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https://github.com/modelscope/FunASR
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paraformer streaming onnx
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游雁
parent
e5dd9cb090
commit
836bf6417e
168
funasr/models/paraformer_streaming/export_meta.py
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168
funasr/models/paraformer_streaming/export_meta.py
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#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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import types
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import torch
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from funasr.register import tables
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def export_rebuild_model(model, **kwargs):
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model.device = kwargs.get("device")
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is_onnx = kwargs.get("type", "onnx") == "onnx"
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encoder_class = tables.encoder_classes.get(kwargs["encoder"]+"Export")
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model.encoder = encoder_class(model.encoder, onnx=is_onnx)
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predictor_class = tables.predictor_classes.get(kwargs["predictor"]+"Export")
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model.predictor = predictor_class(model.predictor, onnx=is_onnx)
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decoder_class = tables.decoder_classes.get(kwargs["decoder"]+"Export")
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model.decoder = decoder_class(model.decoder, onnx=is_onnx)
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from funasr.utils.torch_function import sequence_mask
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model.make_pad_mask = sequence_mask(kwargs['max_seq_len'], flip=False)
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model.forward = types.MethodType(export_forward, model)
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model.export_dummy_inputs = types.MethodType(export_dummy_inputs, model)
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model.export_input_names = types.MethodType(export_input_names, model)
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model.export_output_names = types.MethodType(export_output_names, model)
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model.export_dynamic_axes = types.MethodType(export_dynamic_axes, model)
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model.export_name = types.MethodType(export_name, model)
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return model
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def export_rebuild_model(model, **kwargs):
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# self.device = kwargs.get("device")
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is_onnx = kwargs.get("type", "onnx") == "onnx"
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encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export")
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model.encoder = encoder_class(model.encoder, onnx=is_onnx)
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predictor_class = tables.predictor_classes.get(kwargs["predictor"] + "Export")
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model.predictor = predictor_class(model.predictor, onnx=is_onnx)
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if kwargs["decoder"] == "ParaformerSANMDecoder":
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kwargs["decoder"] = "ParaformerSANMDecoderOnline"
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decoder_class = tables.decoder_classes.get(kwargs["decoder"] + "Export")
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model.decoder = decoder_class(model.decoder, onnx=is_onnx)
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from funasr.utils.torch_function import sequence_mask
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model.make_pad_mask = sequence_mask(max_seq_len=None, flip=False)
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import copy
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import types
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encoder_model = copy.copy(model)
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decoder_model = copy.copy(model)
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# encoder
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encoder_model.forward = types.MethodType(export_encoder_forward, encoder_model)
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encoder_model.export_dummy_inputs = types.MethodType(export_encoder_dummy_inputs, encoder_model)
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encoder_model.export_input_names = types.MethodType(export_encoder_input_names, encoder_model)
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encoder_model.export_output_names = types.MethodType(export_encoder_output_names, encoder_model)
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encoder_model.export_dynamic_axes = types.MethodType(export_encoder_dynamic_axes, encoder_model)
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encoder_model.export_name = types.MethodType(export_encoder_name, encoder_model)
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# decoder
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decoder_model.forward = types.MethodType(export_decoder_forward, decoder_model)
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decoder_model.export_dummy_inputs = types.MethodType(export_decoder_dummy_inputs, decoder_model)
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decoder_model.export_input_names = types.MethodType(export_decoder_input_names, decoder_model)
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decoder_model.export_output_names = types.MethodType(export_decoder_output_names, decoder_model)
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decoder_model.export_dynamic_axes = types.MethodType(export_decoder_dynamic_axes, decoder_model)
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decoder_model.export_name = types.MethodType(export_decoder_name, decoder_model)
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return encoder_model, decoder_model
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def export_encoder_forward(
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self,
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speech: torch.Tensor,
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speech_lengths: torch.Tensor,
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):
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# a. To device
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batch = {"speech": speech, "speech_lengths": speech_lengths, "online": True}
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# batch = to_device(batch, device=self.device)
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enc, enc_len = self.encoder(**batch)
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mask = self.make_pad_mask(enc_len)[:, None, :]
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alphas, _ = self.predictor.forward_cnn(enc, mask)
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return enc, enc_len, alphas
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def export_encoder_dummy_inputs(self):
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speech = torch.randn(2, 30, 560)
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speech_lengths = torch.tensor([6, 30], dtype=torch.int32)
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return (speech, speech_lengths)
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def export_encoder_input_names(self):
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return ['speech', 'speech_lengths']
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def export_encoder_output_names(self):
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return ['enc', 'enc_len', 'alphas']
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def export_encoder_dynamic_axes(self):
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return {
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'speech': {
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0: 'batch_size',
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1: 'feats_length'
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},
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'speech_lengths': {
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0: 'batch_size',
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},
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'enc': {
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0: 'batch_size',
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1: 'feats_length'
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},
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'enc_len': {
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0: 'batch_size',
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},
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'alphas': {
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0: 'batch_size',
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1: 'feats_length'
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},
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}
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def export_encoder_name(self):
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return "model.onnx"
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def export_decoder_forward(
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self,
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enc: torch.Tensor,
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enc_len: torch.Tensor,
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acoustic_embeds: torch.Tensor,
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acoustic_embeds_len: torch.Tensor,
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*args,
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):
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decoder_out, out_caches = self.decoder(enc, enc_len, acoustic_embeds, acoustic_embeds_len, *args)
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sample_ids = decoder_out.argmax(dim=-1)
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return decoder_out, sample_ids, out_caches
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def export_decoder_dummy_inputs(self):
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dummy_inputs = self.decoder.get_dummy_inputs(enc_size=self.encoder._output_size)
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return dummy_inputs
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def export_decoder_input_names(self):
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return self.decoder.get_input_names()
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def export_decoder_output_names(self):
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return self.decoder.get_output_names()
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def export_decoder_dynamic_axes(self):
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return self.decoder.get_dynamic_axes()
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def export_decoder_name(self):
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return "decoder.onnx"
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@ -562,130 +562,8 @@ class ParaformerStreaming(Paraformer):
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ibest_writer["text"][key[0]] = text_postprocessed
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ibest_writer["text"][key[0]] = text_postprocessed
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return result, meta_data
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return result, meta_data
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def export(
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self,
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max_seq_len=512,
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**kwargs,
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):
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self.device = kwargs.get("device")
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is_onnx = kwargs.get("type", "onnx") == "onnx"
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encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export")
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self.encoder = encoder_class(self.encoder, onnx=is_onnx)
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predictor_class = tables.predictor_classes.get(kwargs["predictor"] + "Export")
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def export(self, **kwargs):
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self.predictor = predictor_class(self.predictor, onnx=is_onnx)
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from .export_meta import export_rebuild_model
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models = export_rebuild_model(model=self, **kwargs)
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if kwargs["decoder"] == "ParaformerSANMDecoder":
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return models
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kwargs["decoder"] = "ParaformerSANMDecoderOnline"
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decoder_class = tables.decoder_classes.get(kwargs["decoder"] + "Export")
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self.decoder = decoder_class(self.decoder, onnx=is_onnx)
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from funasr.utils.torch_function import sequence_mask
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self.make_pad_mask = sequence_mask(max_seq_len, flip=False)
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import copy
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import types
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encoder_model = copy.copy(self)
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decoder_model = copy.copy(self)
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# encoder
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encoder_model.forward = types.MethodType(ParaformerStreaming.export_encoder_forward, encoder_model)
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encoder_model.export_dummy_inputs = types.MethodType(ParaformerStreaming.export_encoder_dummy_inputs, encoder_model)
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encoder_model.export_input_names = types.MethodType(ParaformerStreaming.export_encoder_input_names, encoder_model)
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encoder_model.export_output_names = types.MethodType(ParaformerStreaming.export_encoder_output_names, encoder_model)
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encoder_model.export_dynamic_axes = types.MethodType(ParaformerStreaming.export_encoder_dynamic_axes, encoder_model)
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encoder_model.export_name = types.MethodType(ParaformerStreaming.export_encoder_name, encoder_model)
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# decoder
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decoder_model.forward = types.MethodType(ParaformerStreaming.export_decoder_forward, decoder_model)
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decoder_model.export_dummy_inputs = types.MethodType(ParaformerStreaming.export_decoder_dummy_inputs, decoder_model)
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decoder_model.export_input_names = types.MethodType(ParaformerStreaming.export_decoder_input_names, decoder_model)
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decoder_model.export_output_names = types.MethodType(ParaformerStreaming.export_decoder_output_names, decoder_model)
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decoder_model.export_dynamic_axes = types.MethodType(ParaformerStreaming.export_decoder_dynamic_axes, decoder_model)
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decoder_model.export_name = types.MethodType(ParaformerStreaming.export_decoder_name, decoder_model)
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return encoder_model, decoder_model
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def export_encoder_forward(
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self,
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speech: torch.Tensor,
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speech_lengths: torch.Tensor,
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):
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# a. To device
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batch = {"speech": speech, "speech_lengths": speech_lengths, "online": True}
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# batch = to_device(batch, device=self.device)
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enc, enc_len = self.encoder(**batch)
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mask = self.make_pad_mask(enc_len)[:, None, :]
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alphas, _ = self.predictor.forward_cnn(enc, mask)
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return enc, enc_len, alphas
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def export_encoder_dummy_inputs(self):
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speech = torch.randn(2, 30, 560)
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speech_lengths = torch.tensor([6, 30], dtype=torch.int32)
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return (speech, speech_lengths)
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def export_encoder_input_names(self):
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return ['speech', 'speech_lengths']
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def export_encoder_output_names(self):
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return ['enc', 'enc_len', 'alphas']
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def export_encoder_dynamic_axes(self):
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return {
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'speech': {
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0: 'batch_size',
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1: 'feats_length'
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},
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'speech_lengths': {
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0: 'batch_size',
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},
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'enc': {
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0: 'batch_size',
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1: 'feats_length'
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},
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'enc_len': {
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0: 'batch_size',
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},
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'alphas': {
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0: 'batch_size',
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1: 'feats_length'
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},
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}
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def export_encoder_name(self):
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return "model.onnx"
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def export_decoder_forward(
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self,
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enc: torch.Tensor,
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enc_len: torch.Tensor,
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acoustic_embeds: torch.Tensor,
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acoustic_embeds_len: torch.Tensor,
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*args,
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):
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decoder_out, out_caches = self.decoder(enc, enc_len, acoustic_embeds, acoustic_embeds_len, *args)
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sample_ids = decoder_out.argmax(dim=-1)
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return decoder_out, sample_ids, out_caches
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def export_decoder_dummy_inputs(self):
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dummy_inputs = self.decoder.get_dummy_inputs(enc_size=self.encoder._output_size)
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return dummy_inputs
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def export_decoder_input_names(self):
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return self.decoder.get_input_names()
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def export_decoder_output_names(self):
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return self.decoder.get_output_names()
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def export_decoder_dynamic_axes(self):
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return self.decoder.get_dynamic_axes()
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def export_decoder_name(self):
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return "decoder.onnx"
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