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12 days ago
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Fix batched inputs longer than target_length (clips mixed) and keep the last full segment
Verified signatureFor inputs longer than `target_length` (1008 frames in the released configs, ~10.1 s), `AudioTransformerMAE_Encoder.forward` splits the spectrogram into segments and encodes all of them as one batch. This PR fixes two problems in that path. **1. Clips in a batch get mixed.** `torch.flatten(splits, 0, 1)` orders the first axis as (segment, batch), but `torch.reshape(x, (x.shape[0] // n_splits, -1, C))` reads it back as (batch, segment). With batch size > 1, each output row contains segments of other clips. Batch size 1 is not affected, which is why single-clip checks look fine. The fix reshapes as (segment, batch, time, C), swaps the first two axes and joins segments along time. The pip `dasheng` package encodes each segment separately and concatenates along time, so it already behaves this way. **2. The last segment is dropped when the length is an exact multiple of `target_length`.** The `else` branch stacks `splits[:-1]` even though the last split is full, so e.g. 2 x 1008 input fr…8fbd233
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Fix `Tensor.item() cannot be called on meta tensors` error during loading with transformers>=5 (#1)
- Fix `Tensor.item() cannot be called on meta tensors` error during loading with transformers>=5 (b694d074577fdd74ddd12c77f459a8e2774eb351) Co-authored-by: zhoukz <zhoukz@users.noreply.huggingface.co>d29a721
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Update feature_extraction_dasheng.py
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