Add main FAMA data README

README.md

@@ -1,3 +1,146 @@
----
-license: cc-by-4.0
----
+---
+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
+---
+
+<img src="https://huggingface.co/FBK-MT/fama-small/resolve/main/FAMA.png" align="center" width="100%">
+
+### 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](https://huggingface.co/collections/FBK-MT/fama-683425df3fb2b3171e0cdc9e).
+The ASR section of FAMA is derived from the [MOSEL data collection](https://github.com/hlt-mt/mosel), including the automatic
+transcripts obtained with Whisper and available in the [HuggingFace MOSEL Dataset](https://huggingface.co/datasets/FBK-MT/mosel).
+The ASR is further augmented with automatically transcribed speech from the 
+[YouTube-Commons dataset](https://huggingface.co/datasets/PleIAs/YouTube-Commons).
+The ST section is composed of gold-labeled ST datasets and the automatic translations of the ASR datasets with 
+[MADALAD-400 3B-MT](https://huggingface.co/google/madlad400-3b-mt). 
+The complete list of datasets for both tasks are reported in the [Dataset Statistics](#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](https://github.com/hlt-mt/mosel)
+- **MOSEL Pseudolabels:** [MOSEL HuggingFace](https://huggingface.co/datasets/FBK-MT/mosel)
+- **YouTube-Commons:** [YouTube-Commons](https://huggingface.co/datasets/PleIAs/YouTube-Commons)
+- **Paper:** [FAMA: The First Large-Scale Open-Science Speech Foundation Model for English and Italian](https://huggingface.co/papers/2505.22759)
+
+## 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](https://github.com/hlt-mt/fbk-llm) repository and to the 
+[MOSEL data card on HuggingFace](https://huggingface.co/datasets/FBK-MT/mosel).
+
+To download and process YouTube-Commons, please refer to the 
+[dedicated YouTube-Commons README](https://huggingface.co/datasets/FBK-MT/fama-data/blob/main/scripts/YouTube-Commons-README.md).
+
+The code used to produce all translations with [MADALAD-400 3B-MT](https://huggingface.co/google/madlad400-3b-mt) is the following:
+```
+```
+
+The script used for filtering the ST datasets is 
+[`filter_tsv_based_on_ratio`](https://huggingface.co/datasets/FBK-MT/fama-data/blob/main/scripts/filter_tsv_based_on_ratio.py) 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}
+}
+```
+
+## Dataset Card Contact
+[@spapi](https://huggingface.co/spapi)