---
title: mms-300m
canonical_url: "https://www.modelscope.cn/models/facebook/mms-300m"
md_url: "https://www.modelscope.cn/models/facebook/mms-300m.md"
repository: facebook/mms-300m
last_updated: 2025-05-20
license: cc-by-nc-4.0
model_type:
  - wav2vec2
architectures:
  - Wav2Vec2ForPreTraining
library_name:
  - pytorch
frameworks:
  - pytorch
language:
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  - af
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  - ar
  - as
  - av
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  - bm
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  - ms
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  - qu
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  - qu
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  - zu
  - za
downloads: 343
stars: 0
tags:
  - mms
---

# mms-300m

> mms-300m - facebook 在 ModelScope 开源的模型。Massively Multilingual Speech (MMS) - 300m

facebook/mms-300m 是 ModelScope 魔搭社区上的机器学习模型，采用 cc-by-nc-4.0 许可。

- **Repository**: facebook/mms-300m
- **License**: cc-by-nc-4.0
- **Tags**: mms
- **Downloads**: 343
- **Stars**: 0
- **Last updated**: 2025-05-20

Source: https://www.modelscope.cn/models/facebook/mms-300m

---

# Massively Multilingual Speech (MMS) - 300m

Facebook's MMS counting *300m* parameters.

MMS is Facebook AI's massive multilingual pretrained model for speech ("MMS"). 
It is pretrained in with [Wav2Vec2's self-supervised training objective](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) on about 500,000 hours of speech data in over 1,400 languages.

When using the model make sure that your speech input is sampled at 16kHz. 

**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](#how-to-finetune) or [**this blog**](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for more information about ASR.

## Table Of Content

- [How to Finetune](#how-to-finetune)
- [Model details](#model-details)
- [Additional links](#additional-links)

## How to finetune

Coming soon...

## Model details

- **Developed by:** Vineel Pratap et al.
- **Model type:** Multi-Lingual Automatic Speech Recognition model
- **Language(s):** 1000+ languages
- **License:** CC-BY-NC 4.0 license
- **Num parameters**: 300 million
- **Cite as:**

      @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}
      }

## Additional Links

- [Blog post]( )
- [Transformers documentation](https://huggingface.co/docs/transformers/main/en/model_doc/mms).
- [Paper](https://arxiv.org/abs/2305.13516)
- [GitHub Repository](https://github.com/facebookresearch/fairseq/tree/main/examples/mms#asr)
- [Other **MMS** checkpoints](https://huggingface.co/models?other=mms)
- MMS ASR fine-tuned checkpoints:
  - [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all)
  - [facebook/mms-1b-l1107](https://huggingface.co/facebook/mms-1b-l1107)
  - [facebook/mms-1b-fl102](https://huggingface.co/facebook/mms-1b-fl102)
- [Official Space](https://huggingface.co/spaces/facebook/MMS)