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from torchaudio.models import Conformer
from torchaudio.models.rnnt import _TimeReduction
from transformers import PretrainedConfig, PreTrainedModel
import torch
from torch import nn
from typing import List, Tuple, Optional
class ConformerConfig(PretrainedConfig):
model_type = 'conformer'
class ConformerEncoder(PreTrainedModel):
config_class = ConformerConfig
def __init__(
self,
config,
) -> None:
super().__init__(config)
self.time_reduction = _TimeReduction(config.time_reduction_stride)
self.input_linear = torch.nn.Linear(
config.input_dim * config.time_reduction_stride,
config.conformer_input_dim)
self.conformer = Conformer(
num_layers=config.conformer_num_layers,
input_dim=config.conformer_input_dim,
ffn_dim=config.conformer_ffn_dim,
num_heads=config.conformer_num_heads,
depthwise_conv_kernel_size=config.conformer_depthwise_conv_kernel_size,
dropout=config.conformer_dropout,
use_group_norm=True,
convolution_first=True,
)
self.output_linear = torch.nn.Linear(config.conformer_input_dim, config.output_dim)
def forward(self, inputs, lengths, labels=None):
time_reduction_out, time_reduction_lengths = self.time_reduction(inputs, lengths)
input_linear_out = self.input_linear(time_reduction_out)
x, input_lengths = self.conformer(input_linear_out, time_reduction_lengths)
logits = self.output_linear(x)
loss = None
if labels is not None:
labels_mask = labels >= 0
target_lengths = labels_mask.sum(-1)
flattened_targets = labels.masked_select(labels_mask)
log_probs = nn.functional.log_softmax(
logits,
dim=-1,
dtype=torch.float32
).transpose(0, 1)
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=self.config.ctc_loss_reduction,
zero_infinity=self.config.ctc_zero_infinity,
)
output = (logits, input_lengths)
return ((loss,) + output) if loss is not None else output
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