Massively Multilingual Speech (MMS) Finetuned LID This checkpoint is a model fine tuned for speech language identification (LID) and part of Facebook's Massive Multilingual Speech project. This checkpoint is based on the Wav2Vec2 architecture and classifies raw audio input to a probability distribution over 256 output classes (each class representing a language). The checkpoint consists of 1 billion parameters and has been fine tuned from facebook/mms 1b on 256 languages. Table Of Content Example Supported Languages Model details Additional links Example This MMS checkpoint can be used with Transformers to identify the spoken language of an audio. It can recognize the following 256 languages. Let's look at a simple example. First, we install transformers and some other libraries Note : In order to use MMS you need to have at least transformers = 4.30 installed. If the 4.30 version is not yet available on PyPI make sure to install transformers from source: Next, we load a couple of audio samples via datasets . Make sure that the audio data is sampled to 16000 kHz. Next, we load the model and processor Now we process the audio data, pass the processed audio data to the model to class…
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