text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
self.spectrogram_length = spectrogram_length
self.num_channels = num_channels
self.patch_size = patch_size
self.freq_len = feature_size // self.patch_size[1]
self.n_fft = n_fft
self.hop_length = sampling_rate // hop_length_to_sampling_rate
self.sampling_rate = sampling_ra... | 10,281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/feature_extraction_tvlt.py |
def _np_extract_fbank_features(self, waveform: np.array) -> np.ndarray:
"""
Compute the log-mel spectrogram of the provided audio, gives similar results to Whisper's original torch
implementation with 1e-5 tolerance.
"""
log_spec = spectrogram(
waveform,
w... | 10,281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/feature_extraction_tvlt.py |
def __call__(
self,
raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],
return_tensors: Optional[Union[str, TensorType]] = None,
return_attention_mask: Optional[bool] = True,
sampling_rate: Optional[int] = None,
resample: bool = False,
... | 10,281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/feature_extraction_tvlt.py |
Args:
raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`):
The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float
values, a list of numpy arrays or a list of list of float values. Must be mono channel... | 10,281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/feature_extraction_tvlt.py |
<Tip>
For TvltTransformer models, `attention_mask` should alwys be passed for batched inference, to avoid
subtle bugs.
</Tip>
sampling_rate (`int`, *optional*):
The sampling rate at which the `raw_speech` input was sampled. It is strongly re... | 10,281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/feature_extraction_tvlt.py |
- **audio_values** -- Audio values to be fed to a model, of shape (batch_size, num_channels, height,
width).
- **audio_mask** -- Audio masks to be fed to a model, of shape (batch_size, num_audio_patches).
"""
if sampling_rate is not None:
if sampling_rate != self.... | 10,281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/feature_extraction_tvlt.py |
is_batched_numpy = isinstance(raw_speech, np.ndarray) and len(raw_speech.shape) > 1
if is_batched_numpy and len(raw_speech.shape) > 2:
raise ValueError(f"Only mono-channel audio is supported for input to {self}")
is_batched = is_batched_numpy or (
isinstance(raw_speech, (list, tu... | 10,281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/feature_extraction_tvlt.py |
# Convert audio signals to log mel spectrograms, truncate by time axis
audio_features = [
self._np_extract_fbank_features(waveform.squeeze()).T[: self.spectrogram_length] for waveform in raw_speech
]
if isinstance(audio_features[0], List):
audio_features = [np.asarray(fea... | 10,281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/feature_extraction_tvlt.py |
# convert into correct format for padding
max_time_len = max_patch_len // self.freq_len * self.patch_size[0] # The maximum audio size in a batch
padded_audio_features = np.ones([len(audio_features), 1, max_time_len, self.feature_size]).astype(np.float32)
padded_audio_features = padded_audio_fea... | 10,281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/feature_extraction_tvlt.py |
class MCTCTProcessor(ProcessorMixin):
r"""
Constructs a MCTCT processor which wraps a MCTCT feature extractor and a MCTCT tokenizer into a single processor.
[`MCTCTProcessor`] offers all the functionalities of [`MCTCTFeatureExtractor`] and [`AutoTokenizer`]. See the
[`~MCTCTProcessor.__call__`] and [`~... | 10,282 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/processing_mctct.py |
def __call__(self, *args, **kwargs):
"""
When used in normal mode, this method forwards all its arguments to MCTCTFeatureExtractor's
[`~MCTCTFeatureExtractor.__call__`] and returns its output. If used in the context
[`~MCTCTProcessor.as_target_processor`] this method forwards all its arg... | 10,282 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/processing_mctct.py |
if "raw_speech" in kwargs:
warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.")
audio = kwargs.pop("raw_speech")
else:
audio = kwargs.pop("audio", None)
sampling_rate = kwargs.pop("sampling_rate", None)
text = kwargs.po... | 10,282 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/processing_mctct.py |
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to AutoTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please refer
to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def pad(se... | 10,282 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/processing_mctct.py |
input_features = kwargs.pop("input_features", None)
labels = kwargs.pop("labels", None)
if len(args) > 0:
input_features = args[0]
args = args[1:]
if input_features is not None:
input_features = self.feature_extractor.pad(input_features, *args, **kwargs)
... | 10,282 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/processing_mctct.py |
@contextmanager
def as_target_processor(self):
"""
Temporarily sets the tokenizer for processing the input. Useful for encoding the labels when fine-tuning MCTCT.
"""
warnings.warn(
"`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can pr... | 10,282 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/processing_mctct.py |
class MCTCTFeatureExtractor(SequenceFeatureExtractor):
r"""
Constructs a M-CTC-T feature extractor.
This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains
most of the main methods. Users should refer to this superclass for more information re... | 10,283 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/feature_extraction_mctct.py |
Args:
feature_size (`int`, defaults to 80):
The feature dimension of the extracted features. This is the number of mel_frequency
sampling_rate (`int`, defaults to 16000):
The sampling rate at which the audio files should be digitalized expressed in hertz (Hz).
padding_val... | 10,283 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/feature_extraction_mctct.py |
Constant multiplied in applying Pre-emphasis before DFT.
mel_floor (`float` defaults to 1.0):
Minimum value of mel frequency banks.
normalize_means (`bool`, *optional*, defaults to `True`):
Whether or not to zero-mean normalize the extracted features.
normalize_vars (`boo... | 10,283 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/feature_extraction_mctct.py |
model_input_names = ["input_features", "attention_mask"]
def __init__(
self,
feature_size=80,
sampling_rate=16000,
padding_value=0.0,
hop_length=10,
win_length=25,
win_function="hamming_window",
frame_signal_scale=32768.0,
preemphasis_coeff=0.... | 10,283 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/feature_extraction_mctct.py |
self.feature_size = feature_size
self.sampling_rate = sampling_rate
self.padding_value = padding_value
self.hop_length = hop_length
self.win_length = win_length
self.frame_signal_scale = frame_signal_scale
self.preemphasis_coeff = preemphasis_coeff
self.mel_floor ... | 10,283 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/feature_extraction_mctct.py |
def _extract_mfsc_features(self, one_waveform: np.array) -> np.ndarray:
"""
Extracts MFSC Features for one waveform vector (unbatched). Adapted from Flashlight's C++ MFSC code.
"""
if self.win_function == "hamming_window":
window = window_function(window_length=self.sample_si... | 10,283 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/feature_extraction_mctct.py |
msfc_features = spectrogram(
one_waveform * self.frame_signal_scale,
window=window,
frame_length=self.sample_size,
hop_length=self.sample_stride,
fft_length=self.n_fft,
center=False,
preemphasis=self.preemphasis_coeff,
mel_f... | 10,283 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/feature_extraction_mctct.py |
def normalize(
self, input_features: List[np.ndarray], attention_mask: Optional[np.ndarray] = None
) -> List[np.ndarray]:
lengths = attention_mask.sum(-1) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(x, n, self.padding_value) for x, n in... | 10,283 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/feature_extraction_mctct.py |
def __call__(
self,
raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],
padding: Union[bool, str, PaddingStrategy] = False,
max_length: Optional[int] = None,
truncation: bool = False,
pad_to_multiple_of: Optional[int] = None,
return_at... | 10,283 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/feature_extraction_mctct.py |
Args:
raw_speech (`torch.Tensor`, `np.ndarray`, `List[float]`, `List[torch.Tensor]`, `List[np.ndarray]`, `List[List[float]]`):
The sequence or batch of sequences to be padded. Each sequence can be a tensor, a numpy array, a list
of float values, a list of tensors, a list of n... | 10,283 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/feature_extraction_mctct.py |
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that ... | 10,283 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/feature_extraction_mctct.py |
This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
`>= 7.5` (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128.
return_attention_mask (`bool`, *optional*):
Whether to return the attention mas... | 10,283 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/feature_extraction_mctct.py |
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return Numpy `np.ndarray` objects.
sampling_rate (`int`, *optional*):
The sampling rate at which the `raw_speech` input was sampled. It is strongly re... | 10,283 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/feature_extraction_mctct.py |
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
f" {self.sampling_rate}. Please make sure that the provided `raw_s... | 10,283 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/feature_extraction_mctct.py |
is_batched_numpy = isinstance(raw_speech, np.ndarray) and len(raw_speech.shape) > 1
if is_batched_numpy and len(raw_speech.shape) > 2:
raise ValueError(f"Only mono-channel audio is supported for input to {self}")
is_batched = is_batched_numpy or (
isinstance(raw_speech, (list, tu... | 10,283 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/feature_extraction_mctct.py |
# extract fbank features
features = [self._extract_mfsc_features(one_waveform) for one_waveform in raw_speech]
# convert into correct format for padding
encoded_inputs = BatchFeature({"input_features": features})
padded_inputs = self.pad(
encoded_inputs,
padding... | 10,283 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/feature_extraction_mctct.py |
if self.normalize_means or self.normalize_vars:
attention_mask = (
np.array(attention_mask, dtype=np.int32)
if self._get_padding_strategies(padding, max_length=max_length) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
... | 10,283 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/feature_extraction_mctct.py |
class MCTCTConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MCTCTModel`]. It is used to instantiate an
M-CTC-T model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar... | 10,284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/configuration_mctct.py |
Args:
vocab_size (`int`, *optional*, defaults to 8065):
Vocabulary size of the M-CTC-T model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`MCTCTModel`].
hidden_size (`int`, *optional*, defaults to 1536):
Dime... | 10,284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/configuration_mctct.py |
max_position_embeddings (`int`, *optional*, defaults to 920):
The maximum sequence length that this model might ever be used with (after log-mel spectrogram extraction).
layer_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the layer normalization layers.
l... | 10,284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/configuration_mctct.py |
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.3):
The dropout ratio for the attention probabilities.
pad_token_id (`int`, *optional*, defaults to 1):
The tokenizer in... | 10,284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/configuration_mctct.py |
num_conv_layers (`int`, *optional*, defaults to 1):
Number of convolution layers before applying transformer encoder layers.
conv_kernel (`Sequence[int]`, *optional*, defaults to `(7,)`):
The kernel size of the 1D convolution applied before transformer layers. `len(conv_kernel)` must be ... | 10,284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/configuration_mctct.py |
ctc_loss_reduction (`str`, *optional*, defaults to `"sum"`):
Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an
instance of [`MCTCTForCTC`].
ctc_zero_infinity (`bool`, *optional*, defaults to `False`):
Whether to zero infinite... | 10,284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/configuration_mctct.py |
Example:
```python
>>> from transformers import MCTCTConfig, MCTCTModel
>>> # Initializing a M-CTC-T mctct-large style configuration
>>> configuration = MCTCTConfig()
>>> # Initializing a model (with random weights) from the mctct-large style configuration
>>> model = MCTCTModel(configuration... | 10,284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/configuration_mctct.py |
def __init__(
self,
vocab_size=8065,
hidden_size=1536,
num_hidden_layers=36,
intermediate_size=6144,
num_attention_heads=4,
attention_head_dim=384,
max_position_embeddings=920,
layer_norm_eps=1e-5,
layerdrop=0.3,
hidden_act="relu",
... | 10,284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/configuration_mctct.py |
self.num_hidden_layers = num_hidden_layers
self.intermediate_size = intermediate_size
self.num_attention_heads = num_attention_heads
self.attention_head_dim = attention_head_dim
self.max_position_embeddings = max_position_embeddings
self.layer_norm_eps = layer_norm_eps
se... | 10,284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/configuration_mctct.py |
self.ctc_zero_infinity = ctc_zero_infinity | 10,284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/configuration_mctct.py |
# prevents config testing fail with exporting to json
self.conv_kernel = list(conv_kernel)
self.conv_stride = list(conv_stride)
if len(self.conv_kernel) != self.num_conv_layers:
raise ValueError(
"Configuration for convolutional module is incorrect. "
... | 10,284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/configuration_mctct.py |
class MCTCTConv1dSubsampler(nn.Module):
"""
Convolutional subsampler: a stack of 1D convolution (along temporal dimension) followed by non-linear activation
via gated linear units (https://arxiv.org/abs/1911.08460)
"""
def __init__(self, config):
super().__init__()
self.config = con... | 10,285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
self.out_channels = config.hidden_size * 2 # considering GLU halving
self.kernel_size = config.conv_kernel
self.stride = config.conv_stride
# NOTE: MCTCT by construction only uses one convolution kernel. I've made this flexible to allow for
# multiple layers of convolutions, but not su... | 10,285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
def forward(self, input_features):
# NOTE: in reference to the NOTE in __init__, right now it just calculates padding as if
# there will be just one conv layer.
padding = sum([size // 2 for size in self.kernel_size]) # (7, 7) -> (3, 3)
input_features = torch.nn.functional.pad(input_fea... | 10,285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
class MCTCTEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embedd... | 10,286 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
self.register_buffer(
"token_type_ids",
torch.zeros... | 10,286 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
# issue #5664
if token_type_ids is None:
if hasattr(... | 10,286 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings | 10,286 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
class MCTCTSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the numbe... | 10,287 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
self.max_position_embeddings = config.max_position_embeddings
self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
self.is_decoder = config.is_decoder
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_he... | 10,287 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
def relative_position_embedding_rotate(self, scores):
# NOTE: should re-evaluate whether this re-implementation was truly necessary
# or the reason why my complete re-haul worked was due to some other part
# of the code. Adding this and the reshape fortrain code seems very undesirable.
s... | 10,287 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
halfpoint = hidden_state // 2
scores = scores[:, halfpoint : halfpoint + seq_len].transpose(1, 2) # e.g. [10, 14, 14, 4]
return scores.permute(0, 3, 1, 2)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
output_attentions=False,
):... | 10,287 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
# relative key position embeddings
positional_embedding = self.distance_embedding.weight
relative_position_scores = torch.einsum("lh, bche -> bcle", positional_embedding, query_layer.transpose(2, 3))
relative_position_scores = self.relative_position_embedding_rotate(relative_position_scores)
... | 10,287 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).flatten(start_dim=-2)
outputs = (context_layer, attention_probs) ... | 10,287 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
class MCTCTLayerNorm(nn.Module):
def __init__(self):
super().__init__()
self.singleton_weight = nn.Parameter(torch.ones(1))
self.singleton_bias = nn.Parameter(torch.zeros(1))
def forward(self, hidden_states):
return (hidden_states * self.singleton_weight) + self.singleton_bias | 10,288 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
class MCTCTSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.dense = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Drop... | 10,289 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
class MCTCTAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = MCTCTSelfAttention(config)
self.output = MCTCTSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, inde... | 10,290 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
... | 10,290 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
class MCTCTIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
... | 10,291 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
class MCTCTOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_p... | 10,292 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
class MCTCTLayer(nn.Module):
def __init__(self, config: MCTCTConfig):
super().__init__()
self.seq_len_dim = 1
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.intermediate = MCTCTIntermediate(config)
self.attention = MCTCTAttention(config)
self.is_... | 10,293 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
outputs = (layer_output,) + outputs
return outputs
def feed_forward_chunk(self, attention_output):
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output | 10,293 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
class MCTCTPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MCTCTConfig
base_model_prefix = "mctct"
main_input_name = "input_features"
supports_gradient_check... | 10,294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
def _init_weights(self, module):
"""Initialize the weights"""
std = self.config.initializer_range
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
... | 10,294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_() | 10,294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor):
"""
Computes the output length of the convolutional layers
"""
dilation = 1
for _, kernel_sz, stride in zip(
range(self.config.num_conv_layers), self.config.conv_kernel, self.config.conv_stri... | 10,294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
# subsampled_lengths = attention_mask.sum(-1)
subsampled_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1))
bsz = attention_mask.size()[0]
attention_mask = torch.zeros(
(bsz, feature_vector_length), dtype=attention_mask.dtype, device=attention_mask.device
... | 10,294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
class MCTCTEncoder(MCTCTPreTrainedModel):
def __init__(self, config: MCTCTConfig):
super().__init__(config)
self.hidden_dropout_prob = config.hidden_dropout_prob
self.layer_norm = MCTCTLayerNorm()
self.conv = MCTCTConv1dSubsampler(config)
self.layers = nn.ModuleList([MCTCTLa... | 10,295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
def forward(
self,
input_features: torch.Tensor,
attention_mask: torch.Tensor,
head_mask: torch.Tensor,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
) -> Union[Tuple, BaseModelOutput]:
output_attentions... | 10,295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
hidden_states = nn.functional.dropout(inputs_embeds, p=self.hidden_dropout_prob, training=self.training)
# expand attention_mask
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _prepare_4d_attention_mask(attention_mask, inpu... | 10,295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
synced_gpus = is_deepspeed_zero3_enabled() or is_fsdp_managed_module(self)
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)... | 10,295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
skip_the_layer = True if self.training and (dropout_probability < self.config.layerdrop) else False
if not skip_the_layer or synced_gpus:
# under fsdp or deepspeed zero3 all gpus must run in sync
if self.gradient_checkpointing and self.training:
layer_outp... | 10,295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
if skip_the_layer:
layer_outputs = (None, None)
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(... | 10,295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
class MCTCTModel(MCTCTPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.encoder = MCTCTEncoder(config)
# Initialize weights and apply final processing
self.post_init() | 10,296 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
@add_start_docstrings_to_model_forward(MCTCT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
modality="audio",
expected_output=_EXPECTED_OUTPUT_SHAP... | 10,296 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 10,296 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
if input_features is None:
raise ValueError("You have to specify input_features.")
encoder_outputs = self.encoder(
input_features,
attention_mask=attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_state... | 10,296 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
class MCTCTForCTC(MCTCTPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.mctct = MCTCTModel(config)
if config.vocab_size is None:
raise ValueError(
f"You are trying to instantiate {self.__class__} with a configuration that "
... | 10,297 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
@add_start_docstrings_to_model_forward(MCTCT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_CTC_EXPECTED_OUTPUT,
expected_loss=_CTC_EXPECTED_LOSS,
)
def forward(... | 10,297 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
the sequence length of the output logits. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`.
All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ...,
config.vocab_size - 1]`.
"""
if labels is not None and labels.max() >= self.... | 10,297 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.mctct(
input_features,
attention_mask=attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_stat... | 10,297 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
loss = None
if labels is not None:
# retrieve loss input_lengths from attention_mask
attention_mask = (
attention_mask
if attention_mask is not None
else torch.ones(input_features.shape[:-1], dtype=torch.long)
)
inpu... | 10,297 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
with torch.backends.cudnn.flags(enabled=False):
loss = nn.functional.ctc_loss(
log_probs,
flattened_targets,
input_lengths,
target_lengths,
blank=self.config.pad_token_id,
reduction=se... | 10,297 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py |
class NezhaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of an [`NezhaModel`]. It is used to instantiate an Nezha
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar ... | 10,298 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/configuration_nezha.py |
Args:
vocab_size (`int`, optional, defaults to 21128):
Vocabulary size of the NEZHA model. Defines the different tokens that can be represented by the
*inputs_ids* passed to the forward method of [`NezhaModel`].
hidden_size (`int`, optional, defaults to 768):
Dimensio... | 10,298 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/configuration_nezha.py |
hidden_dropout_prob (`float`, optional, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, optional, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_posit... | 10,298 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/configuration_nezha.py |
classifier_dropout (`float`, optional, defaults to 0.1):
The dropout ratio for attached classifiers.
is_decoder (`bool`, *optional*, defaults to `False`):
Whether the model is used as a decoder or not. If `False`, the model is used as an encoder. | 10,298 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/configuration_nezha.py |
Example:
```python
>>> from transformers import NezhaConfig, NezhaModel
>>> # Initializing an Nezha configuration
>>> configuration = NezhaConfig()
>>> # Initializing a model (with random weights) from the Nezha-base style configuration model
>>> model = NezhaModel(configuration)
>>> # A... | 10,298 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/configuration_nezha.py |
def __init__(
self,
vocab_size=21128,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
... | 10,298 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/configuration_nezha.py |
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
... | 10,298 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/configuration_nezha.py |
class NezhaRelativePositionsEncoding(nn.Module):
"""Implement the Functional Relative Position Encoding"""
def __init__(self, length, depth, max_relative_position=127):
super().__init__()
vocab_size = max_relative_position * 2 + 1
range_vec = torch.arange(length)
range_mat = ran... | 10,299 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
flat_relative_positions_matrix = final_mat.view(-1)
one_hot_relative_positions_matrix = torch.nn.functional.one_hot(
flat_relative_positions_matrix, num_classes=vocab_size
).float()
positions_encoding = torch.matmul(one_hot_relative_positions_matrix, embeddings_table)
my_shap... | 10,299 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
class NezhaEmbeddings(nn.Module):
"""Construct the embeddings from word and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.token_type_embeddings = n... | 10,300 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
) -> torch.Tensor:
if input_ids is not None:
input_shape = input_ids.size()
else:
... | 10,300 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
# issue #5664
if token_type_ids is None:
if hasattr(... | 10,300 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
class NezhaSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads (... | 10,301 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.relative_positions_encoding = NezhaRelativePositionsEncoding(
length=config.max_position_embeddings,
depth=self.attention_head_size,
max_relative_position=config.max_relative_position,
)
self.... | 10,301 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
... | 10,301 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
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