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Open-weight model · Text to speech

mms-tts-bam

by AI at Meta facebook/mms-tts-bam

mms - vits pipelinetag: text-to-speech This repository contains the Bamanankan (bam) language text-to-speech (TTS) model checkpoint.

Parameters36M
Context
Weights290.6 MB
Licensecc-by-nc-4.0
AccessOpen weights
Monthly Downloads163.7k

Runs On

What it takes to serve mms-tts-bam (36M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 0.1 GB 0.1 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 0.0 GB 0.0 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 0.0 GB 0.0 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.

Model Card

mms - vits pipelinetag: text-to-speech This repository contains the Bamanankan (bam) language text-to-speech (TTS) model checkpoint. This model is part of Facebook's Massively Multilingual Speech project, aiming to provide speech technology across a diverse range of languages. You can find more details about the supported languages and their ISO 639-3 codes in the MMS Language Coverage Overview, and see all MMS-TTS checkpoints on the Hugging Face Hub: facebook/mms-tts. MMS-TTS is available in the Transformers library from version 4.33 onwards. VITS (Variational Inference with adversarial learning for end-to-end Text-to-Speech) is an end-to-end speech synthesis model that predicts a speech…

Excerpt from the card by AI at Meta, licensed cc-by-nc-4.0.

Configuration

Architecture
VitsModel
Layers
6
Hidden size
192
Attention heads
2
Vocabulary size
36
Stored precision
float32
Model type
vits

Identity and Version

Repository
facebook/mms-tts-bam
Publisher
AI at Meta
Task
Text to speech
Modality
Audio
Library
transformers
Parameters
36M parameters
Languages
mms
Revision
11905f69c2982bf15106044f035d957815aafb1d
First published
2023-09-01
Last updated
2023-09-01

Files and Weights

8 files, 290.6 MB in total. The weights are 2 files totalling 290.6 MB in bin, safetensors.

Weights2 files · 290.6 MB
Configuration2 files · 1.7 KB
Tokenizer2 files · 682 B
Documentation1 file · 4.0 KB
Repository1 file · 1.5 KB
Every file
FileTypeSizeSHA-256
model.safetensorsWeights145.2 MB 0bbc837195a4
pytorch_model.binWeights145.4 MB 474cd9962859
config.jsonConfiguration1.6 KB
special_tokens_map.jsonConfiguration47 B
README.mdDocumentation4.0 KB
.gitattributesRepository1.5 KB
tokenizer_config.jsonTokenizer287 B
vocab.jsonTokenizer395 B

License and Download

License
cc-by-nc-4.0
Access
Open weights, no gate
Download size
290.6 MB
Download from AI at Meta

Released by AI at Meta through its official repository on Hugging Face. Read the license.

Built From

Memory Requirements

PrecisionWeights in memory
As published290.6 MB
16-bit0.1 GB
8-bit0.0 GB
4-bit0.0 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About mms-tts-bam

How much GPU memory does mms-tts-bam need?

About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (36M parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run mms-tts-bam on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use mms-tts-bam commercially?

Not without separate permission. mms-tts-bam is released under Creative Commons Attribution-NonCommercial 4.0. CC BY-NC 4.0 permits sharing and adapting with credit for non-commercial purposes only. Commercial use needs separate permission from the rights holder.

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