"""Decoding methods for seq2seq autoregressive model.
Authors
* Ju-Chieh Chou 2020
* Peter Plantinga 2020
* Mirco Ravanelli 2020
* Sung-Lin Yeh 2020
"""
import torch
import speechbrain as sb
from speechbrain.decoders.ctc import CTCPrefixScorer
[docs]class S2SBaseSearcher(torch.nn.Module):
"""S2SBaseSearcher class to be inherited by other
decoding approaches for seq2seq model.
Arguments
---------
bos_index : int
The index of the beginning-of-sequence (bos) token.
eos_index : int
The index of end-of-sequence token.
min_decode_radio : float
The ratio of minimum decoding steps to the length of encoder states.
max_decode_radio : float
The ratio of maximum decoding steps to the length of encoder states.
Returns
-------
predictions
Outputs as Python list of lists, with "ragged" dimensions; padding
has been removed.
scores
The sum of log probabilities (and possibly
additional heuristic scores) for each prediction.
"""
def __init__(
self, bos_index, eos_index, min_decode_ratio, max_decode_ratio,
):
super(S2SBaseSearcher, self).__init__()
self.bos_index = bos_index
self.eos_index = eos_index
self.min_decode_ratio = min_decode_ratio
self.max_decode_ratio = max_decode_ratio
[docs] def forward(self, enc_states, wav_len):
"""This method should implement the forward algorithm of decoding method.
Arguments
---------
enc_states : torch.Tensor
The precomputed encoder states to be used when decoding.
(ex. the encoded speech representation to be attended).
wav_len : torch.Tensor
The speechbrain-style relative length.
"""
raise NotImplementedError
[docs] def forward_step(self, inp_tokens, memory, enc_states, enc_lens):
"""This method should implement one step of
forwarding operation in the autoregressive model.
Arguments
---------
inp_tokens : torch.Tensor
The input tensor of the current timestep.
memory : No limit
The memory variables input for this timestep.
(ex. RNN hidden states).
enc_states : torch.Tensor
The encoder states to be attended.
enc_lens : torch.Tensor
The actual length of each enc_states sequence.
Returns
-------
log_probs : torch.Tensor
Log-probabilities of the current timestep output.
memory : No limit
The memory variables generated in this timestep.
(ex. RNN hidden states).
attn : torch.Tensor
The attention weight for doing penalty.
"""
raise NotImplementedError
[docs] def reset_mem(self, batch_size, device):
"""This method should implement the resetting of
memory variables for the seq2seq model.
E.g., initializing zero vector as initial hidden states.
Arguments
---------
batch_size : int
The size of the batch.
device : torch.device
The device to put the initial variables.
Return
------
memory : No limit
The initial memory variable.
"""
raise NotImplementedError
[docs] def lm_forward_step(self, inp_tokens, memory):
"""This method should implement one step of
forwarding operation for language model.
Arguments
---------
inp_tokens : torch.Tensor
The input tensor of the current timestep.
memory : No limit
The momory variables input for this timestep.
(e.g., RNN hidden states).
Return
------
log_probs : torch.Tensor
Log-probabilities of the current timestep output.
memory : No limit
The memory variables generated in this timestep.
(e.g., RNN hidden states).
"""
raise NotImplementedError
[docs] def reset_lm_mem(self, batch_size, device):
"""This method should implement the resetting of
memory variables in the language model.
E.g., initializing zero vector as initial hidden states.
Arguments
---------
batch_size : int
The size of the batch.
device : torch.device
The device to put the initial variables.
Return
------
memory : No limit
The initial memory variable.
"""
raise NotImplementedError
[docs]class S2SGreedySearcher(S2SBaseSearcher):
"""This class implements the general forward-pass of
greedy decoding approach. See also S2SBaseSearcher().
"""
[docs] def forward(self, enc_states, wav_len):
"""This method performs a greedy search.
Arguments
---------
enc_states : torch.Tensor
The precomputed encoder states to be used when decoding.
(ex. the encoded speech representation to be attended).
wav_len : torch.Tensor
The speechbrain-style relative length.
"""
enc_lens = torch.round(enc_states.shape[1] * wav_len).int()
device = enc_states.device
batch_size = enc_states.shape[0]
memory = self.reset_mem(batch_size, device=device)
# Using bos as the first input
inp_tokens = (
enc_states.new_zeros(batch_size).fill_(self.bos_index).long()
)
log_probs_lst = []
max_decode_steps = int(enc_states.shape[1] * self.max_decode_ratio)
for t in range(max_decode_steps):
log_probs, memory, _ = self.forward_step(
inp_tokens, memory, enc_states, enc_lens
)
log_probs_lst.append(log_probs)
inp_tokens = log_probs.argmax(dim=-1)
log_probs = torch.stack(log_probs_lst, dim=1)
scores, predictions = log_probs.max(dim=-1)
scores = scores.sum(dim=1).tolist()
predictions = batch_filter_seq2seq_output(
predictions, eos_id=self.eos_index
)
return predictions, scores
[docs]class S2SRNNGreedySearcher(S2SGreedySearcher):
"""
This class implements the greedy decoding
for AttentionalRNNDecoder (speechbrain/nnet/RNN.py).
See also S2SBaseSearcher() and S2SGreedySearcher().
Arguments
---------
embedding : torch.nn.Module
An embedding layer.
decoder : torch.nn.Module
Attentional RNN decoder.
linear : torch.nn.Module
A linear output layer.
**kwargs
see S2SBaseSearcher, arguments are directly passed.
Example
-------
>>> emb = torch.nn.Embedding(5, 3)
>>> dec = sb.nnet.RNN.AttentionalRNNDecoder(
... "gru", "content", 3, 3, 1, enc_dim=7, input_size=3
... )
>>> lin = sb.nnet.linear.Linear(n_neurons=5, input_size=3)
>>> searcher = S2SRNNGreedySearcher(
... embedding=emb,
... decoder=dec,
... linear=lin,
... bos_index=4,
... eos_index=4,
... min_decode_ratio=0,
... max_decode_ratio=1,
... )
>>> enc = torch.rand([2, 6, 7])
>>> wav_len = torch.rand([2])
>>> hyps, scores = searcher(enc, wav_len)
"""
def __init__(self, embedding, decoder, linear, **kwargs):
super(S2SRNNGreedySearcher, self).__init__(**kwargs)
self.emb = embedding
self.dec = decoder
self.fc = linear
self.softmax = torch.nn.LogSoftmax(dim=-1)
[docs] def reset_mem(self, batch_size, device):
"""When doing greedy search, keep hidden state (hs) adn context vector (c)
as memory.
"""
hs = None
self.dec.attn.reset()
c = torch.zeros(batch_size, self.dec.attn_dim, device=device)
return hs, c
[docs] def forward_step(self, inp_tokens, memory, enc_states, enc_lens):
"""Performs a step in the implemented beamsearcher."""
hs, c = memory
e = self.emb(inp_tokens)
dec_out, hs, c, w = self.dec.forward_step(
e, hs, c, enc_states, enc_lens
)
log_probs = self.softmax(self.fc(dec_out))
return log_probs, (hs, c), w
[docs]class S2SBeamSearcher(S2SBaseSearcher):
"""This class implements the beam-search algorithm for the seq2seq model.
See also S2SBaseSearcher().
Arguments
---------
bos_index : int
The index of beginning-of-sequence token.
eos_index : int
The index of end-of-sequence token.
min_decode_radio : float
The ratio of minimum decoding steps to length of encoder states.
max_decode_radio : float
The ratio of maximum decoding steps to length of encoder states.
beam_size : int
The width of beam.
topk : int
The number of hypothesis to return. (default: 1)
return_log_probs : bool
Whether to return log-probabilities. (default: False)
using_eos_threshold : bool
Whether to use eos threshold. (default: true)
eos_threshold : float
The threshold coefficient for eos token (default: 1.5). See 3.1.2 in
reference: https://arxiv.org/abs/1904.02619
length_normalization : bool
Whether to divide the scores by the length. (default: True)
length_rewarding : float
The coefficient of length rewarding (γ).
log P(y|x) + λ log P_LM(y) + γ*len(y). (default: 0.0)
coverage_penalty: float
The coefficient of coverage penalty (η).
log P(y|x) + λ log P_LM(y) + γ*len(y) + η*coverage(x,y). (default: 0.0)
Reference: https://arxiv.org/pdf/1612.02695.pdf, https://arxiv.org/pdf/1808.10792.pdf
lm_weight : float
The weight of LM when performing beam search (λ).
log P(y|x) + λ log P_LM(y). (default: 0.0)
ctc_weight : float
The weight of CTC probabilities when performing beam search (λ).
(1-λ) log P(y|x) + λ log P_CTC(y|x). (default: 0.0)
blank_index : int
The index of the blank token.
ctc_score_mode: str
Default: "full"
CTC prefix scoring on "partial" token or "full: token.
ctc_window_size: int
Default: 0
Compute the ctc scores over the time frames using windowing based on attention peaks.
If 0, no windowing applied.
using_max_attn_shift: bool
Whether using the max_attn_shift constraint. (default: False)
max_attn_shift: int
Beam search will block the beams that attention shift more
than max_attn_shift.
Reference: https://arxiv.org/abs/1904.02619
minus_inf : float
DefaultL -1e20
The value of minus infinity to block some path
of the search.
"""
def __init__(
self,
bos_index,
eos_index,
min_decode_ratio,
max_decode_ratio,
beam_size,
topk=1,
return_log_probs=False,
using_eos_threshold=True,
eos_threshold=1.5,
length_normalization=True,
length_rewarding=0,
coverage_penalty=0.0,
lm_weight=0.0,
lm_modules=None,
ctc_weight=0.0,
blank_index=0,
ctc_score_mode="full",
ctc_window_size=0,
using_max_attn_shift=False,
max_attn_shift=60,
minus_inf=-1e20,
):
super(S2SBeamSearcher, self).__init__(
bos_index, eos_index, min_decode_ratio, max_decode_ratio,
)
self.beam_size = beam_size
self.topk = topk
self.return_log_probs = return_log_probs
self.length_normalization = length_normalization
self.length_rewarding = length_rewarding
self.coverage_penalty = coverage_penalty
self.coverage = None
if self.length_normalization and self.length_rewarding > 0:
raise ValueError(
"length normalization is not compatible with length rewarding."
)
self.using_eos_threshold = using_eos_threshold
self.eos_threshold = eos_threshold
self.using_max_attn_shift = using_max_attn_shift
self.max_attn_shift = max_attn_shift
self.lm_weight = lm_weight
self.lm_modules = lm_modules
# ctc related
self.ctc_weight = ctc_weight
self.blank_index = blank_index
self.att_weight = 1.0 - ctc_weight
assert (
0.0 <= self.ctc_weight <= 1.0
), "ctc_weight should not > 1.0 and < 0.0"
if self.ctc_weight > 0.0:
if len({self.bos_index, self.eos_index, self.blank_index}) < 3:
raise ValueError(
"To perform joint ATT/CTC decoding, set blank, eos and bos to different indexes."
)
# ctc already initialized
self.minus_inf = minus_inf
self.ctc_score_mode = ctc_score_mode
self.ctc_window_size = ctc_window_size
def _check_full_beams(self, hyps, beam_size):
"""This method checks whether hyps has been full.
Arguments
---------
hyps : List
This list contains batch_size number.
Each inside list contains a list stores all the hypothesis for this sentence.
beam_size : int
The number of beam_size.
Returns
-------
bool
Whether the hyps has been full.
"""
hyps_len = [len(lst) for lst in hyps]
beam_size = [self.beam_size for _ in range(len(hyps_len))]
if hyps_len == beam_size:
return True
else:
return False
def _check_attn_shift(self, attn, prev_attn_peak):
"""This method checks whether attention shift is more than attn_shift.
Arguments
---------
attn : torch.Tensor
The attention to be checked.
prev_attn_peak : torch.Tensor
The previous attention peak place.
Returns
-------
cond : torch.BoolTensor
Each element represents whether the beam is within the max_shift range.
attn_peak : torch.Tensor
The peak of the attn tensor.
"""
# Block the candidates that exceed the max shift
_, attn_peak = torch.max(attn, dim=1)
lt_cond = attn_peak <= (prev_attn_peak + self.max_attn_shift)
mt_cond = attn_peak > (prev_attn_peak - self.max_attn_shift)
# True if not exceed limit
# Multiplication equals to element-wise and for tensor
cond = (lt_cond * mt_cond).unsqueeze(1)
return cond, attn_peak
def _check_eos_threshold(self, log_probs):
"""
This method checks whether eos log-probabilities exceed threshold.
Arguments
---------
log_probs : torch.Tensor
The log-probabilities.
Return
------
cond : torch.BoolTensor
Each element represents whether the eos log-probabilities will be kept.
"""
max_probs, _ = torch.max(log_probs, dim=-1)
eos_probs = log_probs[:, self.eos_index]
cond = eos_probs > (self.eos_threshold * max_probs)
return cond
def _update_hyp_and_scores(
self,
inp_tokens,
alived_seq,
alived_log_probs,
hyps_and_scores,
scores,
timesteps,
):
"""This method will update hyps and scores if inp_tokens are eos.
Arguments
---------
inp_tokens : torch.Tensor
The current output.
alived_seq : torch.Tensor
The tensor to store the alived_seq.
alived_log_probs : torch.Tensor
The tensor to store the alived_log_probs.
hyps_and_scores : list
To store generated hypotheses and scores.
scores : torch.Tensor
The final scores of beam search.
timesteps : float
The current timesteps. This is for length rewarding.
Returns
-------
is_eos : torch.BoolTensor
Each element represents whether the token is eos.
"""
is_eos = inp_tokens.eq(self.eos_index)
(eos_indices,) = torch.nonzero(is_eos, as_tuple=True)
# Store the hypothesis and their scores when reaching eos.
if eos_indices.shape[0] > 0:
for index in eos_indices:
# convert to int
index = index.item()
batch_id = torch.div(
index, self.beam_size, rounding_mode="floor"
)
if len(hyps_and_scores[batch_id]) == self.beam_size:
continue
hyp = alived_seq[index, :]
log_probs = alived_log_probs[index, :]
final_scores = scores[index] + self.length_rewarding * (
timesteps + 1
)
hyps_and_scores[batch_id].append((hyp, log_probs, final_scores))
return is_eos
def _get_top_score_prediction(self, hyps_and_scores, topk):
"""This method sorts the scores and return corresponding hypothesis and log probs.
Arguments
---------
hyps_and_scores : list
To store generated hypotheses and scores.
topk : int
Number of hypothesis to return.
Returns
-------
topk_hyps : torch.Tensor (batch, topk, max length of token_id sequences)
This tensor stores the topk predicted hypothesis.
topk_scores : torch.Tensor (batch, topk)
The length of each topk sequence in the batch.
topk_lengths : torch.Tensor (batch, topk)
This tensor contains the final scores of topk hypotheses.
topk_log_probs : list
The log probabilities of each hypotheses.
"""
top_hyps, top_log_probs, top_scores, top_lengths = [], [], [], []
batch_size = len(hyps_and_scores)
# Collect hypotheses
for i in range(len(hyps_and_scores)):
hyps, log_probs, scores = zip(*hyps_and_scores[i])
top_hyps += hyps
top_scores += scores
top_log_probs += log_probs
top_lengths += [len(hyp) for hyp in hyps]
top_hyps = torch.nn.utils.rnn.pad_sequence(
top_hyps, batch_first=True, padding_value=0
)
top_scores = torch.stack((top_scores), dim=0).view(batch_size, -1)
top_lengths = torch.tensor(
top_lengths, dtype=torch.int, device=top_scores.device
)
# Get topk indices
topk_scores, indices = top_scores.topk(self.topk, dim=-1)
indices = (indices + self.beam_offset.unsqueeze(1)).view(
batch_size * self.topk
)
# Select topk hypotheses
topk_hyps = torch.index_select(top_hyps, dim=0, index=indices,)
topk_hyps = topk_hyps.view(batch_size, self.topk, -1)
topk_lengths = torch.index_select(top_lengths, dim=0, index=indices,)
topk_lengths = topk_lengths.view(batch_size, self.topk)
topk_log_probs = [top_log_probs[index.item()] for index in indices]
return topk_hyps, topk_scores, topk_lengths, topk_log_probs
[docs] def forward(self, enc_states, wav_len): # noqa: C901
"""Applies beamsearch and returns the predicted tokens."""
enc_lens = torch.round(enc_states.shape[1] * wav_len).int()
device = enc_states.device
batch_size = enc_states.shape[0]
memory = self.reset_mem(batch_size * self.beam_size, device=device)
if self.lm_weight > 0:
lm_memory = self.reset_lm_mem(batch_size * self.beam_size, device)
if self.ctc_weight > 0:
# (batch_size * beam_size, L, vocab_size)
ctc_outputs = self.ctc_forward_step(enc_states)
ctc_scorer = CTCPrefixScorer(
ctc_outputs,
enc_lens,
batch_size,
self.beam_size,
self.blank_index,
self.eos_index,
self.ctc_window_size,
)
ctc_memory = None
# Inflate the enc_states and enc_len by beam_size times
enc_states = inflate_tensor(enc_states, times=self.beam_size, dim=0)
enc_lens = inflate_tensor(enc_lens, times=self.beam_size, dim=0)
# Using bos as the first input
inp_tokens = (
torch.zeros(batch_size * self.beam_size, device=device)
.fill_(self.bos_index)
.long()
)
# The first index of each sentence.
self.beam_offset = (
torch.arange(batch_size, device=device) * self.beam_size
)
# initialize sequence scores variables.
sequence_scores = torch.empty(
batch_size * self.beam_size, device=device
)
sequence_scores.fill_(float("-inf"))
# keep only the first to make sure no redundancy.
sequence_scores.index_fill_(0, self.beam_offset, 0.0)
# keep the hypothesis that reaches eos and their corresponding score and log_probs.
hyps_and_scores = [[] for _ in range(batch_size)]
# keep the sequences that still not reaches eos.
alived_seq = torch.empty(
batch_size * self.beam_size, 0, device=device
).long()
# Keep the log-probabilities of alived sequences.
alived_log_probs = torch.empty(
batch_size * self.beam_size, 0, device=device
)
min_decode_steps = int(enc_states.shape[1] * self.min_decode_ratio)
max_decode_steps = int(enc_states.shape[1] * self.max_decode_ratio)
# Initialize the previous attention peak to zero
# This variable will be used when using_max_attn_shift=True
prev_attn_peak = torch.zeros(batch_size * self.beam_size, device=device)
for t in range(max_decode_steps):
# terminate condition
if self._check_full_beams(hyps_and_scores, self.beam_size):
break
log_probs, memory, attn = self.forward_step(
inp_tokens, memory, enc_states, enc_lens
)
log_probs = self.att_weight * log_probs
# Keep the original value
log_probs_clone = log_probs.clone().reshape(batch_size, -1)
vocab_size = log_probs.shape[-1]
if self.using_max_attn_shift:
# Block the candidates that exceed the max shift
cond, attn_peak = self._check_attn_shift(attn, prev_attn_peak)
log_probs = mask_by_condition(
log_probs, cond, fill_value=self.minus_inf
)
prev_attn_peak = attn_peak
# Set eos to minus_inf when less than minimum steps.
if t < min_decode_steps:
log_probs[:, self.eos_index] = self.minus_inf
# Set the eos prob to minus_inf when it doesn't exceed threshold.
if self.using_eos_threshold:
cond = self._check_eos_threshold(log_probs)
log_probs[:, self.eos_index] = mask_by_condition(
log_probs[:, self.eos_index],
cond,
fill_value=self.minus_inf,
)
# adding LM scores to log_prob if lm_weight > 0
if self.lm_weight > 0:
lm_log_probs, lm_memory = self.lm_forward_step(
inp_tokens, lm_memory
)
log_probs = log_probs + self.lm_weight * lm_log_probs
# adding CTC scores to log_prob if ctc_weight > 0
if self.ctc_weight > 0:
g = alived_seq
# block blank token
log_probs[:, self.blank_index] = self.minus_inf
if self.ctc_weight != 1.0 and self.ctc_score_mode == "partial":
# pruning vocab for ctc_scorer
_, ctc_candidates = log_probs.topk(
self.beam_size * 2, dim=-1
)
else:
ctc_candidates = None
ctc_log_probs, ctc_memory = ctc_scorer.forward_step(
g, ctc_memory, ctc_candidates, attn
)
log_probs = log_probs + self.ctc_weight * ctc_log_probs
scores = sequence_scores.unsqueeze(1).expand(-1, vocab_size)
scores = scores + log_probs
# length normalization
if self.length_normalization:
scores = scores / (t + 1)
# keep topk beams
scores, candidates = scores.view(batch_size, -1).topk(
self.beam_size, dim=-1
)
# The input for the next step, also the output of current step.
inp_tokens = (candidates % vocab_size).view(
batch_size * self.beam_size
)
scores = scores.view(batch_size * self.beam_size)
sequence_scores = scores
# recover the length normalization
if self.length_normalization:
sequence_scores = sequence_scores * (t + 1)
# The index of which beam the current top-K output came from in (t-1) timesteps.
predecessors = (
torch.div(candidates, vocab_size, rounding_mode="floor")
+ self.beam_offset.unsqueeze(1).expand_as(candidates)
).view(batch_size * self.beam_size)
# Permute the memory to synchoronize with the output.
memory = self.permute_mem(memory, index=predecessors)
if self.lm_weight > 0:
lm_memory = self.permute_lm_mem(lm_memory, index=predecessors)
if self.ctc_weight > 0:
ctc_memory = ctc_scorer.permute_mem(ctc_memory, candidates)
# If using_max_attn_shift, then the previous attn peak has to be permuted too.
if self.using_max_attn_shift:
prev_attn_peak = torch.index_select(
prev_attn_peak, dim=0, index=predecessors
)
# Add coverage penalty
if self.coverage_penalty > 0:
cur_attn = torch.index_select(attn, dim=0, index=predecessors)
# coverage: cumulative attention probability vector
if t == 0:
# Init coverage
self.coverage = cur_attn
# the attn of transformer is [batch_size*beam_size, current_step, source_len]
if len(cur_attn.size()) > 2:
self.converage = torch.sum(cur_attn, dim=1)
else:
# Update coverage
self.coverage = torch.index_select(
self.coverage, dim=0, index=predecessors
)
self.coverage = self.coverage + cur_attn
# Compute coverage penalty and add it to scores
penalty = torch.max(
self.coverage, self.coverage.clone().fill_(0.5)
).sum(-1)
penalty = penalty - self.coverage.size(-1) * 0.5
penalty = penalty.view(batch_size * self.beam_size)
penalty = (
penalty / (t + 1) if self.length_normalization else penalty
)
scores = scores - penalty * self.coverage_penalty
# Update alived_seq
alived_seq = torch.cat(
[
torch.index_select(alived_seq, dim=0, index=predecessors),
inp_tokens.unsqueeze(1),
],
dim=-1,
)
# Takes the log-probabilities
beam_log_probs = log_probs_clone[
torch.arange(batch_size).unsqueeze(1), candidates
].reshape(batch_size * self.beam_size)
alived_log_probs = torch.cat(
[
torch.index_select(
alived_log_probs, dim=0, index=predecessors
),
beam_log_probs.unsqueeze(1),
],
dim=-1,
)
is_eos = self._update_hyp_and_scores(
inp_tokens,
alived_seq,
alived_log_probs,
hyps_and_scores,
scores,
timesteps=t,
)
# Block the paths that have reached eos.
sequence_scores.masked_fill_(is_eos, float("-inf"))
if not self._check_full_beams(hyps_and_scores, self.beam_size):
# Using all eos to fill-up the hyps.
eos = (
torch.zeros(batch_size * self.beam_size, device=device)
.fill_(self.eos_index)
.long()
)
_ = self._update_hyp_and_scores(
eos,
alived_seq,
alived_log_probs,
hyps_and_scores,
scores,
timesteps=max_decode_steps,
)
(
topk_hyps,
topk_scores,
topk_lengths,
log_probs,
) = self._get_top_score_prediction(hyps_and_scores, topk=self.topk,)
# pick the best hyp
predictions = topk_hyps[:, 0, :]
predictions = batch_filter_seq2seq_output(
predictions, eos_id=self.eos_index
)
if self.return_log_probs:
return predictions, topk_scores, log_probs
else:
return predictions, topk_scores
[docs] def ctc_forward_step(self, x):
"""Applies a ctc step during bramsearch."""
logits = self.ctc_fc(x)
log_probs = self.softmax(logits)
return log_probs
[docs] def permute_mem(self, memory, index):
"""This method permutes the seq2seq model memory
to synchronize the memory index with the current output.
Arguments
---------
memory : No limit
The memory variable to be permuted.
index : torch.Tensor
The index of the previous path.
Return
------
The variable of the memory being permuted.
"""
raise NotImplementedError
[docs] def permute_lm_mem(self, memory, index):
"""This method permutes the language model memory
to synchronize the memory index with the current output.
Arguments
---------
memory : No limit
The memory variable to be permuted.
index : torch.Tensor
The index of the previous path.
Returns
-------
The variable of the memory being permuted.
"""
raise NotImplementedError
[docs]class S2SRNNBeamSearcher(S2SBeamSearcher):
"""
This class implements the beam search decoding
for AttentionalRNNDecoder (speechbrain/nnet/RNN.py).
See also S2SBaseSearcher(), S2SBeamSearcher().
Arguments
---------
embedding : torch.nn.Module
An embedding layer.
decoder : torch.nn.Module
Attentional RNN decoder.
linear : torch.nn.Module
A linear output layer.
temperature : float
Temperature factor applied to softmax. It changes the probability
distribution, being softer when T>1 and sharper with T<1.
**kwargs
see S2SBeamSearcher, arguments are directly passed.
Example
-------
>>> emb = torch.nn.Embedding(5, 3)
>>> dec = sb.nnet.RNN.AttentionalRNNDecoder(
... "gru", "content", 3, 3, 1, enc_dim=7, input_size=3
... )
>>> lin = sb.nnet.linear.Linear(n_neurons=5, input_size=3)
>>> ctc_lin = sb.nnet.linear.Linear(n_neurons=5, input_size=7)
>>> searcher = S2SRNNBeamSearcher(
... embedding=emb,
... decoder=dec,
... linear=lin,
... ctc_linear=ctc_lin,
... bos_index=4,
... eos_index=4,
... blank_index=4,
... min_decode_ratio=0,
... max_decode_ratio=1,
... beam_size=2,
... )
>>> enc = torch.rand([2, 6, 7])
>>> wav_len = torch.rand([2])
>>> hyps, scores = searcher(enc, wav_len)
"""
def __init__(
self,
embedding,
decoder,
linear,
ctc_linear=None,
temperature=1.0,
**kwargs,
):
super(S2SRNNBeamSearcher, self).__init__(**kwargs)
self.emb = embedding
self.dec = decoder
self.fc = linear
self.ctc_fc = ctc_linear
if self.ctc_weight > 0.0 and self.ctc_fc is None:
raise ValueError(
"To perform joint ATT/CTC decoding, ctc_fc is required."
)
self.softmax = torch.nn.LogSoftmax(dim=-1)
self.temperature = temperature
[docs] def reset_mem(self, batch_size, device):
"""Needed to reset the memory during beamsearch."""
hs = None
self.dec.attn.reset()
c = torch.zeros(batch_size, self.dec.attn_dim, device=device)
return hs, c
[docs] def forward_step(self, inp_tokens, memory, enc_states, enc_lens):
"""Performs a step in the implemented beamsearcher."""
with torch.no_grad():
hs, c = memory
e = self.emb(inp_tokens)
dec_out, hs, c, w = self.dec.forward_step(
e, hs, c, enc_states, enc_lens
)
log_probs = self.softmax(self.fc(dec_out) / self.temperature)
# average attn weight of heads when attn_type is multiheadlocation
if self.dec.attn_type == "multiheadlocation":
w = torch.mean(w, dim=1)
return log_probs, (hs, c), w
[docs] def permute_mem(self, memory, index):
"""Memory permutation during beamsearch."""
hs, c = memory
# shape of hs: [num_layers, batch_size, n_neurons]
if isinstance(hs, tuple):
hs_0 = torch.index_select(hs[0], dim=1, index=index)
hs_1 = torch.index_select(hs[1], dim=1, index=index)
hs = (hs_0, hs_1)
else:
hs = torch.index_select(hs, dim=1, index=index)
c = torch.index_select(c, dim=0, index=index)
if self.dec.attn_type == "location":
self.dec.attn.prev_attn = torch.index_select(
self.dec.attn.prev_attn, dim=0, index=index
)
return (hs, c)
[docs]class S2SRNNBeamSearchLM(S2SRNNBeamSearcher):
"""This class implements the beam search decoding
for AttentionalRNNDecoder (speechbrain/nnet/RNN.py) with LM.
See also S2SBaseSearcher(), S2SBeamSearcher(), S2SRNNBeamSearcher().
Arguments
---------
embedding : torch.nn.Module
An embedding layer.
decoder : torch.nn.Module
Attentional RNN decoder.
linear : torch.nn.Module
A linear output layer.
language_model : torch.nn.Module
A language model.
temperature_lm : float
Temperature factor applied to softmax. It changes the probability
distribution, being softer when T>1 and sharper with T<1.
**kwargs
Arguments to pass to S2SBeamSearcher.
Example
-------
>>> from speechbrain.lobes.models.RNNLM import RNNLM
>>> emb = torch.nn.Embedding(5, 3)
>>> dec = sb.nnet.RNN.AttentionalRNNDecoder(
... "gru", "content", 3, 3, 1, enc_dim=7, input_size=3
... )
>>> lin = sb.nnet.linear.Linear(n_neurons=5, input_size=3)
>>> lm = RNNLM(output_neurons=5, return_hidden=True)
>>> searcher = S2SRNNBeamSearchLM(
... embedding=emb,
... decoder=dec,
... linear=lin,
... language_model=lm,
... bos_index=4,
... eos_index=4,
... blank_index=4,
... min_decode_ratio=0,
... max_decode_ratio=1,
... beam_size=2,
... lm_weight=0.5,
... )
>>> enc = torch.rand([2, 6, 7])
>>> wav_len = torch.rand([2])
>>> hyps, scores = searcher(enc, wav_len)
"""
def __init__(
self,
embedding,
decoder,
linear,
language_model,
temperature_lm=1.0,
**kwargs,
):
super(S2SRNNBeamSearchLM, self).__init__(
embedding, decoder, linear, **kwargs
)
self.lm = language_model
self.lm.eval()
self.log_softmax = sb.nnet.activations.Softmax(apply_log=True)
self.temperature_lm = temperature_lm
[docs] def lm_forward_step(self, inp_tokens, memory):
"""Applies a step to the LM during beamsearch."""
with torch.no_grad():
logits, hs = self.lm(inp_tokens, hx=memory)
log_probs = self.log_softmax(logits / self.temperature_lm)
return log_probs, hs
[docs] def permute_lm_mem(self, memory, index):
"""This is to permute lm memory to synchronize with current index
during beam search. The order of beams will be shuffled by scores
every timestep to allow batched beam search.
Further details please refer to speechbrain/decoder/seq2seq.py.
"""
if isinstance(memory, tuple):
memory_0 = torch.index_select(memory[0], dim=1, index=index)
memory_1 = torch.index_select(memory[1], dim=1, index=index)
memory = (memory_0, memory_1)
else:
memory = torch.index_select(memory, dim=1, index=index)
return memory
[docs] def reset_lm_mem(self, batch_size, device):
"""Needed to reset the LM memory during beamsearch."""
# set hidden_state=None, pytorch RNN will automatically set it to
# zero vectors.
return None
[docs]def inflate_tensor(tensor, times, dim):
"""This function inflates the tensor for times along dim.
Arguments
---------
tensor : torch.Tensor
The tensor to be inflated.
times : int
The tensor will inflate for this number of times.
dim : int
The dim to be inflated.
Returns
-------
torch.Tensor
The inflated tensor.
Example
-------
>>> tensor = torch.Tensor([[1,2,3], [4,5,6]])
>>> new_tensor = inflate_tensor(tensor, 2, dim=0)
>>> new_tensor
tensor([[1., 2., 3.],
[1., 2., 3.],
[4., 5., 6.],
[4., 5., 6.]])
"""
return torch.repeat_interleave(tensor, times, dim=dim)
[docs]def mask_by_condition(tensor, cond, fill_value):
"""This function will mask some element in the tensor with fill_value, if condition=False.
Arguments
---------
tensor : torch.Tensor
The tensor to be masked.
cond : torch.BoolTensor
This tensor has to be the same size as tensor.
Each element represents whether to keep the value in tensor.
fill_value : float
The value to fill in the masked element.
Returns
-------
torch.Tensor
The masked tensor.
Example
-------
>>> tensor = torch.Tensor([[1,2,3], [4,5,6]])
>>> cond = torch.BoolTensor([[True, True, False], [True, False, False]])
>>> mask_by_condition(tensor, cond, 0)
tensor([[1., 2., 0.],
[4., 0., 0.]])
"""
tensor = torch.where(
cond, tensor, torch.Tensor([fill_value]).to(tensor.device)
)
return tensor
def _update_mem(inp_tokens, memory):
"""This function is for updating the memory for transformer searches.
it is called at each decoding step. When being called, it appends the
predicted token of the previous step to existing memory.
Arguments:
-----------
inp_tokens : tensor
Predicted token of the previous decoding step.
memory : tensor
Contains all the predicted tokens.
"""
if memory is None:
return inp_tokens.unsqueeze(1)
return torch.cat([memory, inp_tokens.unsqueeze(1)], dim=-1)
[docs]def batch_filter_seq2seq_output(prediction, eos_id=-1):
"""Calling batch_size times of filter_seq2seq_output.
Arguments
---------
prediction : list of torch.Tensor
A list containing the output ints predicted by the seq2seq system.
eos_id : int, string
The id of the eos.
Returns
------
list
The output predicted by seq2seq model.
Example
-------
>>> predictions = [torch.IntTensor([1,2,3,4]), torch.IntTensor([2,3,4,5,6])]
>>> predictions = batch_filter_seq2seq_output(predictions, eos_id=4)
>>> predictions
[[1, 2, 3], [2, 3]]
"""
outputs = []
for p in prediction:
res = filter_seq2seq_output(p.tolist(), eos_id=eos_id)
outputs.append(res)
return outputs
[docs]def filter_seq2seq_output(string_pred, eos_id=-1):
"""Filter the output until the first eos occurs (exclusive).
Arguments
---------
string_pred : list
A list containing the output strings/ints predicted by the seq2seq system.
eos_id : int, string
The id of the eos.
Returns
------
list
The output predicted by seq2seq model.
Example
-------
>>> string_pred = ['a','b','c','d','eos','e']
>>> string_out = filter_seq2seq_output(string_pred, eos_id='eos')
>>> string_out
['a', 'b', 'c', 'd']
"""
if isinstance(string_pred, list):
try:
eos_index = next(
i for i, v in enumerate(string_pred) if v == eos_id
)
except StopIteration:
eos_index = len(string_pred)
string_out = string_pred[:eos_index]
else:
raise ValueError("The input must be a list.")
return string_out