code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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def forward(
self,
prev_output_tokens,
encoder_out: Optional[Dict[str, List[Tensor]]] = None,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
features_only: bool = False,
alignment_layer: Optional[int] = 0,
alignment_heads: Optional[int... |
Args:
prev_output_tokens (LongTensor): previous decoder outputs of shape
`(batch, tgt_len)`, for teacher forcing
encoder_out (optional): output from the encoder, used for
encoder-side attention
incremental_state (dict, optional): dictionary us... | forward | python | OFA-Sys/OFA | fairseq/examples/pointer_generator/pointer_generator_src/transformer_pg.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/pointer_generator/pointer_generator_src/transformer_pg.py | Apache-2.0 |
def output_layer(
self,
features: Tensor,
attn: Tensor,
src_tokens: Tensor,
p_gens: Tensor
) -> Tensor:
"""
Project features to the vocabulary size and mix with the attention
distributions.
"""
if self.force_p_gen is not None:
... |
Project features to the vocabulary size and mix with the attention
distributions.
| output_layer | python | OFA-Sys/OFA | fairseq/examples/pointer_generator/pointer_generator_src/transformer_pg.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/pointer_generator/pointer_generator_src/transformer_pg.py | Apache-2.0 |
def get_normalized_probs(
self,
net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]],
log_probs: bool,
sample: Optional[Dict[str, Tensor]] = None,
):
"""
Get normalized probabilities (or log probs) from a net's output.
Pointer-generator netwo... |
Get normalized probabilities (or log probs) from a net's output.
Pointer-generator network output is already normalized.
| get_normalized_probs | python | OFA-Sys/OFA | fairseq/examples/pointer_generator/pointer_generator_src/transformer_pg.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/pointer_generator/pointer_generator_src/transformer_pg.py | Apache-2.0 |
def main():
"""
Helper script to encode raw text with the GPT-2 BPE using multiple processes.
The encoder.json and vocab.bpe files can be obtained here:
- https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json
- https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe
"""
parser = ar... |
Helper script to encode raw text with the GPT-2 BPE using multiple processes.
The encoder.json and vocab.bpe files can be obtained here:
- https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json
- https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe
| main | python | OFA-Sys/OFA | fairseq/examples/roberta/multiprocessing_bpe_encoder.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/roberta/multiprocessing_bpe_encoder.py | Apache-2.0 |
def encode_lines(self, lines):
"""
Encode a set of lines. All lines will be encoded together.
"""
enc_lines = []
for line in lines:
line = line.strip()
if len(line) == 0 and not self.args.keep_empty:
return ["EMPTY", None]
token... |
Encode a set of lines. All lines will be encoded together.
| encode_lines | python | OFA-Sys/OFA | fairseq/examples/roberta/multiprocessing_bpe_encoder.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/roberta/multiprocessing_bpe_encoder.py | Apache-2.0 |
def get_examples(data_dir, set_type):
"""
Extract paragraph and question-answer list from each json file
"""
examples = []
levels = ["middle", "high"]
set_type_c = set_type.split("-")
if len(set_type_c) == 2:
levels = [set_type_c[1]]
set_type = set_type_c[0]
for level in... |
Extract paragraph and question-answer list from each json file
| get_examples | python | OFA-Sys/OFA | fairseq/examples/roberta/preprocess_RACE.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/roberta/preprocess_RACE.py | Apache-2.0 |
def main():
"""
Helper script to extract paragraphs questions and answers from RACE datasets.
"""
parser = argparse.ArgumentParser()
parser.add_argument(
"--input-dir",
help="input directory for downloaded RACE dataset",
)
parser.add_argument(
"--output-dir",
... |
Helper script to extract paragraphs questions and answers from RACE datasets.
| main | python | OFA-Sys/OFA | fairseq/examples/roberta/preprocess_RACE.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/roberta/preprocess_RACE.py | Apache-2.0 |
def load_dictionary(cls, filename):
"""Load the dictionary from the filename
Args:
filename (str): the filename
"""
dictionary = Dictionary.load(filename)
dictionary.add_symbol("<mask>")
return dictionary | Load the dictionary from the filename
Args:
filename (str): the filename
| load_dictionary | python | OFA-Sys/OFA | fairseq/examples/roberta/commonsense_qa/commonsense_qa_task.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/roberta/commonsense_qa/commonsense_qa_task.py | Apache-2.0 |
def load_dataset(
self, split, epoch=1, combine=False, data_path=None, return_only=False, **kwargs
):
"""Load a given dataset split.
Args:
split (str): name of the split (e.g., train, valid, test)
"""
def binarize(s, append_bos=False):
if self.bpe is... | Load a given dataset split.
Args:
split (str): name of the split (e.g., train, valid, test)
| load_dataset | python | OFA-Sys/OFA | fairseq/examples/roberta/commonsense_qa/commonsense_qa_task.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/roberta/commonsense_qa/commonsense_qa_task.py | Apache-2.0 |
def add_args(parser):
"""Add criterion-specific arguments to the parser."""
parser.add_argument("--wsc-margin-alpha", type=float, metavar="A", default=1.0)
parser.add_argument("--wsc-margin-beta", type=float, metavar="B", default=0.0)
parser.add_argument(
"--wsc-cross-entropy... | Add criterion-specific arguments to the parser. | add_args | python | OFA-Sys/OFA | fairseq/examples/roberta/wsc/wsc_criterion.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/roberta/wsc/wsc_criterion.py | Apache-2.0 |
def clean_cache(
self,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
end_id: Optional[int] = None,
):
"""
Clean cache in the monotonic layers.
The cache is generated because of a forward pass of decoder has run but no prediction,
so that... |
Clean cache in the monotonic layers.
The cache is generated because of a forward pass of decoder has run but no prediction,
so that the self attention key value in decoder is written in the incremental state.
end_id is the last idx of the layers
| clean_cache | python | OFA-Sys/OFA | fairseq/examples/simultaneous_translation/models/transformer_monotonic_attention.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/simultaneous_translation/models/transformer_monotonic_attention.py | Apache-2.0 |
def extract_features(
self,
prev_output_tokens,
encoder_out: Optional[Dict[str, List[Tensor]]],
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
full_context_alignment: bool = False, # unused
alignment_layer: Optional[int] = None, # unused
... |
Similar to *forward* but only return features.
Returns:
tuple:
- the decoder's features of shape `(batch, tgt_len, embed_dim)`
- a dictionary with any model-specific outputs
| extract_features | python | OFA-Sys/OFA | fairseq/examples/simultaneous_translation/models/transformer_monotonic_attention.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/simultaneous_translation/models/transformer_monotonic_attention.py | Apache-2.0 |
def energy_from_qk(
self,
query: Tensor,
key: Tensor,
energy_type: str,
key_padding_mask: Optional[Tensor] = None,
bias: int = 0
):
"""
Compute energy from query and key
q_func_value is a tuple looks like
(q_proj_func, q_tensor)
... |
Compute energy from query and key
q_func_value is a tuple looks like
(q_proj_func, q_tensor)
q_tensor size: bsz, tgt_len, emb_dim
k_tensor size: bsz, src_len, emb_dim
key_padding_mask size: bsz, src_len
attn_mask: bsz, src_len
| energy_from_qk | python | OFA-Sys/OFA | fairseq/examples/simultaneous_translation/modules/monotonic_multihead_attention.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/simultaneous_translation/modules/monotonic_multihead_attention.py | Apache-2.0 |
def monotonic_attention_process_infer(
self,
query: Optional[Tensor],
key: Optional[Tensor],
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
):
"""
Monotonic attention at inference time
Notice that this function is designed for simuleval n... |
Monotonic attention at inference time
Notice that this function is designed for simuleval not sequence_generator
| monotonic_attention_process_infer | python | OFA-Sys/OFA | fairseq/examples/simultaneous_translation/modules/monotonic_multihead_attention.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/simultaneous_translation/modules/monotonic_multihead_attention.py | Apache-2.0 |
def monotonic_attention_process_train(
self,
query: Optional[Tensor],
key: Optional[Tensor],
key_padding_mask: Optional[Tensor] = None,
):
"""
Calculating monotonic attention process for training
Including:
stepwise probability: p_choose
... |
Calculating monotonic attention process for training
Including:
stepwise probability: p_choose
expected hard alignment: alpha
expected soft attention: beta
| monotonic_attention_process_train | python | OFA-Sys/OFA | fairseq/examples/simultaneous_translation/modules/monotonic_multihead_attention.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/simultaneous_translation/modules/monotonic_multihead_attention.py | Apache-2.0 |
def forward(
self,
query: Optional[Tensor],
key: Optional[Tensor],
value: Optional[Tensor],
key_padding_mask: Optional[Tensor] = None,
attn_mask: Optional[Tensor] = None,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
need_weig... |
query: tgt_len, bsz, embed_dim
key: src_len, bsz, embed_dim
value: src_len, bsz, embed_dim
| forward | python | OFA-Sys/OFA | fairseq/examples/simultaneous_translation/modules/monotonic_multihead_attention.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/simultaneous_translation/modules/monotonic_multihead_attention.py | Apache-2.0 |
def forward(
self,
x,
encoder_out: Optional[Tensor] = None,
encoder_padding_mask: Optional[Tensor] = None,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
prev_self_attn_state: Optional[List[Tensor]] = None,
prev_attn_state: Optional[Li... |
Args:
x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`
encoder_padding_mask (ByteTensor, optional): binary
ByteTensor of shape `(batch, src_len)` where padding
elements are indicated by ``1``.
need_attn (bool, optional): r... | forward | python | OFA-Sys/OFA | fairseq/examples/simultaneous_translation/modules/monotonic_transformer_layer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/simultaneous_translation/modules/monotonic_transformer_layer.py | Apache-2.0 |
def exclusive_cumprod(tensor, dim: int, eps: float = 1e-10):
"""
Implementing exclusive cumprod.
There is cumprod in pytorch, however there is no exclusive mode.
cumprod(x) = [x1, x1x2, x2x3x4, ..., prod_{i=1}^n x_i]
exclusive means
cumprod(x) = [1, x1, x1x2, x1x2x3, ..., prod_{i=1}^{n-1} x_i]
... |
Implementing exclusive cumprod.
There is cumprod in pytorch, however there is no exclusive mode.
cumprod(x) = [x1, x1x2, x2x3x4, ..., prod_{i=1}^n x_i]
exclusive means
cumprod(x) = [1, x1, x1x2, x1x2x3, ..., prod_{i=1}^{n-1} x_i]
| exclusive_cumprod | python | OFA-Sys/OFA | fairseq/examples/simultaneous_translation/utils/functions.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/simultaneous_translation/utils/functions.py | Apache-2.0 |
def safe_cumprod(tensor, dim: int, eps: float = 1e-10):
"""
An implementation of cumprod to prevent precision issue.
cumprod(x)
= [x1, x1x2, x1x2x3, ....]
= [exp(log(x1)), exp(log(x1) + log(x2)), exp(log(x1) + log(x2) + log(x3)), ...]
= exp(cumsum(log(x)))
"""
if (tensor + eps < 0).any(... |
An implementation of cumprod to prevent precision issue.
cumprod(x)
= [x1, x1x2, x1x2x3, ....]
= [exp(log(x1)), exp(log(x1) + log(x2)), exp(log(x1) + log(x2) + log(x3)), ...]
= exp(cumsum(log(x)))
| safe_cumprod | python | OFA-Sys/OFA | fairseq/examples/simultaneous_translation/utils/functions.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/simultaneous_translation/utils/functions.py | Apache-2.0 |
def expected_soft_attention(
alpha: Tensor,
soft_energy: Tensor,
padding_mask: Optional[Tensor] = None,
chunk_size: Optional[int] = None,
eps: float = 1e-10
):
"""
Function to compute expected soft attention for
monotonic infinite lookback attention from
expected alignment and soft e... |
Function to compute expected soft attention for
monotonic infinite lookback attention from
expected alignment and soft energy.
Reference:
Monotonic Chunkwise Attention
https://arxiv.org/abs/1712.05382
Monotonic Infinite Lookback Attention for Simultaneous Machine Translation
https://a... | expected_soft_attention | python | OFA-Sys/OFA | fairseq/examples/simultaneous_translation/utils/monotonic_attention.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/simultaneous_translation/utils/monotonic_attention.py | Apache-2.0 |
def mass_preservation(
alpha: Tensor,
padding_mask: Optional[Tensor] = None,
left_padding: bool = False
):
"""
Function to compute the mass perservation for alpha.
This means that the residual weights of alpha will be assigned
to the last token.
Reference:
Monotonic Infinite Lookbac... |
Function to compute the mass perservation for alpha.
This means that the residual weights of alpha will be assigned
to the last token.
Reference:
Monotonic Infinite Lookback Attention for Simultaneous Machine Translation
https://arxiv.org/abs/1906.05218
alpha: bsz, tgt_len, src_len
pa... | mass_preservation | python | OFA-Sys/OFA | fairseq/examples/simultaneous_translation/utils/monotonic_attention.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/simultaneous_translation/utils/monotonic_attention.py | Apache-2.0 |
def learnable_p_choose(
energy,
noise_mean: float = 0.0,
noise_var: float = 0.0,
training: bool = True
):
"""
Calculating step wise prob for reading and writing
1 to read, 0 to write
energy: bsz, tgt_len, src_len
"""
noise = 0
if training:
# add noise here to encoura... |
Calculating step wise prob for reading and writing
1 to read, 0 to write
energy: bsz, tgt_len, src_len
| learnable_p_choose | python | OFA-Sys/OFA | fairseq/examples/simultaneous_translation/utils/p_choose_strategy.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/simultaneous_translation/utils/p_choose_strategy.py | Apache-2.0 |
def get_tokens(self, idxs):
"""Normalize tokens by handling CTC blank, ASG replabels, etc."""
idxs = (g[0] for g in it.groupby(idxs))
idxs = filter(lambda x: x != self.blank, idxs)
return torch.LongTensor(list(idxs)) | Normalize tokens by handling CTC blank, ASG replabels, etc. | get_tokens | python | OFA-Sys/OFA | fairseq/examples/speech_recognition/w2l_decoder.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_recognition/w2l_decoder.py | Apache-2.0 |
def get_timesteps(self, token_idxs: List[int]) -> List[int]:
"""Returns frame numbers corresponding to every non-blank token.
Parameters
----------
token_idxs : List[int]
IDs of decoded tokens.
Returns
-------
List[int]
Frame numbers corr... | Returns frame numbers corresponding to every non-blank token.
Parameters
----------
token_idxs : List[int]
IDs of decoded tokens.
Returns
-------
List[int]
Frame numbers corresponding to every non-blank token.
| get_timesteps | python | OFA-Sys/OFA | fairseq/examples/speech_recognition/w2l_decoder.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_recognition/w2l_decoder.py | Apache-2.0 |
def score(self, state: LMState, token_index: int, no_cache: bool = False):
"""
Evaluate language model based on the current lm state and new word
Parameters:
-----------
state: current lm state
token_index: index of the word
(can be lexicon index then... |
Evaluate language model based on the current lm state and new word
Parameters:
-----------
state: current lm state
token_index: index of the word
(can be lexicon index then you should store inside LM the
mapping between indices of lexic... | score | python | OFA-Sys/OFA | fairseq/examples/speech_recognition/w2l_decoder.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_recognition/w2l_decoder.py | Apache-2.0 |
def forward(self, model, sample, reduction="sum", log_probs=True):
"""Computes the cross entropy with accuracy metric for the given sample.
This is similar to CrossEntropyCriterion in fairseq, but also
computes accuracy metrics as part of logging
Args:
logprobs (Torch.tenso... | Computes the cross entropy with accuracy metric for the given sample.
This is similar to CrossEntropyCriterion in fairseq, but also
computes accuracy metrics as part of logging
Args:
logprobs (Torch.tensor) of shape N, T, D i.e.
batchsize, timesteps, dimensions
... | forward | python | OFA-Sys/OFA | fairseq/examples/speech_recognition/criterions/cross_entropy_acc.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_recognition/criterions/cross_entropy_acc.py | Apache-2.0 |
def size(self, index):
"""Return an example's size as a float or tuple. This value is used when
filtering a dataset with ``--max-positions``."""
return (
self.frame_sizes[index],
len(self.tgt[index]) if self.tgt is not None else 0,
) | Return an example's size as a float or tuple. This value is used when
filtering a dataset with ``--max-positions``. | size | python | OFA-Sys/OFA | fairseq/examples/speech_recognition/data/asr_dataset.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_recognition/data/asr_dataset.py | Apache-2.0 |
def _collate_frames(self, frames):
"""Convert a list of 2d frames into a padded 3d tensor
Args:
frames (list): list of 2d frames of size L[i]*f_dim. Where L[i] is
length of i-th frame and f_dim is static dimension of features
Returns:
3d tensor of size len... | Convert a list of 2d frames into a padded 3d tensor
Args:
frames (list): list of 2d frames of size L[i]*f_dim. Where L[i] is
length of i-th frame and f_dim is static dimension of features
Returns:
3d tensor of size len(frames)*len_max*f_dim where len_max is max of... | _collate_frames | python | OFA-Sys/OFA | fairseq/examples/speech_recognition/data/collaters.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_recognition/data/collaters.py | Apache-2.0 |
def collate(self, samples):
"""
utility function to collate samples into batch for speech recognition.
"""
if len(samples) == 0:
return {}
# parse samples into torch tensors
parsed_samples = []
for s in samples:
# skip invalid samples
... |
utility function to collate samples into batch for speech recognition.
| collate | python | OFA-Sys/OFA | fairseq/examples/speech_recognition/data/collaters.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_recognition/data/collaters.py | Apache-2.0 |
def lengths_to_encoder_padding_mask(lengths, batch_first=False):
"""
convert lengths (a 1-D Long/Int tensor) to 2-D binary tensor
Args:
lengths: a (B, )-shaped tensor
Return:
max_length: maximum length of B sequences
encoder_padding_mask: a (max_length, B) binary mask, where
... |
convert lengths (a 1-D Long/Int tensor) to 2-D binary tensor
Args:
lengths: a (B, )-shaped tensor
Return:
max_length: maximum length of B sequences
encoder_padding_mask: a (max_length, B) binary mask, where
[t, b] = 0 for t < lengths[b] and 1 otherwise
TODO:
k... | lengths_to_encoder_padding_mask | python | OFA-Sys/OFA | fairseq/examples/speech_recognition/data/data_utils.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_recognition/data/data_utils.py | Apache-2.0 |
def encoder_padding_mask_to_lengths(
encoder_padding_mask, max_lengths, batch_size, device
):
"""
convert encoder_padding_mask (2-D binary tensor) to a 1-D tensor
Conventionally, encoder output contains a encoder_padding_mask, which is
a 2-D mask in a shape (T, B), whose (t, b) element indicate whe... |
convert encoder_padding_mask (2-D binary tensor) to a 1-D tensor
Conventionally, encoder output contains a encoder_padding_mask, which is
a 2-D mask in a shape (T, B), whose (t, b) element indicate whether
encoder_out[t, b] is a valid output (=0) or not (=1). Occasionally, we
need to convert this ... | encoder_padding_mask_to_lengths | python | OFA-Sys/OFA | fairseq/examples/speech_recognition/data/data_utils.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_recognition/data/data_utils.py | Apache-2.0 |
def pack_replabels(tokens, dictionary, max_reps):
"""
Pack a token sequence so that repeated symbols are replaced by replabels
"""
if len(tokens) == 0 or max_reps <= 0:
return tokens
replabel_value_to_idx = [0] * (max_reps + 1)
for i in range(1, max_reps + 1):
replabel_value_to_... |
Pack a token sequence so that repeated symbols are replaced by replabels
| pack_replabels | python | OFA-Sys/OFA | fairseq/examples/speech_recognition/data/replabels.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_recognition/data/replabels.py | Apache-2.0 |
def unpack_replabels(tokens, dictionary, max_reps):
"""
Unpack a token sequence so that replabels are replaced by repeated symbols
"""
if len(tokens) == 0 or max_reps <= 0:
return tokens
replabel_idx_to_value = {}
for i in range(1, max_reps + 1):
replabel_idx_to_value[dictionary... |
Unpack a token sequence so that replabels are replaced by repeated symbols
| unpack_replabels | python | OFA-Sys/OFA | fairseq/examples/speech_recognition/data/replabels.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_recognition/data/replabels.py | Apache-2.0 |
def __init__(
self,
input_feat_per_channel,
vggblock_config=DEFAULT_ENC_VGGBLOCK_CONFIG,
transformer_config=DEFAULT_ENC_TRANSFORMER_CONFIG,
encoder_output_dim=512,
in_channels=1,
transformer_context=None,
transformer_sampling=None,
):
"""constr... | constructor for VGGTransformerEncoder
Args:
- input_feat_per_channel: feature dim (not including stacked,
just base feature)
- in_channel: # input channels (e.g., if stack 8 feature vector
together, this is 8)
- vggblock_config: configuration of... | __init__ | python | OFA-Sys/OFA | fairseq/examples/speech_recognition/models/vggtransformer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_recognition/models/vggtransformer.py | Apache-2.0 |
def forward(self, src_tokens, src_lengths, **kwargs):
"""
src_tokens: padded tensor (B, T, C * feat)
src_lengths: tensor of original lengths of input utterances (B,)
"""
bsz, max_seq_len, _ = src_tokens.size()
x = src_tokens.view(bsz, max_seq_len, self.in_channels, self.i... |
src_tokens: padded tensor (B, T, C * feat)
src_lengths: tensor of original lengths of input utterances (B,)
| forward | python | OFA-Sys/OFA | fairseq/examples/speech_recognition/models/vggtransformer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_recognition/models/vggtransformer.py | Apache-2.0 |
def parse_transformer_context(self, transformer_context):
"""
transformer_context can be the following:
- None; indicates no context is used, i.e.,
transformer can access full context
- a tuple/list of two int; indicates left and right context,
any number <0 i... |
transformer_context can be the following:
- None; indicates no context is used, i.e.,
transformer can access full context
- a tuple/list of two int; indicates left and right context,
any number <0 indicates infinite context
* e.g., (5, 6) indicates th... | parse_transformer_context | python | OFA-Sys/OFA | fairseq/examples/speech_recognition/models/vggtransformer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_recognition/models/vggtransformer.py | Apache-2.0 |
def parse_transformer_sampling(self, transformer_sampling, num_layers):
"""
parsing transformer sampling configuration
Args:
- transformer_sampling, accepted input:
* None, indicating no sampling
* an Iterable with int (>0) as element
- nu... |
parsing transformer sampling configuration
Args:
- transformer_sampling, accepted input:
* None, indicating no sampling
* an Iterable with int (>0) as element
- num_layers, expected number of transformer layers, must match with
the ... | parse_transformer_sampling | python | OFA-Sys/OFA | fairseq/examples/speech_recognition/models/vggtransformer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_recognition/models/vggtransformer.py | Apache-2.0 |
def slice(self, embedding, padding_mask, attn_mask, sampling_factor):
"""
embedding is a (T, B, D) tensor
padding_mask is a (B, T) tensor or None
attn_mask is a (T, T) tensor or None
"""
embedding = embedding[::sampling_factor, :, :]
if padding_mask is not None:
... |
embedding is a (T, B, D) tensor
padding_mask is a (B, T) tensor or None
attn_mask is a (T, T) tensor or None
| slice | python | OFA-Sys/OFA | fairseq/examples/speech_recognition/models/vggtransformer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_recognition/models/vggtransformer.py | Apache-2.0 |
def lengths_to_attn_mask(self, input_lengths, subsampling_factor=1):
"""
create attention mask according to sequence lengths and transformer
context
Args:
- input_lengths: (B, )-shape Int/Long tensor; input_lengths[b] is
the length of b-th sequence
... |
create attention mask according to sequence lengths and transformer
context
Args:
- input_lengths: (B, )-shape Int/Long tensor; input_lengths[b] is
the length of b-th sequence
- subsampling_factor: int
* Note that the left_context and right... | lengths_to_attn_mask | python | OFA-Sys/OFA | fairseq/examples/speech_recognition/models/vggtransformer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_recognition/models/vggtransformer.py | Apache-2.0 |
def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None):
"""
Args:
prev_output_tokens (LongTensor): previous decoder outputs of shape
`(batch, tgt_len)`, for input feeding/teacher forcing
encoder_out (Tensor, optional): output from the enco... |
Args:
prev_output_tokens (LongTensor): previous decoder outputs of shape
`(batch, tgt_len)`, for input feeding/teacher forcing
encoder_out (Tensor, optional): output from the encoder, used for
encoder-side attention
incremental_state (dict): d... | forward | python | OFA-Sys/OFA | fairseq/examples/speech_recognition/models/vggtransformer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_recognition/models/vggtransformer.py | Apache-2.0 |
def LinearizedConv1d(in_channels, out_channels, kernel_size, dropout=0, **kwargs):
"""Weight-normalized Conv1d layer optimized for decoding"""
m = LinearizedConvolution(in_channels, out_channels, kernel_size, **kwargs)
std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels))
nn.init.nor... | Weight-normalized Conv1d layer optimized for decoding | LinearizedConv1d | python | OFA-Sys/OFA | fairseq/examples/speech_recognition/models/vggtransformer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_recognition/models/vggtransformer.py | Apache-2.0 |
def merge_shards(self) -> None:
"""Merges all shard files into shard 0, then removes shard suffix."""
shard_id = self.data_parallel_rank
num_shards = self.data_parallel_world_size
if self.data_parallel_world_size > 1:
def merge_shards_with_root(fname: str) -> None:
... | Merges all shard files into shard 0, then removes shard suffix. | merge_shards | python | OFA-Sys/OFA | fairseq/examples/speech_recognition/new/infer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_recognition/new/infer.py | Apache-2.0 |
def get_wer_file(cfg: InferConfig) -> Path:
"""Hashes the decoding parameters to a unique file ID."""
base_path = "wer"
if cfg.decoding.results_path is not None:
base_path = os.path.join(cfg.decoding.results_path, base_path)
if cfg.decoding.unique_wer_file:
yaml_str = OmegaConf.to_yaml(... | Hashes the decoding parameters to a unique file ID. | get_wer_file | python | OFA-Sys/OFA | fairseq/examples/speech_recognition/new/infer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_recognition/new/infer.py | Apache-2.0 |
def main(cfg: InferConfig) -> float:
"""Entry point for main processing logic.
Args:
cfg: The inferance configuration to use.
wer: Optional shared memory pointer for returning the WER. If not None,
the final WER value will be written here instead of being returned.
Returns:
... | Entry point for main processing logic.
Args:
cfg: The inferance configuration to use.
wer: Optional shared memory pointer for returning the WER. If not None,
the final WER value will be written here instead of being returned.
Returns:
The final WER if `wer` is None, otherwi... | main | python | OFA-Sys/OFA | fairseq/examples/speech_recognition/new/infer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_recognition/new/infer.py | Apache-2.0 |
def score(
self,
state: LMState,
token_index: int,
no_cache: bool = False,
) -> Tuple[LMState, int]:
"""
Evaluate language model based on the current lm state and new word
Parameters:
-----------
state: current lm state
token_index: ind... |
Evaluate language model based on the current lm state and new word
Parameters:
-----------
state: current lm state
token_index: index of the word
(can be lexicon index then you should store inside LM the
mapping between indices of lexic... | score | python | OFA-Sys/OFA | fairseq/examples/speech_recognition/new/decoders/flashlight_decoder.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_recognition/new/decoders/flashlight_decoder.py | Apache-2.0 |
def get_asr_dataset_from_json(data_json_path, tgt_dict):
"""
Parse data json and create dataset.
See scripts/asr_prep_json.py which pack json from raw files
Json example:
{
"utts": {
"4771-29403-0025": {
"input": {
"length_ms": 170,
"path": "/... |
Parse data json and create dataset.
See scripts/asr_prep_json.py which pack json from raw files
Json example:
{
"utts": {
"4771-29403-0025": {
"input": {
"length_ms": 170,
"path": "/tmp/file1.flac"
},
"output": {
... | get_asr_dataset_from_json | python | OFA-Sys/OFA | fairseq/examples/speech_recognition/tasks/speech_recognition.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_recognition/tasks/speech_recognition.py | Apache-2.0 |
def get_wer_alignment_codes(hyp_str, ref_str):
"""
INPUT: hypothesis string, reference string
OUTPUT: List of alignment codes (intermediate results from WER computation)
"""
t = WERTransformer(hyp_str, ref_str, verbose=0)
return t.ed_.align(str2toks(ref_str), str2toks(hyp_str)).codes |
INPUT: hypothesis string, reference string
OUTPUT: List of alignment codes (intermediate results from WER computation)
| get_wer_alignment_codes | python | OFA-Sys/OFA | fairseq/examples/speech_recognition/utils/wer_utils.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_recognition/utils/wer_utils.py | Apache-2.0 |
def batch_mel_spectral_distortion(
y1, y2, sr, normalize_type="path", mel_fn=None
):
"""
https://arxiv.org/pdf/2011.03568.pdf
Same as Mel Cepstral Distortion, but computed on log-mel spectrograms.
"""
if mel_fn is None or mel_fn.sample_rate != sr:
mel_fn = torchaudio.transforms.MelS... | ERROR: type should be string, got "\n https://arxiv.org/pdf/2011.03568.pdf\n\n Same as Mel Cepstral Distortion, but computed on log-mel spectrograms.\n " | batch_mel_spectral_distortion | python | OFA-Sys/OFA | fairseq/examples/speech_synthesis/utils.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_synthesis/utils.py | Apache-2.0 |
def gross_pitch_error(true_t, true_f, est_t, est_f):
"""The relative frequency in percent of pitch estimates that are
outside a threshold around the true pitch. Only frames that are
considered pitched by both the ground truth and the estimator (if
applicable) are considered.
"""
correct_frames ... | The relative frequency in percent of pitch estimates that are
outside a threshold around the true pitch. Only frames that are
considered pitched by both the ground truth and the estimator (if
applicable) are considered.
| gross_pitch_error | python | OFA-Sys/OFA | fairseq/examples/speech_synthesis/utils.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_synthesis/utils.py | Apache-2.0 |
def run_asr(asr_dir, split, w2v_ckpt, w2v_label, res_dir):
"""
results will be saved at
{res_dir}/{ref,hypo}.word-{w2v_ckpt.filename}-{split}.txt
"""
cmd = ["python", "-m", "examples.speech_recognition.infer"]
cmd += [str(asr_dir.resolve())]
cmd += ["--task", "audio_finetuning", "--nbest", "... |
results will be saved at
{res_dir}/{ref,hypo}.word-{w2v_ckpt.filename}-{split}.txt
| run_asr | python | OFA-Sys/OFA | fairseq/examples/speech_synthesis/evaluation/eval_asr.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_synthesis/evaluation/eval_asr.py | Apache-2.0 |
def compute_error_rate(hyp_wrd_path, ref_wrd_path, unit="word"):
"""each line is "<text> (None-<index>)" """
tokenize_line = {
"word": lambda x: re.sub(r" \(.*\)$", "", x.rstrip()).split(),
"char": lambda x: list(re.sub(r" \(.*\)$", "", x.rstrip()))
}.get(unit)
if tokenize_line is None:
... | each line is "<text> (None-<index>)" | compute_error_rate | python | OFA-Sys/OFA | fairseq/examples/speech_synthesis/evaluation/eval_asr.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_synthesis/evaluation/eval_asr.py | Apache-2.0 |
def difference_function(x, n, tau_max):
"""
Compute difference function of data x. This solution is implemented directly
with Numpy fft.
:param x: audio data
:param n: length of data
:param tau_max: integration window size
:return: difference function
:rtype: list
"""
x = np.a... |
Compute difference function of data x. This solution is implemented directly
with Numpy fft.
:param x: audio data
:param n: length of data
:param tau_max: integration window size
:return: difference function
:rtype: list
| difference_function | python | OFA-Sys/OFA | fairseq/examples/speech_synthesis/evaluation/eval_f0.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_synthesis/evaluation/eval_f0.py | Apache-2.0 |
def cumulative_mean_normalized_difference_function(df, n):
"""
Compute cumulative mean normalized difference function (CMND).
:param df: Difference function
:param n: length of data
:return: cumulative mean normalized difference function
:rtype: list
"""
# scipy method
cmn_df = df[... |
Compute cumulative mean normalized difference function (CMND).
:param df: Difference function
:param n: length of data
:return: cumulative mean normalized difference function
:rtype: list
| cumulative_mean_normalized_difference_function | python | OFA-Sys/OFA | fairseq/examples/speech_synthesis/evaluation/eval_f0.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_synthesis/evaluation/eval_f0.py | Apache-2.0 |
def get_pitch(cmdf, tau_min, tau_max, harmo_th=0.1):
"""
Return fundamental period of a frame based on CMND function.
:param cmdf: Cumulative Mean Normalized Difference function
:param tau_min: minimum period for speech
:param tau_max: maximum period for speech
:param harmo_th: harmonicity thre... |
Return fundamental period of a frame based on CMND function.
:param cmdf: Cumulative Mean Normalized Difference function
:param tau_min: minimum period for speech
:param tau_max: maximum period for speech
:param harmo_th: harmonicity threshold to determine if it is necessary to
compute pitch f... | get_pitch | python | OFA-Sys/OFA | fairseq/examples/speech_synthesis/evaluation/eval_f0.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_synthesis/evaluation/eval_f0.py | Apache-2.0 |
def valid_length(self, length):
"""
Return the nearest valid length to use with the model so that
there is no time steps left over in a convolutions, e.g. for all
layers, size of the input - kernel_size % stride = 0.
If the mixture has a valid length, the estimated sources
... |
Return the nearest valid length to use with the model so that
there is no time steps left over in a convolutions, e.g. for all
layers, size of the input - kernel_size % stride = 0.
If the mixture has a valid length, the estimated sources
will have exactly the same length.
... | valid_length | python | OFA-Sys/OFA | fairseq/examples/speech_synthesis/preprocessing/denoiser/demucs.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_synthesis/preprocessing/denoiser/demucs.py | Apache-2.0 |
def fast_conv(conv, x):
"""
Faster convolution evaluation if either kernel size is 1
or length of sequence is 1.
"""
batch, chin, length = x.shape
chout, chin, kernel = conv.weight.shape
assert batch == 1
if kernel == 1:
x = x.view(chin, length)
out = th.addmm(conv.bias.v... |
Faster convolution evaluation if either kernel size is 1
or length of sequence is 1.
| fast_conv | python | OFA-Sys/OFA | fairseq/examples/speech_synthesis/preprocessing/denoiser/demucs.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_synthesis/preprocessing/denoiser/demucs.py | Apache-2.0 |
def flush(self):
"""
Flush remaining audio by padding it with zero. Call this
when you have no more input and want to get back the last chunk of audio.
"""
pending_length = self.pending.shape[1]
padding = th.zeros(
self.demucs.chin, self.total_length, device=s... |
Flush remaining audio by padding it with zero. Call this
when you have no more input and want to get back the last chunk of audio.
| flush | python | OFA-Sys/OFA | fairseq/examples/speech_synthesis/preprocessing/denoiser/demucs.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_synthesis/preprocessing/denoiser/demucs.py | Apache-2.0 |
def feed(self, wav):
"""
Apply the model to mix using true real time evaluation.
Normalization is done online as is the resampling.
"""
begin = time.time()
demucs = self.demucs
resample_buffer = self.resample_buffer
stride = self.stride
resample = ... |
Apply the model to mix using true real time evaluation.
Normalization is done online as is the resampling.
| feed | python | OFA-Sys/OFA | fairseq/examples/speech_synthesis/preprocessing/denoiser/demucs.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_synthesis/preprocessing/denoiser/demucs.py | Apache-2.0 |
def get_model(args):
"""
Load local model package or torchhub pre-trained model.
"""
if args.model_path:
logger.info("Loading model from %s", args.model_path)
pkg = torch.load(args.model_path)
model = deserialize_model(pkg)
elif args.dns64:
logger.info("Loading pre-tr... |
Load local model package or torchhub pre-trained model.
| get_model | python | OFA-Sys/OFA | fairseq/examples/speech_synthesis/preprocessing/denoiser/pretrained.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_synthesis/preprocessing/denoiser/pretrained.py | Apache-2.0 |
def capture_init(init):
"""capture_init.
Decorate `__init__` with this, and you can then
recover the *args and **kwargs passed to it in `self._init_args_kwargs`
"""
@functools.wraps(init)
def __init__(self, *args, **kwargs):
self._init_args_kwargs = (args, kwargs)
init(self, *ar... | capture_init.
Decorate `__init__` with this, and you can then
recover the *args and **kwargs passed to it in `self._init_args_kwargs`
| capture_init | python | OFA-Sys/OFA | fairseq/examples/speech_synthesis/preprocessing/denoiser/utils.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_synthesis/preprocessing/denoiser/utils.py | Apache-2.0 |
def colorize(text, color):
"""
Display text with some ANSI color in the terminal.
"""
code = f"\033[{color}m"
restore = "\033[0m"
return "".join([code, text, restore]) |
Display text with some ANSI color in the terminal.
| colorize | python | OFA-Sys/OFA | fairseq/examples/speech_synthesis/preprocessing/denoiser/utils.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_synthesis/preprocessing/denoiser/utils.py | Apache-2.0 |
def set_requires_grad(nets, requires_grad=False):
"""Set requies_grad=Fasle for all the networks to avoid unnecessary
computations
Parameters:
nets (network list) -- a list of networks
requires_grad (bool) -- whether the networks require gradients or not
"""
if not isinstance(nets... | Set requies_grad=Fasle for all the networks to avoid unnecessary
computations
Parameters:
nets (network list) -- a list of networks
requires_grad (bool) -- whether the networks require gradients or not
| set_requires_grad | python | OFA-Sys/OFA | fairseq/examples/speech_synthesis/preprocessing/speaker_embedder/__init__.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_synthesis/preprocessing/speaker_embedder/__init__.py | Apache-2.0 |
def read_wave(path):
"""Reads a .wav file.
Takes the path, and returns (PCM audio data, sample rate).
"""
with contextlib.closing(wave.open(path, 'rb')) as wf:
num_channels = wf.getnchannels()
assert num_channels == 1
sample_width = wf.getsampwidth()
assert sample_width =... | Reads a .wav file.
Takes the path, and returns (PCM audio data, sample rate).
| read_wave | python | OFA-Sys/OFA | fairseq/examples/speech_synthesis/preprocessing/vad/__init__.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_synthesis/preprocessing/vad/__init__.py | Apache-2.0 |
def write_wave(path, audio, sample_rate):
"""Writes a .wav file.
Takes path, PCM audio data, and sample rate.
"""
with contextlib.closing(wave.open(path, 'wb')) as wf:
wf.setnchannels(1)
wf.setsampwidth(2)
wf.setframerate(sample_rate)
wf.writeframes(audio) | Writes a .wav file.
Takes path, PCM audio data, and sample rate.
| write_wave | python | OFA-Sys/OFA | fairseq/examples/speech_synthesis/preprocessing/vad/__init__.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_synthesis/preprocessing/vad/__init__.py | Apache-2.0 |
def frame_generator(frame_duration_ms, audio, sample_rate):
"""Generates audio frames from PCM audio data.
Takes the desired frame duration in milliseconds, the PCM data, and
the sample rate.
Yields Frames of the requested duration.
"""
n = int(sample_rate * (frame_duration_ms / 1000.0) * 2)
... | Generates audio frames from PCM audio data.
Takes the desired frame duration in milliseconds, the PCM data, and
the sample rate.
Yields Frames of the requested duration.
| frame_generator | python | OFA-Sys/OFA | fairseq/examples/speech_synthesis/preprocessing/vad/__init__.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_synthesis/preprocessing/vad/__init__.py | Apache-2.0 |
def vad_collector(sample_rate, frame_duration_ms,
padding_duration_ms, vad, frames):
"""Filters out non-voiced audio frames.
Given a webrtcvad.Vad and a source of audio frames, yields only
the voiced audio.
Uses a padded, sliding window algorithm over the audio frames.
When more th... | Filters out non-voiced audio frames.
Given a webrtcvad.Vad and a source of audio frames, yields only
the voiced audio.
Uses a padded, sliding window algorithm over the audio frames.
When more than 90% of the frames in the window are voiced (as
reported by the VAD), the collector triggers and begins ... | vad_collector | python | OFA-Sys/OFA | fairseq/examples/speech_synthesis/preprocessing/vad/__init__.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_synthesis/preprocessing/vad/__init__.py | Apache-2.0 |
def __init__(
self,
task,
sentence_avg,
guide_alpha,
text_input_cost_ratio,
label_smoothing,
disable_text_guide_update_num=0,
attentive_cost_regularization=0,
):
"""
guide_alpha: alpha to inteplate nll and kd loss
tex... |
guide_alpha: alpha to inteplate nll and kd loss
text_input_cost_ratio: loss ratio for text only input data
label_smoothing: label smoothing ratio
disable_text_guide_update_num: only use nll loss for the first N updates
attentive_cost_regularization: ratio fo... | __init__ | python | OFA-Sys/OFA | fairseq/examples/speech_text_joint_to_text/criterions/text_guide_cross_entropy_acc.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_text_joint_to_text/criterions/text_guide_cross_entropy_acc.py | Apache-2.0 |
def guide_loss_and_acc(self, model, lprobs, lprobs_teacher, target, reduce=True):
""" lprobs_teacher is used as guide for lprobs """
if self.alpha == 0.0 or model.num_updates < self.disable_update_num:
return self.compute_loss_and_acc(model, lprobs, target, reduction=('sum' if reduce else 'n... | lprobs_teacher is used as guide for lprobs | guide_loss_and_acc | python | OFA-Sys/OFA | fairseq/examples/speech_text_joint_to_text/criterions/text_guide_cross_entropy_acc.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_text_joint_to_text/criterions/text_guide_cross_entropy_acc.py | Apache-2.0 |
def set_shared_layer(cls, share_level, src_layer, tgt_layer):
"""
share parameters from tgt_layer to src_layer
share_level:
0: share everything
1: share everything but different model
2: share weight but not bias, layernorm
"""
if share_level =... |
share parameters from tgt_layer to src_layer
share_level:
0: share everything
1: share everything but different model
2: share weight but not bias, layernorm
| set_shared_layer | python | OFA-Sys/OFA | fairseq/examples/speech_text_joint_to_text/models/s2t_dualinputtransformer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_text_joint_to_text/models/s2t_dualinputtransformer.py | Apache-2.0 |
def forward(
self,
src_tokens,
src_lengths=None,
src_txt_tokens=None,
src_txt_lengths=None,
**kwargs
):
"""
Args:
src_tokens: padded tensor (B, T, C * feat)
src_lengths: tensor of original lengths of input utterances (speech) (B... |
Args:
src_tokens: padded tensor (B, T, C * feat)
src_lengths: tensor of original lengths of input utterances (speech) (B,)
src_txt_tokens: padded tensor (B, T)
src_txt_lengths: tensor of original lengths of input utterances (text) (B,)
| forward | python | OFA-Sys/OFA | fairseq/examples/speech_text_joint_to_text/models/s2t_dualinputtransformer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_text_joint_to_text/models/s2t_dualinputtransformer.py | Apache-2.0 |
def forward(
self,
prev_output_tokens,
encoder_out,
incremental_state=None,
has_txt_input=False,
**kwargs
):
"""
Args:
prev_output_tokens (LongTensor): previous decoder outputs of shape
`(batch, tgt_len)`, for input feeding/... |
Args:
prev_output_tokens (LongTensor): previous decoder outputs of shape
`(batch, tgt_len)`, for input feeding/teacher forcing. If there are
two or more input during training, they will share the same prev_output_tokens
encoder_out (tuple[Tensor]): output... | forward | python | OFA-Sys/OFA | fairseq/examples/speech_text_joint_to_text/models/s2t_dualinputtransformer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_text_joint_to_text/models/s2t_dualinputtransformer.py | Apache-2.0 |
def forward(
self,
src_tokens,
src_lengths,
prev_output_tokens,
use_encoder_outputs=False,
src_txt_tokens=None,
src_txt_lengths=None,
mode="sup_speech",
**kwargs
):
"""
Run the forward pass for an encoder-decoder model.
... |
Run the forward pass for an encoder-decoder model.
First feed a batch of source tokens through the encoder. Then, feed the
encoder output and previous decoder outputs (i.e., teacher forcing) to
the decoder to produce the next outputs::
encoder_out = self.encoder(src_tokens... | forward | python | OFA-Sys/OFA | fairseq/examples/speech_text_joint_to_text/models/s2t_dualinputtransformer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_text_joint_to_text/models/s2t_dualinputtransformer.py | Apache-2.0 |
def dup_pho(sent, dup_v_num, dup_c_num):
"""
duplicate phoneme defined as cmudict
http://www.speech.cs.cmu.edu/cgi-bin/cmudict
"""
if dup_v_num == 1 and dup_c_num == 1:
return sent
ns = []
for p in sent:
ns.append(p)
if re.search(r"\d$", p):
for i in range... |
duplicate phoneme defined as cmudict
http://www.speech.cs.cmu.edu/cgi-bin/cmudict
| dup_pho | python | OFA-Sys/OFA | fairseq/examples/speech_text_joint_to_text/scripts/g2p_encode.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_text_joint_to_text/scripts/g2p_encode.py | Apache-2.0 |
def __getitem__(
self, n: int
) -> Tuple[Tensor, int, str, str, Optional[str], str, str]:
"""Load the n-th sample from the dataset.
Args:
n (int): The index of the sample to be loaded
Returns:
tuple: ``(waveform, sample_rate, sentence, translation, speaker_i... | Load the n-th sample from the dataset.
Args:
n (int): The index of the sample to be loaded
Returns:
tuple: ``(waveform, sample_rate, sentence, translation, speaker_id,
sample_id)``
| __getitem__ | python | OFA-Sys/OFA | fairseq/examples/speech_to_text/prep_covost_data.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/speech_to_text/prep_covost_data.py | Apache-2.0 |
def corpus_bleu(
list_of_references,
hypotheses,
weights=(0.25, 0.25, 0.25, 0.25),
smoothing_function=None,
auto_reweigh=False,
averaging_mode="geometric",
no_length_penalty=False
):
"""
Calculate a single corpus-level BLEU score (aka. system-level BLEU) for all
the hypotheses an... |
Calculate a single corpus-level BLEU score (aka. system-level BLEU) for all
the hypotheses and their respective references.
Instead of averaging the sentence level BLEU scores (i.e. marco-average
precision), the original BLEU metric (Papineni et al. 2002) accounts for
the micro-average precision (... | corpus_bleu | python | OFA-Sys/OFA | fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/misc/bleu_utils.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/misc/bleu_utils.py | Apache-2.0 |
def window_sumsquare(window, n_frames, hop_length=200, win_length=800,
n_fft=800, dtype=np.float32, norm=None):
"""
# from librosa 0.6
Compute the sum-square envelope of a window function at a given hop length.
This is used to estimate modulation effects induced by windowing
ob... |
# from librosa 0.6
Compute the sum-square envelope of a window function at a given hop length.
This is used to estimate modulation effects induced by windowing
observations in short-time fourier transforms.
Parameters
----------
window : string, tuple, number, callable, or list-like
... | window_sumsquare | python | OFA-Sys/OFA | fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/audio_processing.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/audio_processing.py | Apache-2.0 |
def english_cleaners(text):
'''Pipeline for English text, including number and abbreviation expansion.'''
text = convert_to_ascii(text)
text = lowercase(text)
text = expand_numbers(text)
text = expand_abbreviations(text)
text = collapse_whitespace(text)
return text | Pipeline for English text, including number and abbreviation expansion. | english_cleaners | python | OFA-Sys/OFA | fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/cleaners.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/cleaners.py | Apache-2.0 |
def forward(self, x, lengths=None):
"""Average pooling across time steps (dim=1) with optionally lengths.
Args:
x: torch.Tensor of shape (N, T, ...)
lengths: None or torch.Tensor of shape (N,)
dim: dimension to pool
"""
if lengths is None:
... | Average pooling across time steps (dim=1) with optionally lengths.
Args:
x: torch.Tensor of shape (N, T, ...)
lengths: None or torch.Tensor of shape (N,)
dim: dimension to pool
| forward | python | OFA-Sys/OFA | fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/layers.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/layers.py | Apache-2.0 |
def mel_spectrogram(self, y):
"""Computes mel-spectrograms from a batch of waves
PARAMS
------
y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1]
RETURNS
-------
mel_output: torch.FloatTensor of shape (B, n_mel_channels, T)
"""
asse... | Computes mel-spectrograms from a batch of waves
PARAMS
------
y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1]
RETURNS
-------
mel_output: torch.FloatTensor of shape (B, n_mel_channels, T)
| mel_spectrogram | python | OFA-Sys/OFA | fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/layers.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/layers.py | Apache-2.0 |
def get_alignment_energies(self, query, processed_memory,
attention_weights_cat):
"""
PARAMS
------
query: decoder output (batch, n_mel_channels * n_frames_per_step)
processed_memory: processed encoder outputs (B, T_in, attention_dim)
attent... |
PARAMS
------
query: decoder output (batch, n_mel_channels * n_frames_per_step)
processed_memory: processed encoder outputs (B, T_in, attention_dim)
attention_weights_cat: cumulative and prev. att weights (B, 2, max_time)
RETURNS
-------
alignment (batch... | get_alignment_energies | python | OFA-Sys/OFA | fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/model.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/model.py | Apache-2.0 |
def forward(self, attention_hidden_state, memory, processed_memory,
attention_weights_cat, mask):
"""
PARAMS
------
attention_hidden_state: attention rnn last output
memory: encoder outputs
processed_memory: processed encoder outputs
attention_weig... |
PARAMS
------
attention_hidden_state: attention rnn last output
memory: encoder outputs
processed_memory: processed encoder outputs
attention_weights_cat: previous and cummulative attention weights
mask: binary mask for padded data
| forward | python | OFA-Sys/OFA | fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/model.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/model.py | Apache-2.0 |
def get_go_frame(self, memory):
""" Gets all zeros frames to use as first decoder input
PARAMS
------
memory: decoder outputs
RETURNS
-------
decoder_input: all zeros frames
"""
B = memory.size(0)
decoder_input = Variable(memory.data.new(
... | Gets all zeros frames to use as first decoder input
PARAMS
------
memory: decoder outputs
RETURNS
-------
decoder_input: all zeros frames
| get_go_frame | python | OFA-Sys/OFA | fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/model.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/model.py | Apache-2.0 |
def initialize_decoder_states(self, memory, obs_and_lat, mask):
""" Initializes attention rnn states, decoder rnn states, attention
weights, attention cumulative weights, attention context, stores memory
and stores processed memory
PARAMS
------
memory: Encoder outputs
... | Initializes attention rnn states, decoder rnn states, attention
weights, attention cumulative weights, attention context, stores memory
and stores processed memory
PARAMS
------
memory: Encoder outputs
obs_and_lat: Observed and latent attribute embeddings
mask: M... | initialize_decoder_states | python | OFA-Sys/OFA | fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/model.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/model.py | Apache-2.0 |
def parse_decoder_inputs(self, decoder_inputs):
""" Prepares decoder inputs, i.e. mel outputs
PARAMS
------
decoder_inputs: inputs used for teacher-forced training, i.e. mel-specs
RETURNS
-------
inputs: processed decoder inputs
"""
# (B, n_mel_c... | Prepares decoder inputs, i.e. mel outputs
PARAMS
------
decoder_inputs: inputs used for teacher-forced training, i.e. mel-specs
RETURNS
-------
inputs: processed decoder inputs
| parse_decoder_inputs | python | OFA-Sys/OFA | fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/model.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/model.py | Apache-2.0 |
def parse_decoder_outputs(self, mel_outputs, gate_outputs, alignments):
""" Prepares decoder outputs for output
PARAMS
------
mel_outputs:
gate_outputs: gate output energies
alignments:
RETURNS
-------
mel_outputs:
gate_outpust: gate outpu... | Prepares decoder outputs for output
PARAMS
------
mel_outputs:
gate_outputs: gate output energies
alignments:
RETURNS
-------
mel_outputs:
gate_outpust: gate output energies
alignments:
| parse_decoder_outputs | python | OFA-Sys/OFA | fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/model.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/model.py | Apache-2.0 |
def decode(self, decoder_input):
""" Decoder step using stored states, attention and memory
PARAMS
------
decoder_input: previous mel output
RETURNS
-------
mel_output:
gate_output: gate output energies
attention_weights:
"""
cell_... | Decoder step using stored states, attention and memory
PARAMS
------
decoder_input: previous mel output
RETURNS
-------
mel_output:
gate_output: gate output energies
attention_weights:
| decode | python | OFA-Sys/OFA | fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/model.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/model.py | Apache-2.0 |
def forward(self, memory, obs_and_lat, decoder_inputs, memory_lengths):
""" Decoder forward pass for training
PARAMS
------
memory: Encoder outputs
obs_and_lat: Observed and latent attribute embeddings
decoder_inputs: Decoder inputs for teacher forcing. i.e. mel-specs
... | Decoder forward pass for training
PARAMS
------
memory: Encoder outputs
obs_and_lat: Observed and latent attribute embeddings
decoder_inputs: Decoder inputs for teacher forcing. i.e. mel-specs
memory_lengths: Encoder output lengths for attention masking.
RETURNS... | forward | python | OFA-Sys/OFA | fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/model.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/model.py | Apache-2.0 |
def inference(self, memory, obs_and_lat, ret_has_eos=False):
""" Decoder inference
PARAMS
------
memory: Encoder outputs
obs_and_lat: Observed and latent attribute embeddings
RETURNS
-------
mel_outputs: mel outputs from the decoder
gate_outputs: ... | Decoder inference
PARAMS
------
memory: Encoder outputs
obs_and_lat: Observed and latent attribute embeddings
RETURNS
-------
mel_outputs: mel outputs from the decoder
gate_outputs: gate outputs from the decoder
alignments: sequence of attention ... | inference | python | OFA-Sys/OFA | fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/model.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/model.py | Apache-2.0 |
def text_to_sequence(text, cleaner_names):
'''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
The text can optionally have ARPAbet sequences enclosed in curly braces embedded
in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street."
Args:
text: string... | Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
The text can optionally have ARPAbet sequences enclosed in curly braces embedded
in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street."
Args:
text: string to convert to a sequence
cleaner_names: n... | text_to_sequence | python | OFA-Sys/OFA | fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/text.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/text.py | Apache-2.0 |
def read_binary_audio(bin_data, tar_sr=None):
"""
read binary audio (`bytes` or `uint8` `numpy.ndarray`) to `float32`
`numpy.ndarray`
RETURNS:
data (np.ndarray) : audio of shape (n,) or (2, n)
tar_sr (int) : sample rate
"""
data, ori_sr = sf.read(io.BytesIO(bin_data), dtype='flo... |
read binary audio (`bytes` or `uint8` `numpy.ndarray`) to `float32`
`numpy.ndarray`
RETURNS:
data (np.ndarray) : audio of shape (n,) or (2, n)
tar_sr (int) : sample rate
| read_binary_audio | python | OFA-Sys/OFA | fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/utils.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/utils.py | Apache-2.0 |
def read_audio(fname):
""" Load an audio file and return PCM along with the sample rate """
wav, sr = sf.read(fname)
assert sr == 16e3
return wav, 16e3 | Load an audio file and return PCM along with the sample rate | read_audio | python | OFA-Sys/OFA | fairseq/examples/wav2vec/wav2vec_featurize.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/wav2vec/wav2vec_featurize.py | Apache-2.0 |
def ordered_indices(self):
"""Return an ordered list of indices. Batches will be constructed based
on this order."""
if self.shuffle:
order = [np.random.permutation(len(self))]
else:
order = [np.arange(len(self))]
if self.sort_by_length:
order... | Return an ordered list of indices. Batches will be constructed based
on this order. | ordered_indices | python | OFA-Sys/OFA | fairseq/examples/wav2vec/unsupervised/data/extracted_features_dataset.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/wav2vec/unsupervised/data/extracted_features_dataset.py | Apache-2.0 |
def read_audio(self, fname):
"""Load an audio file and return PCM along with the sample rate"""
wav, sr = sf.read(fname)
assert sr == 16e3
return wav | Load an audio file and return PCM along with the sample rate | read_audio | python | OFA-Sys/OFA | fairseq/examples/wav2vec/unsupervised/scripts/wav2vec_cluster_faiss.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/wav2vec/unsupervised/scripts/wav2vec_cluster_faiss.py | Apache-2.0 |
def setup_task(cls, cfg: UnpairedAudioTextConfig, **kwargs):
"""Setup the task (e.g., load dictionaries).
Args:
cfg (AudioPretrainingConfig): configuration of this task
"""
dict_path = os.path.join(cfg.text_data, "dict.txt")
if os.path.exists(dict_path):
... | Setup the task (e.g., load dictionaries).
Args:
cfg (AudioPretrainingConfig): configuration of this task
| setup_task | python | OFA-Sys/OFA | fairseq/examples/wav2vec/unsupervised/tasks/unpaired_audio_text.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/wav2vec/unsupervised/tasks/unpaired_audio_text.py | Apache-2.0 |
def load_checkpoint(cfg: CheckpointConfig, trainer, **passthrough_args):
"""
Load a checkpoint and restore the training iterator.
*passthrough_args* will be passed through to
``trainer.get_train_iterator``.
"""
reset_optimizer = cfg.reset_optimizer
reset_lr_scheduler = cfg.reset_lr_schedul... |
Load a checkpoint and restore the training iterator.
*passthrough_args* will be passed through to
``trainer.get_train_iterator``.
| load_checkpoint | python | OFA-Sys/OFA | fairseq/fairseq/checkpoint_utils.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/checkpoint_utils.py | Apache-2.0 |
def load_checkpoint_to_cpu(path, arg_overrides=None, load_on_all_ranks=False):
"""Loads a checkpoint to CPU (with upgrading for backward compatibility).
If doing single-GPU training or if the checkpoint is only being loaded by at
most one process on each node (current default behavior is for only rank 0
... | Loads a checkpoint to CPU (with upgrading for backward compatibility).
If doing single-GPU training or if the checkpoint is only being loaded by at
most one process on each node (current default behavior is for only rank 0
to read the checkpoint from disk), load_on_all_ranks should be False to
avoid er... | load_checkpoint_to_cpu | python | OFA-Sys/OFA | fairseq/fairseq/checkpoint_utils.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/checkpoint_utils.py | Apache-2.0 |
def load_model_ensemble(
filenames,
arg_overrides: Optional[Dict[str, Any]] = None,
task=None,
strict=True,
suffix="",
num_shards=1,
state=None,
):
"""Loads an ensemble of models.
Args:
filenames (List[str]): checkpoint files to load
arg_overrides (Dict[str,Any], opt... | Loads an ensemble of models.
Args:
filenames (List[str]): checkpoint files to load
arg_overrides (Dict[str,Any], optional): override model args that
were used during model training
task (fairseq.tasks.FairseqTask, optional): task to use for loading
| load_model_ensemble | python | OFA-Sys/OFA | fairseq/fairseq/checkpoint_utils.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/checkpoint_utils.py | Apache-2.0 |
def checkpoint_paths(path, pattern=r"checkpoint(\d+)\.pt", keep_match=False):
"""Retrieves all checkpoints found in `path` directory.
Checkpoints are identified by matching filename to the specified pattern. If
the pattern contains groups, the result will be sorted by the first group in
descending orde... | Retrieves all checkpoints found in `path` directory.
Checkpoints are identified by matching filename to the specified pattern. If
the pattern contains groups, the result will be sorted by the first group in
descending order.
| checkpoint_paths | python | OFA-Sys/OFA | fairseq/fairseq/checkpoint_utils.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/checkpoint_utils.py | Apache-2.0 |
def _upgrade_state_dict(state):
"""Helper for upgrading old model checkpoints."""
# add optimizer_history
if "optimizer_history" not in state:
state["optimizer_history"] = [
{"criterion_name": "CrossEntropyCriterion", "best_loss": state["best_loss"]}
]
state["last_optimi... | Helper for upgrading old model checkpoints. | _upgrade_state_dict | python | OFA-Sys/OFA | fairseq/fairseq/checkpoint_utils.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/checkpoint_utils.py | Apache-2.0 |
def prune_state_dict(state_dict, model_cfg: Optional[DictConfig]):
"""Prune the given state_dict if desired for LayerDrop
(https://arxiv.org/abs/1909.11556).
Training with LayerDrop allows models to be robust to pruning at inference
time. This function prunes state_dict to allow smaller models to be lo... | Prune the given state_dict if desired for LayerDrop
(https://arxiv.org/abs/1909.11556).
Training with LayerDrop allows models to be robust to pruning at inference
time. This function prunes state_dict to allow smaller models to be loaded
from a larger model and re-maps the existing state_dict for this ... | prune_state_dict | python | OFA-Sys/OFA | fairseq/fairseq/checkpoint_utils.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/checkpoint_utils.py | Apache-2.0 |
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