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Refactor beam search and update results. (#177)

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Fangjun Kuang 2022-01-18 16:40:19 +08:00 committed by GitHub
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3 changed files with 121 additions and 43 deletions
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2
README.md
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@ -84,7 +84,7 @@ The best WER using beam search with beam size 4 is:
| | test-clean | test-other |
|-----|------------|------------|
| WER | 2.76 | 6.97 |
| WER | 2.68 | 6.72 |
Note: No auxiliary losses are used in the training and no LMs are used
in the decoding.

15
egs/librispeech/ASR/RESULTS.md
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@ -13,8 +13,8 @@ The WERs are
| | test-clean | test-other | comment |
|---------------------------|------------|------------|------------------------------------------|
| greedy search | 2.77 | 7.07 | --epoch 30, --avg 13, --max-duration 100 |
| beam search (beam size 4) | 2.76 | 6.97 | |
| greedy search | 2.69 | 6.81 | --epoch 71, --avg 15, --max-duration 100 |
| beam search (beam size 4) | 2.68 | 6.72 | --epoch 71, --avg 15, --max-duration 100 |
The training command for reproducing is given below:
@ -23,7 +23,7 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
./transducer_stateless/train.py \
--world-size 4 \
--num-epochs 30 \
--num-epochs 76 \
--start-epoch 0 \
--exp-dir transducer_stateless/exp-full \
--full-libri 1 \
@ -32,12 +32,12 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
```
The tensorboard training log can be found at
<https://tensorboard.dev/experiment/6fnVojoUQTmEJVq1yG34Vw/>
<https://tensorboard.dev/experiment/qGdqzHnxS0WJ695OXfZDzA/#scalars&_smoothingWeight=0>
The decoding command is:
```
epoch=36
avg=13
epoch=71
avg=15
## greedy search
./transducer_stateless/decode.py \
@ -58,6 +58,9 @@ avg=13
--beam-size 4
```
You can find a pretrained model by visiting
<https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-01-10>
#### Conformer encoder + LSTM decoder
Using commit `8187d6236c2926500da5ee854f758e621df803cc`.

145
egs/librispeech/ASR/transducer_stateless/beam_search.py
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@ -118,7 +118,7 @@ class Hypothesis:
class HypothesisList(object):
def __init__(self, data: Optional[Dict[str, Hypothesis]] = None):
def __init__(self, data: Optional[Dict[str, Hypothesis]] = None) -> None:
"""
Args:
data:
@ -130,11 +130,10 @@ class HypothesisList(object):
self._data = data
@property
def data(self):
def data(self) -> Dict[str, Hypothesis]:
return self._data
# def add(self, ys: List[int], log_prob: float):
def add(self, hyp: Hypothesis):
def add(self, hyp: Hypothesis) -> None:
"""Add a Hypothesis to `self`.
If `hyp` already exists in `self`, its probability is updated using
@ -159,7 +158,8 @@ class HypothesisList(object):
length_norm:
If True, the `log_prob` of a hypothesis is normalized by the
number of tokens in it.
Returns:
Return the hypothesis that has the largest `log_prob`.
"""
if length_norm:
return max(
@ -171,6 +171,9 @@ class HypothesisList(object):
def remove(self, hyp: Hypothesis) -> None:
"""Remove a given hypothesis.
Caution:
`self` is modified **in-place**.
Args:
hyp:
The hypothesis to be removed from `self`.
@ -189,10 +192,10 @@ class HypothesisList(object):
Returns:
Return a new HypothesisList containing all hypotheses from `self`
that have `log_prob` being greater than the given `threshold`.
with `log_prob` being greater than the given `threshold`.
"""
ans = HypothesisList()
for key, hyp in self._data.items():
for _, hyp in self._data.items():
if hyp.log_prob > threshold:
ans.add(hyp) # shallow copy
return ans
@ -222,6 +225,93 @@ class HypothesisList(object):
return ", ".join(s)
def run_decoder(
ys: List[int],
model: Transducer,
decoder_cache: Dict[str, torch.Tensor],
) -> torch.Tensor:
"""Run the neural decoder model for a given hypothesis.
Args:
ys:
The current hypothesis.
model:
The transducer model.
decoder_cache:
Cache to save computations.
Returns:
Return a 1-D tensor of shape (decoder_out_dim,) containing
output of `model.decoder`.
"""
context_size = model.decoder.context_size
key = "_".join(map(str, ys[-context_size:]))
if key in decoder_cache:
return decoder_cache[key]
device = model.device
decoder_input = torch.tensor([ys[-context_size:]], device=device).reshape(
1, context_size
)
decoder_out = model.decoder(decoder_input, need_pad=False)
decoder_cache[key] = decoder_out
return decoder_out
def run_joiner(
key: str,
model: Transducer,
encoder_out: torch.Tensor,
decoder_out: torch.Tensor,
encoder_out_len: torch.Tensor,
decoder_out_len: torch.Tensor,
joint_cache: Dict[str, torch.Tensor],
):
"""Run the joint network given outputs from the encoder and decoder.
Args:
key:
A key into the `joint_cache`.
model:
The transducer model.
encoder_out:
A tensor of shape (1, 1, encoder_out_dim).
decoder_out:
A tensor of shape (1, 1, decoder_out_dim).
encoder_out_len:
A tensor with value [1].
decoder_out_len:
A tensor with value [1].
joint_cache:
A dict to save computations.
Returns:
Return a tensor from the output of log-softmax.
Its shape is (vocab_size,).
"""
if key in joint_cache:
return joint_cache[key]
logits = model.joiner(
encoder_out,
decoder_out,
encoder_out_len,
decoder_out_len,
)
# TODO(fangjun): Scale the blank posterior
log_prob = logits.log_softmax(dim=-1)
# log_prob is (1, 1, 1, vocab_size)
log_prob = log_prob.squeeze()
# Now log_prob is (vocab_size,)
joint_cache[key] = log_prob
return log_prob
def beam_search(
model: Transducer,
encoder_out: torch.Tensor,
@ -288,36 +378,21 @@ def beam_search(
y_star = A.get_most_probable()
A.remove(y_star)
cached_key = y_star.key
if cached_key not in decoder_cache:
decoder_input = torch.tensor(
[y_star.ys[-context_size:]], device=device
).reshape(1, context_size)
decoder_out = model.decoder(decoder_input, need_pad=False)
decoder_cache[cached_key] = decoder_out
else:
decoder_out = decoder_cache[cached_key]
cached_key += f"-t-{t}"
if cached_key not in joint_cache:
logits = model.joiner(
current_encoder_out,
decoder_out,
encoder_out_len,
decoder_out_len,
decoder_out = run_decoder(
ys=y_star.ys, model=model, decoder_cache=decoder_cache
)
# TODO(fangjun): Ccale the blank posterior
log_prob = logits.log_softmax(dim=-1)
# log_prob is (1, 1, 1, vocab_size)
log_prob = log_prob.squeeze()
# Now log_prob is (vocab_size,)
joint_cache[cached_key] = log_prob
else:
log_prob = joint_cache[cached_key]
key = "_".join(map(str, y_star.ys[-context_size:]))
key += f"-t-{t}"
log_prob = run_joiner(
key=key,
model=model,
encoder_out=current_encoder_out,
decoder_out=decoder_out,
encoder_out_len=encoder_out_len,
decoder_out_len=decoder_out_len,
joint_cache=joint_cache,
)
# First, process the blank symbol
skip_log_prob = log_prob[blank_id]