test

语帆

2024-02-22 6d4a5c19310be72e4dc12dc9471670868451dda6

 funasr/models/lcbnet/model.py

@@ -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.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 @@
        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 @@
        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(
@@ -91,6 +117,9 @@
        self.specaug = specaug
        self.normalize = normalize
        self.encoder = encoder
        self.text_encoder = text_encoder
        self.fusion_encoder = fusion_encoder
        self.bias_predictor = bias_predictor

        if not hasattr(self.encoder, "interctc_use_conditioning"):
            self.encoder.interctc_use_conditioning = False