speech_recognition/FunASR
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Merge pull request #153 from alibaba-damo-academy/dev_gzf

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Dev gzf
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zhifu gao 2023-02-25 19:42:25 +08:00 committed by GitHub
commit dae8f7472d
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5 changed files with 134 additions and 14 deletions
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107
funasr/export/export_model.py
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@ -58,7 +58,7 @@ class ASRModelExportParaformer:
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)
@ -106,6 +106,110 @@ class ASRModelExportParaformer:
# 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=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,
@ -117,6 +221,7 @@ class ASRModelExportParaformer:
dynamic_axes=model.get_dynamic_axes()
)
if __name__ == '__main__':
import sys

1
funasr/export/models/__init__.py
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@ -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):

2
funasr/export/models/e2e_asr_paraformer.py
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@ -59,7 +59,7 @@ class Paraformer(nn.Module):
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)

38
funasr/export/models/predictor/cif.py
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@ -16,6 +16,11 @@ def sequence_mask(lengths, maxlen=None, dtype=torch.float32, device=None):
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 @@ class CifPredictorV2(nn.Module):
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 @@ def cif(hidden, alphas, threshold: float):
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

0
scan.py Normal file
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