license: cc-by-4.0
task_categories:
- translation
- automatic-speech-recognition
language:
- it
- en
multilinguality:
- multilingual
pretty_name: FAMA-data
tags:
- speech
- speech-to-text
- open-source
- speech translation
- ST
- ASR
- audio
- text
size_categories:
- 100K<n<1M
configs:
- config_name: en
data_files:
- split: train_commonvoice
path: train_commonvoice_en-it.tsv
- split: train_covost2
path: train_covost2_en-it.tsv
- split: train_fleurs
path: train_fleurs_en-it.tsv
- split: train_librilight_large
path: train_librilightlarge_en-it.tsv
- split: train_librilight_medium
path: train_librilightmedium_en-it.tsv
- split: train_librilight_small
path: train_librilightsmall_en-it.tsv
- split: train_librispeech
path: train_librispeech_en-it.tsv
- split: train_mls
path: train_mls_en-it.tsv
- split: train_voxpopuli
path: train_voxpopuli_en-it.tsv
- split: train_voxpopuliasr
path: train_voxpopuliasr_en-it.tsv
- split: train_youtubecommons
path: train_youtubecommons_en-it.tsv
- config_name: it
data_files:
- split: train_commonvoice
path: train_commonvoice_it-en.tsv
- split: train_covost2
path: train_covost2_it-en.tsv
- split: train_fleurs
path: train_fleurs_it-en.tsv
- split: train_mls
path: train_mls_it-en.tsv
- split: train_voxpopuli
path: train_voxpopuli_it-en.tsv
- split: train_voxpopuliasr
path: train_voxpopuliasr_it-en.tsv
- split: train_youtubecommons
path: train_youtubecommons_it-en.tsv
Dataset Description, Collection, and Source
The FAMA training data is the collection of English and Italian datasets for automatic speech recognition (ASR) and speech translation (ST) used to train the FAMA models family. The ASR section of FAMA is derived from the MOSEL data collection, including the automatic transcripts obtained with Whisper and available in the HuggingFace MOSEL Dataset. The ASR is further augmented with automatically transcribed speech from the YouTube-Commons dataset. The ST section is composed of gold-labeled ST datasets and the automatic translations of the ASR datasets with MADALAD-400 3B-MT. The complete list of datasets for both tasks are reported in the Dataset Statistics.
- Curated by: Sara Papi, Marco Gaido, Luisa Bentivogli, Alessio Brutti, Mauro Cettolo, Roberto Gretter, Marco Matassoni, Mohamed Nabih, and Matteo Negri
- Funded by: FAIR, Meetween, and CINECA
- Shared by: Fondazione Bruno Kessler
License
- CC-BY-4.0
Dataset Sources
- MOSEL Collection: MOSEL GitHub
- MOSEL Pseudolabels: MOSEL HuggingFace
- YouTube-Commons: YouTube-Commons
- Paper: FAMA: The First Large-Scale Open-Science Speech Foundation Model for English and Italian
Dataset Structure
Data Config
The dataset is split into multiple tsv files corresponding to the dataset name and the source and target languages, either Italian (it) and English (en), containing both the ASR transcript and translation in the other language.
Data Field
id: unique id of the segment (text, e.g.: "5NTUCHeZuds_0")
audio: filename (text, e.g. "5NTUCHeZuds.wav")
offset: start of the segment, in seconds (float, e.g. "0.020")
duration: duration of the segments, in seconds (float, e.g. "5.946")
speaker: id of the speaker (text, e.g. "000")
src_lang: id of the source language (ISO 639-1 code, e.g. "it", "en")
src_text: recognized text (text, e.g. "Grazie a tutti.")
tgt_lang: id of the source language (ISO 639-1 code, e.g. "it", "en")
tgt_text: translated text (text, e.g. "Thank you all.")
ASR: True/False - indicates whether the sample has been used for ASR training
ST: True/False - indicates whether the sample has been used for ST training
Dataset Statistics
The full list of FAMA training datasets, together with the number of hours for each language/language pair and the type of labels (A for automatic and G for gold labels) is reported below for both ASR and ST tasks.
Automatic Speech Recognition (ASR)
| Dataset | English (h) | Italian (h) | Label |
|---|---|---|---|
| CommonVoice v18 | 1,746 | 250 | G |
| CoVoST2 | 420 | 28 | G |
| FLEURS | 7 | 9 | G |
| LibriSpeech | 358 | - | G |
| MOSEL | 66,301 | 21,775 | A |
| MLS | 44,600 | 247 | G |
| VoxPopuli-ASR | 519 | 74 | G |
| YouTube-Commons | 14,200 | 1,828 | A |
| TOTAL | 128,152 | 24,211 | G+A |
Speech Translation (ST)
| Dataset | English (h) | Italian (h) | Label |
|---|---|---|---|
| CommonVoice v18 | 1,746 | 250 | A |
| CoVoST2 | 420 | 28 | A |
| LibriSpeech | 358 | - | A |
| MOSEL | 66,301 | 21,775 | A |
| MLS | 44,600 | 247 | A |
| VoxPopuli-ASR | 519 | 74 | A |
| YouTube-Commons | 14,200 | 1,828 | A |
| TOTAL (A) | 128,144 | 24,202 | A |
| FILTERED (A) | 123,777 | 23,445 | A |
| CoVoST2 | 420 | 28 | G |
| FLEURS | 7 | 9 | G |
| TOTAL | 124,204 | 23,482 | G+A |
Dataset Creation
To reproduce the MOSEL-derived datasets (all but YouTube-Commons), please refer to the MOSEL README in the fbk-llm repository and to the MOSEL data card on HuggingFace.
To download and process YouTube-Commons, please refer to the dedicated YouTube-Commons README.
The code used to produce all translations with MADALAD-400 3B-MT is the following:
import os
import sys
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
modelname = "google/madlad400-3b-mt"
batch_size = {$BATCH_SIZE}
tlang = {$LANGUAGE}
class BatchedMT:
def __init__(self, tokenizer, model):
self.buffer_lines = []
self.model = model
if torch.cuda.is_available():
self.model = self.model.cuda()
self.tokenizer = tokenizer
def process_line(self, line):
self.buffer_lines.append(line.strip())
if len(self.buffer_lines) >= BATCHSIZE:
self.print_translations()
self.buffer_lines = []
def print_translations(self):
outs = self._do_translate()
for s in outs:
print(s)
def _do_translate(self):
tokens = self.tokenizer(self.buffer_lines, return_tensors="pt", padding=True)
if torch.cuda.is_available():
tokens = {k: v.cuda() for k, v in tokens.items()}
translated = self.model.generate(**tokens, max_new_tokens=512)
return [self.tokenizer.decode(t, skip_special_tokens=True) for t in translated]
def close(self):
if len(self.buffer_lines) > 0:
self.print_translations()
self.buffer_lines = []
mt = BatchedMT(
AutoTokenizer.from_pretrained(modelname),
AutoModelForSeq2SeqLM.from_pretrained(modelname))
for input_line in sys.stdin:
mt.process_line("<2" + tlang + "> " + input_line)
mt.close()
where the input text is passad as stdin, {$BATCH_SIZE} is the batch size supported on your machine
and {$LANGUAGE} is either en for Italian to English translation and it for English to Italian translation.
The script used for filtering the ST datasets is
filter_tsv_based_on_ratio and
available in the scripts folder of this repository.
For English speech datasets, we set --threshold-min 0.75 and --threshold-max 1.45
while, for the Italian speech datasets, --threshold-min 0.65 and --threshold-max 1.35.
Citation
@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}
}