FAMA models achieve remarkable results, with ASR and ST improvements on average across languages compared to OWSM,
and is competitive in terms of ASR performance with the Whisper model family while being up to 8 times faster.
All the artifacts used for realizing FAMA models, including codebase, datasets, and models
themself are released under OS-compliant licenses, promoting a more
responsible creation of models in our community.
It is available in 2 sizes, with 2 variants for ASR only:
FAMA models are supported in Hugging Face 🤗 Transformers.
To run the model, first install the Transformers and Datasets libraries.
pip install transformers==4.48.1 datasets
To perform a single inference on a sample audio file using the
pipeline
class, run:
python
1import torch
2from transformers import AutoProcessor, pipeline
3from datasets import load_dataset
45model_id ="FBK-MT/fama-medium-asr"6processor = AutoProcessor.from_pretrained(model_id)78device ="cuda:0"if torch.cuda.is_available()else"cpu"9tgt_lang ="en"1011# Force the model to start with the language tag12lang_tag ="<lang:{}>".format(tgt_lang)13lang_tag_id = processor.tokenizer.convert_tokens_to_ids(lang_tag)1415generate_kwargs ={"num_beams":5,"no_repeat_ngram_size":5,"forced_bos_token_id": lang_tag_id}1617pipe = pipeline(18"automatic-speech-recognition",19 model=model_id,20 trust_remote_code=True,21 torch_dtype=torch.float32,22 device=device,23 return_timestamps=False,24 generate_kwargs=generate_kwargs
25)2627dataset = load_dataset("distil-whisper/librispeech_asr_dummy","clean", split="validation")28sample = dataset[0]["audio"]2930result = pipe(sample)31print(result["text"])
Where tgt_lang is the target language (either en or it). The source languages has not to be specified.
To run the inference on a local audio file audio.wav, call the pipeline with:
result = pipe("audio.wav")
To perform a batch inference with size batch_size, run:
result = pipe(["audio_1.wav", "audio_2.wav"], batch_size=2)
For the inference, we suggest converting the audio files in wav format with 16kHz sampling rate and 1 channel.
Results
We evaluate FAMA-ASR on ASR using popular open-source datasets such as CommonVoice, Multilingual LibriSpeech (MLS), and VoxPopuli.
The metric used is WER (↓).
We also benchmark FAMA in terms of computational time and maximum batch size supported on HuggingFace against Whisper and SeamlessM4T models. The metric used is the inverse real time factor (xRTF).
Key highlights:
FAMA achieves up to 4.2 WER improvement on average across languages compared to OWSM v3.1
FAMA is up to 8 times faster than Whisper large-v3 while achieving comparable performance
Automatic Speech Recogniton (ASR)
Model/Dataset WER (↓)
CommonVoice-en
CommonVoice-it
MLS-en
MLS-it
VoxPopuli-en
VoxPopuli-it
AVG-en
AVG-it
Whisper medium
14.5
10.4
14.2
15.9
8.1
26.8
12.3
17.7
Whisper large-v3
11.2
6.5
5.0
8.8
7.1
18.8
7.8
11.4
OWSM v3.1 medium
11.9
12.5
6.6
19.3
8.4
24.0
9.0
18.6
SeamlessM4T medium
10.7
7.8
8.8
11.3
10.2
18.2
9.9
12.4
SeamlessM4T v2-large
7.7
5.0
6.4
8.5
6.9
16.6
7.0
10.0
FAMA-ASR small
13.8
8.9
5.8
12.6
7.2
15.7
8.9
12.4
FAMA-ASR medium
11.7
7.1
5.1
12.2
7.0
15.9
7.9
11.7
FAMA small
13.7
8.6
5.8
12.8
7.3
15.6
8.9
12.3
FAMA medium
11.5
7.0
5.2
13.9
7.2
15.9
8.0
12.3
Computational Time and Maximum Batch Size
Model
Batch Size
xRTF en (↑)
xRTF it (↑)
xRTF AVG (↑)
Whisper medium
8
13.3
10.9
12.1
Whisper large-v3
4
7.9
6.5
7.2
SeamlessM4T medium
2
28.5
26.2
27.4
SeamlessM4T v2-large
2
13.7
13.3
13.5
FAMA small
16
57.4
56.0
56.7
FAMA medium
8
39.5
41.2
40.4
License
We release the FAMA model weights, and training data under the CC-BY 4.0 license.
The training data can be found in FAMA Training Data.
The original FBK-fairseq codebase used to train the model is released under the Apache 2.0 license.
Citation
If you use FAMA in your work, please cite:
@misc{papi2025fama,
title={FAMA: The First Large-Scale Open-Science Speech Foundation Model for English and Italian},
author={Sara Papi and Marco Gaido and Luisa Bentivogli and Alessio Brutti and Mauro Cettolo and Roberto Gretter and Marco Matassoni and Mohamed Nabih and Matteo Negri},
year={2025}
}
