librispeech s2 for kaldi feat

examples/librispeech/README.md

 # ASR
+
 * s0 is for deepspeech2 offline
 * s1 is for transformer/conformer/U2
+* s2 is for transformer/conformer/U2 w/ kaldi feat

examples/librispeech/s2/README.md

+# LibriSpeech
+
+## Data
+
+| Data Subset | Duration in Seconds |
+| data/manifest.train |  0.83s ~ 29.735s |
+| data/manifest.dev | 1.065 ~ 35.155s |  
+| data/manifest.test-clean | 1.285s ~ 34.955s |
+
+## Conformer
+
+| Model | Params | Config | Augmentation| Test set | Decode method | Loss | WER |  
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| conformer | 47.63 M | conf/conformer.yaml | spec_aug + shift | test-clean | attention | 6.35 | 0.030162 |  
+| conformer | 47.63 M | conf/conformer.yaml | spec_aug + shift | test-clean | ctc_greedy_search | 6.35 | 0.037910 |  
+| conformer | 47.63 M | conf/conformer.yaml | spec_aug + shift | test-clean | ctc_prefix_beam_search | 6.35 | 0.037761 |  
+| conformer | 47.63 M | conf/conformer.yaml | spec_aug + shift | test-clean | attention_rescoring | 6.35 | 0.032115 |  
+
+
+### Test w/o length filter
+
+| Model | Params | Config | Augmentation| Test set | Decode method | Loss | WER |  
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| conformer | 47.63 M | conf/conformer.yaml | spec_aug + shift | test-clean-all | attention | 6.35 | 0.057117 |  
+
+## Chunk Conformer
+
+| Model | Params | Config | Augmentation| Test set | Decode method | Chunk Size & Left Chunks | Loss | WER |  
+| --- | --- | --- | --- | --- | --- | --- | --- | --- |  
+| conformer | 47.63 M | conf/chunk_conformer.yaml | spec_aug + shift | test-clean | attention | 16, -1 | 7.01250648 | 0.069548 |  
+| conformer | 47.63 M | conf/chunk_conformer.yaml | spec_aug + shift | test-clean | ctc_greedy_search | 16, -1 | 7.01250648 | 0.094753 |  
+| conformer | 47.63 M | conf/chunk_conformer.yaml | spec_aug + shift | test-clean | ctc_prefix_beam_search | 16, -1 | 7.01250648 | - |  
+| conformer | 47.63 M | conf/chunk_conformer.yaml | spec_aug + shift | test-clean | attention_rescoring | 16, -1 | 7.01250648 | - |  
+
+
+## Transformer
+
+| Model | Params | Config | Augmentation| Test set | Decode method | Loss | WER |  
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| transformer | 32.52 M | conf/transformer.yaml | spec_aug + shift | test-clean | attention | 6.98 | 0.036 |  
+
+### Test w/o length filter
+
+| Model | Params | Config | Augmentation| Test set | Decode method | Loss | WER |  
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| transformer | 32.52 M | conf/transformer.yaml | spec_aug + shift | test-clean-all | attention | 6.98 | 0.066500 |  
+

examples/librispeech/s2/conf/augmentation.json

+[
+  {
+    "type": "shift",
+    "params": {
+      "min_shift_ms": -5,
+      "max_shift_ms": 5
+    },
+    "prob": 1.0
+  },
+  {
+    "type": "speed",
+    "params": {
+      "min_speed_rate": 0.9,
+      "max_speed_rate": 1.1,
+      "num_rates": 3
+    },
+    "prob": 0.0
+  },
+  {
+    "type": "specaug",
+    "params": {
+      "F": 10,
+      "T": 50,
+      "n_freq_masks": 2,
+      "n_time_masks": 2,
+      "p": 1.0,
+      "W": 80,
+      "adaptive_number_ratio": 0,
+      "adaptive_size_ratio": 0,
+      "max_n_time_masks": 20
+    },
+    "prob": 1.0
+  }
+]

examples/librispeech/s2/conf/chunk_conformer.yaml

+# https://yaml.org/type/float.html
+data:
+  train_manifest: data/manifest.train
+  dev_manifest: data/manifest.dev
+  test_manifest: data/manifest.test
+  min_input_len: 0.5
+  max_input_len: 20.0
+  min_output_len: 0.0
+  max_output_len: 400.0
+  min_output_input_ratio: 0.05
+  max_output_input_ratio: 10.0
+
+collator:
+  vocab_filepath: data/vocab.txt 
+  unit_type: 'spm'
+  spm_model_prefix: 'data/bpe_unigram_5000'
+  mean_std_filepath: ""
+  augmentation_config: conf/augmentation.json
+  batch_size: 16
+  raw_wav: True  # use raw_wav or kaldi feature
+  specgram_type: fbank #linear, mfcc, fbank
+  feat_dim: 80
+  delta_delta: False
+  dither: 1.0
+  target_sample_rate: 16000
+  max_freq: None
+  n_fft: None
+  stride_ms: 10.0
+  window_ms: 25.0
+  use_dB_normalization: True
+  target_dB: -20
+  random_seed: 0
+  keep_transcription_text: False
+  sortagrad: True 
+  shuffle_method: batch_shuffle
+  num_workers: 2
+
+
+# network architecture
+model:
+    cmvn_file: "data/mean_std.json"
+    cmvn_file_type: "json"
+    # encoder related
+    encoder: conformer
+    encoder_conf:
+        output_size: 256    # dimension of attention
+        attention_heads: 4
+        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 input type, you can chose conv2d, conv2d6 and conv2d8
+        normalize_before: True
+        use_cnn_module: True
+        cnn_module_kernel: 15
+        activation_type: 'swish'
+        pos_enc_layer_type: 'rel_pos'
+        selfattention_layer_type: 'rel_selfattn'
+        causal: True
+        use_dynamic_chunk: true
+        cnn_module_norm: 'layer_norm' # using nn.LayerNorm makes model converge faster
+        use_dynamic_left_chunk: false
+
+    # decoder related
+    decoder: transformer
+    decoder_conf:
+        attention_heads: 4
+        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
+
+    # hybrid CTC/attention
+    model_conf:
+        ctc_weight: 0.3
+        lsm_weight: 0.1     # label smoothing option
+        length_normalized_loss: false
+
+
+training:
+  n_epoch: 240
+  accum_grad: 8
+  global_grad_clip: 5.0
+  optim: adam
+  optim_conf:
+    lr: 0.001
+    weight_decay: 1e-06
+  scheduler: warmuplr     # pytorch v1.1.0+ required
+  scheduler_conf:
+    warmup_steps: 25000
+    lr_decay: 1.0
+  log_interval: 100
+  checkpoint:
+    kbest_n: 50
+    latest_n: 5
+
+
+decoding:
+  batch_size: 128
+  error_rate_type: wer
+  decoding_method: attention  # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
+  lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
+  alpha: 2.5
+  beta: 0.3
+  beam_size: 10
+  cutoff_prob: 1.0
+  cutoff_top_n: 0
+  num_proc_bsearch: 8
+  ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
+  decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
+      # <0: for decoding, use full chunk.
+      # >0: for decoding, use fixed chunk size as set.
+      # 0: used for training, it's prohibited here. 
+  num_decoding_left_chunks: -1  # number of left chunks for decoding. Defaults to -1.
+  simulate_streaming: true  # simulate streaming inference. Defaults to False.
+
+

examples/librispeech/s2/conf/chunk_transformer.yaml

+# https://yaml.org/type/float.html
+data:
+  train_manifest: data/manifest.train
+  dev_manifest: data/manifest.dev
+  test_manifest: data/manifest.test
+  min_input_len: 0.5  # second
+  max_input_len: 20.0 # second
+  min_output_len: 0.0 # tokens
+  max_output_len: 400.0 # tokens
+  min_output_input_ratio: 0.05
+  max_output_input_ratio: 10.0
+
+collator:
+  vocab_filepath: data/vocab.txt 
+  unit_type: 'spm'
+  spm_model_prefix: 'data/bpe_unigram_5000'
+  mean_std_filepath: ""
+  augmentation_config: conf/augmentation.json
+  batch_size: 64
+  raw_wav: True  # use raw_wav or kaldi feature
+  specgram_type: fbank #linear, mfcc, fbank
+  feat_dim: 80
+  delta_delta: False
+  dither: 1.0
+  target_sample_rate: 16000
+  max_freq: None
+  n_fft: None
+  stride_ms: 10.0
+  window_ms: 25.0
+  use_dB_normalization: True
+  target_dB: -20
+  random_seed: 0
+  keep_transcription_text: False
+  sortagrad: True 
+  shuffle_method: batch_shuffle
+  num_workers: 2
+
+
+# network architecture
+model:
+    cmvn_file: "data/mean_std.json"
+    cmvn_file_type: "json"
+    # encoder related
+    encoder: transformer
+    encoder_conf:
+        output_size: 256    # dimension of attention
+        attention_heads: 4
+        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 input type, you can chose conv2d, conv2d6 and conv2d8
+        normalize_before: true
+        use_dynamic_chunk: true
+        use_dynamic_left_chunk: false
+
+    # decoder related
+    decoder: transformer
+    decoder_conf:
+        attention_heads: 4
+        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
+
+    # hybrid CTC/attention
+    model_conf:
+        ctc_weight: 0.3
+        lsm_weight: 0.1     # label smoothing option
+        length_normalized_loss: false
+
+
+training:
+  n_epoch: 120
+  accum_grad: 1
+  global_grad_clip: 5.0
+  optim: adam
+  optim_conf:
+    lr: 0.001
+    weight_decay: 1e-06
+  scheduler: warmuplr     # pytorch v1.1.0+ required
+  scheduler_conf:
+    warmup_steps: 25000
+    lr_decay: 1.0
+  log_interval: 100
+  checkpoint:
+    kbest_n: 50
+    latest_n: 5
+
+
+decoding:
+  batch_size: 64
+  error_rate_type: wer
+  decoding_method: attention  # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
+  lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
+  alpha: 2.5
+  beta: 0.3
+  beam_size: 10
+  cutoff_prob: 1.0
+  cutoff_top_n: 0
+  num_proc_bsearch: 8
+  ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
+  decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
+      # <0: for decoding, use full chunk.
+      # >0: for decoding, use fixed chunk size as set.
+      # 0: used for training, it's prohibited here. 
+  num_decoding_left_chunks: -1  # number of left chunks for decoding. Defaults to -1.
+  simulate_streaming: true  # simulate streaming inference. Defaults to False.
+
+

examples/librispeech/s2/conf/conformer.yaml

+# https://yaml.org/type/float.html
+data:
+  train_manifest: data/manifest.train
+  dev_manifest: data/manifest.dev
+  test_manifest: data/manifest.test-clean
+  min_input_len: 0.5  # seconds
+  max_input_len: 20.0 # seconds
+  min_output_len: 0.0 # tokens
+  max_output_len: 400.0 # tokens
+  min_output_input_ratio: 0.05
+  max_output_input_ratio: 10.0
+
+collator:
+  vocab_filepath: data/vocab.txt 
+  unit_type: 'spm'
+  spm_model_prefix: 'data/bpe_unigram_5000'
+  mean_std_filepath: ""
+  augmentation_config: conf/augmentation.json
+  batch_size: 16
+  raw_wav: True  # use raw_wav or kaldi feature
+  specgram_type: fbank #linear, mfcc, fbank
+  feat_dim: 80
+  delta_delta: False
+  dither: 1.0
+  target_sample_rate: 16000
+  max_freq: None
+  n_fft: None
+  stride_ms: 10.0
+  window_ms: 25.0
+  use_dB_normalization: True
+  target_dB: -20
+  random_seed: 0
+  keep_transcription_text: False
+  sortagrad: True 
+  shuffle_method: batch_shuffle
+  num_workers: 2
+
+
+# network architecture
+model:
+    cmvn_file: "data/mean_std.json"
+    cmvn_file_type: "json"
+    # encoder related
+    encoder: conformer
+    encoder_conf:
+        output_size: 256    # dimension of attention
+        attention_heads: 4
+        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 input type, you can chose conv2d, conv2d6 and conv2d8
+        normalize_before: True
+        use_cnn_module: True
+        cnn_module_kernel: 15
+        activation_type: 'swish'
+        pos_enc_layer_type: 'rel_pos'
+        selfattention_layer_type: 'rel_selfattn'
+
+    # decoder related
+    decoder: transformer
+    decoder_conf:
+        attention_heads: 4
+        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
+
+    # hybrid CTC/attention
+    model_conf:
+        ctc_weight: 0.3
+        lsm_weight: 0.1     # label smoothing option
+        length_normalized_loss: false
+
+
+training:
+  n_epoch: 120
+  accum_grad: 8
+  global_grad_clip: 3.0
+  optim: adam
+  optim_conf:
+    lr: 0.004
+    weight_decay: 1e-06
+  scheduler: warmuplr     # pytorch v1.1.0+ required
+  scheduler_conf:
+    warmup_steps: 25000
+    lr_decay: 1.0
+  log_interval: 100
+  checkpoint:
+    kbest_n: 50
+    latest_n: 5
+
+
+decoding:
+  batch_size: 64
+  error_rate_type: wer
+  decoding_method: attention  # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
+  lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
+  alpha: 2.5
+  beta: 0.3
+  beam_size: 10
+  cutoff_prob: 1.0
+  cutoff_top_n: 0
+  num_proc_bsearch: 8
+  ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
+  decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
+      # <0: for decoding, use full chunk.
+      # >0: for decoding, use fixed chunk size as set.
+      # 0: used for training, it's prohibited here. 
+  num_decoding_left_chunks: -1  # number of left chunks for decoding. Defaults to -1.
+  simulate_streaming: False  # simulate streaming inference. Defaults to False.
+
+

examples/librispeech/s2/conf/transformer.yaml

+# https://yaml.org/type/float.html
+data:
+  train_manifest: data/manifest.train
+  dev_manifest: data/manifest.dev
+  test_manifest: data/manifest.test-clean
+  min_input_len: 0.5  # second
+  max_input_len: 20.0 # second
+  min_output_len: 0.0 # tokens
+  max_output_len: 400.0 # tokens
+  min_output_input_ratio: 0.05
+  max_output_input_ratio: 10.0
+
+collator:
+  vocab_filepath: data/vocab.txt
+  unit_type: 'spm'
+  spm_model_prefix: 'data/bpe_unigram_5000'
+  mean_std_filepath: ""
+  augmentation_config: conf/augmentation.json
+  batch_size: 64
+  raw_wav: True  # use raw_wav or kaldi feature
+  specgram_type: fbank #linear, mfcc, fbank
+  feat_dim: 80
+  delta_delta: False
+  dither: 1.0
+  target_sample_rate: 16000
+  max_freq: None
+  n_fft: None
+  stride_ms: 10.0
+  window_ms: 25.0
+  use_dB_normalization: True
+  target_dB: -20
+  random_seed: 0
+  keep_transcription_text: False
+  sortagrad: True 
+  shuffle_method: batch_shuffle
+  num_workers: 2
+
+
+# network architecture
+model:
+    cmvn_file: "data/mean_std.json"
+    cmvn_file_type: "json"
+    # encoder related
+    encoder: transformer
+    encoder_conf:
+        output_size: 256    # dimension of attention
+        attention_heads: 4
+        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 input type, you can chose conv2d, conv2d6 and conv2d8
+        normalize_before: true
+
+    # decoder related
+    decoder: transformer
+    decoder_conf:
+        attention_heads: 4
+        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
+
+    # hybrid CTC/attention
+    model_conf:
+        ctc_weight: 0.3
+        lsm_weight: 0.1     # label smoothing option
+        length_normalized_loss: false
+
+
+training:
+  n_epoch: 120
+  accum_grad: 2
+  global_grad_clip: 5.0
+  optim: adam
+  optim_conf:
+    lr: 0.004
+    weight_decay: 1e-06
+  scheduler: warmuplr     # pytorch v1.1.0+ required
+  scheduler_conf:
+    warmup_steps: 25000
+    lr_decay: 1.0
+  log_interval: 100
+  checkpoint:
+    kbest_n: 50
+    latest_n: 5
+
+
+decoding:
+  batch_size: 64
+  error_rate_type: wer
+  decoding_method: attention  # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
+  lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
+  alpha: 2.5
+  beta: 0.3
+  beam_size: 10
+  cutoff_prob: 1.0
+  cutoff_top_n: 0
+  num_proc_bsearch: 8
+  ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
+  decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
+      # <0: for decoding, use full chunk.
+      # >0: for decoding, use fixed chunk size as set.
+      # 0: used for training, it's prohibited here. 
+  num_decoding_left_chunks: -1  # number of left chunks for decoding. Defaults to -1.
+  simulate_streaming: False  # simulate streaming inference. Defaults to False.
+
+

examples/librispeech/s2/local/align.sh

+#!/bin/bash
+
+if [ $# != 2 ];then
+    echo "usage: ${0} config_path ckpt_path_prefix"
+    exit -1
+fi
+
+ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+echo "using $ngpu gpus..."
+
+device=gpu
+if [ ${ngpu} == 0 ];then
+    device=cpu
+fi
+config_path=$1
+ckpt_prefix=$2
+
+batch_size=1
+output_dir=${ckpt_prefix}
+mkdir -p ${output_dir}
+
+# align dump in `result_file`
+# .tier, .TextGrid dump in `dir of result_file`
+python3 -u ${BIN_DIR}/alignment.py \
+--device ${device} \
+--nproc 1 \
+--config ${config_path} \
+--result_file ${output_dir}/${type}.align \
+--checkpoint_path ${ckpt_prefix} \
+--opts decoding.batch_size ${batch_size}
+
+if [ $? -ne 0 ]; then
+    echo "Failed in ctc alignment!"
+    exit 1
+fi
+
+exit 0

examples/librispeech/s2/local/data.sh

+#!/bin/bash
+
+stage=-1
+stop_stage=100
+
+# bpemode (unigram or bpe)
+nbpe=5000
+bpemode=unigram
+bpeprefix="data/bpe_${bpemode}_${nbpe}"
+
+source ${MAIN_ROOT}/utils/parse_options.sh
+
+
+mkdir -p data
+TARGET_DIR=${MAIN_ROOT}/examples/dataset
+mkdir -p ${TARGET_DIR}
+
+if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
+    # download data, generate manifests
+    python3 ${TARGET_DIR}/librispeech/librispeech.py \
+    --manifest_prefix="data/manifest" \
+    --target_dir="${TARGET_DIR}/librispeech" \
+    --full_download="True"
+
+    if [ $? -ne 0 ]; then
+        echo "Prepare LibriSpeech failed. Terminated."
+        exit 1
+    fi
+
+    for set in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do
+        mv data/manifest.${set} data/manifest.${set}.raw
+    done
+
+    rm -rf data/manifest.train.raw data/manifest.dev.raw  data/manifest.test.raw
+    for set in train-clean-100 train-clean-360 train-other-500; do
+        cat data/manifest.${set}.raw >> data/manifest.train.raw
+    done
+
+    for set in dev-clean dev-other; do
+        cat data/manifest.${set}.raw >> data/manifest.dev.raw
+    done
+
+    for set in test-clean test-other; do
+        cat data/manifest.${set}.raw >> data/manifest.test.raw
+    done
+fi
+
+if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+    # build vocabulary
+    python3 ${MAIN_ROOT}/utils/build_vocab.py \
+    --unit_type "spm" \
+    --spm_vocab_size=${nbpe} \
+    --spm_mode ${bpemode} \
+    --spm_model_prefix ${bpeprefix} \
+    --vocab_path="data/vocab.txt" \
+    --manifest_paths="data/manifest.train.raw"
+
+    if [ $? -ne 0 ]; then
+        echo "Build vocabulary failed. Terminated."
+        exit 1
+    fi
+fi
+
+
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+    # compute mean and stddev for normalizer
+    num_workers=$(nproc)
+    python3 ${MAIN_ROOT}/utils/compute_mean_std.py \
+    --manifest_path="data/manifest.train.raw" \
+    --num_samples=-1 \
+    --specgram_type="fbank" \
+    --feat_dim=80 \
+    --delta_delta=false \
+    --sample_rate=16000 \
+    --stride_ms=10.0 \
+    --window_ms=25.0 \
+    --use_dB_normalization=False \
+    --num_workers=${num_workers} \
+    --output_path="data/mean_std.json"
+
+    if [ $? -ne 0 ]; then
+        echo "Compute mean and stddev failed. Terminated."
+        exit 1
+    fi
+fi
+
+
+if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+    # format manifest with tokenids, vocab size
+    for set in train dev test dev-clean dev-other test-clean test-other; do
+    {
+        python3 ${MAIN_ROOT}/utils/format_data.py \
+        --feat_type "raw" \
+        --cmvn_path "data/mean_std.json" \
+        --unit_type "spm" \
+        --spm_model_prefix ${bpeprefix} \
+        --vocab_path="data/vocab.txt" \
+        --manifest_path="data/manifest.${set}.raw" \
+        --output_path="data/manifest.${set}"
+
+        if [ $? -ne 0 ]; then
+            echo "Formt mnaifest failed. Terminated."
+            exit 1
+        fi
+    }&
+    done
+    wait
+fi
+
+echo "LibriSpeech Data preparation done."
+exit 0

examples/librispeech/s2/local/download_lm_en.sh

+#!/bin/bash
+
+. ${MAIN_ROOT}/utils/utility.sh
+
+DIR=data/lm
+mkdir -p ${DIR}
+
+URL=https://deepspeech.bj.bcebos.com/en_lm/common_crawl_00.prune01111.trie.klm
+MD5="099a601759d467cd0a8523ff939819c5"
+TARGET=${DIR}/common_crawl_00.prune01111.trie.klm
+
+echo "Download language model ..."
+download $URL $MD5 $TARGET
+if [ $? -ne 0 ]; then
+    echo "Fail to download the language model!"
+    exit 1
+fi
+
+
+exit 0

examples/librispeech/s2/local/export.sh

+#!/bin/bash
+
+if [ $# != 3 ];then
+    echo "usage: $0 config_path ckpt_prefix jit_model_path"
+    exit -1
+fi
+
+ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+echo "using $ngpu gpus..."
+
+config_path=$1
+ckpt_path_prefix=$2
+jit_model_export_path=$3
+
+device=gpu
+if [ ${ngpu} == 0 ];then
+    device=cpu
+fi
+
+python3 -u ${BIN_DIR}/export.py \
+--device ${device} \
+--nproc ${ngpu} \
+--config ${config_path} \
+--checkpoint_path ${ckpt_path_prefix} \
+--export_path ${jit_model_export_path}
+
+
+if [ $? -ne 0 ]; then
+    echo "Failed in export!"
+    exit 1
+fi
+
+
+exit 0

examples/librispeech/s2/local/test.sh

+#!/bin/bash
+
+if [ $# != 2 ];then
+    echo "usage: ${0} config_path ckpt_path_prefix"
+    exit -1
+fi
+
+ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+echo "using $ngpu gpus..."
+
+device=gpu
+if [ ${ngpu} == 0 ];then
+    device=cpu
+fi
+
+config_path=$1
+ckpt_prefix=$2
+
+chunk_mode=false
+if [[ ${config_path} =~ ^.*chunk_.*yaml$ ]];then
+    chunk_mode=true
+fi
+echo "chunk mode ${chunk_mode}"
+
+
+# download language model
+#bash local/download_lm_en.sh
+#if [ $? -ne 0 ]; then
+#    exit 1
+#fi
+
+for type in attention ctc_greedy_search; do
+    echo "decoding ${type}"
+    if [ ${chunk_mode} == true ];then
+        # stream decoding only support batchsize=1
+        batch_size=1
+    else
+        batch_size=64
+    fi
+    python3 -u ${BIN_DIR}/test.py \
+    --device ${device} \
+    --nproc 1 \
+    --config ${config_path} \
+    --result_file ${ckpt_prefix}.${type}.rsl \
+    --checkpoint_path ${ckpt_prefix} \
+    --opts decoding.decoding_method ${type} decoding.batch_size ${batch_size}
+
+    if [ $? -ne 0 ]; then
+        echo "Failed in evaluation!"
+        exit 1
+    fi
+done
+
+for type in ctc_prefix_beam_search attention_rescoring; do
+    echo "decoding ${type}"
+    batch_size=1
+    python3 -u ${BIN_DIR}/test.py \
+    --device ${device} \
+    --nproc 1 \
+    --config ${config_path} \
+    --result_file ${ckpt_prefix}.${type}.rsl \
+    --checkpoint_path ${ckpt_prefix} \
+    --opts decoding.decoding_method ${type} decoding.batch_size ${batch_size}
+
+    if [ $? -ne 0 ]; then
+        echo "Failed in evaluation!"
+        exit 1
+    fi
+done
+
+
+exit 0

examples/librispeech/s2/local/train.sh

+#!/bin/bash
+
+if [ $# != 2 ];then
+    echo "usage: CUDA_VISIBLE_DEVICES=0 ${0} config_path ckpt_name"
+    exit -1
+fi
+
+ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+echo "using $ngpu gpus..."
+
+config_path=$1
+ckpt_name=$2
+
+device=gpu
+if [ ${ngpu} == 0 ];then
+    device=cpu
+fi
+echo "using ${device}..."
+
+mkdir -p exp
+
+python3 -u ${BIN_DIR}/train.py \
+--device ${device} \
+--nproc ${ngpu} \
+--config ${config_path} \
+--output exp/${ckpt_name}
+
+if [ $? -ne 0 ]; then
+    echo "Failed in training!"
+    exit 1
+fi
+
+exit 0

examples/librispeech/s2/path.sh

+export MAIN_ROOT=${PWD}/../../../
+
+export PATH=${MAIN_ROOT}:${PWD}/utils:${PATH}
+export LC_ALL=C
+
+# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
+export PYTHONIOENCODING=UTF-8
+export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
+
+export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib/
+
+
+MODEL=u2
+export BIN_DIR=${MAIN_ROOT}/deepspeech/exps/${MODEL}/bin

examples/librispeech/s2/run.sh

+#!/bin/bash
+set -e
+source path.sh
+
+stage=0
+stop_stage=100
+conf_path=conf/transformer.yaml
+avg_num=30
+source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
+
+avg_ckpt=avg_${avg_num}
+ckpt=$(basename ${conf_path} | awk -F'.' '{print $1}')
+echo "checkpoint name ${ckpt}"
+
+if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+    # prepare data
+    bash ./local/data.sh || exit -1
+fi
+
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+    # train model, all `ckpt` under `exp` dir
+    CUDA_VISIBLE_DEVICES=4,5,6,7 ./local/train.sh ${conf_path}  ${ckpt}
+fi
+
+if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+    # avg n best model
+    avg.sh exp/${ckpt}/checkpoints ${avg_num}
+fi
+
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+    # test ckpt avg_n
+    CUDA_VISIBLE_DEVICES=7 ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
+fi
+
+if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
+    # ctc alignment of test data
+    CUDA_VISIBLE_DEVICES=0 ./local/align.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
+fi
+
+if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
+    # export ckpt avg_n
+    CUDA_VISIBLE_DEVICES= ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
+fi

examples/librispeech/s2/utils

+../../../utils/
\ No newline at end of file