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语帆 2024-02-22 14:24:54 +08:00
parent 0871fa6e0d
commit 0e416eacbf
1 changed files with 30 additions and 4 deletions
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funasr/models/lcbnet/model.py
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@ -1,3 +1,8 @@
#!/usr/bin/env python3
# -*- encoding: utf-8 -*-
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
# MIT License (https://opensource.org/licenses/MIT)
import logging
from typing import Union, Dict, List, Tuple, Optional
@ -17,10 +22,13 @@ from funasr.utils import postprocess_utils
from funasr.utils.datadir_writer import DatadirWriter
from funasr.register import tables
@tables.register("model_classes", "Transformer")
class Transformer(nn.Module):
"""CTC-attention hybrid Encoder-Decoder model"""
@tables.register("model_classes", "LCBNet")
class LCBNet(nn.Module):
"""
Author: Speech Lab of DAMO Academy, Alibaba Group
LCB-NET: LONG-CONTEXT BIASING FOR AUDIO-VISUAL SPEECH RECOGNITION
https://arxiv.org/abs/2401.06390
"""
def __init__(
self,
@ -32,10 +40,19 @@ class Transformer(nn.Module):
encoder_conf: dict = None,
decoder: str = None,
decoder_conf: dict = None,
text_encoder: str = None,
text_encoder_conf: dict = None,
bias_predictor: str = None,
bias_predictor_conf: dict = None,
fusion_encoder: str = None,
fusion_encoder_conf: dict = None,
ctc: str = None,
ctc_conf: dict = None,
ctc_weight: float = 0.5,
interctc_weight: float = 0.0,
select_num: int = 2,
select_length: int = 3,
insert_blank: bool = True,
input_size: int = 80,
vocab_size: int = -1,
ignore_id: int = -1,
@ -66,6 +83,15 @@ class Transformer(nn.Module):
encoder_class = tables.encoder_classes.get(encoder)
encoder = encoder_class(input_size=input_size, **encoder_conf)
encoder_output_size = encoder.output_size()
# lcbnet modules: text encoder, fusion encoder and bias predictor
text_encoder_class = tables.encoder_classes.get(text_encoder)
text_encoder = text_encoder_class(input_size=vocab_size, **text_encoder_conf)
fusion_encoder_class = tables.encoder_classes.get(fusion_encoder)
fusion_encoder = fusion_encoder_class(**fusion_encoder_conf)
bias_predictor_class = tables.encoder_classes.get_class(bias_predictor)
bias_predictor = bias_predictor_class(args.bias_predictor_conf)
if decoder is not None:
decoder_class = tables.decoder_classes.get(decoder)
decoder = decoder_class(