From 4e0fcee2a915641e7f39d62c389bee561d849e19 Mon Sep 17 00:00:00 2001
From: jmwang66 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期一, 19 六月 2023 20:28:23 +0800
Subject: [PATCH] Merge branch 'main' into dev_wjm_infer
---
funasr/tasks/asr.py | 152 ++++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 152 insertions(+), 0 deletions(-)
diff --git a/funasr/tasks/asr.py b/funasr/tasks/asr.py
index 8244856..7338513 100644
--- a/funasr/tasks/asr.py
+++ b/funasr/tasks/asr.py
@@ -38,6 +38,7 @@
from funasr.models.decoder.transformer_decoder import ParaformerDecoderSAN
from funasr.models.decoder.transformer_decoder import TransformerDecoder
from funasr.models.decoder.contextual_decoder import ContextualParaformerDecoder
+from funasr.models.decoder.transformer_decoder import SAAsrTransformerDecoder
from funasr.models.e2e_asr import ASRModel
from funasr.models.decoder.rnnt_decoder import RNNTDecoder
from funasr.models.joint_net.joint_network import JointNetwork
@@ -45,6 +46,7 @@
from funasr.models.e2e_asr_contextual_paraformer import NeatContextualParaformer
from funasr.models.e2e_tp import TimestampPredictor
from funasr.models.e2e_asr_mfcca import MFCCA
+from funasr.models.e2e_sa_asr import SAASRModel
from funasr.models.e2e_uni_asr import UniASR
from funasr.models.e2e_asr_transducer import TransducerModel, UnifiedTransducerModel
from funasr.models.encoder.abs_encoder import AbsEncoder
@@ -54,6 +56,7 @@
from funasr.models.encoder.sanm_encoder import SANMEncoder, SANMEncoderChunkOpt
from funasr.models.encoder.transformer_encoder import TransformerEncoder
from funasr.models.encoder.mfcca_encoder import MFCCAEncoder
+from funasr.models.encoder.resnet34_encoder import ResNet34Diar
from funasr.models.frontend.abs_frontend import AbsFrontend
from funasr.models.frontend.default import DefaultFrontend
from funasr.models.frontend.default import MultiChannelFrontend
@@ -134,6 +137,7 @@
timestamp_prediction=TimestampPredictor,
rnnt=TransducerModel,
rnnt_unified=UnifiedTransducerModel,
+ sa_asr=SAASRModel,
),
type_check=FunASRModel,
default="asr",
@@ -175,6 +179,27 @@
type_check=AbsEncoder,
default="rnn",
)
+asr_encoder_choices = ClassChoices(
+ "asr_encoder",
+ classes=dict(
+ conformer=ConformerEncoder,
+ transformer=TransformerEncoder,
+ rnn=RNNEncoder,
+ sanm=SANMEncoder,
+ sanm_chunk_opt=SANMEncoderChunkOpt,
+ data2vec_encoder=Data2VecEncoder,
+ mfcca_enc=MFCCAEncoder,
+ ),
+ type_check=AbsEncoder,
+ default="rnn",
+)
+spk_encoder_choices = ClassChoices(
+ "spk_encoder",
+ classes=dict(
+ resnet34_diar=ResNet34Diar,
+ ),
+ default="resnet34_diar",
+)
postencoder_choices = ClassChoices(
name="postencoder",
classes=dict(
@@ -197,6 +222,7 @@
paraformer_decoder_sanm=ParaformerSANMDecoder,
paraformer_decoder_san=ParaformerDecoderSAN,
contextual_paraformer_decoder=ContextualParaformerDecoder,
+ sa_decoder=SAAsrTransformerDecoder,
),
type_check=AbsDecoder,
default="rnn",
@@ -328,6 +354,12 @@
type=str2bool,
default=True,
help="whether to split text using <space>",
+ )
+ group.add_argument(
+ "--max_spk_num",
+ type=int_or_none,
+ default=None,
+ help="A text mapping int-id to token",
)
group.add_argument(
"--seg_dict_file",
@@ -1495,3 +1527,123 @@
#assert check_return_type(model)
return model
+
+
+class ASRTaskSAASR(ASRTask):
+ # If you need more than one optimizers, change this value
+ num_optimizers: int = 1
+
+ # Add variable objects configurations
+ class_choices_list = [
+ # --frontend and --frontend_conf
+ frontend_choices,
+ # --specaug and --specaug_conf
+ specaug_choices,
+ # --normalize and --normalize_conf
+ normalize_choices,
+ # --model and --model_conf
+ model_choices,
+ # --preencoder and --preencoder_conf
+ preencoder_choices,
+ # --encoder and --encoder_conf
+ # --asr_encoder and --asr_encoder_conf
+ asr_encoder_choices,
+ # --spk_encoder and --spk_encoder_conf
+ spk_encoder_choices,
+ # --decoder and --decoder_conf
+ decoder_choices,
+ ]
+
+ # If you need to modify train() or eval() procedures, change Trainer class here
+ trainer = Trainer
+
+ @classmethod
+ def build_model(cls, args: argparse.Namespace):
+ assert check_argument_types()
+ if isinstance(args.token_list, str):
+ with open(args.token_list, encoding="utf-8") as f:
+ token_list = [line.rstrip() for line in f]
+
+ # Overwriting token_list to keep it as "portable".
+ args.token_list = list(token_list)
+ elif isinstance(args.token_list, (tuple, list)):
+ token_list = list(args.token_list)
+ else:
+ raise RuntimeError("token_list must be str or list")
+ vocab_size = len(token_list)
+ logging.info(f"Vocabulary size: {vocab_size}")
+
+ # 1. frontend
+ if args.input_size is None:
+ # Extract features in the model
+ frontend_class = frontend_choices.get_class(args.frontend)
+ if args.frontend == 'wav_frontend' or args.frontend == "multichannelfrontend":
+ frontend = frontend_class(cmvn_file=args.cmvn_file, **args.frontend_conf)
+ else:
+ frontend = frontend_class(**args.frontend_conf)
+ input_size = frontend.output_size()
+ else:
+ # Give features from data-loader
+ args.frontend = None
+ args.frontend_conf = {}
+ frontend = None
+ input_size = args.input_size
+
+ # 2. Data augmentation for spectrogram
+ if args.specaug is not None:
+ specaug_class = specaug_choices.get_class(args.specaug)
+ specaug = specaug_class(**args.specaug_conf)
+ else:
+ specaug = None
+
+ # 3. Normalization layer
+ if args.normalize is not None:
+ normalize_class = normalize_choices.get_class(args.normalize)
+ normalize = normalize_class(**args.normalize_conf)
+ else:
+ normalize = None
+
+ # 5. Encoder
+ asr_encoder_class = asr_encoder_choices.get_class(args.asr_encoder)
+ asr_encoder = asr_encoder_class(input_size=input_size, **args.asr_encoder_conf)
+ spk_encoder_class = spk_encoder_choices.get_class(args.spk_encoder)
+ spk_encoder = spk_encoder_class(input_size=input_size, **args.spk_encoder_conf)
+
+ # 7. Decoder
+ decoder_class = decoder_choices.get_class(args.decoder)
+ decoder = decoder_class(
+ vocab_size=vocab_size,
+ encoder_output_size=asr_encoder.output_size(),
+ **args.decoder_conf,
+ )
+
+ # 8. CTC
+ ctc = CTC(
+ odim=vocab_size, encoder_output_size=asr_encoder.output_size(), **args.ctc_conf
+ )
+
+ # import ipdb;ipdb.set_trace()
+ # 9. Build model
+ try:
+ model_class = model_choices.get_class(args.model)
+ except AttributeError:
+ model_class = model_choices.get_class("asr")
+ model = model_class(
+ vocab_size=vocab_size,
+ frontend=frontend,
+ specaug=specaug,
+ normalize=normalize,
+ asr_encoder=asr_encoder,
+ spk_encoder=spk_encoder,
+ decoder=decoder,
+ ctc=ctc,
+ token_list=token_list,
+ **args.model_conf,
+ )
+
+ # 10. Initialize
+ if args.init is not None:
+ initialize(model, args.init)
+
+ assert check_return_type(model)
+ return model
--
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