Upload ConformerEncoder

config.json

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+{
+  "architectures": [
+    "ConformerEncoder"
+  ],
+  "auto_map": {
+    "AutoConfig": "conformer.ConformerConfig",
+    "AutoModel": "conformer.ConformerEncoder"
+  },
+  "conformer_depthwise_conv_kernel_size": 31,
+  "conformer_dropout": 0.0,
+  "conformer_ffn_dim": 576,
+  "conformer_input_dim": 144,
+  "conformer_num_heads": 4,
+  "conformer_num_layers": 4,
+  "ctc_loss_reduction": "mean",
+  "ctc_zero_infinity": true,
+  "input_dim": 80,
+  "model_type": "conformer",
+  "output_dim": 40,
+  "pad_token_id": 39,
+  "time_reduction_stride": 4,
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.2"
+}

conformer.py

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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

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:392b4943a07d85d92ca96c94743dcf98178b17594a44e1ad3a1bc5e273779713
+size 7994432