From 59f184a622be316b6a75ce053ee8e19e6a7b50ec Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期二, 07 二月 2023 15:19:18 +0800
Subject: [PATCH] export model
---
funasr/export/models/decoder/__init__.py | 0
funasr/export/__init__.py | 0
funasr/export/models/e2e_asr_paraformer.py | 91 ++++
funasr/export/utils/__init__.py | 0
funasr/export/models/modules/feedforward.py | 31 +
funasr/export/models/predictor/cif.py | 168 ++++++++
funasr/export/models/modules/decoder_layer.py | 43 ++
funasr/export/models/modules/encoder_layer.py | 37 +
funasr/export/models/encoder/sanm_encoder.py | 102 ++++
funasr/export/models/modules/multihead_att.py | 135 ++++++
funasr/export/models/encoder/__init__.py | 0
funasr/export/utils/torch_function.py | 68 +++
funasr/export/models/decoder/sanm_decoder.py | 155 +++++++
funasr/export/export_model.py | 91 ++++
funasr/export/models/__init__.py | 91 ++++
funasr/export/models/modules/__init__.py | 0
funasr/export/models/predictor/cif_test.py | 212 ++++++++++
funasr/export/models/predictor/__init__.py | 0
18 files changed, 1,224 insertions(+), 0 deletions(-)
diff --git a/funasr/export/__init__.py b/funasr/export/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/funasr/export/__init__.py
diff --git a/funasr/export/export_model.py b/funasr/export/export_model.py
new file mode 100644
index 0000000..17bc138
--- /dev/null
+++ b/funasr/export/export_model.py
@@ -0,0 +1,91 @@
+from typing import Union, Dict
+from pathlib import Path
+from typeguard import check_argument_types
+
+import os
+import logging
+import torch
+
+from funasr.bin.asr_inference_paraformer import Speech2Text
+from funasr.export.models import get_model
+
+
+
+class ASRModelExportParaformer:
+ def __init__(self, cache_dir: Union[Path, str] = None, onnx: bool = True):
+ assert check_argument_types()
+ if cache_dir is None:
+ cache_dir = Path.home() / "cache" / "export"
+
+ self.cache_dir = Path(cache_dir)
+ self.export_config = dict(
+ feats_dim=560,
+ onnx=onnx,
+ )
+ logging.info("output dir: {}".format(self.cache_dir))
+ self.onnx = onnx
+
+ def export(
+ self,
+ model: Speech2Text,
+ tag_name: str = None,
+ verbose: bool = False,
+ ):
+
+ export_dir = self.cache_dir / tag_name.replace(' ', '-')
+ os.makedirs(export_dir, exist_ok=True)
+
+ # export encoder1
+ self.export_config["model_name"] = "model"
+ model = get_model(
+ model,
+ self.export_config,
+ )
+ if self.onnx:
+ self._export_onnx(model, verbose, export_dir)
+
+ logging.info("output dir: {}".format(export_dir))
+
+
+ def export_from_modelscope(
+ self,
+ tag_name: str = 'damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
+ ):
+
+ from funasr.tasks.asr import ASRTaskParaformer as ASRTask
+ from modelscope.hub.snapshot_download import snapshot_download
+
+ model_dir = snapshot_download(tag_name, cache_dir=self.cache_dir)
+ asr_train_config = os.path.join(model_dir, 'config.yaml')
+ asr_model_file = os.path.join(model_dir, 'model.pb')
+ cmvn_file = os.path.join(model_dir, 'am.mvn')
+ model, asr_train_args = ASRTask.build_model_from_file(
+ asr_train_config, asr_model_file, cmvn_file, 'cpu'
+ )
+ self.export(model, tag_name)
+
+
+
+ def _export_onnx(self, model, verbose, path, enc_size=None):
+ if enc_size:
+ dummy_input = model.get_dummy_inputs(enc_size)
+ else:
+ dummy_input = model.get_dummy_inputs()
+
+ # model_script = torch.jit.script(model)
+ model_script = model #torch.jit.trace(model)
+
+ torch.onnx.export(
+ model_script,
+ dummy_input,
+ os.path.join(path, f'{model.model_name}.onnx'),
+ verbose=verbose,
+ opset_version=12,
+ input_names=model.get_input_names(),
+ output_names=model.get_output_names(),
+ dynamic_axes=model.get_dynamic_axes()
+ )
+
+if __name__ == '__main__':
+ export_model = ASRModelExportParaformer()
+ export_model.export_from_modelscope('damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch')
\ No newline at end of file
diff --git a/funasr/export/models/__init__.py b/funasr/export/models/__init__.py
new file mode 100644
index 0000000..b21b080
--- /dev/null
+++ b/funasr/export/models/__init__.py
@@ -0,0 +1,91 @@
+# from .ctc import CTC
+# from .joint_network import JointNetwork
+#
+# # encoder
+# from espnet2.asr.encoder.rnn_encoder import RNNEncoder as espnetRNNEncoder
+# from espnet2.asr.encoder.vgg_rnn_encoder import VGGRNNEncoder as espnetVGGRNNEncoder
+# from espnet2.asr.encoder.contextual_block_transformer_encoder import ContextualBlockTransformerEncoder as espnetContextualTransformer
+# from espnet2.asr.encoder.contextual_block_conformer_encoder import ContextualBlockConformerEncoder as espnetContextualConformer
+# from espnet2.asr.encoder.transformer_encoder import TransformerEncoder as espnetTransformerEncoder
+# from espnet2.asr.encoder.conformer_encoder import ConformerEncoder as espnetConformerEncoder
+# from funasr.export.models.encoder.rnn import RNNEncoder
+# from funasr.export.models.encoders import TransformerEncoder
+# from funasr.export.models.encoders import ConformerEncoder
+# from funasr.export.models.encoder.contextual_block_xformer import ContextualBlockXformerEncoder
+#
+# # decoder
+# from espnet2.asr.decoder.rnn_decoder import RNNDecoder as espnetRNNDecoder
+# from espnet2.asr.transducer.transducer_decoder import TransducerDecoder as espnetTransducerDecoder
+# from funasr.export.models.decoder.rnn import (
+# RNNDecoder
+# )
+# from funasr.export.models.decoders import XformerDecoder
+# from funasr.export.models.decoders import TransducerDecoder
+#
+# # lm
+# from espnet2.lm.seq_rnn_lm import SequentialRNNLM as espnetSequentialRNNLM
+# from espnet2.lm.transformer_lm import TransformerLM as espnetTransformerLM
+# from .language_models.seq_rnn import SequentialRNNLM
+# from .language_models.transformer import TransformerLM
+#
+# # frontend
+# from espnet2.asr.frontend.s3prl import S3prlFrontend as espnetS3PRLModel
+# from .frontends.s3prl import S3PRLModel
+#
+# from espnet2.asr.encoder.sanm_encoder import SANMEncoder_tf, SANMEncoderChunkOpt_tf
+# from espnet_onnx.export.asr.models.encoders.transformer_sanm import TransformerEncoderSANM_tf
+# from espnet2.asr.decoder.transformer_decoder import FsmnDecoderSCAMAOpt_tf
+# from funasr.export.models.decoders import XformerDecoderSANM
+
+from funasr.models.e2e_asr_paraformer import Paraformer
+from funasr.export.models.e2e_asr_paraformer import Paraformer as Paraformer_export
+
+def get_model(model, export_config=None):
+
+ if isinstance(model, Paraformer):
+ return Paraformer_export(model, **export_config)
+ else:
+ raise "The model is not exist!"
+
+
+# def get_encoder(model, frontend, preencoder, predictor=None, export_config=None):
+# if isinstance(model, espnetRNNEncoder) or isinstance(model, espnetVGGRNNEncoder):
+# return RNNEncoder(model, frontend, preencoder, **export_config)
+# elif isinstance(model, espnetContextualTransformer) or isinstance(model, espnetContextualConformer):
+# return ContextualBlockXformerEncoder(model, **export_config)
+# elif isinstance(model, espnetTransformerEncoder):
+# return TransformerEncoder(model, frontend, preencoder, **export_config)
+# elif isinstance(model, espnetConformerEncoder):
+# return ConformerEncoder(model, frontend, preencoder, **export_config)
+# elif isinstance(model, SANMEncoder_tf) or isinstance(model, SANMEncoderChunkOpt_tf):
+# return TransformerEncoderSANM_tf(model, frontend, preencoder, predictor, **export_config)
+# else:
+# raise "The model is not exist!"
+
+
+#
+# def get_decoder(model, export_config):
+# if isinstance(model, espnetRNNDecoder):
+# return RNNDecoder(model, **export_config)
+# elif isinstance(model, espnetTransducerDecoder):
+# return TransducerDecoder(model, **export_config)
+# elif isinstance(model, FsmnDecoderSCAMAOpt_tf):
+# return XformerDecoderSANM(model, **export_config)
+# else:
+# return XformerDecoder(model, **export_config)
+#
+#
+# def get_lm(model, export_config):
+# if isinstance(model, espnetSequentialRNNLM):
+# return SequentialRNNLM(model, **export_config)
+# elif isinstance(model, espnetTransformerLM):
+# return TransformerLM(model, **export_config)
+#
+#
+# def get_frontend_models(model, export_config):
+# if isinstance(model, espnetS3PRLModel):
+# return S3PRLModel(model, **export_config)
+# else:
+# return None
+#
+
\ No newline at end of file
diff --git a/funasr/export/models/decoder/__init__.py b/funasr/export/models/decoder/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/funasr/export/models/decoder/__init__.py
diff --git a/funasr/export/models/decoder/sanm_decoder.py b/funasr/export/models/decoder/sanm_decoder.py
new file mode 100644
index 0000000..ca2563b
--- /dev/null
+++ b/funasr/export/models/decoder/sanm_decoder.py
@@ -0,0 +1,155 @@
+import os
+
+import torch
+import torch.nn as nn
+
+
+# from espnet.nets.pytorch_backend.transformer.mask import subsequent_mask
+
+from funasr.export.utils.torch_function import MakePadMask
+
+from funasr.modules.attention import MultiHeadedAttentionSANMDecoder
+from funasr.export.models.modules.multihead_att import MultiHeadedAttentionSANMDecoder as MultiHeadedAttentionSANMDecoder_export
+from funasr.modules.attention import MultiHeadedAttentionCrossAtt
+from funasr.export.models.modules.multihead_att import MultiHeadedAttentionCrossAtt as MultiHeadedAttentionCrossAtt_export
+from funasr.modules.positionwise_feed_forward import PositionwiseFeedForwardDecoderSANM
+from funasr.export.models.modules.feedforward import PositionwiseFeedForwardDecoderSANM as PositionwiseFeedForwardDecoderSANM_export
+from funasr.export.models.modules.decoder_layer import DecoderLayerSANM as DecoderLayerSANM_export
+
+
+class ParaformerSANMDecoder(nn.Module):
+ def __init__(self, model,
+ max_seq_len=512,
+ model_name='decoder'):
+ super().__init__()
+ # self.embed = model.embed #Embedding(model.embed, max_seq_len)
+ self.model = model
+ self.make_pad_mask = MakePadMask(max_seq_len, flip=False)
+
+ for i, d in enumerate(self.model.decoders):
+ if isinstance(d.feed_forward, PositionwiseFeedForwardDecoderSANM):
+ d.feed_forward = PositionwiseFeedForwardDecoderSANM_export(d.feed_forward)
+ if isinstance(d.self_attn, MultiHeadedAttentionSANMDecoder):
+ d.self_attn = MultiHeadedAttentionSANMDecoder_export(d.self_attn)
+ if isinstance(d.src_attn, MultiHeadedAttentionCrossAtt):
+ d.src_attn = MultiHeadedAttentionCrossAtt_export(d.src_attn)
+ self.model.decoders[i] = DecoderLayerSANM_export(d)
+
+ if self.model.decoders2 is not None:
+ for i, d in enumerate(self.model.decoders2):
+ if isinstance(d.feed_forward, PositionwiseFeedForwardDecoderSANM):
+ d.feed_forward = PositionwiseFeedForwardDecoderSANM_export(d.feed_forward)
+ if isinstance(d.self_attn, MultiHeadedAttentionSANMDecoder):
+ d.self_attn = MultiHeadedAttentionSANMDecoder_export(d.self_attn)
+ self.model.decoders2[i] = DecoderLayerSANM_export(d)
+
+ for i, d in enumerate(self.model.decoders3):
+ if isinstance(d.feed_forward, PositionwiseFeedForwardDecoderSANM):
+ d.feed_forward = PositionwiseFeedForwardDecoderSANM_export(d.feed_forward)
+ self.model.decoders3[i] = DecoderLayerSANM_export(d)
+
+ self.output_layer = model.output_layer
+ self.after_norm = model.after_norm
+ self.model_name = model_name
+
+ def prepare_mask(self, mask):
+ mask_3d_btd = mask[:, :, None]
+ if len(mask.shape) == 2:
+ mask_4d_bhlt = 1 - mask[:, None, None, :]
+ elif len(mask.shape) == 3:
+ mask_4d_bhlt = 1 - mask[:, None, :]
+ mask_4d_bhlt = mask_4d_bhlt * -10000.0
+
+ return mask_3d_btd, mask_4d_bhlt
+
+ def forward(
+ self,
+ hs_pad: torch.Tensor,
+ hlens: torch.Tensor,
+ ys_in_pad: torch.Tensor,
+ ys_in_lens: torch.Tensor,
+ ):
+
+ tgt = ys_in_pad
+ tgt_mask = self.make_pad_mask(ys_in_lens)
+ tgt_mask, _ = self.prepare_mask(tgt_mask)
+ # tgt_mask = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None]
+
+ memory = hs_pad
+ memory_mask = self.make_pad_mask(hlens)
+ _, memory_mask = self.prepare_mask(memory_mask)
+ # memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]
+
+ x = tgt
+ x, tgt_mask, memory, memory_mask, _ = self.model.decoders(
+ x, tgt_mask, memory, memory_mask
+ )
+ if self.model.decoders2 is not None:
+ x, tgt_mask, memory, memory_mask, _ = self.model.decoders2(
+ x, tgt_mask, memory, memory_mask
+ )
+ x, tgt_mask, memory, memory_mask, _ = self.model.decoders3(
+ x, tgt_mask, memory, memory_mask
+ )
+ x = self.after_norm(x)
+ x = self.output_layer(x)
+
+ return x, ys_in_lens
+
+
+ def get_dummy_inputs(self, enc_size):
+ tgt = torch.LongTensor([0]).unsqueeze(0)
+ memory = torch.randn(1, 100, enc_size)
+ pre_acoustic_embeds = torch.randn(1, 1, enc_size)
+ cache_num = len(self.model.decoders) + len(self.model.decoders2)
+ cache = [
+ torch.zeros((1, self.model.decoders[0].size, self.model.decoders[0].self_attn.kernel_size))
+ for _ in range(cache_num)
+ ]
+ return (tgt, memory, pre_acoustic_embeds, cache)
+
+ def is_optimizable(self):
+ return True
+
+ def get_input_names(self):
+ cache_num = len(self.model.decoders) + len(self.model.decoders2)
+ return ['tgt', 'memory', 'pre_acoustic_embeds'] \
+ + ['cache_%d' % i for i in range(cache_num)]
+
+ def get_output_names(self):
+ cache_num = len(self.model.decoders) + len(self.model.decoders2)
+ return ['y'] \
+ + ['out_cache_%d' % i for i in range(cache_num)]
+
+ def get_dynamic_axes(self):
+ ret = {
+ 'tgt': {
+ 0: 'tgt_batch',
+ 1: 'tgt_length'
+ },
+ 'memory': {
+ 0: 'memory_batch',
+ 1: 'memory_length'
+ },
+ 'pre_acoustic_embeds': {
+ 0: 'acoustic_embeds_batch',
+ 1: 'acoustic_embeds_length',
+ }
+ }
+ cache_num = len(self.model.decoders) + len(self.model.decoders2)
+ ret.update({
+ 'cache_%d' % d: {
+ 0: 'cache_%d_batch' % d,
+ 2: 'cache_%d_length' % d
+ }
+ for d in range(cache_num)
+ })
+ return ret
+
+ def get_model_config(self, path):
+ return {
+ "dec_type": "XformerDecoder",
+ "model_path": os.path.join(path, f'{self.model_name}.onnx'),
+ "n_layers": len(self.model.decoders) + len(self.model.decoders2),
+ "odim": self.model.decoders[0].size
+ }
diff --git a/funasr/export/models/e2e_asr_paraformer.py b/funasr/export/models/e2e_asr_paraformer.py
new file mode 100644
index 0000000..162837a
--- /dev/null
+++ b/funasr/export/models/e2e_asr_paraformer.py
@@ -0,0 +1,91 @@
+import logging
+
+
+import torch
+import torch.nn as nn
+
+from funasr.export.utils.torch_function import MakePadMask
+from funasr.train.abs_espnet_model import AbsESPnetModel
+from funasr.models.encoder.sanm_encoder import SANMEncoder
+from funasr.export.models.encoder.sanm_encoder import SANMEncoder as SANMEncoder_export
+from funasr.models.predictor.cif import CifPredictorV2
+from funasr.export.models.predictor.cif import CifPredictorV2 as CifPredictorV2_export
+from funasr.models.decoder.sanm_decoder import ParaformerSANMDecoder
+from funasr.export.models.decoder.sanm_decoder import ParaformerSANMDecoder as ParaformerSANMDecoder_export
+
+class Paraformer(nn.Module):
+ """
+ Author: Speech Lab, Alibaba Group, China
+ Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
+ https://arxiv.org/abs/2206.08317
+ """
+
+ def __init__(
+ self,
+ model,
+ max_seq_len=512,
+ feats_dim=560,
+ model_name='model',
+ **kwargs,
+ ):
+ super().__init__()
+ if isinstance(model.encoder, SANMEncoder):
+ self.encoder = SANMEncoder_export(model.encoder)
+ if isinstance(model.predictor, CifPredictorV2):
+ self.predictor = CifPredictorV2_export(model.predictor)
+ if isinstance(model.decoder, ParaformerSANMDecoder):
+ self.decoder = ParaformerSANMDecoder_export(model.decoder)
+ self.make_pad_mask = MakePadMask(max_seq_len, flip=False)
+ self.feats_dim = feats_dim
+ self.model_name = model_name
+ self.onnx = False
+ if "onnx" in kwargs:
+ self.onnx = kwargs["onnx"]
+
+ def forward(
+ self,
+ speech: torch.Tensor,
+ speech_lengths: torch.Tensor,
+ ):
+ # a. To device
+ batch = {"speech": speech, "speech_lengths": speech_lengths}
+ # batch = to_device(batch, device=self.device)
+
+ enc, enc_len = self.encoder(**batch)
+ mask = self.make_pad_mask(enc_len)[:, None, :]
+ pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = self.predictor(enc, mask)
+ pre_token_length = pre_token_length.round().long()
+
+ decoder_out, _ = self.decoder(enc, enc_len, pre_acoustic_embeds, pre_token_length)
+ decoder_out = torch.log_softmax(decoder_out, dim=-1)
+
+ return decoder_out, pre_token_length
+
+ # def get_output_size(self):
+ # return self.model.encoders[0].size
+
+ def get_dummy_inputs(self):
+ speech = torch.randn(2, 30, self.feats_dim)
+ speech_lengths = torch.tensor([6, 30]).long()
+ return (speech, speech_lengths)
+
+ def get_input_names(self):
+ return ['speech', 'speech_lengths']
+
+ def get_output_names(self):
+ return ['logits', 'token_num']
+
+ def get_dynamic_axes(self):
+ return {
+ 'speech': {
+ 0: 'batch_size',
+ 1: 'feats_length'
+ },
+ 'speech_lengths': {
+ 0: 'batch_size',
+ },
+ 'logits': {
+ 0: 'batch_size',
+ 1: 'logits_length'
+ },
+ }
\ No newline at end of file
diff --git a/funasr/export/models/encoder/__init__.py b/funasr/export/models/encoder/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/funasr/export/models/encoder/__init__.py
diff --git a/funasr/export/models/encoder/sanm_encoder.py b/funasr/export/models/encoder/sanm_encoder.py
new file mode 100644
index 0000000..ee45732
--- /dev/null
+++ b/funasr/export/models/encoder/sanm_encoder.py
@@ -0,0 +1,102 @@
+import torch
+import torch.nn as nn
+
+from funasr.export.utils.torch_function import MakePadMask
+from funasr.modules.attention import MultiHeadedAttentionSANM
+from funasr.export.models.modules.multihead_att import MultiHeadedAttentionSANM as MultiHeadedAttentionSANM_export
+from funasr.export.models.modules.encoder_layer import EncoderLayerSANM as EncoderLayerSANM_export
+from funasr.modules.positionwise_feed_forward import PositionwiseFeedForward
+from funasr.export.models.modules.feedforward import PositionwiseFeedForward as PositionwiseFeedForward_export
+
+class SANMEncoder(nn.Module):
+ def __init__(
+ self,
+ model,
+ max_seq_len=512,
+ feats_dim=560,
+ model_name='encoder',
+ ):
+ super().__init__()
+ self.embed = model.embed
+ self.model = model
+ self.make_pad_mask = MakePadMask(max_seq_len, flip=False)
+ self.feats_dim = feats_dim
+
+ if hasattr(model, 'encoders0'):
+ for i, d in enumerate(self.model.encoders0):
+ if isinstance(d.self_attn, MultiHeadedAttentionSANM):
+ d.self_attn = MultiHeadedAttentionSANM_export(d.self_attn)
+ if isinstance(d.feed_forward, PositionwiseFeedForward):
+ d.feed_forward = PositionwiseFeedForward_export(d.feed_forward)
+ self.model.encoders0[i] = EncoderLayerSANM_export(d)
+
+ for i, d in enumerate(self.model.encoders):
+ if isinstance(d.self_attn, MultiHeadedAttentionSANM):
+ d.self_attn = MultiHeadedAttentionSANM_export(d.self_attn)
+ if isinstance(d.feed_forward, PositionwiseFeedForward):
+ d.feed_forward = PositionwiseFeedForward_export(d.feed_forward)
+ self.model.encoders[i] = EncoderLayerSANM_export(d)
+
+ self.model_name = model_name
+ self.num_heads = model.encoders[0].self_attn.h
+ self.hidden_size = model.encoders[0].self_attn.linear_out.out_features
+
+
+ def prepare_mask(self, mask):
+ mask_3d_btd = mask[:, :, None]
+ if len(mask.shape) == 2:
+ mask_4d_bhlt = 1 - mask[:, None, None, :]
+ elif len(mask.shape) == 3:
+ mask_4d_bhlt = 1 - mask[:, None, :]
+ mask_4d_bhlt = mask_4d_bhlt * -10000.0
+
+ return mask_3d_btd, mask_4d_bhlt
+
+ def forward(self,
+ speech: torch.Tensor,
+ speech_lengths: torch.Tensor,
+ ):
+
+ mask = self.make_pad_mask(speech_lengths)
+ mask = self.prepare_mask(mask)
+ if self.embed is None:
+ xs_pad = speech
+ else:
+ xs_pad = self.embed(speech)
+
+ encoder_outs = self.model.encoders0(xs_pad, mask)
+ xs_pad, masks = encoder_outs[0], encoder_outs[1]
+
+ encoder_outs = self.model.encoders(xs_pad, mask)
+ xs_pad, masks = encoder_outs[0], encoder_outs[1]
+
+ xs_pad = self.model.after_norm(xs_pad)
+
+ return xs_pad, speech_lengths
+
+ def get_output_size(self):
+ return self.model.encoders[0].size
+
+ def get_dummy_inputs(self):
+ feats = torch.randn(1, 100, self.feats_dim)
+ return (feats)
+
+ def get_input_names(self):
+ return ['feats']
+
+ def get_output_names(self):
+ return ['encoder_out', 'encoder_out_lens', 'predictor_weight']
+
+ def get_dynamic_axes(self):
+ return {
+ 'feats': {
+ 1: 'feats_length'
+ },
+ 'encoder_out': {
+ 1: 'enc_out_length'
+ },
+ 'predictor_weight':{
+ 1: 'pre_out_length'
+ }
+
+ }
diff --git a/funasr/export/models/modules/__init__.py b/funasr/export/models/modules/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/funasr/export/models/modules/__init__.py
diff --git a/funasr/export/models/modules/decoder_layer.py b/funasr/export/models/modules/decoder_layer.py
new file mode 100644
index 0000000..bc306b1
--- /dev/null
+++ b/funasr/export/models/modules/decoder_layer.py
@@ -0,0 +1,43 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import torch
+from torch import nn
+
+
+class DecoderLayerSANM(nn.Module):
+
+ def __init__(
+ self,
+ model
+ ):
+ super().__init__()
+ self.self_attn = model.self_attn
+ self.src_attn = model.src_attn
+ self.feed_forward = model.feed_forward
+ self.norm1 = model.norm1
+ self.norm2 = model.norm2 if hasattr(model, 'norm2') else None
+ self.norm3 = model.norm3 if hasattr(model, 'norm3') else None
+ self.size = model.size
+
+
+ def forward(self, tgt, tgt_mask, memory, memory_mask=None, cache=None):
+
+ residual = tgt
+ tgt = self.norm1(tgt)
+ tgt = self.feed_forward(tgt)
+
+ x = tgt
+ if self.self_attn is not None:
+ tgt = self.norm2(tgt)
+ x, cache = self.self_attn(tgt, tgt_mask, cache=cache)
+ x = residual + x
+
+ if self.src_attn is not None:
+ residual = x
+ x = self.norm3(x)
+ x = residual + self.src_attn(x, memory, memory_mask)
+
+
+ return x, tgt_mask, memory, memory_mask, cache
+
diff --git a/funasr/export/models/modules/encoder_layer.py b/funasr/export/models/modules/encoder_layer.py
new file mode 100644
index 0000000..800a4f7
--- /dev/null
+++ b/funasr/export/models/modules/encoder_layer.py
@@ -0,0 +1,37 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import torch
+from torch import nn
+
+
+class EncoderLayerSANM(nn.Module):
+ def __init__(
+ self,
+ model,
+ ):
+ """Construct an EncoderLayer object."""
+ super().__init__()
+ self.self_attn = model.self_attn
+ self.feed_forward = model.feed_forward
+ self.norm1 = model.norm1
+ self.norm2 = model.norm2
+ self.size = model.size
+
+ def forward(self, x, mask):
+
+ residual = x
+ x = self.norm1(x)
+ x = self.self_attn(x, mask)
+ if x.size(2) == residual.size(2):
+ x = x + residual
+ residual = x
+ x = self.norm2(x)
+ x = self.feed_forward(x)
+ if x.size(2) == residual.size(2):
+ x = x + residual
+
+ return x, mask
+
+
+
diff --git a/funasr/export/models/modules/feedforward.py b/funasr/export/models/modules/feedforward.py
new file mode 100644
index 0000000..9388ae1
--- /dev/null
+++ b/funasr/export/models/modules/feedforward.py
@@ -0,0 +1,31 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+import torch
+import torch.nn as nn
+
+
+class PositionwiseFeedForward(nn.Module):
+ def __init__(self, model):
+ super().__init__()
+ self.w_1 = model.w_1
+ self.w_2 = model.w_2
+ self.activation = model.activation
+
+ def forward(self, x):
+ x = self.activation(self.w_1(x))
+ x = self.w_2(x)
+ return x
+
+
+class PositionwiseFeedForwardDecoderSANM(nn.Module):
+ def __init__(self, model):
+ super().__init__()
+ self.w_1 = model.w_1
+ self.w_2 = model.w_2
+ self.activation = model.activation
+ self.norm = model.norm
+
+ def forward(self, x):
+ x = self.activation(self.w_1(x))
+ x = self.w_2(self.norm(x))
+ return x
\ No newline at end of file
diff --git a/funasr/export/models/modules/multihead_att.py b/funasr/export/models/modules/multihead_att.py
new file mode 100644
index 0000000..377b979
--- /dev/null
+++ b/funasr/export/models/modules/multihead_att.py
@@ -0,0 +1,135 @@
+import os
+import math
+
+import torch
+import torch.nn as nn
+
+class MultiHeadedAttentionSANM(nn.Module):
+ def __init__(self, model):
+ super().__init__()
+ self.d_k = model.d_k
+ self.h = model.h
+ self.linear_out = model.linear_out
+ self.linear_q_k_v = model.linear_q_k_v
+ self.fsmn_block = model.fsmn_block
+ self.pad_fn = model.pad_fn
+
+ self.attn = None
+ self.all_head_size = self.h * self.d_k
+
+ def forward(self, x, mask):
+ mask_3d_btd, mask_4d_bhlt = mask
+ q_h, k_h, v_h, v = self.forward_qkv(x)
+ fsmn_memory = self.forward_fsmn(v, mask_3d_btd)
+ q_h = q_h * self.d_k**(-0.5)
+ scores = torch.matmul(q_h, k_h.transpose(-2, -1))
+ att_outs = self.forward_attention(v_h, scores, mask_4d_bhlt)
+ return att_outs + fsmn_memory
+
+ def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
+ new_x_shape = x.size()[:-1] + (self.h, self.d_k)
+ x = x.view(new_x_shape)
+ return x.permute(0, 2, 1, 3)
+
+ def forward_qkv(self, x):
+
+ q_k_v = self.linear_q_k_v(x)
+ q, k, v = torch.split(q_k_v, int(self.h * self.d_k), dim=-1)
+ q_h = self.transpose_for_scores(q)
+ k_h = self.transpose_for_scores(k)
+ v_h = self.transpose_for_scores(v)
+ return q_h, k_h, v_h, v
+
+ def forward_fsmn(self, inputs, mask):
+
+ # b, t, d = inputs.size()
+ # mask = torch.reshape(mask, (b, -1, 1))
+ inputs = inputs * mask
+ x = inputs.transpose(1, 2)
+ x = self.pad_fn(x)
+ x = self.fsmn_block(x)
+ x = x.transpose(1, 2)
+ x = x + inputs
+ x = x * mask
+ return x
+
+
+ def forward_attention(self, value, scores, mask):
+ scores = scores + mask
+
+ self.attn = torch.softmax(scores, dim=-1)
+ context_layer = torch.matmul(self.attn, value) # (batch, head, time1, d_k)
+
+ context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+ new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+ context_layer = context_layer.view(new_context_layer_shape)
+ return self.linear_out(context_layer) # (batch, time1, d_model)
+
+class MultiHeadedAttentionSANMDecoder(nn.Module):
+ def __init__(self, model):
+ super().__init__()
+ self.fsmn_block = model.fsmn_block
+ self.pad_fn = model.pad_fn
+ self.kernel_size = model.kernel_size
+ self.attn = None
+
+ def forward(self, inputs, mask, cache=None):
+
+ # b, t, d = inputs.size()
+ # mask = torch.reshape(mask, (b, -1, 1))
+ inputs = inputs * mask
+
+ x = inputs.transpose(1, 2)
+ if cache is None:
+ x = self.pad_fn(x)
+ else:
+ x = torch.cat((cache[:, :, 1:], x), dim=2)
+ cache = x
+ x = self.fsmn_block(x)
+ x = x.transpose(1, 2)
+
+ x = x + inputs
+ x = x * mask
+ return x, cache
+
+class MultiHeadedAttentionCrossAtt(nn.Module):
+ def __init__(self, model):
+ super().__init__()
+ self.d_k = model.d_k
+ self.h = model.h
+ self.linear_q = model.linear_q
+ self.linear_k_v = model.linear_k_v
+ self.linear_out = model.linear_out
+ self.attn = None
+ self.all_head_size = self.h * self.d_k
+
+ def forward(self, x, memory, memory_mask):
+ q, k, v = self.forward_qkv(x, memory)
+ scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k)
+ return self.forward_attention(v, scores, memory_mask)
+
+ def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
+ new_x_shape = x.size()[:-1] + (self.h, self.d_k)
+ x = x.view(new_x_shape)
+ return x.permute(0, 2, 1, 3)
+
+ def forward_qkv(self, x, memory):
+ q = self.linear_q(x)
+
+ k_v = self.linear_k_v(memory)
+ k, v = torch.split(k_v, int(self.h * self.d_k), dim=-1)
+ q = self.transpose_for_scores(q)
+ k = self.transpose_for_scores(k)
+ v = self.transpose_for_scores(v)
+ return q, k, v
+
+ def forward_attention(self, value, scores, mask):
+ scores = scores + mask
+
+ self.attn = torch.softmax(scores, dim=-1)
+ context_layer = torch.matmul(self.attn, value) # (batch, head, time1, d_k)
+
+ context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+ new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+ context_layer = context_layer.view(new_context_layer_shape)
+ return self.linear_out(context_layer) # (batch, time1, d_model)
diff --git a/funasr/export/models/predictor/__init__.py b/funasr/export/models/predictor/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/funasr/export/models/predictor/__init__.py
diff --git a/funasr/export/models/predictor/cif.py b/funasr/export/models/predictor/cif.py
new file mode 100644
index 0000000..32a3c13
--- /dev/null
+++ b/funasr/export/models/predictor/cif.py
@@ -0,0 +1,168 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+import torch
+from torch import nn
+import logging
+import numpy as np
+
+
+def sequence_mask(lengths, maxlen=None, dtype=torch.float32, device=None):
+ if maxlen is None:
+ maxlen = lengths.max()
+ row_vector = torch.arange(0, maxlen, 1).to(lengths.device)
+ matrix = torch.unsqueeze(lengths, dim=-1)
+ mask = row_vector < matrix
+ mask = mask.detach()
+
+ return mask.type(dtype).to(device) if device is not None else mask.type(dtype)
+
+
+class CifPredictorV2(nn.Module):
+ def __init__(self, model):
+ super().__init__()
+
+ self.pad = model.pad
+ self.cif_conv1d = model.cif_conv1d
+ self.cif_output = model.cif_output
+ self.threshold = model.threshold
+ self.smooth_factor = model.smooth_factor
+ self.noise_threshold = model.noise_threshold
+ self.tail_threshold = model.tail_threshold
+
+ def forward(self, hidden: torch.Tensor,
+ mask: torch.Tensor,
+ ):
+ h = hidden
+ context = h.transpose(1, 2)
+ queries = self.pad(context)
+ output = torch.relu(self.cif_conv1d(queries))
+ output = output.transpose(1, 2)
+
+ output = self.cif_output(output)
+ alphas = torch.sigmoid(output)
+ alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)
+ mask = mask.transpose(-1, -2).float()
+ alphas = alphas * mask
+
+ alphas = alphas.squeeze(-1)
+
+ token_num = alphas.sum(-1)
+
+ acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)
+
+ return acoustic_embeds, token_num, alphas, cif_peak
+
+ def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):
+ b, t, d = hidden.size()
+ tail_threshold = self.tail_threshold
+
+ zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device)
+ ones_t = torch.ones_like(zeros_t)
+ mask_1 = torch.cat([mask, zeros_t], dim=1)
+ mask_2 = torch.cat([ones_t, mask], dim=1)
+ mask = mask_2 - mask_1
+ tail_threshold = mask * tail_threshold
+ alphas = torch.cat([alphas, tail_threshold], dim=1)
+
+ zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device)
+ hidden = torch.cat([hidden, zeros], dim=1)
+ token_num = alphas.sum(dim=-1)
+ token_num_floor = torch.floor(token_num)
+
+ return hidden, alphas, token_num_floor
+
+@torch.jit.script
+def cif(hidden, alphas, threshold: float):
+ batch_size, len_time, hidden_size = hidden.size()
+ threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)
+
+ # loop varss
+ integrate = torch.zeros([batch_size], device=hidden.device)
+ frame = torch.zeros([batch_size, hidden_size], device=hidden.device)
+ # intermediate vars along time
+ list_fires = []
+ list_frames = []
+
+ for t in range(len_time):
+ alpha = alphas[:, t]
+ distribution_completion = torch.ones([batch_size], device=hidden.device) - integrate
+
+ integrate += alpha
+ list_fires.append(integrate)
+
+ fire_place = integrate >= threshold
+ integrate = torch.where(fire_place,
+ integrate - torch.ones([batch_size], device=hidden.device),
+ integrate)
+ cur = torch.where(fire_place,
+ distribution_completion,
+ alpha)
+ remainds = alpha - cur
+
+ frame += cur[:, None] * hidden[:, t, :]
+ list_frames.append(frame)
+ frame = torch.where(fire_place[:, None].repeat(1, hidden_size),
+ remainds[:, None] * hidden[:, t, :],
+ frame)
+
+ fires = torch.stack(list_fires, 1)
+ frames = torch.stack(list_frames, 1)
+ list_ls = []
+ len_labels = torch.round(alphas.sum(-1)).int()
+ max_label_len = len_labels.max()
+ for b in range(batch_size):
+ fire = fires[b, :]
+ l = torch.index_select(frames[b, :, :], 0, torch.nonzero(fire >= threshold).squeeze())
+ pad_l = torch.zeros([int(max_label_len - l.size(0)), int(hidden_size)], device=hidden.device)
+ list_ls.append(torch.cat([l, pad_l], 0))
+ return torch.stack(list_ls, 0), fires
+
+
+def CifPredictorV2_test():
+ x = torch.rand([2, 21, 2])
+ x_len = torch.IntTensor([6, 21])
+
+ mask = sequence_mask(x_len, maxlen=x.size(1), dtype=x.dtype)
+ x = x * mask[:, :, None]
+
+ predictor_scripts = torch.jit.script(CifPredictorV2(2, 1, 1))
+ # cif_output, cif_length, alphas, cif_peak = predictor_scripts(x, mask=mask[:, None, :])
+ predictor_scripts.save('test.pt')
+ loaded = torch.jit.load('test.pt')
+ cif_output, cif_length, alphas, cif_peak = loaded(x, mask=mask[:, None, :])
+ # print(cif_output)
+ print(predictor_scripts.code)
+ # predictor = CifPredictorV2(2, 1, 1)
+ # cif_output, cif_length, alphas, cif_peak = predictor(x, mask=mask[:, None, :])
+ print(cif_output)
+
+
+def CifPredictorV2_export_test():
+ x = torch.rand([2, 21, 2])
+ x_len = torch.IntTensor([6, 21])
+
+ mask = sequence_mask(x_len, maxlen=x.size(1), dtype=x.dtype)
+ x = x * mask[:, :, None]
+
+ # predictor_scripts = torch.jit.script(CifPredictorV2(2, 1, 1))
+ # cif_output, cif_length, alphas, cif_peak = predictor_scripts(x, mask=mask[:, None, :])
+ predictor = CifPredictorV2(2, 1, 1)
+ predictor_trace = torch.jit.trace(predictor, (x, mask[:, None, :]))
+ predictor_trace.save('test_trace.pt')
+ loaded = torch.jit.load('test_trace.pt')
+
+ x = torch.rand([3, 30, 2])
+ x_len = torch.IntTensor([6, 20, 30])
+ mask = sequence_mask(x_len, maxlen=x.size(1), dtype=x.dtype)
+ x = x * mask[:, :, None]
+ cif_output, cif_length, alphas, cif_peak = loaded(x, mask=mask[:, None, :])
+ print(cif_output)
+ # print(predictor_trace.code)
+ # predictor = CifPredictorV2(2, 1, 1)
+ # cif_output, cif_length, alphas, cif_peak = predictor(x, mask=mask[:, None, :])
+ # print(cif_output)
+
+
+if __name__ == '__main__':
+ # CifPredictorV2_test()
+ CifPredictorV2_export_test()
\ No newline at end of file
diff --git a/funasr/export/models/predictor/cif_test.py b/funasr/export/models/predictor/cif_test.py
new file mode 100644
index 0000000..954c434
--- /dev/null
+++ b/funasr/export/models/predictor/cif_test.py
@@ -0,0 +1,212 @@
+import torch
+from torch import nn
+import logging
+import numpy as np
+
+
+def sequence_mask(lengths, maxlen=None, dtype=torch.float32, device=None):
+ if maxlen is None:
+ maxlen = lengths.max()
+ row_vector = torch.arange(0, maxlen, 1).to(lengths.device)
+ matrix = torch.unsqueeze(lengths, dim=-1)
+ mask = row_vector < matrix
+ mask = mask.detach()
+
+ return mask.type(dtype).to(device) if device is not None else mask.type(dtype)
+
+
+def make_pad_mask(lengths, xs=None, length_dim=-1, maxlen=None):
+
+ if length_dim == 0:
+ raise ValueError("length_dim cannot be 0: {}".format(length_dim))
+
+ if not isinstance(lengths, list):
+ lengths = lengths.tolist()
+ bs = int(len(lengths))
+ if maxlen is None:
+ if xs is None:
+ maxlen = int(max(lengths))
+ else:
+ maxlen = xs.size(length_dim)
+ else:
+ assert xs is None
+ assert maxlen >= int(max(lengths))
+
+ seq_range = torch.arange(0, maxlen, dtype=torch.int64)
+ seq_range_expand = seq_range.unsqueeze(0).expand(bs, maxlen)
+ seq_length_expand = seq_range_expand.new(lengths).unsqueeze(-1)
+ mask = seq_range_expand >= seq_length_expand
+
+ if xs is not None:
+ assert xs.size(0) == bs, (xs.size(0), bs)
+
+ if length_dim < 0:
+ length_dim = xs.dim() + length_dim
+ # ind = (:, None, ..., None, :, , None, ..., None)
+ ind = tuple(
+ slice(None) if i in (0, length_dim) else None for i in range(xs.dim())
+ )
+ mask = mask[ind].expand_as(xs).to(xs.device)
+ return mask
+
+
+
+class CifPredictorV2(nn.Module):
+ def __init__(self,
+ idim: int,
+ l_order: int,
+ r_order: int,
+ threshold: float = 1.0,
+ dropout: float = 0.1,
+ smooth_factor: float = 1.0,
+ noise_threshold: float = 0,
+ tail_threshold: float = 0.0,
+ ):
+ super(CifPredictorV2, self).__init__()
+
+ self.pad = nn.ConstantPad1d((l_order, r_order), 0.0)
+ self.cif_conv1d = nn.Conv1d(idim, idim, l_order + r_order + 1)
+ self.cif_output = nn.Linear(idim, 1)
+ self.dropout = torch.nn.Dropout(p=dropout)
+ self.threshold = threshold
+ self.smooth_factor = smooth_factor
+ self.noise_threshold = noise_threshold
+ self.tail_threshold = tail_threshold
+
+ def forward(self, hidden: torch.Tensor,
+ mask: torch.Tensor,
+ ):
+ h = hidden
+ context = h.transpose(1, 2)
+ queries = self.pad(context)
+ output = torch.relu(self.cif_conv1d(queries))
+ output = output.transpose(1, 2)
+
+ output = self.cif_output(output)
+ alphas = torch.sigmoid(output)
+ alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)
+ mask = mask.transpose(-1, -2).float()
+ alphas = alphas * mask
+
+ alphas = alphas.squeeze(-1)
+
+ token_num = alphas.sum(-1)
+
+ acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)
+
+ return acoustic_embeds, token_num, alphas, cif_peak
+
+ def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):
+ b, t, d = hidden.size()
+ tail_threshold = self.tail_threshold
+
+ zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device)
+ ones_t = torch.ones_like(zeros_t)
+ mask_1 = torch.cat([mask, zeros_t], dim=1)
+ mask_2 = torch.cat([ones_t, mask], dim=1)
+ mask = mask_2 - mask_1
+ tail_threshold = mask * tail_threshold
+ alphas = torch.cat([alphas, tail_threshold], dim=1)
+
+ zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device)
+ hidden = torch.cat([hidden, zeros], dim=1)
+ token_num = alphas.sum(dim=-1)
+ token_num_floor = torch.floor(token_num)
+
+ return hidden, alphas, token_num_floor
+
+@torch.jit.script
+def cif(hidden, alphas, threshold: float):
+ batch_size, len_time, hidden_size = hidden.size()
+ threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)
+
+ # loop varss
+ integrate = torch.zeros([batch_size], device=hidden.device)
+ frame = torch.zeros([batch_size, hidden_size], device=hidden.device)
+ # intermediate vars along time
+ list_fires = []
+ list_frames = []
+
+ for t in range(len_time):
+ alpha = alphas[:, t]
+ distribution_completion = torch.ones([batch_size], device=hidden.device) - integrate
+
+ integrate += alpha
+ list_fires.append(integrate)
+
+ fire_place = integrate >= threshold
+ integrate = torch.where(fire_place,
+ integrate - torch.ones([batch_size], device=hidden.device),
+ integrate)
+ cur = torch.where(fire_place,
+ distribution_completion,
+ alpha)
+ remainds = alpha - cur
+
+ frame += cur[:, None] * hidden[:, t, :]
+ list_frames.append(frame)
+ frame = torch.where(fire_place[:, None].repeat(1, hidden_size),
+ remainds[:, None] * hidden[:, t, :],
+ frame)
+
+ fires = torch.stack(list_fires, 1)
+ frames = torch.stack(list_frames, 1)
+ list_ls = []
+ len_labels = torch.round(alphas.sum(-1)).int()
+ max_label_len = len_labels.max()
+ for b in range(batch_size):
+ fire = fires[b, :]
+ l = torch.index_select(frames[b, :, :], 0, torch.nonzero(fire >= threshold).squeeze())
+ pad_l = torch.zeros([int(max_label_len - l.size(0)), int(hidden_size)], device=hidden.device)
+ list_ls.append(torch.cat([l, pad_l], 0))
+ return torch.stack(list_ls, 0), fires
+
+
+def CifPredictorV2_test():
+ x = torch.rand([2, 21, 2])
+ x_len = torch.IntTensor([6, 21])
+
+ mask = sequence_mask(x_len, maxlen=x.size(1), dtype=x.dtype)
+ x = x * mask[:, :, None]
+
+ predictor_scripts = torch.jit.script(CifPredictorV2(2, 1, 1))
+ # cif_output, cif_length, alphas, cif_peak = predictor_scripts(x, mask=mask[:, None, :])
+ predictor_scripts.save('test.pt')
+ loaded = torch.jit.load('test.pt')
+ cif_output, cif_length, alphas, cif_peak = loaded(x, mask=mask[:, None, :])
+ # print(cif_output)
+ print(predictor_scripts.code)
+ # predictor = CifPredictorV2(2, 1, 1)
+ # cif_output, cif_length, alphas, cif_peak = predictor(x, mask=mask[:, None, :])
+ print(cif_output)
+
+
+def CifPredictorV2_export_test():
+ x = torch.rand([2, 21, 2])
+ x_len = torch.IntTensor([6, 21])
+
+ mask = sequence_mask(x_len, maxlen=x.size(1), dtype=x.dtype)
+ x = x * mask[:, :, None]
+
+ # predictor_scripts = torch.jit.script(CifPredictorV2(2, 1, 1))
+ # cif_output, cif_length, alphas, cif_peak = predictor_scripts(x, mask=mask[:, None, :])
+ predictor = CifPredictorV2(2, 1, 1)
+ predictor_trace = torch.jit.trace(predictor, (x, mask[:, None, :]))
+ predictor_trace.save('test_trace.pt')
+ loaded = torch.jit.load('test_trace.pt')
+
+ x = torch.rand([3, 30, 2])
+ x_len = torch.IntTensor([6, 20, 30])
+ mask = sequence_mask(x_len, maxlen=x.size(1), dtype=x.dtype)
+ x = x * mask[:, :, None]
+ cif_output, cif_length, alphas, cif_peak = loaded(x, mask=mask[:, None, :])
+ print(cif_output)
+ # print(predictor_trace.code)
+ # predictor = CifPredictorV2(2, 1, 1)
+ # cif_output, cif_length, alphas, cif_peak = predictor(x, mask=mask[:, None, :])
+ # print(cif_output)
+
+
+if __name__ == '__main__':
+ # CifPredictorV2_test()
+ CifPredictorV2_export_test()
\ No newline at end of file
diff --git a/funasr/export/utils/__init__.py b/funasr/export/utils/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/funasr/export/utils/__init__.py
diff --git a/funasr/export/utils/torch_function.py b/funasr/export/utils/torch_function.py
new file mode 100644
index 0000000..e8e5e1a
--- /dev/null
+++ b/funasr/export/utils/torch_function.py
@@ -0,0 +1,68 @@
+from typing import Optional
+
+import torch
+import torch.nn as nn
+
+import numpy as np
+
+
+class MakePadMask(nn.Module):
+ def __init__(self, max_seq_len=512, flip=True):
+ super().__init__()
+ if flip:
+ self.mask_pad = torch.Tensor(1 - np.tri(max_seq_len)).type(torch.bool)
+ else:
+ self.mask_pad = torch.Tensor(np.tri(max_seq_len)).type(torch.bool)
+
+ def forward(self, lengths, xs=None, length_dim=-1, maxlen=None):
+ """Make mask tensor containing indices of padded part.
+ This implementation creates the same mask tensor with original make_pad_mask,
+ which can be converted into onnx format.
+ Dimension length of xs should be 2 or 3.
+ """
+ if length_dim == 0:
+ raise ValueError("length_dim cannot be 0: {}".format(length_dim))
+
+ if xs is not None and len(xs.shape) == 3:
+ if length_dim == 1:
+ lengths = lengths.unsqueeze(1).expand(
+ *xs.transpose(1, 2).shape[:2])
+ else:
+ lengths = lengths.unsqueeze(1).expand(*xs.shape[:2])
+
+ if maxlen is not None:
+ m = maxlen
+ elif xs is not None:
+ m = xs.shape[-1]
+ else:
+ m = torch.max(lengths)
+
+ mask = self.mask_pad[lengths - 1][..., :m].type(torch.float32)
+
+ if length_dim == 1:
+ return mask.transpose(1, 2)
+ else:
+ return mask
+
+
+def normalize(input: torch.Tensor, p: float = 2.0, dim: int = 1, out: Optional[torch.Tensor] = None) -> torch.Tensor:
+ if out is None:
+ denom = input.norm(p, dim, keepdim=True).expand_as(input)
+ return input / denom
+ else:
+ denom = input.norm(p, dim, keepdim=True).expand_as(input)
+ return torch.div(input, denom, out=out)
+
+def subsequent_mask(size: torch.Tensor):
+ return torch.ones(size, size).tril()
+
+
+def MakePadMask_test():
+ feats_length = torch.tensor([10]).type(torch.long)
+ mask_fn = MakePadMask()
+ mask = mask_fn(feats_length)
+ print(mask)
+
+
+if __name__ == '__main__':
+ MakePadMask_test()
\ No newline at end of file
--
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