You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
102 lines
3.3 KiB
102 lines
3.3 KiB
# Copyright (c) 2023 speechbrain Authors. All Rights Reserved.
|
|
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
#
|
|
# Modified from speechbrain 2023 (https://github.com/speechbrain/speechbrain/blob/develop/recipes/AISHELL-1/ASR/CTC/hparams/train_with_wav2vec.yaml)
|
|
|
|
# ############################################################################
|
|
# Model: CTC-wav2vec2
|
|
# Encoder: wav2vec2
|
|
# Decoder: -
|
|
# Tokens: Char
|
|
# losses: CTC
|
|
# Training: AISHELL-1
|
|
# Authors: Yingzhi WANG 2022
|
|
# ############################################################################
|
|
|
|
output_folder: !ref data
|
|
cer_file: !ref <output_folder>/cer.txt
|
|
save_folder: !ref <output_folder>/save
|
|
train_log: !ref <output_folder>/train_log.txt
|
|
|
|
# Data files
|
|
data_folder: data/aishell # e,g./path/to/aishell
|
|
|
|
skip_prep: False
|
|
ckpt_interval_minutes: 15 # save checkpoint every N min
|
|
train_data: !ref <output_folder>/train.csv
|
|
valid_data: !ref <output_folder>/dev.csv
|
|
test_data: !ref <output_folder>/test.csv
|
|
|
|
wav2vec2_hub: TencentGameMate/chinese-wav2vec2-large
|
|
|
|
# Training parameters
|
|
number_of_epochs: 80
|
|
lr: 1.0
|
|
lr_wav2vec: 0.0001
|
|
sorting: ascending
|
|
auto_mix_prec: False
|
|
sample_rate: 16000
|
|
|
|
# With data_parallel batch_size is split into N jobs
|
|
# With DDP batch_size is multiplied by N jobs
|
|
# Must be 8 per GPU to fit 32GB of VRAM
|
|
batch_size: 5
|
|
test_batch_size: 1 # need set to 1 when decoding
|
|
|
|
dynamic_batching: False
|
|
dynamic_batch_sampler:
|
|
feats_hop_size: 0.01
|
|
max_batch_len: 15 # in terms of "duration" in annotations by default, second here
|
|
left_bucket_len: 200 # old implementation attributs
|
|
multiplier: 1.1 # old implementation attributs
|
|
shuffle_ex: False # if true re-creates batches at each epoch shuffling examples.
|
|
num_buckets: 10 # floor(log(max_batch_len/left_bucket_len, multiplier)) + 1
|
|
batch_ordering: ascending
|
|
|
|
num_workers: 6
|
|
|
|
# Dataloader options
|
|
train_dataloader_opts:
|
|
batch_size: !ref <batch_size>
|
|
num_workers: !ref <num_workers>
|
|
valid_dataloader_opts:
|
|
batch_size: !ref <test_batch_size>
|
|
num_workers: !ref <num_workers>
|
|
test_dataloader_opts:
|
|
batch_size: !ref <test_batch_size>
|
|
num_workers: !ref <num_workers>
|
|
|
|
wav2vec_output_dim: 1024
|
|
dnn_neurons: 1024
|
|
freeze_wav2vec: False
|
|
dropout: 0.15
|
|
|
|
tokenizer: !apply:paddlenlp.transformers.AutoTokenizer.from_pretrained
|
|
pretrained_model_name_or_path: bert-base-chinese
|
|
# bert-base-chinese tokens length
|
|
output_neurons: 21128
|
|
|
|
# Decoding parameters
|
|
# Be sure that the bos and eos index match with the BPEs ones
|
|
blank_index: 0
|
|
|
|
# AISHELL-1 has spaces between words in the transcripts,
|
|
# which Chinese writing normally does not do.
|
|
# If remove_spaces, spaces are removed
|
|
# from the transcript before computing CER.
|
|
# (e.g., 祝 可爱 的 你 —> 祝可爱的你)
|
|
remove_spaces: True
|
|
split_tokens: !apply:operator.not_ [!ref <remove_spaces>]
|