first commit for takway.ai

funasr/models/contextual_paraformer/export_meta.py

@@ -0,0 +1,113 @@
+#!/usr/bin/env python3
+# -*- encoding: utf-8 -*-
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+#  MIT License  (https://opensource.org/licenses/MIT)
+
+import torch
+import types
+
+from funasr.register import tables
+from funasr.models.seaco_paraformer.export_meta import ContextualEmbedderExport
+
+
+class ContextualEmbedderExport2(ContextualEmbedderExport):
+    def __init__(self, model, **kwargs):
+        super().__init__(model)
+        self.embedding = model.bias_embed
+        model.bias_encoder.batch_first = False
+        self.bias_encoder = model.bias_encoder
+
+
+def export_rebuild_model(model, **kwargs):
+    is_onnx = kwargs.get("type", "onnx") == "onnx"
+
+    encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export")
+    model.encoder = encoder_class(model.encoder, onnx=is_onnx)
+
+    predictor_class = tables.predictor_classes.get(kwargs["predictor"] + "Export")
+    model.predictor = predictor_class(model.predictor, onnx=is_onnx)
+
+    # little difference with bias encoder with seaco paraformer
+    embedder_class = ContextualEmbedderExport2
+    embedder_model = embedder_class(model, onnx=is_onnx)
+
+    if kwargs["decoder"] == "ParaformerSANMDecoder":
+        kwargs["decoder"] = "ParaformerSANMDecoderOnline"
+    decoder_class = tables.decoder_classes.get(kwargs["decoder"] + "Export")
+    model.decoder = decoder_class(model.decoder, onnx=is_onnx)
+
+    from funasr.utils.torch_function import sequence_mask
+
+    model.make_pad_mask = sequence_mask(kwargs["max_seq_len"], flip=False)
+    model.feats_dim = 560
+
+    import copy
+
+    backbone_model = copy.copy(model)
+
+    # backbone
+    backbone_model.forward = types.MethodType(export_backbone_forward, backbone_model)
+    backbone_model.export_dummy_inputs = types.MethodType(
+        export_backbone_dummy_inputs, backbone_model
+    )
+    backbone_model.export_input_names = types.MethodType(
+        export_backbone_input_names, backbone_model
+    )
+    backbone_model.export_output_names = types.MethodType(
+        export_backbone_output_names, backbone_model
+    )
+    backbone_model.export_dynamic_axes = types.MethodType(
+        export_backbone_dynamic_axes, backbone_model
+    )
+    backbone_model.export_name = types.MethodType(export_backbone_name, backbone_model)
+
+    return backbone_model, embedder_model
+
+
+def export_backbone_forward(
+    self,
+    speech: torch.Tensor,
+    speech_lengths: torch.Tensor,
+    bias_embed: torch.Tensor,
+):
+    batch = {"speech": speech, "speech_lengths": speech_lengths}
+
+    enc, enc_len = self.encoder(**batch)
+    mask = self.make_pad_mask(enc_len)[:, None, :]
+    pre_acoustic_embeds, pre_token_length, _, _ = self.predictor(enc, mask)
+    pre_token_length = pre_token_length.floor().type(torch.int32)
+
+    decoder_out, _ = self.decoder(enc, enc_len, pre_acoustic_embeds, pre_token_length, bias_embed)
+    decoder_out = torch.log_softmax(decoder_out, dim=-1)
+
+    return decoder_out, pre_token_length
+
+
+def export_backbone_dummy_inputs(self):
+    speech = torch.randn(2, 30, self.feats_dim)
+    speech_lengths = torch.tensor([6, 30], dtype=torch.int32)
+    bias_embed = torch.randn(2, 1, 512)
+    return (speech, speech_lengths, bias_embed)
+
+
+def export_backbone_input_names(self):
+    return ["speech", "speech_lengths", "bias_embed"]
+
+
+def export_backbone_output_names(self):
+    return ["logits", "token_num"]
+
+
+def export_backbone_dynamic_axes(self):
+    return {
+        "speech": {0: "batch_size", 1: "feats_length"},
+        "speech_lengths": {
+            0: "batch_size",
+        },
+        "bias_embed": {0: "batch_size", 1: "num_hotwords"},
+        "logits": {0: "batch_size", 1: "logits_length"},
+    }
+
+
+def export_backbone_name(self):
+    return "model.onnx"