From 2ae59b6ce06305724e2eaf30b9f9e93447a7832e Mon Sep 17 00:00:00 2001
From: 维石 <shixian.shi@alibaba-inc.com>
Date: 星期一, 22 七月 2024 16:58:27 +0800
Subject: [PATCH] ONNX and torchscript export for sensevoice
---
funasr/models/sense_voice/export_meta.py | 58 +++++++++++++++++++---------------------------------------
1 files changed, 19 insertions(+), 39 deletions(-)
diff --git a/funasr/models/sense_voice/export_meta.py b/funasr/models/sense_voice/export_meta.py
index fe09ee1..449388e 100644
--- a/funasr/models/sense_voice/export_meta.py
+++ b/funasr/models/sense_voice/export_meta.py
@@ -5,30 +5,19 @@
import types
import torch
-import torch.nn as nn
-from funasr.register import tables
+from funasr.utils.torch_function import sequence_mask
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)
-
- 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)
-
- model.export_name = "model"
return model
-
def export_forward(
self,
@@ -38,32 +27,28 @@
textnorm: torch.Tensor,
**kwargs,
):
- speech = speech.to(device=kwargs["device"])
- speech_lengths = speech_lengths.to(device=kwargs["device"])
-
- language_query = self.embed(language).to(speech.device)
-
- textnorm_query = self.embed(textnorm).to(speech.device)
+ # speech = speech.to(device="cuda")
+ # speech_lengths = speech_lengths.to(device="cuda")
+ language_query = self.embed(language.to(speech.device)).unsqueeze(1)
+ textnorm_query = self.embed(textnorm.to(speech.device)).unsqueeze(1)
+
speech = torch.cat((textnorm_query, speech), dim=1)
- speech_lengths += 1
-
+
event_emo_query = self.embed(torch.LongTensor([[1, 2]]).to(speech.device)).repeat(
speech.size(0), 1, 1
)
input_query = torch.cat((language_query, event_emo_query), dim=1)
speech = torch.cat((input_query, speech), dim=1)
- speech_lengths += 3
-
- # Encoder
- encoder_out, encoder_out_lens = self.encoder(speech, speech_lengths)
+
+ speech_lengths_new = speech_lengths + 4
+ encoder_out, encoder_out_lens = self.encoder(speech, speech_lengths_new)
+
if isinstance(encoder_out, tuple):
encoder_out = encoder_out[0]
- # c. Passed the encoder result and the beam search
- ctc_logits = self.ctc.log_softmax(encoder_out)
-
+ ctc_logits = self.ctc.ctc_lo(encoder_out)
+
return ctc_logits, encoder_out_lens
-
def export_dummy_inputs(self):
speech = torch.randn(2, 30, 560)
@@ -72,26 +57,21 @@
textnorm = torch.tensor([15, 15], dtype=torch.int32)
return (speech, speech_lengths, language, textnorm)
-
def export_input_names(self):
return ["speech", "speech_lengths", "language", "textnorm"]
-
def export_output_names(self):
return ["ctc_logits", "encoder_out_lens"]
-
def export_dynamic_axes(self):
return {
"speech": {0: "batch_size", 1: "feats_length"},
- "speech_lengths": {
- 0: "batch_size",
- },
- "logits": {0: "batch_size", 1: "logits_length"},
+ "speech_lengths": {0: "batch_size"},
+ "language": {0: "batch_size"},
+ "textnorm": {0: "batch_size"},
+ "ctc_logits": {0: "batch_size", 1: "logits_length"},
+ "encoder_out_lens": {0: "batch_size"},
}
-
-def export_name(
- self,
-):
+def export_name(self):
return "model.onnx"
--
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