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# Copyright 2022 The HuggingFace Evaluate 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.
from numbers import Number
from typing import TYPE_CHECKING, Any, Callable, Dict, Optional, Tuple, Union
from datasets import Dataset
from typing_extensions import Literal
from ..module import EvaluationModule
from ..utils.file_utils import add_end_docstrings, add_start_docstrings
from .base import EVALUATOR_COMPUTE_RETURN_DOCSTRING, EVALUTOR_COMPUTE_START_DOCSTRING, Evaluator
if TYPE_CHECKING:
from transformers import FeatureExtractionMixin, Pipeline, PreTrainedModel, PreTrainedTokenizer, TFPreTrainedModel
TASK_DOCUMENTATION = r"""
Examples:
<Tip>
Remember that, in order to process audio files, you need ffmpeg installed (https://ffmpeg.org/download.html)
</Tip>
```python
>>> from evaluate import evaluator
>>> from datasets import load_dataset
>>> task_evaluator = evaluator("audio-classification")
>>> data = load_dataset("superb", 'ks', split="test[:40]")
>>> results = task_evaluator.compute(
>>> model_or_pipeline=""superb/wav2vec2-base-superb-ks"",
>>> data=data,
>>> label_column="label",
>>> input_column="file",
>>> metric="accuracy",
>>> label_mapping={0: "yes", 1: "no", 2: "up", 3: "down"}
>>> )
```
<Tip>
The evaluator supports raw audio data as well, in the form of a numpy array. However, be aware that calling
the audio column automatically decodes and resamples the audio files, which can be slow for large datasets.
</Tip>
```python
>>> from evaluate import evaluator
>>> from datasets import load_dataset
>>> task_evaluator = evaluator("audio-classification")
>>> data = load_dataset("superb", 'ks', split="test[:40]")
>>> data = data.map(lambda example: {"audio": example["audio"]["array"]})
>>> results = task_evaluator.compute(
>>> model_or_pipeline=""superb/wav2vec2-base-superb-ks"",
>>> data=data,
>>> label_column="label",
>>> input_column="audio",
>>> metric="accuracy",
>>> label_mapping={0: "yes", 1: "no", 2: "up", 3: "down"}
>>> )
```
"""
class AudioClassificationEvaluator(Evaluator):
"""
Audio classification evaluator.
This audio classification evaluator can currently be loaded from [`evaluator`] using the default task name
`audio-classification`.
Methods in this class assume a data format compatible with the [`transformers.AudioClassificationPipeline`].
"""
PIPELINE_KWARGS = {}
def __init__(self, task="audio-classification", default_metric_name=None):
super().__init__(task, default_metric_name=default_metric_name)
def predictions_processor(self, predictions, label_mapping):
pred_label = [max(pred, key=lambda x: x["score"])["label"] for pred in predictions]
pred_label = [label_mapping[pred] if label_mapping is not None else pred for pred in pred_label]
return {"predictions": pred_label}
@add_start_docstrings(EVALUTOR_COMPUTE_START_DOCSTRING)
@add_end_docstrings(EVALUATOR_COMPUTE_RETURN_DOCSTRING, TASK_DOCUMENTATION)
def compute(
self,
model_or_pipeline: Union[
str, "Pipeline", Callable, "PreTrainedModel", "TFPreTrainedModel" # noqa: F821
] = None,
data: Union[str, Dataset] = None,
subset: Optional[str] = None,
split: Optional[str] = None,
metric: Union[str, EvaluationModule] = None,
tokenizer: Optional[Union[str, "PreTrainedTokenizer"]] = None, # noqa: F821
feature_extractor: Optional[Union[str, "FeatureExtractionMixin"]] = None, # noqa: F821
strategy: Literal["simple", "bootstrap"] = "simple",
confidence_level: float = 0.95,
n_resamples: int = 9999,
device: int = None,
random_state: Optional[int] = None,
input_column: str = "file",
label_column: str = "label",
label_mapping: Optional[Dict[str, Number]] = None,
) -> Tuple[Dict[str, float], Any]:
"""
input_column (`str`, defaults to `"file"`):
The name of the column containing either the audio files or a raw waveform, represented as a numpy array, in the dataset specified by `data`.
label_column (`str`, defaults to `"label"`):
The name of the column containing the labels in the dataset specified by `data`.
label_mapping (`Dict[str, Number]`, *optional*, defaults to `None`):
We want to map class labels defined by the model in the pipeline to values consistent with those
defined in the `label_column` of the `data` dataset.
"""
result = super().compute(
model_or_pipeline=model_or_pipeline,
data=data,
subset=subset,
split=split,
metric=metric,
tokenizer=tokenizer,
feature_extractor=feature_extractor,
strategy=strategy,
confidence_level=confidence_level,
n_resamples=n_resamples,
device=device,
random_state=random_state,
input_column=input_column,
label_column=label_column,
label_mapping=label_mapping,
)
return result
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