Create soccer_net_echoes.py

soccer_net_echoes.py

@@ -0,0 +1,105 @@
+# Copyright 2024 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""ASR Dataset for various football leagues and seasons"""
+
+import json
+import os
+
+import datasets
+
+
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {ASR Dataset for Football Leagues},
+author={Your Name},
+year={2024}
+}
+"""
+
+_DESCRIPTION = """\
+This dataset contains Automatic Speech Recognition (ASR) data for various football leagues and seasons. 
+The dataset includes ASR outputs from Whisper v1, v2, and v3, along with their English-translated versions.
+"""
+
+_HOMEPAGE = "https://github.com/SoccerNet/sn-echoes"
+
+_LICENSE = "Apache License 2.0"
+
+_URLS = {
+    "whisper_v1": "whisper_v1/",
+    "whisper_v1_en":  "wisper_v1_en/",
+    "whisper_v2":  "wisper_v2/",
+    "whisper_v2_en":  "wisper_v2_en/",
+    "whisper_v3":  "wisper_v3/",
+}
+
+
+class FootballASRDataset(datasets.GeneratorBasedBuilder):
+    """ASR Dataset for various football leagues and seasons"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="whisper_v1", version=VERSION, description="Contains ASR from Whisper v1"),
+        datasets.BuilderConfig(name="whisper_v1_en", version=VERSION, description="English-translated datasets from Whisper v1"),
+        # datasets.BuilderConfig(name="whisper_v2", version=VERSION, description="Contains ASR from Whisper v2"),
+        # datasets.BuilderConfig(name="whisper_v2_en", version=VERSION, description="English-translated datasets from Whisper v2"),
+        # datasets.BuilderConfig(name="whisper_v3", version=VERSION, description="Contains ASR from Whisper v3"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "whisper_v1"
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "segment_index": datasets.Value("int32"),
+                "start_time": datasets.Value("float"),
+                "end_time": datasets.Value("float"),
+                "transcribed_text": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        urls = _URLS[self.config.name]
+        data_dir = dl_manager.download_and_extract("https://codeload.github.com/SoccerNet/sn-echoes/zip/refs/heads/main") +"/sn-echoes-main/Dataset/"
+        print("data_dir", {  "data_dir": os.path.join(data_dir+ urls),})
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_dir": os.path.join(data_dir+ urls),
+                },)
+        ]
+
+    def _generate_examples(self, data_dir,):
+        for root, _, files in os.walk(data_dir):
+            for file in files:
+                if file.endswith(".json"):
+                    with open(os.path.join(root, file), encoding="utf-8") as f:
+                        data = json.load(f)
+                        for segment_index, segment_data in data["segments"].items():
+                            yield f"{file}_{segment_index}", {
+                                "segment_index": segment_index,
+                                "start_time": segment_data[0],
+                                "end_time": segment_data[1],
+                                "transcribed_text": segment_data[2],
+                            }