Add Speech Recognition Task (Wav2Vec)

flash/audio/__init__.py

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+from flash.audio.speech_recognition import SpeechRecognition, SpeechRecognitionData  # noqa: F401

flash/audio/speech_recognition/__init__.py

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+# Copyright The PyTorch Lightning team.
+#
+# 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 flash.audio.speech_recognition.data import SpeechRecognitionData  # noqa: F401
+from flash.audio.speech_recognition.model import SpeechRecognition  # noqa: F401

flash/audio/speech_recognition/collate.py

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+# Copyright 2020 The PyTorch Lightning team and The HuggingFace Team. All rights reserved.
+
+# 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 dataclasses import dataclass
+from typing import Dict, List, Optional, Union
+
+import torch
+
+from flash.core.utilities.imports import _SPEECH_RECOGNITION_AVAILABLE
+
+if _SPEECH_RECOGNITION_AVAILABLE:
+    from transformers import Wav2Vec2Processor
+
+
+@dataclass
+class DataCollatorCTCWithPadding:
+    """
+    Data collator that will dynamically pad the inputs received.
+    Args:
+        processor (:class:`~transformers.Wav2Vec2Processor`)
+            The processor used for proccessing the data.
+        padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`,
+            `optional`, defaults to :obj:`True`):
+            Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
+            among:
+            * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+              sequence if provided).
+            * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
+              maximum acceptable input length for the model if that argument is not provided.
+            * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
+              different lengths).
+        max_length (:obj:`int`, `optional`):
+            Maximum length of the ``input_values`` of the returned list and optionally padding length (see above).
+        max_length_labels (:obj:`int`, `optional`):
+            Maximum length of the ``labels`` returned list and optionally padding length (see above).
+        pad_to_multiple_of (:obj:`int`, `optional`):
+            If set will pad the sequence to a multiple of the provided value.
+            This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
+            7.5 (Volta).
+    """
+
+    processor: Wav2Vec2Processor
+    padding: Union[bool, str] = True
+    max_length: Optional[int] = None
+    max_length_labels: Optional[int] = None
+    pad_to_multiple_of: Optional[int] = None
+    pad_to_multiple_of_labels: Optional[int] = None
+
+    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
+        # split inputs and labels since they have to be of different lengths and need
+        # different padding methods
+        input_features = [{"input_values": feature["input_values"]} for feature in features]
+
+        batch = self.processor.pad(
+            input_features,
+            padding=self.padding,
+            max_length=self.max_length,
+            pad_to_multiple_of=self.pad_to_multiple_of,
+            return_tensors="pt",
+        )
+
+        label_features = [{"input_ids": feature.get("labels")} for feature in features]
+        # check to ensure labels exist to collate
+        labels_exist = not any(x['input_ids'] is None for x in label_features)
+        if labels_exist:
+            with self.processor.as_target_processor():
+                labels_batch = self.processor.pad(
+                    label_features,
+                    padding=self.padding,
+                    max_length=self.max_length_labels,
+                    pad_to_multiple_of=self.pad_to_multiple_of_labels,
+                    return_tensors="pt",
+                )
+
+            # replace padding with -100 to ignore loss correctly
+            labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
+
+            batch["labels"] = labels
+
+        return batch