Model reference · open weights
mms-all is an open-weight audio or speech model from Meta. mms-1b-all (FP32) weighs 7.3 GB; the smallest configuration that runs it is RTX 3060 12 GB.
Summary of the facebook/mms-1b-all model card, 2026-10-01
What it is
| Released by | Meta |
|---|---|
| Released | 2023-05-27 |
| Parameters | 965M |
| VRAM | 7.3 GB for the weights |
What it runs on
| Card | One stream | Memory |
|---|---|---|
| RTX 3060 12 GB | fits | 11.6 GB |
| RTX 4060 Ti 16 GB | fits | 15.4 GB |
| RTX 3090 24 GB | fits | 23.4 GB |
| RTX 4090 24 GB | fits | 23.4 GB |
| RTX 5090 32 GB | fits | 31.0 GB |
| L40S 48 GB | fits | 44.0 GB |
| A100 80 GB | fits | 78.2 GB |
| H100 80 GB | fits | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
From the model card
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.
This MMS checkpoint can be used with Transformers to transcribe audio of 1107 different languages. Let's look at a simple example.
First, we install transformers and some other libraries
pip install torch accelerate torchaudio datasets
pip install --upgrade transformers
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:
pip install git+https://github.com/huggingface/transformers.git
Next, we load a couple of audio samples via datasets. Make sure that the audio data is sampled to 16000 kHz.
from datasets import load_dataset, Audio
# English
stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "en", split="test", streaming=True)
stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000))
en_sample = next(iter(stream_data))["audio"]["array"]
# French
stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "fr", split="test", streaming=True)
stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000))
fr_sample = next(iter(stream_data))["audio"]["array"]
Next, we load the model and processor
from transformers import Wav2Vec2ForCTC, AutoProcessor
import torch
model_id = "facebook/mms-1b-all"
processor = AutoProcessor.from_pretrained(model_id)
model = Wav2Vec2ForCTC.from_pretrained(model_id)
Now we process the audio data, pass the processed audio data to the model and transcribe the model output, just like we usually do for Wav2Vec2 models such as facebook/wav2vec2-base-960h
inputs = processor(en_sample, sampling_rate=16_000, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs).logits
ids = torch.argmax(outputs, dim=-1)[0]
transcription = processor.decode(ids)
# 'joe keton disapproved of films and buster also had reservations about the media'
We can now keep the same model in memory and simply switch out the language adapters by calling the convenient load_adapter() function for the model and set_target_lang() for the tokenizer. We pass the target language as an input - "fra" for French.
processor.tokenizer.set_target_lang("fra")
model.load_adapter("fra")
inputs = processor(fr_sample, sampling_rate=16_000, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs).logits
ids = torch.argmax(outputs, dim=-1)[0]
transcription = processor.decode(ids)
# "ce dernier est volé tout au long de l'histoire romaine"
In the same way the language can be switched out for all other supported languages. Please have a look at:
processor.tokenizer.vocab.keys()
For more details, please have a look at the official docs.
This model supports 1162 languages. Unclick the following to toogle all supported languages of this checkpoint in ISO 639-3 code. You can find more details about the languages and their ISO 649-3 codes in the MMS Language Coverage Overview.
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.