conversation dataset.

conversational_dummy.py

@@ -0,0 +1,113 @@
+# coding=utf-8
+# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# 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.
+
+# Lint as: python3
+"""SUPERB: Speech processing Universal PERformance Benchmark."""
+
+
+import datasets
+from datasets.tasks import AutomaticSpeechRecognition
+
+
+_CITATION = ""
+_DESCRIPTION = ""
+
+
+class AsrDummyConfig(datasets.BuilderConfig):
+    """BuilderConfig for Superb."""
+
+    def __init__(
+        self,
+        data_url,
+        url,
+        task_templates=None,
+        **kwargs,
+    ):
+        super().__init__(version=datasets.Version("1.9.0", ""), **kwargs)
+        self.data_url = data_url
+        self.url = url
+        self.task_templates = task_templates
+
+
+class AsrDummy(datasets.GeneratorBasedBuilder):
+    """Superb dataset."""
+
+    BUILDER_CONFIGS = [
+        AsrDummyConfig(
+            name="conversational",
+            description="",
+            url="",
+            data_url="",
+            task_templates=[],
+        )
+    ]
+
+    DEFAULT_CONFIG_NAME = "conversational"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "generated_responses": datasets.features.Sequence(
+                        datasets.Value("string")
+                    ),
+                    "past_user_inputs": datasets.features.Sequence(
+                        datasets.Value("string")
+                    ),
+                    "new_user_input": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=("file",),
+            homepage=self.config.url,
+            citation=_CITATION,
+            task_templates=self.config.task_templates,
+        )
+
+    def _split_generators(self, dl_manager):
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={},
+            ),
+        ]
+
+    def _generate_examples(self):
+        """Generate examples."""
+        # Only odd number to have user prompt
+        textss = [
+            ["Hello there", "Hello There", "Who are you ?"],
+            ["Hello there"],
+            [
+                "Hello there",
+                "Hello There",
+                "Can you help me ?",
+                "Yes what do you need ?",
+                "I am having a problem with your product",
+            ],
+        ]
+        for i, texts in enumerate(textss):
+            key = str(i)
+            past_user_inputs = texts[:-1:2]
+            generated_responses = texts[1::2]
+            new_user_input = texts[-1]
+            example = {
+                "id": key,
+                "generated_responses": generated_responses,
+                "past_user_inputs": past_user_inputs,
+                "new_user_input": new_user_input,
+            }
+            yield key, example