whisper-diarization
Maintainer: thomasmol
231
| Property | Value |
|---|---|
| Model Link | View on Replicate |
| API Spec | View on Replicate |
| Github Link | View on Github |
| Paper Link | View on Arxiv |
Get summaries of the top AI models delivered straight to your inbox:
Model overview
whisper-diarization is a fast audio transcription model that combines the powerful Whisper Large v3 model with speaker diarization from the Pyannote audio library. This model provides accurate transcription with word-level timestamps and the ability to identify different speakers in the audio. Similar models like whisperx and voicecraft also offer advanced speech-to-text capabilities, but whisper-diarization stands out with its speed and ease of use.
Model inputs and outputs
whisper-diarization takes in audio data in various formats, including a direct file URL, a Base64 encoded audio file, or a local audio file path. Users can also provide a prompt containing relevant vocabulary to improve transcription accuracy. The model outputs a list of speaker segments with start and end times, the detected number of speakers, and the language of the spoken words.
Inputs
file_string: Base64 encoded audio filefile_url: Direct URL to an audio filefile: Local audio file pathprompt: Vocabulary to improve transcription accuracygroup_segments: Option to group short segments from the same speakernum_speakers: Specify the number of speakers (leave empty to autodetect)language: Language of the spoken words (leave empty to autodetect)offset_seconds: Offset in seconds for chunked inputs
Outputs
segments: List of speaker segments with start/end times, average log probability, and word-level probabilitiesnum_speakers: Number of detected speakerslanguage: Detected language of the spoken words
Capabilities
whisper-diarization excels at fast and accurate audio transcription, even in noisy or multilingual environments. The model's ability to identify different speakers and provide word-level timestamps makes it a powerful tool for a variety of applications, from meeting recordings to podcast production.
What can I use it for?
whisper-diarization can be used in many industries and applications that require accurate speech-to-text conversion and speaker identification. Some potential use cases include:
- Meeting and interview transcription: Quickly generate transcripts with speaker attribution for remote or in-person meetings, interviews, and conferences.
- Podcast and audio production: Streamline the podcast production workflow by automatically generating transcripts and identifying different speakers.
- Accessibility and subtitling: Provide accurate, time-stamped captions for videos and audio content to improve accessibility.
- Market research and customer service: Analyze audio recordings of customer calls or focus groups to extract insights and improve product or service offerings.
Things to try
One interesting aspect of whisper-diarization is its ability to handle multiple speakers and provide word-level timestamps. This can be particularly useful for applications that require speaker segmentation, such as conversation analysis or audio captioning. You could experiment with the group_segments and num_speakers parameters to see how they affect the model's performance on different types of audio content.
Another area to explore is the use of the prompt parameter to improve transcription accuracy. By providing relevant vocabulary, acronyms, or proper names, you can potentially boost the model's performance on domain-specific content, such as technical jargon or industry-specific terminology.
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
whisperx
36
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.
Updated Invalid Date
speaker-transcription
20
The speaker-transcription model is a powerful AI system that combines speaker diarization and speech transcription capabilities. It was developed by Meronym, a creator on the Replicate platform. This model builds upon two main components: the pyannote.audio speaker diarization pipeline and OpenAI's whisper model for general-purpose English speech transcription. The speaker-transcription model outperforms similar models like whisper-diarization and whisperx by providing more accurate speaker segmentation and identification, as well as high-quality transcription. It can be particularly useful for tasks that require both speaker information and verbatim transcripts, such as interview analysis, podcast processing, or meeting recordings. Model inputs and outputs The speaker-transcription model takes an audio file as input and can optionally accept a prompt string to guide the transcription. The model outputs a JSON file containing the transcribed segments, with each segment associated with a speaker label and timestamps. Inputs Audio**: An audio file in a supported format, such as MP3, AAC, FLAC, OGG, OPUS, or WAV. Prompt (optional)**: A text prompt that can be used to provide additional context for the transcription. Outputs JSON file**: A JSON file with the following structure: segments: A list of transcribed segments, each with a speaker label, start and stop timestamps, and the segment transcript. speakers: Information about the detected speakers, including the total count, labels for each speaker, and embeddings (a vector representation of each speaker's voice). Capabilities The speaker-transcription model excels at accurately identifying and labeling different speakers within an audio recording, while also providing high-quality transcripts of the spoken content. This makes it a valuable tool for a variety of applications, such as interview analysis, podcast processing, or meeting recordings. What can I use it for? The speaker-transcription model can be used for data augmentation and segmentation tasks, where the speaker information and timestamps can be used to improve the accuracy and effectiveness of transcription and captioning models. Additionally, the speaker embeddings generated by the model can be used for speaker recognition, allowing you to match voice profiles against a database of known speakers. Things to try One interesting aspect of the speaker-transcription model is the ability to use a prompt to guide the transcription. By providing additional context about the topic or subject matter, you can potentially improve the accuracy and relevance of the transcripts. Try experimenting with different prompts to see how they affect the output. Another useful feature is the generation of speaker embeddings, which can be used for speaker recognition and identification tasks. Consider exploring ways to leverage these embeddings, such as building a speaker verification system or clustering speakers in large audio datasets.
Updated Invalid Date
whisper
7.4K
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 Whisper model to use, currently only large-v3 is supported Language**: The language spoken in the audio, or None to 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 language input is None) 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 Whisper into 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.
Updated Invalid Date
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.
Updated Invalid Date