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# 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
_CITATION = ""
_DESCRIPTION = ""
class AsrDummyConfig(datasets.BuilderConfig):
"""BuilderConfig for Superb."""
def __init__(
self,
data_url,
url,
**kwargs,
):
super().__init__(version=datasets.Version("1.9.0", ""), **kwargs)
self.data_url = data_url
self.url = url
class AsrDummy(datasets.GeneratorBasedBuilder):
"""Superb dataset."""
BUILDER_CONFIGS = [
AsrDummyConfig(
name="conversational",
description="",
url="",
data_url="",
)
]
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,
)
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
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