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32 changes: 6 additions & 26 deletions nemo/collections/asr/modules/rnnt.py
Original file line number Diff line number Diff line change
Expand Up @@ -845,8 +845,6 @@ def forward(
)

losses = []
wer_numer_list = []
wer_denom_list = []
batch_size = int(encoder_outputs.size(0)) # actual batch size

# Iterate over batch using fused_batch_size steps
Expand Down Expand Up @@ -914,31 +912,14 @@ def forward(
else:
losses = None

# Compute WER for sub batch
# Update WER for sub batch
if compute_wer:
sub_enc = sub_enc.transpose(1, 2) # [B, T, D] -> [B, D, T]
sub_enc = sub_enc.detach()
sub_transcripts = sub_transcripts.detach()

original_log_prediction = self.wer.log_prediction
if original_log_prediction and batch_idx == 0:
self.wer.log_prediction = True
else:
self.wer.log_prediction = False

# Compute the wer (with logging for just 1st sub-batch)
# Update WER on each process without syncing
self.wer.update(sub_enc, sub_enc_lens, sub_transcripts, sub_transcript_lens)
wer, wer_num, wer_denom = self.wer.compute()
self.wer.reset()

wer_numer_list.append(wer_num)
wer_denom_list.append(wer_denom)

# Reset logging default
self.wer.log_prediction = original_log_prediction

else:
wer = None

del sub_enc, sub_transcripts, sub_enc_lens, sub_transcript_lens

Expand All @@ -951,12 +932,11 @@ def forward(

# Collect sub batch wer results
if compute_wer:
wer_num = torch.tensor(wer_numer_list, dtype=torch.long)
wer_denom = torch.tensor(wer_denom_list, dtype=torch.long)

wer_num = wer_num.sum() # global sum of correct words/chars
wer_denom = wer_denom.sum() # global sum of all words/chars
# Sync and all_reduce on all processes, compute global WER
wer, wer_num, wer_denom = self.wer.compute()
self.wer.reset()
else:
wer = None
wer_num = None
wer_denom = None

Expand Down