paraformer streaming onnx

游雁

2024-03-18 836bf6417e7c75710a83518ca1d509558f0bfb46

paraformer streaming onnx

 funasr/models/paraformer_streaming/export_meta.py

New file
@@ -0,0 +1,168 @@
#!/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 types
import torch
from funasr.register import tables


def export_rebuild_model(model, **kwargs):
        model.device = kwargs.get("device")
        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)


        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.forward = types.MethodType(export_forward, model)
        model.export_dummy_inputs = types.MethodType(export_dummy_inputs, model)
        model.export_input_names = types.MethodType(export_input_names, model)
        model.export_output_names = types.MethodType(export_output_names, model)
        model.export_dynamic_axes = types.MethodType(export_dynamic_axes, model)
        model.export_name = types.MethodType(export_name, model)
        
        return model


def export_rebuild_model(model, **kwargs):
    # self.device = kwargs.get("device")
    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)
    
    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(max_seq_len=None, flip=False)
    
    import copy
    import types
    encoder_model = copy.copy(model)
    decoder_model = copy.copy(model)
    
    # encoder
    encoder_model.forward = types.MethodType(export_encoder_forward, encoder_model)
    encoder_model.export_dummy_inputs = types.MethodType(export_encoder_dummy_inputs, encoder_model)
    encoder_model.export_input_names = types.MethodType(export_encoder_input_names, encoder_model)
    encoder_model.export_output_names = types.MethodType(export_encoder_output_names, encoder_model)
    encoder_model.export_dynamic_axes = types.MethodType(export_encoder_dynamic_axes, encoder_model)
    encoder_model.export_name = types.MethodType(export_encoder_name, encoder_model)
    
    # decoder
    decoder_model.forward = types.MethodType(export_decoder_forward, decoder_model)
    decoder_model.export_dummy_inputs = types.MethodType(export_decoder_dummy_inputs, decoder_model)
    decoder_model.export_input_names = types.MethodType(export_decoder_input_names, decoder_model)
    decoder_model.export_output_names = types.MethodType(export_decoder_output_names, decoder_model)
    decoder_model.export_dynamic_axes = types.MethodType(export_decoder_dynamic_axes, decoder_model)
    decoder_model.export_name = types.MethodType(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"

 funasr/models/paraformer_streaming/model.py

@@ -562,130 +562,8 @@
            ibest_writer["text"][key[0]] = text_postprocessed

        return result, meta_data

    def export(
        self,
        max_seq_len=512,
        **kwargs,
    ):
        self.device = kwargs.get("device")
        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 sequence_mask


        self.make_pad_mask = sequence_mask(max_seq_len, flip=False)

        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"
    def export(self, **kwargs):
        from .export_meta import export_rebuild_model
        models = export_rebuild_model(model=self, **kwargs)
        return models