mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
e04489ce4c
* contextual&seaco ONNX export * update ContextualEmbedderExport2 * update ContextualEmbedderExport2 * update code * onnx (#1482) * qwenaudio qwenaudiochat * qwenaudio qwenaudiochat * whisper * whisper * llm * llm * llm * llm * llm * llm * llm * llm * export onnx * export onnx * export onnx * dingding * dingding * llm * doc * onnx * onnx * onnx * onnx * onnx * onnx * v1.0.15 * qwenaudio * qwenaudio * issue doc * update * update * bugfix * onnx * update export calling * update codes * remove useless code * update code --------- Co-authored-by: zhifu gao <zhifu.gzf@alibaba-inc.com>
181 lines
6.5 KiB
Python
181 lines
6.5 KiB
Python
#!/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
|
|
|
|
from funasr.register import tables
|
|
|
|
|
|
class ContextualEmbedderExport(torch.nn.Module):
|
|
def __init__(self,
|
|
model,
|
|
max_seq_len=512,
|
|
feats_dim=560,
|
|
**kwargs,):
|
|
super().__init__()
|
|
self.embedding = model.decoder.embed # model.bias_embed
|
|
model.bias_encoder.batch_first = False
|
|
self.bias_encoder = model.bias_encoder
|
|
|
|
def forward(self, hotword):
|
|
hotword = self.embedding(hotword).transpose(0, 1) # batch second
|
|
hw_embed, (_, _) = self.bias_encoder(hotword)
|
|
return hw_embed
|
|
|
|
def export_dummy_inputs(self):
|
|
hotword = torch.tensor([
|
|
[10, 11, 12, 13, 14, 10, 11, 12, 13, 14],
|
|
[100, 101, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[10, 11, 12, 13, 14, 10, 11, 12, 13, 14],
|
|
[100, 101, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
],
|
|
dtype=torch.int32)
|
|
# hotword_length = torch.tensor([10, 2, 1], dtype=torch.int32)
|
|
return (hotword)
|
|
|
|
def export_input_names(self):
|
|
return ['hotword']
|
|
|
|
def export_output_names(self):
|
|
return ['hw_embed']
|
|
|
|
def export_dynamic_axes(self):
|
|
return {
|
|
'hotword': {
|
|
0: 'num_hotwords',
|
|
},
|
|
'hw_embed': {
|
|
0: 'num_hotwords',
|
|
},
|
|
}
|
|
|
|
def export_name(self):
|
|
return 'model_eb.onnx'
|
|
|
|
|
|
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)
|
|
|
|
# before decoder convert into export class
|
|
embedder_class = ContextualEmbedderExport
|
|
embedder_model = embedder_class(model, onnx=is_onnx)
|
|
|
|
decoder_class = tables.decoder_classes.get(kwargs["decoder"]+"Export")
|
|
model.decoder = decoder_class(model.decoder, onnx=is_onnx)
|
|
|
|
seaco_decoder_class = tables.decoder_classes.get(kwargs["seaco_decoder"]+"Export")
|
|
model.seaco_decoder = seaco_decoder_class(model.seaco_decoder, onnx=is_onnx)
|
|
|
|
from funasr.utils.torch_function import sequence_mask
|
|
model.make_pad_mask = sequence_mask(kwargs["max_seq_len"], flip=False)
|
|
|
|
from funasr.utils.torch_function import sequence_mask
|
|
model.make_pad_mask = sequence_mask(kwargs["max_seq_len"], flip=False)
|
|
model.feats_dim = 560
|
|
model.NOBIAS = 8377
|
|
|
|
import copy
|
|
import types
|
|
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,
|
|
# lmbd: float,
|
|
):
|
|
# a. To device
|
|
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, alphas, pre_peak_index = self.predictor(enc, mask)
|
|
pre_token_length = pre_token_length.floor().type(torch.int32)
|
|
|
|
decoder_out, decoder_hidden, _ = self.decoder(enc, enc_len, pre_acoustic_embeds, pre_token_length, return_hidden=True, return_both=True)
|
|
decoder_out = torch.log_softmax(decoder_out, dim=-1)
|
|
# seaco forward
|
|
B, N, D = bias_embed.shape
|
|
_contextual_length = torch.ones(B) * N
|
|
|
|
# ASF
|
|
hotword_scores = self.seaco_decoder.forward_asf6(bias_embed, _contextual_length, decoder_hidden, pre_token_length)
|
|
hotword_scores = hotword_scores[0].sum(0).sum(0)
|
|
# _ = self.decoder2(bias_embed, _contextual_length, decoder_hidden, pre_token_length)
|
|
# hotword_scores = self.decoder2.model.decoders[-1].attn_mat[0][0].sum(0).sum(0)
|
|
dec_filter = torch.sort(hotword_scores, descending=True)[1][:51]
|
|
contextual_info = bias_embed[:,dec_filter]
|
|
num_hot_word = contextual_info.shape[1]
|
|
_contextual_length = torch.Tensor([num_hot_word]).int().repeat(B).to(enc.device)
|
|
|
|
# again
|
|
cif_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, pre_acoustic_embeds, pre_token_length)
|
|
dec_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, decoder_hidden, pre_token_length)
|
|
merged = cif_attended + dec_attended
|
|
dha_output = self.hotword_output_layer(merged)
|
|
dha_pred = torch.log_softmax(dha_output, dim=-1)
|
|
# merging logits
|
|
dha_ids = dha_pred.max(-1)[-1]
|
|
dha_mask = (dha_ids == self.NOBIAS).int().unsqueeze(-1)
|
|
decoder_out = decoder_out * dha_mask + dha_pred * (1-dha_mask)
|
|
return decoder_out, pre_token_length, alphas
|
|
|
|
def export_backbone_dummy_inputs(self):
|
|
speech = torch.randn(2, 30, self.feats_dim)
|
|
speech_lengths = torch.tensor([15, 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', 'alphas']
|
|
|
|
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'
|
|
},
|
|
'pre_acoustic_embeds': {
|
|
1: 'feats_length1'
|
|
}
|
|
}
|
|
|
|
def export_backbone_name(self):
|
|
return 'model.onnx'
|
|
|