, whether to output only the transcribed text or include segment metadata, and whether to print out memory usage information for debugging purposes. Inputs audio**: The audio file to be transcribed batch_size**: The number of audio segments to process in parallel for faster transcription only_text**: A boolean flag to return only the transcribed text, without segment metadata align_output**: A boolean flag to generate word-level timestamps (currently only works for English) debug**: A boolean flag to print out memory usage information Outputs The transcribed text, optionally with segment-level metadata Capabilities whisperx builds on the strong speech recognition capabilities of the Whisper model, providing accelerated transcription through batch processing. This can be particularly useful for transcribing long audio files or processing multiple audio files in parallel. What can I use it for? whisperx can be used for a variety of applications that require fast and accurate speech-to-text transcription, such as podcast production, video captioning, or meeting minutes generation. The ability to process audio in batches and the option to output only the transcribed text can make the model well-suited for high-volume or real-time transcription scenarios. Things to try One interesting aspect of whisperx is the ability to generate word-level timestamps, which can be useful for applications like video editing or language learning. You can experiment with the align_output parameter to see how this feature performs on your audio files. Another thing to try is leveraging the batch processing capabilities of whisperx to transcribe multiple audio files in parallel, which can significantly reduce the overall processing time for large-scale transcription tasks.

Read more

Updated Invalid Date

AI model preview image

whisper-large-v3

nateraw

Total Score

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.

Read more

Updated Invalid Date

AI model preview image

incredibly-fast-whisper

vaibhavs10

Total Score

2.4K

The incredibly-fast-whisper model is an opinionated CLI tool built on top of the OpenAI Whisper large-v3 model, which is designed to enable blazingly fast audio transcription. Powered by Hugging Face Transformers, Optimum, and Flash Attention 2, the model can transcribe 150 minutes of audio in less than 98 seconds, a significant performance improvement over the standard Whisper model. This tool is part of a community-driven project started by vaibhavs10 to showcase advanced Transformers optimizations. The incredibly-fast-whisper model is comparable to other Whisper-based models like whisperx, whisper-diarization, and metavoice, each of which offers its own unique set of features and optimizations for speech-to-text transcription. Model inputs and outputs Inputs Audio file**: The primary input for the incredibly-fast-whisper model is an audio file, which can be provided as a local file path or a URL. Task**: The model supports two main tasks: transcription (the default) and translation to another language. Language**: The language of the input audio, which can be specified or left as "None" to allow the model to auto-detect the language. Batch size**: The number of parallel batches to compute, which can be adjusted to avoid out-of-memory (OOM) errors. Timestamp format**: The model can output timestamps at either the chunk or word level. Diarization**: The model can use Pyannote.audio to perform speaker diarization, but this requires providing a Hugging Face API token. Outputs The primary output of the incredibly-fast-whisper model is a transcription of the input audio, which can be saved to a JSON file. Capabilities The incredibly-fast-whisper model leverages several advanced optimizations to achieve its impressive transcription speed, including the use of Flash Attention 2 and BetterTransformer. These optimizations allow the model to significantly outperform the standard Whisper large-v3 model in terms of transcription speed, while maintaining high accuracy. What can I use it for? The incredibly-fast-whisper model is well-suited for applications that require real-time or near-real-time audio transcription, such as live captioning, podcast production, or meeting transcription. The model's speed and efficiency make it a compelling choice for these types of use cases, especially when dealing with large amounts of audio data. Things to try One interesting feature of the incredibly-fast-whisper model is its support for the distil-whisper/large-v2 checkpoint, which is a smaller and more efficient version of the Whisper model. Users can experiment with this checkpoint to find the right balance between speed and accuracy for their specific use case. Additionally, the model's ability to leverage Flash Attention 2 and BetterTransformer optimizations opens up opportunities for further experimentation and customization. Users can explore different configurations of these optimizations to see how they impact transcription speed and quality.

Read more

Updated Invalid Date