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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils.rnn import pad_sequence
from typing import Union, List, Tuple
from src.models.model import Model
################################################################################
# Extends Model class for speech recognition, with optional decoding
################################################################################
class Decoder(object):
"""
Code adapted from DeepSpeech PyTorch (https://tinyurl.com/2p89d35e). Base
class for decoder objects, which convert emitted frame-by-frame token
probabilities into a string transcription.
"""
def __init__(self,
labels: Union[List[str], Tuple[str]],
sep_idx: int = None,
blank_idx: int = 0):
"""
Parameters
----------
labels (list): character corresponding to each token index
sep_idx (int): index corresponding to space / separating character
blank_idx (int): index corresponding to blank '_' character
"""
self.labels = labels
self.blank_idx = blank_idx
self.int_to_char = dict([(i, c) for (i, c) in enumerate(labels)])
if sep_idx is None:
# use out-of-bounds index for separating character
sep_idx = len(labels)
if ' ' in labels:
sep_idx = labels.index(' ')
elif '|' in labels:
sep_idx = labels.index('|')
self.sep_idx = sep_idx
else:
self.sep_idx = sep_idx
def get_labels(self):
return self.labels
def get_sep_idx(self):
return self.sep_idx
def get_blank_idx(self):
return self.blank_idx
def __call__(self, emission: torch.Tensor, sizes=None):
return self.decode(emission, sizes)
def decode(self, emission: torch.Tensor, sizes=None):
"""
Decode emitted token probabilities to obtain a string transcription.
Parameters
----------
emission (Tensor): shape (n_batch, n_frames, n_tokens)
sizes (Tensor): length in frames of each emission in batch
"""
raise NotImplementedError
class GreedyCTCDecoder(Decoder):
"""
A simple decoder module to map token probability sequences to transcripts.
Decodes 'greedily' by selecting maximum-probability token at each time step.
Code adapted from DeepSpeech PyTorch (https://tinyurl.com/2p89d35e).
"""
def __init__(self,
labels: Union[List[str], Tuple[str]],
sep_idx: int = None,
blank_idx: int = 0):
super().__init__(labels, sep_idx, blank_idx)
def convert_to_strings(self,
sequences,
sizes=None,
remove_repetitions=False,
return_offsets=False):
"""
Given a list of sequences holding token numbers, return the
corresponding strings. Optionally, collapse repeated token subsequences
and return final length of each processed sequence.
Parameters
----------
sequences (Tensor): shape (n_batch, n_frames); holds argmax token index
for each frame
sizes
remove_repetitions
return_offsets
Returns
-------
"""
strings = []
offsets = [] if return_offsets else None
for i, sequence in enumerate(sequences):
seq_len = sizes[i] if sizes is not None else len(sequence)
string, string_offsets = self.process_string(sequence, seq_len, remove_repetitions)
strings.append(string)
if return_offsets:
offsets.append(string_offsets)
if return_offsets:
return strings, offsets
else:
return strings
def process_string(self,
sequence,
size,
remove_repetitions=False):
string = ''
offsets = []
for i in range(size):
char = self.int_to_char[sequence[i].item()]
if char != self.int_to_char[self.blank_idx]:
# skip repeated characters if specified
if remove_repetitions and i != 0 and \
char == self.int_to_char[sequence[i - 1].item()]:
pass
elif char == self.labels[self.sep_idx]:
string += self.labels[self.sep_idx]
offsets.append(i)
else:
string = string + char
offsets.append(i)
return string, torch.tensor(offsets, dtype=torch.int)
def decode(self, emission, sizes=None):
"""
Returns the argmax decoding given the emitted token probabilities.
According to connectionist temporal classification (CTC), removes
repeated elements in the decoded token sequence, as well as blanks.
Parameters
----------
emission (Tensor): shape (n_batch, n_frames, n_tokens)
sizes (Tensor): length in frames of each emission in batch
Returns
-------
transcription (list[str]): string transcription for each item in batch
offsets (??? frame index per character predicted
"""
if emission.ndim == 2: # require shape (n_batch, n_frames, n_tokens)
emission = emission.unsqueeze(0)
# compute max-probability label at each sequence index
max_probs = torch.argmax(emission, dim=-1) # (n_batch, sequence_len)
strings, offsets = self.convert_to_strings(max_probs,
sizes,
remove_repetitions=True,
return_offsets=True)
return strings, offsets
class SpeechRecognitionModel(Model):
def __init__(self,
model: nn.Module,
decoder: Decoder = None
):
super().__init__()
self.model = model
self.model.eval()
# ensure that list of viable tokens can be retrieved from wrapped model
labels_method = getattr(self.model, "get_labels", None)
labels_attr = getattr(self.model, "labels", None)
if callable(labels_method):
self._get_labels_fn = lambda: self.model.get_labels()
elif labels_attr is not None:
self._get_labels_fn = lambda: self.model.labels
else:
raise ValueError(f'Wrapped model must have method `.get_labels()`'
f' or attribute `.labels`')
# ensure that blank and separator tokens can be retrieved from wrapped
# model
sep_method = getattr(self.model, "get_sep_idx", None)
sep_attr = getattr(self.model, "sep_idx", None)
if callable(sep_method):
self._get_sep_fn = lambda: self.model.get_sep_idx()
elif sep_attr is not None:
self._get_sep_fn = lambda: self.model.sep_idx
else:
raise ValueError(f'Wrapped model must have method `.get_sep_idx()`'
f' or attribute `.sep_idx`')
blank_method = getattr(self.model, "get_blank_idx", None)
blank_attr = getattr(self.model, "blank_idx", None)
if callable(blank_method):
self._get_blank_fn = lambda: self.model.get_blank_idx()
elif blank_attr is not None:
self._get_blank_fn = lambda: self.model.blank_idx
else:
raise ValueError(f'Wrapped model must have method '
f'`.get_blank_idx()` or attribute `.blank_idx`')
# initialize decoder
if decoder is None:
decoder = GreedyCTCDecoder(
labels=self.get_labels(),
blank_idx=self.get_blank_idx(),
sep_idx=self.get_sep_idx()
)
self.decoder = decoder
# translate characters to token indices
self.char_to_idx = {l: i for i, l in enumerate(decoder.labels)}
def get_labels(self):
"""Retrieve a list of valid tokens"""
return self._get_labels_fn()
def get_blank_idx(self):
"""Return index of blank token"""
return self._get_blank_fn()
def get_sep_idx(self):
"""Return index of separator token"""
return self._get_sep_fn()
def forward(self, x: torch.Tensor):
return self.model.forward(x)
def transcribe(self, x: torch.Tensor, return_alignment: bool = False):
if return_alignment:
return self.decoder(self.model(x))
else:
return self.decoder(self.model(x))[0]
def load_weights(self, path: str):
"""
Load weights from checkpoint file
"""
# check if file exists
if not path or not os.path.isfile(path):
return
model_state = self.model.state_dict()
loaded_state = torch.load(path)
for name, param in loaded_state.items():
origname = name
if name not in model_state:
print("{} is not in the model.".format(origname))
continue
if model_state[name].size() != loaded_state[origname].size():
print(
"Wrong parameter length: {}, model: {}, loaded: {}".format(
origname,
model_state[name].size(),
loaded_state[origname].size()
)
)
continue
model_state[name].copy_(param)
def extract_features(
self,
x: torch.Tensor
) -> List[torch.Tensor]:
"""
Extract deep features.
:param x: input
:return: a list of tensors holding intermediate activations / features
"""
try:
return self.model.extract_features(x)
except AttributeError:
return []
def _str_to_tensor(self, seq: str):
token_indices = [self.char_to_idx[c] for c in seq]
return torch.as_tensor(token_indices, dtype=torch.long)
def match_predict(self,
y_pred: Union[List[str], torch.Tensor],
y_true: Union[List[str], torch.Tensor]):
"""
Determine whether (batched) target pairs are equivalent.
"""
n_batch = len(y_pred)
y_true_lengths = None
# convert ground-truth transcriptions to tensor form
if isinstance(y_true, list):
y_true = [self._str_to_tensor(t) for t in y_true]
y_true_lengths = [t.shape[-1] for t in y_true]
y_true = pad_sequence(
y_true,
batch_first=True
) # (n_batch, max_seq_len)
if y_true_lengths is None:
y_true_lengths = [y_true.shape[-1]] * n_batch
# convert predicted transcriptions to tensor form
if isinstance(y_pred, list):
y_pred = [self._str_to_tensor(t) for t in y_pred]
y_pred = pad_sequence(
y_pred,
batch_first=True
) # (n_batch, max_seq_len)
length_diff = max(0, y_true.shape[-1] - y_pred.shape[-1])
if length_diff:
y_pred = F.pad(y_pred, (0, length_diff))
matches = []
for i in range(n_batch):
matches.append(
torch.all(
y_pred[i, ..., :y_true_lengths[i]] == y_true[i, ..., :y_true_lengths[i]]
)
)
return torch.as_tensor(matches)
"""
# masked comparison
use which one as dimension to select --- true or pred?
pred lengths may be unnecessary! just select to true length
"""
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