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Use pre-sorted text to generate token ids for attention decoder. (#98)

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* Use pre-sorted text to generate token ids for attention decoder.

See https://github.com/k2-fsa/icefall/issues/97
for more details.

* Fix typos.
This commit is contained in:
Fangjun Kuang 2021-10-29 13:46:41 +08:00 committed by GitHub
parent 12d647d899
commit 42b437bea6
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 37 additions and 50 deletions
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23
egs/librispeech/ASR/conformer_ctc/train.py
View File

@ -362,19 +362,22 @@ def compute_loss(
if params.att_rate != 0.0: if params.att_rate != 0.0:
with torch.set_grad_enabled(is_training): with torch.set_grad_enabled(is_training):
if hasattr(model, "module"): mmodel = model.module if hasattr(model, "module") else model
att_loss = model.module.decoder_forward( # Note: We need to generate an unsorted version of token_ids
encoder_memory, # `encode_supervisions()` called above sorts text, but
memory_mask, # encoder_memory and memory_mask are not sorted, so we
token_ids=token_ids, # use an unsorted version `supervisions["text"]` to regenerate
sos_id=graph_compiler.sos_id, # the token_ids
eos_id=graph_compiler.eos_id, #
# See https://github.com/k2-fsa/icefall/issues/97
# for more details
unsorted_token_ids = graph_compiler.texts_to_ids(
supervisions["text"]
) )
else: att_loss = mmodel.decoder_forward(
att_loss = model.decoder_forward(
encoder_memory, encoder_memory,
memory_mask, memory_mask,
token_ids=token_ids, token_ids=unsorted_token_ids,
sos_id=graph_compiler.sos_id, sos_id=graph_compiler.sos_id,
eos_id=graph_compiler.eos_id, eos_id=graph_compiler.eos_id,
) )

14
egs/librispeech/ASR/conformer_mmi/train-with-attention.py
View File

@ -394,18 +394,10 @@ def compute_loss(
mmi_loss = loss_fn(dense_fsa_vec=dense_fsa_vec, texts=texts) mmi_loss = loss_fn(dense_fsa_vec=dense_fsa_vec, texts=texts)
if params.att_rate != 0.0: if params.att_rate != 0.0:
token_ids = graph_compiler.texts_to_ids(texts) token_ids = graph_compiler.texts_to_ids(supervisions["text"])
with torch.set_grad_enabled(is_training): with torch.set_grad_enabled(is_training):
if hasattr(model, "module"): mmodel = model.module if hasattr(model, "module") else model
att_loss = model.module.decoder_forward( att_loss = mmodel.decoder_forward(
encoder_memory,
memory_mask,
token_ids=token_ids,
sos_id=graph_compiler.sos_id,
eos_id=graph_compiler.eos_id,
)
else:
att_loss = model.decoder_forward(
encoder_memory, encoder_memory,
memory_mask, memory_mask,
token_ids=token_ids, token_ids=token_ids,

14
egs/librispeech/ASR/conformer_mmi/train.py
View File

@ -394,18 +394,10 @@ def compute_loss(
mmi_loss = loss_fn(dense_fsa_vec=dense_fsa_vec, texts=texts) mmi_loss = loss_fn(dense_fsa_vec=dense_fsa_vec, texts=texts)
if params.att_rate != 0.0: if params.att_rate != 0.0:
token_ids = graph_compiler.texts_to_ids(texts) token_ids = graph_compiler.texts_to_ids(supervisions["text"])
with torch.set_grad_enabled(is_training): with torch.set_grad_enabled(is_training):
if hasattr(model, "module"): mmodel = model.module if hasattr(model, "module") else model
att_loss = model.module.decoder_forward( att_loss = mmodel.decoder_forward(
encoder_memory,
memory_mask,
token_ids=token_ids,
sos_id=graph_compiler.sos_id,
eos_id=graph_compiler.eos_id,
)
else:
att_loss = model.decoder_forward(
encoder_memory, encoder_memory,
memory_mask, memory_mask,
token_ids=token_ids, token_ids=token_ids,