建立分批同步基线（基础文件）
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from __future__ import annotations
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from collections.abc import Sequence
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import torch
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from torch import Tensor
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from .types import AlignmentOutput, MODALITIES, SequenceBatch
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def uniform_time_intervals(durations: Tensor, grid_size: int) -> Tensor:
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"""Return ``[B, K, 2]`` equal-duration windows in seconds."""
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if durations.ndim != 1 or grid_size < 1:
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raise ValueError("durations must be [B] and grid_size must be positive")
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if bool((durations <= 0).any()) or not bool(torch.isfinite(durations).all()):
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raise ValueError("durations must be finite and positive")
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edges = torch.linspace(
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0.0, 1.0, grid_size + 1, device=durations.device, dtype=durations.dtype
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)[None, :] * durations[:, None]
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return torch.stack((edges[:, :-1], edges[:, 1:]), dim=-1)
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def word_intervals_to_grid(word_intervals: Tensor, grid_size: int) -> Tensor:
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"""Resample ordered word spans into K consecutive text-order intervals.
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Each grid slot covers an equal share of transcript word order. Its time
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boundaries are interpolated from forced-alignment word boundaries, so long
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and short words retain their actual duration on the audio/video timeline.
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"""
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if word_intervals.ndim != 2 or word_intervals.shape[1] != 2:
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raise ValueError("word_intervals must have shape [word_count, 2]")
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if word_intervals.shape[0] == 0 or grid_size < 1:
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raise ValueError("at least one word interval and a positive grid size are required")
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intervals = word_intervals.to(dtype=torch.float32)
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if not bool(torch.isfinite(intervals).all()):
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raise ValueError("word intervals must be finite")
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if bool((intervals[:, 1] < intervals[:, 0]).any()):
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raise ValueError("word interval end must not precede its start")
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if bool((intervals[1:, 0] < intervals[:-1, 0]).any()):
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raise ValueError("word intervals must be ordered by start time")
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word_count = intervals.shape[0]
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if word_count == 1:
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boundaries = torch.cat((intervals[:1, 0], intervals[:1, 1]))
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else:
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between = (intervals[:-1, 1] + intervals[1:, 0]) / 2
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boundaries = torch.cat((intervals[:1, 0], between, intervals[-1:, 1]))
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boundaries = torch.cummax(boundaries, dim=0).values
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positions = torch.linspace(
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0, word_count, grid_size + 1, device=intervals.device, dtype=intervals.dtype
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)
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left = positions.floor().long().clamp(max=word_count)
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right = (left + 1).clamp(max=word_count)
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fraction = (positions - left.to(positions.dtype)).unsqueeze(-1)
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time_edges = boundaries[left] + fraction.squeeze(-1) * (boundaries[right] - boundaries[left])
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return torch.stack((time_edges[:-1], time_edges[1:]), dim=-1)
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def index_alignment(valid: Tensor, grid_size: int) -> tuple[Tensor, int]:
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"""Map equal ranges of valid sequence order to K grid slots."""
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if valid.ndim != 2 or valid.dtype != torch.bool:
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raise ValueError("valid must be a boolean [B, L] tensor")
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if grid_size < 1:
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raise ValueError("grid_size must be positive")
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batch_size, length = valid.shape
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result = torch.zeros(batch_size, grid_size, length, device=valid.device, dtype=torch.float32)
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fallback_count = 0
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for batch_index in range(batch_size):
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positions = torch.nonzero(valid[batch_index], as_tuple=False).flatten()
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count = positions.numel()
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if count == 0:
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raise ValueError("each sample must contain a valid position")
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for grid_index in range(grid_size):
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start = (grid_index * count) // grid_size
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end = ((grid_index + 1) * count) // grid_size
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if start == end:
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source_index = min(int((grid_index + 0.5) * count / grid_size), count - 1)
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result[batch_index, grid_index, positions[source_index]] = 1.0
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fallback_count += 1
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else:
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chosen = positions[start:end]
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result[batch_index, grid_index, chosen] = 1.0 / chosen.numel()
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return result, fallback_count
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def interval_alignment(times: Tensor, valid: Tensor, intervals: Tensor) -> tuple[Tensor, int]:
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"""Create row-normalized interval membership weights with nearest-time fallback."""
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if times.ndim != 2 or valid.shape != times.shape or valid.dtype != torch.bool:
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raise ValueError("times and valid must have matching [B, L] shapes")
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if intervals.ndim != 3 or intervals.shape[0] != times.shape[0] or intervals.shape[2] != 2:
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raise ValueError("intervals must have shape [B, K, 2]")
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batch_size, length = times.shape
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grid_size = intervals.shape[1]
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result = torch.zeros(batch_size, grid_size, length, device=times.device, dtype=torch.float32)
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fallback_count = 0
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for batch_index in range(batch_size):
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valid_positions = torch.nonzero(valid[batch_index], as_tuple=False).flatten()
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valid_times = times[batch_index, valid_positions]
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for grid_index in range(grid_size):
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start, end = intervals[batch_index, grid_index]
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is_last = grid_index == grid_size - 1
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in_window = (valid_times >= start) & (
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(valid_times <= end) if is_last else (valid_times < end)
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)
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chosen = valid_positions[in_window]
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if chosen.numel() > 0:
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result[batch_index, grid_index, chosen] = 1.0 / chosen.numel()
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else:
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center = (start + end) / 2
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nearest = valid_positions[torch.argmin((valid_times - center).abs())]
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result[batch_index, grid_index, nearest] = 1.0
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fallback_count += 1
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return result, fallback_count
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def _output_from_weights(
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sequences: dict[str, SequenceBatch],
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weights: dict[str, Tensor],
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fallbacks: dict[str, int],
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) -> AlignmentOutput:
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aligned = {name: torch.bmm(weights[name].to(sequences[name].features.dtype), sequences[name].features)
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for name in MODALITIES}
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output = AlignmentOutput(weights=weights, aligned=aligned, fallback_rows=fallbacks)
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output.validate({name: sequences[name].valid for name in MODALITIES})
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return output
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def align_fixed_windows(
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sequences: dict[str, SequenceBatch], durations: Tensor, grid_size: int = 50
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) -> AlignmentOutput:
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"""M2: average each modality inside the same K equal-duration windows."""
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intervals = uniform_time_intervals(durations, grid_size)
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weights: dict[str, Tensor] = {}
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fallbacks: dict[str, int] = {}
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for name in MODALITIES:
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weights[name], fallbacks[name] = interval_alignment(
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sequences[name].times, sequences[name].valid, intervals
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)
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return _output_from_weights(sequences, weights, fallbacks)
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def align_forced_timestamps(
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sequences: dict[str, SequenceBatch],
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word_intervals: Sequence[Tensor],
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grid_size: int = 50,
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) -> AlignmentOutput:
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"""M1: use forced word timestamps to define text-ordered grid intervals."""
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batch_size = sequences["text"].features.shape[0]
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if len(word_intervals) != batch_size:
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raise ValueError("provide one ordered word-interval array per sample")
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device = sequences["text"].features.device
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intervals = torch.stack(
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[word_intervals_to_grid(spans.to(device), grid_size) for spans in word_intervals], dim=0
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)
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weights: dict[str, Tensor] = {}
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fallbacks: dict[str, int] = {}
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for name in MODALITIES:
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weights[name], fallbacks[name] = interval_alignment(
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sequences[name].times, sequences[name].valid, intervals
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)
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return _output_from_weights(sequences, weights, fallbacks)
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def make_block_mask(
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batch_size: int,
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grid_size: int,
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ratio: float,
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device: torch.device | str,
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generator: torch.Generator | None = None,
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) -> Tensor:
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"""Sample one continuous masked interval per sequence on the common grid."""
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if not 0 < ratio < 1:
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raise ValueError("ratio must be between zero and one")
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if batch_size < 1 or grid_size < 2:
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raise ValueError("batch_size must be positive and grid_size at least two")
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block_length = min(max(1, round(grid_size * ratio)), grid_size - 1)
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mask = torch.zeros(batch_size, grid_size, dtype=torch.bool, device=device)
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starts = torch.randint(
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0,
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grid_size - block_length + 1,
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(batch_size,),
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device=device,
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generator=generator,
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)
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offsets = torch.arange(block_length, device=device)
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mask[torch.arange(batch_size, device=device)[:, None], starts[:, None] + offsets] = True
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return mask
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