mms-300m

Maintained By
facebook

MMS-300M

PropertyValue
Parameter Count300 Million
Model TypeMultilingual Speech Recognition
Languages Supported1400+
LicenseCC-BY-NC 4.0
AuthorsVineel Pratap et al.

What is mms-300m?

MMS-300M is Facebook AI's groundbreaking multilingual speech model that represents a significant advancement in speech recognition technology. This model is pretrained using Wav2Vec2's self-supervised training objective on approximately 500,000 hours of speech data across more than 1,400 languages, making it one of the most comprehensive multilingual speech models available.

Implementation Details

The model is built on advanced speech processing architecture and requires input speech to be sampled at 16kHz. It's designed as a foundation model that needs to be fine-tuned for specific downstream tasks such as Automatic Speech Recognition (ASR), Translation, or Classification.

  • Pretrained on 500,000+ hours of speech data
  • Supports over 1,400 languages
  • Uses Wav2Vec2's self-supervised training methodology
  • Requires 16kHz audio input sampling

Core Capabilities

  • Multilingual speech processing
  • Adaptable for various speech-related tasks
  • Robust performance across diverse languages
  • Foundation for ASR and translation systems

Frequently Asked Questions

Q: What makes this model unique?

MMS-300M's uniqueness lies in its massive multilingual coverage of over 1,400 languages and its efficient architecture with 300 million parameters, making it both powerful and relatively lightweight compared to larger models in the series.

Q: What are the recommended use cases?

The model is best suited for fine-tuning in tasks such as Automatic Speech Recognition, Speech Translation, and Speech Classification. It's particularly valuable for developing applications that need to handle multiple languages or work with low-resource languages.

🍰 Interesting in building your own agents?
PromptLayer provides Huggingface integration tools to manage and monitor prompts with your whole team. Get started here.