From dae8f7472dee1f630d45828c186f028c05cb298f Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期六, 25 二月 2023 19:42:25 +0800
Subject: [PATCH] Merge pull request #153 from alibaba-damo-academy/dev_gzf
---
funasr/export/models/e2e_asr_paraformer.py | 2
funasr/export/models/predictor/cif.py | 38 ++++++++----
scan.py | 0
funasr/export/export_model.py | 107 +++++++++++++++++++++++++++++++++++
funasr/export/models/__init__.py | 1
5 files changed, 134 insertions(+), 14 deletions(-)
diff --git a/funasr/export/export_model.py b/funasr/export/export_model.py
index 3c73152..933a927 100644
--- a/funasr/export/export_model.py
+++ b/funasr/export/export_model.py
@@ -58,7 +58,7 @@
if enc_size:
dummy_input = model.get_dummy_inputs(enc_size)
else:
- dummy_input = model.get_dummy_inputs_txt()
+ dummy_input = model.get_dummy_inputs()
# model_script = torch.jit.script(model)
model_script = torch.jit.trace(model, dummy_input)
@@ -111,12 +111,117 @@
dummy_input,
os.path.join(path, f'{model.model_name}.onnx'),
verbose=verbose,
+ opset_version=14,
+ input_names=model.get_input_names(),
+ output_names=model.get_output_names(),
+ dynamic_axes=model.get_dynamic_axes()
+ )
+
+
+class ASRModelExport:
+ def __init__(self, cache_dir: Union[Path, str] = None, onnx: bool = True):
+ assert check_argument_types()
+ self.set_all_random_seed(0)
+ 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=False,
+ )
+ print("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,
+ )
+ model.eval()
+ # self._export_onnx(model, verbose, export_dir)
+ if self.onnx:
+ self._export_onnx(model, verbose, export_dir)
+ else:
+ self._export_torchscripts(model, verbose, export_dir)
+
+ print("output dir: {}".format(export_dir))
+
+ def _export_torchscripts(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_txt()
+
+ # model_script = torch.jit.script(model)
+ model_script = torch.jit.trace(model, dummy_input)
+ model_script.save(os.path.join(path, f'{model.model_name}.torchscripts'))
+
+ def set_all_random_seed(self, seed: int):
+ random.seed(seed)
+ np.random.seed(seed)
+ torch.random.manual_seed(seed)
+
+ def export(self,
+ tag_name: str = 'damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
+ mode: str = 'paraformer',
+ ):
+
+ model_dir = tag_name
+ if model_dir.startswith('damo/'):
+ from modelscope.hub.snapshot_download import snapshot_download
+ model_dir = snapshot_download(model_dir, 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')
+ json_file = os.path.join(model_dir, 'configuration.json')
+ if mode is None:
+ import json
+ with open(json_file, 'r') as f:
+ config_data = json.load(f)
+ mode = config_data['model']['model_config']['mode']
+ if mode.startswith('paraformer'):
+ from funasr.tasks.asr import ASRTaskParaformer as ASRTask
+ elif mode.startswith('uniasr'):
+ from funasr.tasks.asr import ASRTaskUniASR as ASRTask
+
+ 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__':
import sys
diff --git a/funasr/export/models/__init__.py b/funasr/export/models/__init__.py
index ca2c813..27a65af 100644
--- a/funasr/export/models/__init__.py
+++ b/funasr/export/models/__init__.py
@@ -1,5 +1,6 @@
from funasr.models.e2e_asr_paraformer import Paraformer
from funasr.export.models.e2e_asr_paraformer import Paraformer as Paraformer_export
+from funasr.models.e2e_uni_asr import UniASR
def get_model(model, export_config=None):
diff --git a/funasr/export/models/e2e_asr_paraformer.py b/funasr/export/models/e2e_asr_paraformer.py
index bf5ed1e..5424a0a 100644
--- a/funasr/export/models/e2e_asr_paraformer.py
+++ b/funasr/export/models/e2e_asr_paraformer.py
@@ -59,7 +59,7 @@
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().type(torch.int32)
+ pre_token_length = pre_token_length.floor().type(torch.int32)
decoder_out, _ = self.decoder(enc, enc_len, pre_acoustic_embeds, pre_token_length)
decoder_out = torch.log_softmax(decoder_out, dim=-1)
diff --git a/funasr/export/models/predictor/cif.py b/funasr/export/models/predictor/cif.py
index 5518cb8..c8df7f3 100644
--- a/funasr/export/models/predictor/cif.py
+++ b/funasr/export/models/predictor/cif.py
@@ -16,6 +16,11 @@
return mask.type(dtype).to(device) if device is not None else mask.type(dtype)
+def sequence_mask_scripts(lengths, maxlen:int):
+ row_vector = torch.arange(0, maxlen, 1).type(lengths.dtype).to(lengths.device)
+ matrix = torch.unsqueeze(lengths, dim=-1)
+ mask = row_vector < matrix
+ return mask.type(torch.float32).to(lengths.device)
class CifPredictorV2(nn.Module):
def __init__(self, model):
@@ -71,28 +76,29 @@
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)
+ integrate = torch.zeros([batch_size], dtype=alphas.dtype, device=hidden.device)
+ frame = torch.zeros([batch_size, hidden_size], dtype=hidden.dtype, 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
+ distribution_completion = torch.ones([batch_size], dtype=alphas.dtype, 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 - torch.ones([batch_size], dtype=alphas.dtype, device=hidden.device),
integrate)
cur = torch.where(fire_place,
distribution_completion,
@@ -107,12 +113,20 @@
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()
+ # list_ls = []
+ len_labels = torch.round(alphas.sum(-1)).type(torch.int32)
+ # max_label_len = int(torch.max(len_labels).item())
+ # print("type: {}".format(type(max_label_len)))
+ fire_idxs = fires >= threshold
+ frame_fires = torch.zeros_like(hidden)
+ max_label_len = frames[0, fire_idxs[0]].size(0)
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
+ # fire = fires[b, :]
+ frame_fire = frames[b, fire_idxs[b]]
+ frame_len = frame_fire.size(0)
+ frame_fires[b, :frame_len, :] = frame_fire
+
+ if frame_len >= max_label_len:
+ max_label_len = frame_len
+ frame_fires = frame_fires[:, :max_label_len, :]
+ return frame_fires, fires
diff --git a/scan.py b/scan.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/scan.py
--
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