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Merge branch 'dev_infer' of https://github.com/alibaba/FunASR into dev_infer

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嘉渊 2023-05-18 14:10:11 +08:00
commit 17eaf419c0
3 changed files with 36 additions and 57 deletions
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2
funasr/bin/asr_infer.py
View File

@ -1581,7 +1581,7 @@ class Speech2TextTransducer:
d = ModelDownloader()
kwargs.update(**d.download_and_unpack(model_tag))
return Speech2Text(**kwargs)
return Speech2TextTransducer(**kwargs)
class Speech2TextSAASR:

43
funasr/build_utils/build_asr_model.py
View File

@ -87,6 +87,8 @@ model_choices = ClassChoices(
contextual_paraformer=ContextualParaformer,
mfcca=MFCCA,
timestamp_prediction=TimestampPredictor,
rnnt=TransducerModel,
rnnt_unified=UnifiedTransducerModel,
),
default="asr",
)
@ -367,7 +369,7 @@ def build_asr_model(args):
token_list=token_list,
**args.model_conf,
)
elif args.model == "rnnt":
elif args.model == "rnnt" or args.model == "rnnt_unified":
# 5. Decoder
encoder_output_size = encoder.output_size()
@ -396,34 +398,21 @@ def build_asr_model(args):
**args.joint_network_conf,
)
model_class = model_choices.get_class(args.model)
# 7. Build model
if hasattr(encoder, 'unified_model_training') and encoder.unified_model_training:
model = UnifiedTransducerModel(
vocab_size=vocab_size,
token_list=token_list,
frontend=frontend,
specaug=specaug,
normalize=normalize,
encoder=encoder,
decoder=decoder,
att_decoder=att_decoder,
joint_network=joint_network,
**args.model_conf,
)
model = model_class(
vocab_size=vocab_size,
token_list=token_list,
frontend=frontend,
specaug=specaug,
normalize=normalize,
encoder=encoder,
decoder=decoder,
att_decoder=att_decoder,
joint_network=joint_network,
**args.model_conf,
)
else:
model = TransducerModel(
vocab_size=vocab_size,
token_list=token_list,
frontend=frontend,
specaug=specaug,
normalize=normalize,
encoder=encoder,
decoder=decoder,
att_decoder=att_decoder,
joint_network=joint_network,
**args.model_conf,
)
else:
raise NotImplementedError("Not supported model: {}".format(args.model))

48
funasr/tasks/asr.py
View File

@ -132,6 +132,8 @@ model_choices = ClassChoices(
neatcontextual_paraformer=NeatContextualParaformer,
mfcca=MFCCA,
timestamp_prediction=TimestampPredictor,
rnnt=TransducerModel,
rnnt_unified=UnifiedTransducerModel,
),
type_check=FunASRModel,
default="asr",
@ -1453,7 +1455,7 @@ class ASRTransducerTask(ASRTask):
decoder_output_size = decoder.output_size
if getattr(args, "decoder", None) is not None:
att_decoder_class = decoder_choices.get_class(args.att_decoder)
att_decoder_class = decoder_choices.get_class(args.decoder)
att_decoder = att_decoder_class(
vocab_size=vocab_size,
@ -1471,35 +1473,23 @@ class ASRTransducerTask(ASRTask):
)
# 7. Build model
try:
model_class = model_choices.get_class(args.model)
except AttributeError:
model_class = model_choices.get_class("asr")
if hasattr(encoder, 'unified_model_training') and encoder.unified_model_training:
model = UnifiedTransducerModel(
vocab_size=vocab_size,
token_list=token_list,
frontend=frontend,
specaug=specaug,
normalize=normalize,
encoder=encoder,
decoder=decoder,
att_decoder=att_decoder,
joint_network=joint_network,
**args.model_conf,
)
else:
model = TransducerModel(
vocab_size=vocab_size,
token_list=token_list,
frontend=frontend,
specaug=specaug,
normalize=normalize,
encoder=encoder,
decoder=decoder,
att_decoder=att_decoder,
joint_network=joint_network,
**args.model_conf,
)
model = model_class(
vocab_size=vocab_size,
token_list=token_list,
frontend=frontend,
specaug=specaug,
normalize=normalize,
encoder=encoder,
decoder=decoder,
att_decoder=att_decoder,
joint_network=joint_network,
**args.model_conf,
)
# 8. Initialize model
if args.init is not None:
raise NotImplementedError(