[examples] better results on wenetspeech using revised transcripts

examples/wenetspeech/s0/README.md

@@ -38,6 +38,19 @@
 | attention rescoring - 16      | 7.57 % N=328207 C=307065 S=15169 D=5973 I=3687 | 10.13 % N=414285 C=376854 S=28486 D=8945 I=4541 | 15.55 % N=220358 C=191270 S=21136 D=7952 I=5184 |
 | attention - full    | 7.73 % N=328207 C=306688 S=13166 D=8353 I=3845 | 9.44 % N=414285 C=378096 S=24532 D=11657 I=2908 | 14.98 % N=220358 C=191881 S=15303 D=13174 I=4540 |
 
+## U2++ conformer (text\_fixed, see https://github.com/wenet-e2e/WenetSpeech/discussions/54)
+
+* Feature info: using fbank feature, with dither 1.0, with cmvn
+* Training info: lr 0.001, batch size dynamic36000, 8 gpus on 3090, acc_grad 4, 130k steps, 4.6 days
+* Decoding info: ctc_weight 0.5, reverse_weight 0.0, average_num 5, blank penalty 0.0, length penalty 0.0
+* PR link: https://github.com/wenet-e2e/wenet/pull/2371
+
+| Decoding mode - Chunk size    | Dev  | Test\_Net | Test\_Meeting |
+|:-----------------------------:|:----:|:---------:|:-------------:|
+| ctc prefix beam search - full      | 6.26 % N=328207 C=310671 S=15612 D=1924 I=3002 | 9.46 % N=414285 C=381373 S=26013 D=6899 I=6295 | 12.52 % N=220358 C=194801 S=19209 D=6348 I=2042 |
+| attention rescoring - full    | 5.90 % N=328207 C=311721 S=14597 D=1889 I=2888 | 8.96 % N=414092 C=380232 S=27606 D=6254 I=3222 | 11.99 % N=220358 C=195808 S=18243 D=6307 I=1878 |
+| attention - full    | 5.87 % N=328207 C=311922 S=14204 D=2081 I=2987 | 8.87 % N=414092 C=381014 S=27359 D=5719 I=3650 | 11.79 % N=220358 C=196484 S=17378 D=6496 I=2108 |
+
 ## U2++ conformer (wenetspeech plus aishell4)
 
 * Feature info: using fbank feature, with dither 1.0, with cmvn

examples/wenetspeech/s0/conf/train_u2++_conformer.yaml

@@ -0,0 +1,116 @@
+encoder: conformer
+encoder_conf:
+  activation_type: swish
+  attention_dropout_rate: 0.1
+  attention_heads: 8
+  causal: true
+  cnn_module_kernel: 15
+  cnn_module_norm: layer_norm
+  dropout_rate: 0.1
+  gradient_checkpointing: true
+  input_layer: conv2d
+  linear_units: 2048
+  normalize_before: true
+  num_blocks: 12
+  output_size: 512
+  pos_enc_layer_type: rel_pos
+  positional_dropout_rate: 0.1
+  selfattention_layer_type: rel_selfattn
+  use_cnn_module: true
+  use_dynamic_chunk: true
+  use_dynamic_left_chunk: false
+
+decoder: bitransformer
+decoder_conf:
+  attention_heads: 8
+  dropout_rate: 0.1
+  gradient_checkpointing: true
+  linear_units: 2048
+  num_blocks: 3
+  positional_dropout_rate: 0.1
+  r_num_blocks: 3
+  self_attention_dropout_rate: 0.1
+  src_attention_dropout_rate: 0.1
+
+tokenizer: char
+tokenizer_conf:
+  bpe_path: null
+  is_multilingual: false
+  non_lang_syms_path: null
+  num_languages: 1
+  special_tokens:
+    <blank>: 0
+    <eos>: 2
+    <sos>: 2
+    <unk>: 1
+  split_with_space: false
+  symbol_table_path: data/dict/lang_char.txt
+
+ctc: ctc
+ctc_conf:
+  ctc_blank_id: 0
+
+cmvn: global_cmvn
+cmvn_conf:
+  cmvn_file: data/train_l/global_cmvn
+  is_json_cmvn: true
+
+model: asr_model
+model_conf:
+  ctc_weight: 0.3
+  length_normalized_loss: false
+  lsm_weight: 0.1
+  reverse_weight: 0.3
+
+dataset: asr
+dataset_conf:
+  batch_conf:
+    batch_size: 32
+    batch_type: dynamic
+    max_frames_in_batch: 36000
+  fbank_conf:
+    dither: 1.0
+    frame_length: 25
+    frame_shift: 10
+    num_mel_bins: 80
+  filter_conf:
+    max_length: 4096
+    max_output_input_ratio: 0.25
+    min_length: 10
+    token_max_length: 200
+    token_min_length: 1
+  resample_conf:
+    resample_rate: 16000
+  shuffle: true
+  shuffle_conf:
+    shuffle_size: 5000
+  sort: true
+  sort_conf:
+    sort_size: 1000
+  spec_aug: true
+  spec_aug_conf:
+    max_f: 30
+    max_t: 50
+    num_f_mask: 2
+    num_t_mask: 2
+  spec_sub: true
+  spec_sub_conf:
+    max_t: 30
+    num_t_sub: 3
+  spec_trim: true
+  spec_trim_conf:
+    max_t: 30
+  speed_perturb: true
+
+grad_clip: 5
+accum_grad: 4
+max_epoch: 1  # NOTE(xcsong): Configure the epoch in run.sh
+log_interval: 100
+save_interval: 1000  # NOTE(xcsong): we use step_save instead of epoch_save for large datasets
+
+optim: adam
+optim_conf:
+  lr: 0.001
+scheduler: warmuplr
+scheduler_conf:
+  warmup_steps: 50000

examples/wenetspeech/s0/run.sh

@@ -17,7 +17,18 @@ fi
 # if you don't want to utilize all available GPU resources.
 export CUDA_VISIBLE_DEVICES="${gpu_list}"
 echo "CUDA_VISIBLE_DEVICES is ${CUDA_VISIBLE_DEVICES}"
-stage=0
+
+cuda_visible_devices=${CUDA_VISIBLE_DEVICES:-""}
+if [ -z "$cuda_visible_devices" ]; then
+  echo "CUDA_VISIBLE_DEVICES is not set. Using default device_ids."
+  device_ids=(0 1 2 3 4 5 6 7)
+else
+  IFS=',' read -r -a device_ids <<< "$cuda_visible_devices"
+  echo "Using CUDA_VISIBLE_DEVICES: $cuda_visible_devices"
+fi
+echo "Parsed device_ids: ${device_ids[@]}"
+
+stage=4
 stop_stage=5
 
 # You should change the following two parameters for multiple machine training,
@@ -36,22 +47,34 @@ train_set=train_`echo $set | tr 'A-Z' 'a-z'`
 dev_set=dev
 test_sets="test_net test_meeting"
 
-train_config=conf/train_conformer.yaml
+# NOTE(xcsong): we use step_save instead of epoch_save for large datasets
+epoch=100
+
+train_config=conf/train_u2++_conformer.yaml
 checkpoint=
+dir=exp/u2pp_conformer
+
 cmvn_sampling_divisor=20 # 20 means 5% of the training data to estimate cmvn
-dir=exp/conformer
 
 decode_checkpoint=
 average_checkpoint=true
-average_num=10
-decode_modes="attention_rescoring ctc_prefix_beam_search"
+average_num=5
+average_mode=step
+max_step=88888888
+decode_modes="ctc_greedy_search ctc_prefix_beam_search attention attention_rescoring"
 
 train_engine=torch_ddp
 
-deepspeed_config=../../aishell/s0/conf/ds_stage2.json
-deepspeed_save_states="model_only"
+deepspeed_config=../whisper/conf/ds_stage1.json
+deepspeed_save_states="model+optimizer"
 
 dict=data/dict/lang_char.txt
+decoding_chunk_size=
+ctc_weight=0.5
+reverse_weight=0.0
+blank_penalty=0.0
+length_penalty=0.0
+decode_batch=16
 
 . tools/parse_options.sh || exit 1;
 
@@ -133,19 +156,33 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
   else
     echo "$0: using torch ddp"
   fi
+
+  # repeat data.list, we use step_save instead of epoch_save for large datasets
+  train_data=data/$train_set/data.list.repeat${epoch}
+  if [ ! -f "${train_data}" ]; then
+    echo "repeat data/$train_set/data.list ${epoch} times"
+    for (( i=1; i<=$epoch; i++ ))
+    do
+        cat "data/$train_set/data.list" >> "${train_data}"
+    done
+    echo "save new data.list in ${train_data}, it will be used for training"
+  else
+    echo "${train_data} already exists."
+  fi
+
   echo "$0: num_nodes is $num_nodes, proc_per_node is $num_gpus"
   torchrun --nnodes=$num_nodes --nproc_per_node=$num_gpus --rdzv_endpoint=$HOST_NODE_ADDR \
            --rdzv_id=2023 --rdzv_backend="c10d" \
     wenet/bin/train.py \
       --train_engine ${train_engine} \
       --config $train_config \
       --data_type "shard" \
-      --train_data data/$train_set/data.list \
+      --train_data ${train_data} \
       --cv_data data/$dev_set/data.list \
       ${checkpoint:+--checkpoint $checkpoint} \
       --model_dir $dir \
       --ddp.dist_backend $dist_backend \
-      --num_workers 8 \
+      --num_workers 2 \
       --pin_memory \
       --deepspeed_config ${deepspeed_config} \
       --deepspeed.save_states ${deepspeed_save_states}
@@ -154,37 +191,52 @@ fi
 if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
   echo "Test model"
   if [ ${average_checkpoint} == true ]; then
-    decode_checkpoint=$dir/avg${average_num}.pt
+    decode_checkpoint=$dir/avg${average_num}_mode${average_mode}_max${max_step}.pt
     echo "do model average and final checkpoint is $decode_checkpoint"
     python wenet/bin/average_model.py \
         --dst_model $decode_checkpoint \
         --src_path $dir  \
         --num ${average_num} \
+        --mode ${average_mode} \
+        --max_step ${max_step} \
         --val_best
   fi
   # Specify decoding_chunk_size if it's a unified dynamic chunk trained model
   # -1 for full chunk
-  decoding_chunk_size=
-  ctc_weight=0.5
-  reverse_weight=0.0
-  blank_penalty=2.5
+  i=0
   for testset in ${test_sets} ${dev_set}; do
   {
     base=$(basename $decode_checkpoint)
-    result_dir=$dir/${testset}_${base}_chunk${decoding_chunk_size}_ctc${ctc_weight}_reverse${reverse_weight}_blankpenalty${blank_penalty}
-    python wenet/bin/recognize.py --gpu 0 \
+    result_dir=$dir/${testset}_${base}_chunk${decoding_chunk_size}_ctc${ctc_weight}_reverse${reverse_weight}_blankpenalty${blank_penalty}_lengthpenalty${length_penalty}
+    mkdir -p ${result_dir}
+    device_id=${device_ids[i % ${#device_ids[@]}]}
+    echo "Testing ${testset} on GPU ${device_id}"
+    python wenet/bin/recognize.py --gpu ${device_id} \
       --modes $decode_modes \
       --config $dir/train.yaml \
       --data_type "shard" \
       --test_data data/$testset/data.list \
       --checkpoint $decode_checkpoint \
       --beam_size 10 \
-      --batch_size 32 \
+      --batch_size ${decode_batch} \
       --blank_penalty ${blank_penalty} \
+      --length_penalty ${length_penalty} \
       --ctc_weight $ctc_weight \
       --reverse_weight $reverse_weight \
       --result_dir $result_dir \
-      ${decoding_chunk_size:+--decoding_chunk_size $decoding_chunk_size}
+      ${decoding_chunk_size:+--decoding_chunk_size $decoding_chunk_size} &
+    ((i++))
+    if [[ $device_id -eq $((num_gpus - 1)) ]]; then
+      wait
+    fi
+  }
+  done
+  wait
+  for testset in ${test_sets} ${dev_set}; do
+  {
+    base=$(basename $decode_checkpoint)
+    result_dir=$dir/${testset}_${base}_chunk${decoding_chunk_size}_ctc${ctc_weight}_reverse${reverse_weight}_blankpenalty${blank_penalty}_lengthpenalty${length_penalty}
+    mkdir -p ${result_dir}
     for mode in ${decode_modes}; do
       python tools/compute-wer.py --char=1 --v=1 \
         data/$testset/text $result_dir/$mode/text > $result_dir/$mode/wer

examples/wenetspeech/whisper/README.md

@@ -55,6 +55,19 @@ python local/modify_ckpt.py \
 |      attention      | 7.27 % N=328207 C=308016 S=11392 D=8799 I=3672  |  7.90 % N=414097 C=383382 S=18954 D=11761 I=2018    |   13.00 % N=220358 C=194417 S=11788 D=14153 I=2705     |
 | attention_rescoring | 8.95 % N=328207 C=305892 S=16696 D=5619 I=7056  |    10.83 % N=414097 C=371515 S=30229 D=12353 I=2269    |    15.64 % N=220358 C=193717 S=18669 D=7972 I=7812     |
 
+## Whisper-largev3 (conv1d2, full-parameter tuning) Result (text\_fixed, see https://github.com/wenet-e2e/WenetSpeech/discussions/54)
+
+* Feature info: using log_mel_spectrogram feature, no cmvn
+* Training info: bf16, deepspeed stage1, activation checkpointing, batch dynamic12000, acc_grad 8, 8 * 3090 gpu, 48k steps (about 6 days), conf/finetune_whisper_largev3.yaml
+* Decoding info: ctc_weight 0.0, average_num 5
+* PR link: https://github.com/wenet-e2e/wenet/pull/2371
+
+|   decoding_method   |  Dev | Test\_Net | Test\_Meeting |
+|:-------------------:|:----:|:---------:|:-------------:|
+|  ctc_greedy_search  | 7.09 % N=328207 C=308643 S=16976 D=2588 I=3709  | 10.98 % N=414092 C=373301 S=33375 D=7416 I=4697 | 12.84 % N=220358 C=194928 S=18398 D=7032 I=2862 |
+|      attention      | 4.66 % N=328207 C=315591 S=10352 D=2264 I=2692  | 6.54 % N=414092 C=389523 S=19101 D=5468 I=2513 | 8.84 % N=220358 C=202722 S=11296 D=6340 I=1839  |
+| attention_rescoring | 5.99 % N=328207 C=311106 S=14807 D=2294 I=2547  | 9.27 % N=414092 C=378406 S=28993 D=6693 I=2715 | 11.47 % N=220358 C=197013 S=16716 D=6629 I=1923 |
+
 # Frequently Asked Questions
 
 - Q: Why are there so many insertion errors in the decoding results of CTC and attention_rescoring?