Commit
•
b9e9c01
1
Parent(s):
0eac7e3
Upload ConformerEncoder
Browse files- config.json +25 -0
- conformer.py +66 -0
- model.safetensors +3 -0
config.json
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{
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"_name_or_path": "tiny/checkpoint-16600",
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"architectures": [
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"ConformerEncoder"
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],
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"auto_map": {
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"AutoConfig": "conformer.ConformerConfig",
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"AutoModel": "conformer.ConformerEncoder"
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},
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"conformer_depthwise_conv_kernel_size": 31,
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"conformer_dropout": 0.1,
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"conformer_ffn_dim": 576,
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"conformer_input_dim": 144,
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"conformer_num_heads": 4,
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"conformer_num_layers": 8,
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"ctc_loss_reduction": "mean",
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"ctc_zero_infinity": true,
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"input_dim": 80,
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"model_type": "conformer",
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"output_dim": 40,
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"pad_token_id": 39,
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"time_reduction_stride": 4,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.35.2"
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}
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conformer.py
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from torchaudio.models import Conformer
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from torchaudio.models.rnnt import _TimeReduction
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from transformers import PretrainedConfig, PreTrainedModel
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import torch
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from torch import nn
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from typing import List, Tuple, Optional
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class ConformerConfig(PretrainedConfig):
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model_type = 'conformer'
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class ConformerEncoder(PreTrainedModel):
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config_class = ConformerConfig
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def __init__(
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self,
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config,
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) -> None:
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super().__init__(config)
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self.time_reduction = _TimeReduction(config.time_reduction_stride)
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self.input_linear = torch.nn.Linear(
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config.input_dim * config.time_reduction_stride,
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config.conformer_input_dim)
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self.conformer = Conformer(
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num_layers=config.conformer_num_layers,
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input_dim=config.conformer_input_dim,
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ffn_dim=config.conformer_ffn_dim,
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num_heads=config.conformer_num_heads,
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depthwise_conv_kernel_size=config.conformer_depthwise_conv_kernel_size,
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dropout=config.conformer_dropout,
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use_group_norm=True,
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convolution_first=True,
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)
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self.output_linear = torch.nn.Linear(config.conformer_input_dim, config.output_dim)
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def forward(self, inputs, lengths, labels=None):
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time_reduction_out, time_reduction_lengths = self.time_reduction(inputs, lengths)
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input_linear_out = self.input_linear(time_reduction_out)
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x, input_lengths = self.conformer(input_linear_out, time_reduction_lengths)
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logits = self.output_linear(x)
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loss = None
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if labels is not None:
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labels_mask = labels >= 0
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target_lengths = labels_mask.sum(-1)
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flattened_targets = labels.masked_select(labels_mask)
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log_probs = nn.functional.log_softmax(
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logits,
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dim=-1,
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dtype=torch.float32
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).transpose(0, 1)
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with torch.backends.cudnn.flags(enabled=False):
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loss = nn.functional.ctc_loss(
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log_probs,
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flattened_targets,
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input_lengths,
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target_lengths,
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blank=self.config.pad_token_id,
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reduction=self.config.ctc_loss_reduction,
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zero_infinity=self.config.ctc_zero_infinity,
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)
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output = (logits, input_lengths)
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return ((loss,) + output) if loss is not None else output
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:bafaee0f2a0d7ada1201d14c875e94b40d0c8560c86421dca23acfd31968da84
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size 7905192
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