chore: import upstream snapshot with attribution

2026-07-13 13:24:13 +08:00
commit 1037506f2e
6050 changed files with 1731598 additions and 0 deletions
@@ -0,0 +1,122 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+
+import numpy as np
+from fairseq.data import FairseqDataset
+
+from . import data_utils
+from .collaters import Seq2SeqCollater
+
+
+class AsrDataset(FairseqDataset):
+    """
+    A dataset representing speech and corresponding transcription.
+
+    Args:
+        aud_paths: (List[str]): A list of str with paths to audio files.
+        aud_durations_ms (List[int]): A list of int containing the durations of
+            audio files.
+        tgt (List[torch.LongTensor]): A list of LongTensors containing the indices
+            of target transcriptions.
+        tgt_dict (~fairseq.data.Dictionary): target vocabulary.
+        ids (List[str]): A list of utterance IDs.
+        speakers (List[str]): A list of speakers corresponding to utterances.
+        num_mel_bins (int): Number of triangular mel-frequency bins (default: 80)
+        frame_length (float): Frame length in milliseconds (default: 25.0)
+        frame_shift (float): Frame shift in milliseconds (default: 10.0)
+    """
+
+    def __init__(
+        self,
+        aud_paths,
+        aud_durations_ms,
+        tgt,
+        tgt_dict,
+        ids,
+        speakers,
+        num_mel_bins=80,
+        frame_length=25.0,
+        frame_shift=10.0,
+    ):
+        assert frame_length > 0
+        assert frame_shift > 0
+        assert all(x > frame_length for x in aud_durations_ms)
+        self.frame_sizes = [
+            int(1 + (d - frame_length) / frame_shift) for d in aud_durations_ms
+        ]
+
+        assert len(aud_paths) > 0
+        assert len(aud_paths) == len(aud_durations_ms)
+        assert len(aud_paths) == len(tgt)
+        assert len(aud_paths) == len(ids)
+        assert len(aud_paths) == len(speakers)
+        self.aud_paths = aud_paths
+        self.tgt_dict = tgt_dict
+        self.tgt = tgt
+        self.ids = ids
+        self.speakers = speakers
+        self.num_mel_bins = num_mel_bins
+        self.frame_length = frame_length
+        self.frame_shift = frame_shift
+
+        self.s2s_collater = Seq2SeqCollater(
+            0,
+            1,
+            pad_index=self.tgt_dict.pad(),
+            eos_index=self.tgt_dict.eos(),
+            move_eos_to_beginning=True,
+        )
+
+    def __getitem__(self, index):
+        import torchaudio
+        import torchaudio.compliance.kaldi as kaldi
+
+        tgt_item = self.tgt[index] if self.tgt is not None else None
+
+        path = self.aud_paths[index]
+        if not os.path.exists(path):
+            raise FileNotFoundError("Audio file not found: {}".format(path))
+        sound, sample_rate = torchaudio.load_wav(path)
+        output = kaldi.fbank(
+            sound,
+            num_mel_bins=self.num_mel_bins,
+            frame_length=self.frame_length,
+            frame_shift=self.frame_shift,
+        )
+        output_cmvn = data_utils.apply_mv_norm(output)
+
+        return {"id": index, "data": [output_cmvn.detach(), tgt_item]}
+
+    def __len__(self):
+        return len(self.aud_paths)
+
+    def collater(self, samples):
+        """Merge a list of samples to form a mini-batch.
+
+        Args:
+            samples (List[int]): sample indices to collate
+
+        Returns:
+            dict: a mini-batch suitable for forwarding with a Model
+        """
+        return self.s2s_collater.collate(samples)
+
+    def num_tokens(self, index):
+        return self.frame_sizes[index]
+
+    def size(self, index):
+        """Return an example's size as a float or tuple. This value is used when
+        filtering a dataset with ``--max-positions``."""
+        return (
+            self.frame_sizes[index],
+            len(self.tgt[index]) if self.tgt is not None else 0,
+        )
+
+    def ordered_indices(self):
+        """Return an ordered list of indices. Batches will be constructed based
+        on this order."""
+        return np.arange(len(self))