From 95cf2646fa6dae67bf53354f4ed5e81780d8fee9 Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 11 三月 2024 14:43:08 +0800
Subject: [PATCH] onnx (#1460)
---
funasr/models/paraformer_streaming/model.py | 130 +++++++++++++++++++++++++++++++++++++++++++
1 files changed, 130 insertions(+), 0 deletions(-)
diff --git a/funasr/models/paraformer_streaming/model.py b/funasr/models/paraformer_streaming/model.py
index 4cf20de..cebbfc1 100644
--- a/funasr/models/paraformer_streaming/model.py
+++ b/funasr/models/paraformer_streaming/model.py
@@ -561,4 +561,134 @@
return result, meta_data
+ def export(
+ self,
+ max_seq_len=512,
+ **kwargs,
+ ):
+
+ is_onnx = kwargs.get("type", "onnx") == "onnx"
+ encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export")
+ self.encoder = encoder_class(self.encoder, onnx=is_onnx)
+
+ predictor_class = tables.predictor_classes.get(kwargs["predictor"] + "Export")
+ self.predictor = predictor_class(self.predictor, onnx=is_onnx)
+
+ if kwargs["decoder"] == "ParaformerSANMDecoder":
+ kwargs["decoder"] = "ParaformerSANMDecoderOnline"
+ decoder_class = tables.decoder_classes.get(kwargs["decoder"] + "Export")
+ self.decoder = decoder_class(self.decoder, onnx=is_onnx)
+
+ from funasr.utils.torch_function import MakePadMask
+ from funasr.utils.torch_function import sequence_mask
+
+ if is_onnx:
+ self.make_pad_mask = MakePadMask(max_seq_len, flip=False)
+ else:
+ self.make_pad_mask = sequence_mask(max_seq_len, flip=False)
+
+ self.forward = self._export_forward
+ import copy
+ import types
+ encoder_model = copy.copy(self)
+ decoder_model = copy.copy(self)
+
+ # encoder
+ encoder_model.forward = types.MethodType(ParaformerStreaming._export_encoder_forward, encoder_model)
+ encoder_model.export_dummy_inputs = types.MethodType(ParaformerStreaming.export_encoder_dummy_inputs, encoder_model)
+ encoder_model.export_input_names = types.MethodType(ParaformerStreaming.export_encoder_input_names, encoder_model)
+ encoder_model.export_output_names = types.MethodType(ParaformerStreaming.export_encoder_output_names, encoder_model)
+ encoder_model.export_dynamic_axes = types.MethodType(ParaformerStreaming.export_encoder_dynamic_axes, encoder_model)
+ encoder_model.export_name = types.MethodType(ParaformerStreaming.export_encoder_name, encoder_model)
+
+ # decoder
+ decoder_model.forward = types.MethodType(ParaformerStreaming._export_decoder_forward, decoder_model)
+ decoder_model.export_dummy_inputs = types.MethodType(ParaformerStreaming.export_decoder_dummy_inputs, decoder_model)
+ decoder_model.export_input_names = types.MethodType(ParaformerStreaming.export_decoder_input_names, decoder_model)
+ decoder_model.export_output_names = types.MethodType(ParaformerStreaming.export_decoder_output_names, decoder_model)
+ decoder_model.export_dynamic_axes = types.MethodType(ParaformerStreaming.export_decoder_dynamic_axes, decoder_model)
+ decoder_model.export_name = types.MethodType(ParaformerStreaming.export_decoder_name, decoder_model)
+
+ return encoder_model, decoder_model
+
+ def _export_encoder_forward(
+ self,
+ speech: torch.Tensor,
+ speech_lengths: torch.Tensor,
+ ):
+ # a. To device
+ batch = {"speech": speech, "speech_lengths": speech_lengths, "online": True}
+ # batch = to_device(batch, device=self.device)
+
+ enc, enc_len = self.encoder(**batch)
+ mask = self.make_pad_mask(enc_len)[:, None, :]
+ alphas, _ = self.predictor.forward_cnn(enc, mask)
+
+ return enc, enc_len, alphas
+
+ def export_encoder_dummy_inputs(self):
+ speech = torch.randn(2, 30, 560)
+ speech_lengths = torch.tensor([6, 30], dtype=torch.int32)
+ return (speech, speech_lengths)
+
+ def export_encoder_input_names(self):
+ return ['speech', 'speech_lengths']
+
+ def export_encoder_output_names(self):
+ return ['enc', 'enc_len', 'alphas']
+
+ def export_encoder_dynamic_axes(self):
+ return {
+ 'speech': {
+ 0: 'batch_size',
+ 1: 'feats_length'
+ },
+ 'speech_lengths': {
+ 0: 'batch_size',
+ },
+ 'enc': {
+ 0: 'batch_size',
+ 1: 'feats_length'
+ },
+ 'enc_len': {
+ 0: 'batch_size',
+ },
+ 'alphas': {
+ 0: 'batch_size',
+ 1: 'feats_length'
+ },
+ }
+
+ def export_encoder_name(self):
+ return "model.onnx"
+
+ def _export_decoder_forward(
+ self,
+ enc: torch.Tensor,
+ enc_len: torch.Tensor,
+ acoustic_embeds: torch.Tensor,
+ acoustic_embeds_len: torch.Tensor,
+ *args,
+ ):
+ decoder_out, out_caches = self.decoder(enc, enc_len, acoustic_embeds, acoustic_embeds_len, *args)
+ sample_ids = decoder_out.argmax(dim=-1)
+
+ return decoder_out, sample_ids, out_caches
+
+ def export_decoder_dummy_inputs(self):
+ dummy_inputs = self.decoder.get_dummy_inputs(enc_size=self.encoder._output_size)
+ return dummy_inputs
+
+ def export_decoder_input_names(self):
+
+ return self.decoder.get_input_names()
+
+ def export_decoder_output_names(self):
+
+ return self.decoder.get_output_names()
+
+ def export_decoder_dynamic_axes(self):
+ return self.decoder.get_dynamic_axes()
+ def export_decoder_name(self):
+ return "decoder.onnx"
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