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hirofumi0810 avatar hirofumi0810 commented on June 8, 2024

@Cescfangs yes

from neural_sp.

Cescfangs avatar Cescfangs commented on June 8, 2024

@Cescfangs yes

Thanks for the reply, and I'm curious about the improvement of this mWER tuning, say 5% relative wer reduction?

from neural_sp.

Cescfangs avatar Cescfangs commented on June 8, 2024

neural_sp/neural_sp/models/criterion.py

Lines 12 to 39 in 2b10b9c

class MBR(torch.autograd.Function):
"""Minimum Bayes Risk (MBR) training.
Args:
vocab (int): number of nodes in softmax layer
"""
@staticmethod
def forward(ctx, log_probs, hyps, exp_risk, grad_input):
"""Forward pass.
Args:
log_probs (FloatTensor): `[B * nbest, L, vocab]`
hyps (LongTensor): `[B * nbest, L]`
exp_risk (FloatTensor): `[1]` (for forward)
grad_input (FloatTensor): `[1]` (for backward)
Returns:
loss (FloatTensor): `[1]`
"""
ctx.save_for_backward(grad_input)
return exp_risk
@staticmethod
def backward(ctx, grad_output):
grads, = ctx.saved_tensors
# grads = torch.mul(grads, grad_output)
return grads, None, None, None

Also, I am a little confused about the “mbr” loss, the inputs are not used in backward function, how does the grad flow to model params?

from neural_sp.

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