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README.md

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+---
+language:
+- en
+- de
+- es
+- fr
+- it
+- ko
+- nl
+- pt
+- ru
+- zh
+license: apache-2.0
+task_categories:
+- translation
+- automatic-speech-recognition
+---
+
+# Spite Dataset
+
+Pseudolabeled speech translation data with quality annotations from multiple metrics. This version uses transcripts from [GigaSpeech](https://huggingface.co/datasets/speechcolab/gigaspeech) and translations from [Tower-Plus-9B](https://huggingface.co/Unbabel/Tower-Plus-9B).
+
+## Configs
+- en_de
+- en_es
+- en_fr
+- en_it
+- en_ko
+- en_nl
+- en_pt
+- en_ru
+- en_zh
+
+## Usage
+
+```python
+from datasets import load_dataset
+ds = load_dataset("bpop/spite-CV16-Euro9B", "en_pt")
+```

data/en_de.parquet

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data/en_fr.parquet

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spite.py

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+import datasets
+
+_LANGUAGE_PAIRS = {
+    "en_de": "data/en_de.parquet",
+    "en_es": "data/en_es.parquet",
+    "en_fr": "data/en_fr.parquet",
+    "en_it": "data/en_it.parquet",
+    "en_ko": "data/en_ko.parquet",
+    "en_nl": "data/en_nl.parquet",
+    "en_pt": "data/en_pt.parquet",
+    "en_ru": "data/en_ru.parquet",
+    "en_zh": "data/en_zh.parquet",
+}
+
+VERSION = datasets.Version("1.0.0")
+
+
+class Spite(datasets.GeneratorBasedBuilder):
+    VERSION = VERSION
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name=lang_pair,
+            version=VERSION,
+            description=f"Training data for language pair: {lang_pair}",
+        )
+        for lang_pair in _LANGUAGE_PAIRS
+    ]
+
+    DEFAULT_CONFIG_NAME = "en_de"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description="Pseudolabeled speech translation dataset",
+            features=datasets.Features({
+                "src": datasets.Value("string"),
+                "mt": datasets.Value("string"),
+                'cometqe_22': datasets.Value("float64"),
+                'xcomet_xl': datasets.Value("float64"),
+                'blaser2_src': datasets.Value("float64"),
+                'audio_length': datasets.Value("float64"),
+                "example_id": datasets.Value("string"),
+                "index": datasets.Value("int64")
+                # add more columns if needed
+            }),
+            supervised_keys=None,
+        )
+
+    def _split_generators(self, dl_manager):
+        parquet_path = dl_manager.download(_LANGUAGE_PAIRS[self.config.name])
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": parquet_path},
+            )
+        ]
+
+    def _generate_examples(self, filepath):
+        import pyarrow.parquet as pq
+
+        table = pq.read_table(filepath)
+        for idx, row in enumerate(table.to_pylist()):
+            yield idx, row