MMS is Facebook AI's massive multilingual pretrained model for speech ("MMS"). It is pretrained in with Wav2Vec2's self-supervised training objective on about 500,000 hours of speech data in over 1,400 languages. **Note**: This model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Translation, or Classification. Check out the **How-to-fine section or **this blog** for more information about ASR. @article{pratap2023mms, title={Scaling Speech Technology to 1,000+ Languages}, author={Vineel Pratap and Andros Tjandra and Bowen Shi and Paden Tomasello and Arun Babu and Sayani Kundu and Ali Elkahky and Zhaoheng Ni and Apoorv Vyas and Maryam Fazel-Zarandi and Alexei Baevski and Yossi Adi and Xiaohui Zhang and Wei-Ning Hsu and Alexis Conneau and Michael Auli}, journal={arXiv}, year={2023} }
This checkpoint is a model fine-tuned for multi-lingual ASR and part of Facebook's Massive Multilingual Speech project. This checkpoint is based on the Wav2Vec2 architecture and makes use of adapter models to transcribe 1000+ languages. The checkpoint consists of **1 billion parameters** and has been fine-tuned from facebook/mms-1b on 1162 languages. - Example - Supported Languages - Model details - Additional links This MMS checkpoint can be used with Transformers to transcribe audio of 1107 different languages. Let's look at a simple example.
This checkpoint is a model fine-tuned for multi-lingual ASR and part of Facebook's Massive Multilingual Speech project. This checkpoint is based on the Wav2Vec2 architecture and makes use of adapter models to transcribe 100+ languages. The checkpoint consists of **1 billion parameters** and has been fine-tuned from facebook/mms-1b on 102 languages of Fleurs. This MMS checkpoint can be used with Transformers to transcribe audio of 1107 different languages. Let's look at a simple example. **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:
MMS is Facebook AI's massive multilingual pretrained model for speech ("MMS"). It is pretrained in with Wav2Vec2's self-supervised training objective on about 500,000 hours of speech data in over 1,400 languages. **Note**: This model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Translation, or Classification. Check out the **How-to-fine section or **this blog** for more information about ASR. - How to Finetune - Model details - Additional links
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 1024 output classes (each class representing a language). The checkpoint consists of **1 billion parameters** and has been fine-tuned from facebook/mms-1b on 1024 languages. - Example - Supported Languages - Model details - Additional links This MMS checkpoint can be used with Transformers to identify the spoken language of an audio. It can recognize the following 1024 languages.
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 126 output classes (each class representing a language). The checkpoint consists of **1 billion parameters** and has been fine-tuned from facebook/mms-1b on 126 languages. This MMS checkpoint can be used with Transformers to identify the spoken language of an audio. It can recognize the following 126 languages. **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:
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 2048 output classes (each class representing a language). The checkpoint consists of **1 billion parameters** and has been fine-tuned from facebook/mms-1b on 2048 languages. This MMS checkpoint can be used with Transformers to identify the spoken language of an audio. It can recognize the following 2048 languages. **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:
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. - Example - Supported Languages - Model details - Additional links This MMS checkpoint can be used with Transformers to identify the spoken language of an audio. It can recognize the following 256 languages.
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 4017 output classes (each class representing a language). The checkpoint consists of **1 billion parameters** and has been fine-tuned from facebook/mms-1b on 4017 languages. - Example - Supported Languages - Model details - Additional links This MMS checkpoint can be used with Transformers to identify the spoken language of an audio. It can recognize the following 4017 languages.
