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[cli/paraformer] ali-paraformer inference #2067

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merged 21 commits into from
Oct 30, 2023
Merged

[cli/paraformer] ali-paraformer inference #2067

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000c7af
[cli/paraformer] ali-paraformer load and infer work
Mddct Oct 16, 2023
6967ef5
fix lint
Mddct Oct 20, 2023
c8cccdc
export jit and load work
Mddct Oct 23, 2023
852165c
reuse init_model.py
Mddct Oct 23, 2023
e8fa013
mv the intermediate files to the assets directory
Mddct Oct 23, 2023
0a98847
merge main
Mddct Oct 23, 2023
e987b00
model.decodde work && recognize.py work
Mddct Oct 23, 2023
3d25e2e
rm positionwise_feed_forward.py/lfr.py
Mddct Oct 23, 2023
aedd43b
Merge branch 'main' into Mddct-cli-paraformer
Mddct Oct 24, 2023
d264297
refactor search
Mddct Oct 24, 2023
3f45af7
merge main
Mddct Oct 24, 2023
30b5677
merge main
Mddct Oct 24, 2023
20146f4
cli work
Mddct Oct 24, 2023
e3ec8e7
fix lint
Mddct Oct 24, 2023
b1b44df
fix att mask && batch infer
Mddct Oct 30, 2023
cd9c659
search confidence works
Mddct Oct 30, 2023
0b0eea7
merge main
Mddct Oct 30, 2023
c11aefe
merge main
Mddct Oct 30, 2023
daff617
fix linux dtype
Mddct Oct 30, 2023
9e810d1
fix label type
Mddct Oct 30, 2023
b765c12
revert init_model.py and add init_model in export_jit
Mddct Oct 30, 2023
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merge main
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@Mddct
Mddct committed Oct 30, 2023
commit c11aefeb9e92c5dc954cbb236615c1871e338ef5
12 changes: 10 additions & 2 deletions wenet/cli/paraformer_model.py
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Expand Up @@ -19,7 +19,7 @@ def __init__(self, model_dir: str) -> None:
self.char_dict = {v: k for k, v in symbol_table.items()}
self.eos = 2

def transcribe(self, audio_file: str):
def transcribe(self, audio_file: str, tokens_info: bool = False) -> dict:
waveform, sample_rate = torchaudio.load(audio_file, normalize=False)
waveform = waveform.to(torch.float)
feats = kaldi.fbank(waveform,
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The default window in the FunASR frontend is hamming. You can find more details here. However, the default window in kaldi.fbank is povey, as specified here. This different window maybe a little mismatch. As mentioned in line 44 of this document:

"povey" is a window I made to be similar to Hamming but to go to zero at the edges, it's pow((0.5 - 0.5cos(n/N2*pi)), 0.85) I just don't think the Hamming window makes sense as a windowing function.

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pr welcome

Expand All @@ -36,7 +36,6 @@ def transcribe(self, audio_file: str):

res = paraformer_greedy_search(decoder_out, token_num)[0]

tokens_info = True
result = {}
result['confidence'] = res.confidence
# # TODO(Mddct): deal with '@@' and 'eos'
Expand All @@ -56,3 +55,12 @@ def transcribe(self, audio_file: str):

# result = ''.join(hyp)
return result

def align(self, audio_file: str, label: str) -> dict:
raise NotImplementedError


def load_model(language: str = None, model_dir: str = None) -> Paraformer:
if model_dir is None:
model_dir = Hub.get_model_by_lang(language)
return Paraformer(model_dir)
16 changes: 13 additions & 3 deletions wenet/cli/transcribe.py
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Expand Up @@ -14,6 +14,7 @@

import argparse

from wenet.cli.paraformer_model import load_model as load_paraformer
from wenet.cli.model import load_model


Expand All @@ -25,7 +26,6 @@ def get_args():
choices=[
'chinese',
'english',
'chinese-paraformer',
],
default='chinese',
help='language type')
Expand All @@ -41,14 +41,24 @@ def get_args():
parser.add_argument('--align',
action='store_true',
help='force align the input audio and transcript')
parser.add_argument('--label', type=str, help='the input label to align')
parser.add_argument('--label',
type=bool,
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default=False,
help='the input label to align')
parser.add_argument('--paraformer',
action='store_true',
help='whether to use the best chinese model')
args = parser.parse_args()
return args


def main():
args = get_args()
model = load_model(args.language, args.model_dir)

if args.paraformer:
model = load_paraformer(args.language, args.model_dir)
else:
model = load_model(args.language, args.model_dir)
if args.align:
result = model.align(args.audio_file, args.label)
else:
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