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Whisper large-v3-turbo

Efficient automatic speech recognition model

#Multi-language support
#Voice translation
#Zero-shot learning
#Automatic speech recognition
Whisper large-v3-turbo

Product Details

Whisper large-v3-turbo is an advanced automatic speech recognition (ASR) and speech translation model proposed by OpenAI. It is trained on over 5 million hours of labeled data and is able to generalize to many datasets and domains in a zero-shot setting. This model is a fine-tuned version of Whisper large-v3, with the decoding layers reduced from 32 to 4 to increase speed, but may slightly reduce quality.

Main Features

1
Supports speech recognition and translation in 99 languages
2
Ability to generalize to multiple datasets and domains in a zero-shot setting
3
Improve model running speed by reducing the number of decoding layers
4
Supports block-by-block processing of long audio files
5
Compatible with all Whisper decoding strategies, such as temperature fallback and conditions based on the previous token
6
Automatically predict the language of source audio
7
Supports speech transcription and speech translation tasks
8
Ability to predict timestamps, providing sentence-level or word-level time stamps

How to Use

1
First, install the Transformers library as well as the Datasets and Accelerate libraries.
2
Use AutoModelForSpeechSeq2Seq and AutoProcessor to load models and processors from Hugging Face Hub.
3
Create a pipeline for automatic speech recognition through the pipeline class.
4
Load and prepare audio data, either the sample dataset from Hugging Face Hub or a local audio file.
5
Call the pipeline with audio data as input and get the transcription results.
6
If desired, additional decoding strategies can be enabled by setting the generate_kwargs parameter.
7
If you need to perform speech translation, you can specify the task type by setting the task parameter to 'translate'.
8
If you need to predict timestamps, you can set the return_timestamps parameter to True.

Target Users

The target audience includes AI researchers, developers and enterprises in need of efficient speech recognition solutions. Due to its multi-language support and fast processing capabilities, it is especially suitable for users who need to process large and diverse speech data.

Examples

Used for real-time speech to text conversion to improve the efficiency of meeting notes

Integrated into mobile applications to provide multi-language voice translation services

For transcribing and analyzing long-form speech content such as interviews and lectures

Quick Access

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Categories

💼 productive forces
› AI speech recognition
› AI speech to text

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