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fix(paraformer): CIF crashes (IndexError) on segments with no fired boundaries #3028

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Merged
LauraGPT merged 1 commit into from
Jun 23, 2026
Merged

fix(paraformer): CIF crashes (IndexError) on segments with no fired boundaries #3028

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18 changes: 18 additions & 0 deletions funasr/models/paraformer/cif_predictor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -616,6 +616,15 @@ def cif_v1_export(hidden, alphas, threshold: float):
device = hidden.device
dtype = hidden.dtype
batch_size, len_time, hidden_size = hidden.size()
if fire_idxs.sum() == 0:
# No integration boundaries fired (e.g. a very short / silent
# segment): return zero frames instead of indexing into the
# resulting empty tensors, which raised an IndexError.
max_label_len = torch.round(alphas.sum(-1)).int().max()
return (
torch.zeros(batch_size, max_label_len, hidden_size, dtype=dtype, device=device),
fires,
)
Comment on lines +619 to +627

@gemini-code-assist gemini-code-assist Bot Jun 23, 2026

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critical

In cif_v1_export, fire_idxs and fires are referenced before they are defined. This will raise a NameError (or fail TorchScript compilation) at runtime.

Since alphas are non-negative, the condition fire_idxs.sum() == 0 is mathematically equivalent to torch.floor(alphas.sum(-1)).sum() == 0. We can use this check to avoid any undefined variable issues and completely bypass the need to compute fire_idxs beforehand in the early-return case. In this case, fires is simply the cumulative sum of alphas cast to float32.

Suggested change
if fire_idxs.sum() == 0:
# No integration boundaries fired (e.g. a very short / silent
# segment): return zero frames instead of indexing into the
# resulting empty tensors, which raised an IndexError.
max_label_len = torch.round(alphas.sum(-1)).int().max()
return (
torch.zeros(batch_size, max_label_len, hidden_size, dtype=dtype, device=device),
fires,
)
if torch.floor(alphas.sum(-1)).sum() == 0:
# No integration boundaries fired (e.g. a very short / silent
# segment): return zero frames instead of indexing into the
# resulting empty tensors, which raised an IndexError.
max_label_len = torch.round(alphas.sum(-1)).int().max()
fires = torch.cumsum(alphas, dim=1, dtype=torch.float64).to(torch.float32)
return (
torch.zeros(batch_size, max_label_len, hidden_size, dtype=dtype, device=device),
fires,
)

threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)

frames = torch.zeros(batch_size, len_time, hidden_size, dtype=dtype, device=device)
Expand Down Expand Up @@ -853,6 +862,15 @@ def cif_v1(hidden, alphas, threshold):
device = hidden.device
dtype = hidden.dtype
batch_size, len_time, hidden_size = hidden.size()
if fire_idxs.sum() == 0:
# No integration boundaries fired (e.g. a very short / silent
# segment): return zero frames instead of indexing into the
# resulting empty tensors, which raised an IndexError.
max_label_len = torch.round(alphas.sum(-1)).int().max()
return (
torch.zeros(batch_size, max_label_len, hidden_size, dtype=dtype, device=device),
fires,
)
# frames = torch.zeros(batch_size, len_time, hidden_size, dtype=dtype, device=device)
# prefix_sum_hidden = torch.cumsum(alphas.unsqueeze(-1).tile((1, 1, hidden_size)) * hidden, dim=1)
frames = torch.zeros(batch_size, len_time, hidden_size, dtype=dtype, device=device)
Expand Down