update repo

嘉渊

2023-05-25 5635bfec22948447387613b6c9d5a0c5dbbd5ac4

update repo

 egs/wenetspeech/conformer/conf/decode_asr_transformer_5beam.yaml

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beam_size: 5
penalty: 0.0
maxlenratio: 0.0
minlenratio: 0.0
ctc_weight: 0.5
lm_weight: 0.7

 egs/wenetspeech/conformer/conf/train_asr_conformer.yaml

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# network architecture
# encoder related
encoder: conformer
encoder_conf:
    output_size: 512    # dimension of attention
    attention_heads: 8
    linear_units: 2048  # the number of units of position-wise feed forward
    num_blocks: 12      # the number of encoder blocks
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    attention_dropout_rate: 0.0
    input_layer: conv2d # encoder architecture type
    normalize_before: true
    rel_pos_type: latest
    pos_enc_layer_type: rel_pos
    selfattention_layer_type: rel_selfattn
    activation_type: swish
    macaron_style: true
    use_cnn_module: true
    cnn_module_kernel: 15

# decoder related
decoder: transformer
decoder_conf:
    attention_heads: 8
    linear_units: 2048
    num_blocks: 6
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    self_attention_dropout_rate: 0.0
    src_attention_dropout_rate: 0.0

# CTC realted
ctc_conf:
    ignore_nan_grad: true

# frontend related
frontend: wav_frontend
frontend_conf:
    fs: 16000
    window: hamming
    n_mels: 80
    frame_length: 25
    frame_shift: 10
    lfr_m: 1
    lfr_n: 1

# hybrid CTC/attention
model_conf:
    ctc_weight: 0.3
    lsm_weight: 0.1     # label smoothing option
    length_normalized_loss: false

# optimization related
accum_grad: 4
grad_clip: 5
patience: none
max_epoch: 30
val_scheduler_criterion:
    - valid
    - acc
best_model_criterion:
-   - valid
    - acc
    - max
keep_nbest_models: 10

optim: adam
optim_conf:
   lr: 0.0015
scheduler: warmuplr
scheduler_conf:
   warmup_steps: 30000

specaug: specaug
specaug_conf:
    apply_time_warp: true
    time_warp_window: 5
    time_warp_mode: bicubic
    apply_freq_mask: true
    freq_mask_width_range:
    - 0
    - 30
    num_freq_mask: 2
    apply_time_mask: true
    time_mask_width_range:
    - 0
    - 40
    num_time_mask: 2

dataset_conf:
    data_names: speech,text
    data_types: sound,text
    shuffle: True
    shuffle_conf:
        shuffle_size: 2048
        sort_size: 500
    batch_conf:
        batch_type: token
        batch_size: 32000
    num_workers: 8

log_interval: 50
normalize: None

 egs/wenetspeech/conformer/path.sh

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export FUNASR_DIR=$PWD/../../..

# NOTE(kan-bayashi): Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
export PYTHONIOENCODING=UTF-8
export PATH=$FUNASR_DIR/funasr/bin:$PATH

 egs/wenetspeech/conformer/run.sh

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#!/usr/bin/env bash

. ./path.sh || exit 1;

# machines configuration
CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
gpu_num=8
count=1
gpu_inference=true  # Whether to perform gpu decoding, set false for cpu decoding
# for gpu decoding, inference_nj=ngpu*njob; for cpu decoding, inference_nj=njob
njob=5
train_cmd=utils/run.pl
infer_cmd=utils/run.pl

 egs/wenetspeech/conformer/utils

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../../aishell/transformer/utils