Hui Zhang
3 years ago
committed by
GitHub
commit
20d6f9ae4b
@ -1,3 +1,6 @@
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# ASR
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* s0 is for deepspeech2 offline
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* s1 is for transformer/conformer/U2
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* s2 is for transformer/conformer/U2 w/ kaldi feat
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need install Kaldi
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@ -0,0 +1,42 @@
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# LibriSpeech
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## Data
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| Data Subset | Duration in Seconds |
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| data/manifest.train | 0.83s ~ 29.735s |
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| data/manifest.dev | 1.065 ~ 35.155s |
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| data/manifest.test-clean | 1.285s ~ 34.955s |
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## Conformer
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| Model | Params | Config | Augmentation| Test set | Decode method | Loss | WER |
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| --- | --- | --- | --- | --- | --- | --- | --- |
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| conformer | 47.63 M | conf/conformer.yaml | spec_aug + shift | test-clean | attention | - | - |
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| conformer | 47.63 M | conf/conformer.yaml | spec_aug + shift | test-clean | ctc_greedy_search | | |
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| conformer | 47.63 M | conf/conformer.yaml | spec_aug + shift | test-clean | ctc_prefix_beam_search | | |
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| conformer | 47.63 M | conf/conformer.yaml | spec_aug + shift | test-clean | attention_rescoring | | |
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### Test w/o length filter
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| Model | Params | Config | Augmentation| Test set | Decode method | Loss | WER |
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| --- | --- | --- | --- | --- | --- | --- | --- |
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| conformer | 47.63 M | conf/conformer.yaml | spec_aug + shift | test-clean-all | attention | | |
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## Chunk Conformer
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| Model | Params | Config | Augmentation| Test set | Decode method | Chunk Size & Left Chunks | Loss | WER |
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| --- | --- | --- | --- | --- | --- | --- | --- | --- |
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| conformer | 47.63 M | conf/chunk_conformer.yaml | spec_aug + shift | test-clean | attention | 16, -1 | | |
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| conformer | 47.63 M | conf/chunk_conformer.yaml | spec_aug + shift | test-clean | ctc_greedy_search | 16, -1 | | |
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| conformer | 47.63 M | conf/chunk_conformer.yaml | spec_aug + shift | test-clean | ctc_prefix_beam_search | 16, -1 | | - |
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| conformer | 47.63 M | conf/chunk_conformer.yaml | spec_aug + shift | test-clean | attention_rescoring | 16, -1 | | - |
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## Transformer
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| Model | Params | Config | Augmentation| Test set | Decode method | Loss | WER |
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| --- | --- | --- | --- | --- | --- | --- | --- |
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| transformer | 32.52 M | conf/transformer.yaml | spec_aug + shift | test-clean | attention | | |
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### Test w/o length filter
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| Model | Params | Config | Augmentation| Test set | Decode method | Loss | WER |
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| --- | --- | --- | --- | --- | --- | --- | --- |
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| transformer | 32.52 M | conf/transformer.yaml | spec_aug + shift | test-clean-all | attention | | |
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@ -0,0 +1,34 @@
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[
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{
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"type": "shift",
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"params": {
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"min_shift_ms": -5,
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"max_shift_ms": 5
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},
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"prob": 1.0
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},
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{
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"type": "speed",
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"params": {
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"min_speed_rate": 0.9,
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"max_speed_rate": 1.1,
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"num_rates": 3
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},
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"prob": 0.0
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},
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{
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"type": "specaug",
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"params": {
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"F": 10,
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"T": 50,
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"n_freq_masks": 2,
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"n_time_masks": 2,
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"p": 1.0,
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"W": 80,
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"adaptive_number_ratio": 0,
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"adaptive_size_ratio": 0,
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"max_n_time_masks": 20
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},
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"prob": 1.0
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}
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]
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@ -0,0 +1,120 @@
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# https://yaml.org/type/float.html
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data:
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train_manifest: data/manifest.train
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dev_manifest: data/manifest.dev
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test_manifest: data/manifest.test
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min_input_len: 0.5
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max_input_len: 20.0
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min_output_len: 0.0
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max_output_len: 400.0
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min_output_input_ratio: 0.05
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max_output_input_ratio: 10.0
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collator:
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vocab_filepath: data/vocab.txt
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unit_type: 'spm'
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spm_model_prefix: 'data/bpe_unigram_5000'
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mean_std_filepath: ""
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augmentation_config: conf/augmentation.json
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batch_size: 16
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raw_wav: True # use raw_wav or kaldi feature
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specgram_type: fbank #linear, mfcc, fbank
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feat_dim: 80
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delta_delta: False
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dither: 1.0
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target_sample_rate: 16000
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max_freq: None
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n_fft: None
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stride_ms: 10.0
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window_ms: 25.0
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use_dB_normalization: True
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target_dB: -20
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random_seed: 0
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keep_transcription_text: False
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sortagrad: True
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shuffle_method: batch_shuffle
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num_workers: 2
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# network architecture
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model:
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cmvn_file: "data/mean_std.json"
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cmvn_file_type: "json"
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# encoder related
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encoder: conformer
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encoder_conf:
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output_size: 256 # dimension of attention
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attention_heads: 4
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linear_units: 2048 # the number of units of position-wise feed forward
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num_blocks: 12 # the number of encoder blocks
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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attention_dropout_rate: 0.0
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input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
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normalize_before: True
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use_cnn_module: True
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cnn_module_kernel: 15
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activation_type: 'swish'
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pos_enc_layer_type: 'rel_pos'
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selfattention_layer_type: 'rel_selfattn'
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causal: True
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use_dynamic_chunk: true
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cnn_module_norm: 'layer_norm' # using nn.LayerNorm makes model converge faster
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use_dynamic_left_chunk: false
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# decoder related
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decoder: transformer
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decoder_conf:
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attention_heads: 4
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linear_units: 2048
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num_blocks: 6
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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self_attention_dropout_rate: 0.0
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src_attention_dropout_rate: 0.0
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# hybrid CTC/attention
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model_conf:
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ctc_weight: 0.3
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lsm_weight: 0.1 # label smoothing option
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length_normalized_loss: false
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training:
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n_epoch: 240
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accum_grad: 8
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global_grad_clip: 5.0
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optim: adam
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optim_conf:
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lr: 0.001
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weight_decay: 1e-06
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scheduler: warmuplr # pytorch v1.1.0+ required
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scheduler_conf:
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warmup_steps: 25000
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lr_decay: 1.0
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log_interval: 100
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checkpoint:
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kbest_n: 50
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latest_n: 5
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decoding:
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batch_size: 128
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error_rate_type: wer
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decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
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lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
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alpha: 2.5
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beta: 0.3
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beam_size: 10
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cutoff_prob: 1.0
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cutoff_top_n: 0
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num_proc_bsearch: 8
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ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
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decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
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# <0: for decoding, use full chunk.
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# >0: for decoding, use fixed chunk size as set.
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# 0: used for training, it's prohibited here.
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num_decoding_left_chunks: -1 # number of left chunks for decoding. Defaults to -1.
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simulate_streaming: true # simulate streaming inference. Defaults to False.
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@ -0,0 +1,113 @@
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# https://yaml.org/type/float.html
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data:
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train_manifest: data/manifest.train
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dev_manifest: data/manifest.dev
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test_manifest: data/manifest.test
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min_input_len: 0.5 # second
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max_input_len: 20.0 # second
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min_output_len: 0.0 # tokens
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max_output_len: 400.0 # tokens
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min_output_input_ratio: 0.05
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max_output_input_ratio: 10.0
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collator:
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vocab_filepath: data/vocab.txt
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unit_type: 'spm'
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spm_model_prefix: 'data/bpe_unigram_5000'
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mean_std_filepath: ""
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augmentation_config: conf/augmentation.json
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batch_size: 64
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raw_wav: True # use raw_wav or kaldi feature
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specgram_type: fbank #linear, mfcc, fbank
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feat_dim: 80
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delta_delta: False
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dither: 1.0
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target_sample_rate: 16000
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max_freq: None
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n_fft: None
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stride_ms: 10.0
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window_ms: 25.0
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use_dB_normalization: True
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target_dB: -20
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random_seed: 0
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keep_transcription_text: False
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sortagrad: True
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shuffle_method: batch_shuffle
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num_workers: 2
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# network architecture
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model:
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cmvn_file: "data/mean_std.json"
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cmvn_file_type: "json"
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# encoder related
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encoder: transformer
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encoder_conf:
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output_size: 256 # dimension of attention
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attention_heads: 4
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linear_units: 2048 # the number of units of position-wise feed forward
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num_blocks: 12 # the number of encoder blocks
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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attention_dropout_rate: 0.0
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input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
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normalize_before: true
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use_dynamic_chunk: true
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use_dynamic_left_chunk: false
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# decoder related
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decoder: transformer
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decoder_conf:
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attention_heads: 4
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linear_units: 2048
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num_blocks: 6
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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self_attention_dropout_rate: 0.0
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src_attention_dropout_rate: 0.0
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# hybrid CTC/attention
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model_conf:
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ctc_weight: 0.3
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lsm_weight: 0.1 # label smoothing option
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length_normalized_loss: false
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training:
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n_epoch: 120
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accum_grad: 1
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global_grad_clip: 5.0
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optim: adam
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optim_conf:
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lr: 0.001
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weight_decay: 1e-06
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scheduler: warmuplr # pytorch v1.1.0+ required
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scheduler_conf:
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warmup_steps: 25000
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lr_decay: 1.0
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log_interval: 100
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checkpoint:
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kbest_n: 50
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latest_n: 5
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decoding:
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batch_size: 64
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error_rate_type: wer
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decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
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lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
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alpha: 2.5
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beta: 0.3
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beam_size: 10
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cutoff_prob: 1.0
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cutoff_top_n: 0
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num_proc_bsearch: 8
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ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
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decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
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# <0: for decoding, use full chunk.
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# >0: for decoding, use fixed chunk size as set.
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# 0: used for training, it's prohibited here.
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num_decoding_left_chunks: -1 # number of left chunks for decoding. Defaults to -1.
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simulate_streaming: true # simulate streaming inference. Defaults to False.
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@ -0,0 +1,116 @@
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# https://yaml.org/type/float.html
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data:
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train_manifest: data/manifest.train
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dev_manifest: data/manifest.dev
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test_manifest: data/manifest.test-clean
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min_input_len: 0.5 # seconds
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max_input_len: 20.0 # seconds
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min_output_len: 0.0 # tokens
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max_output_len: 400.0 # tokens
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min_output_input_ratio: 0.05
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max_output_input_ratio: 10.0
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collator:
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vocab_filepath: data/vocab.txt
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unit_type: 'spm'
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spm_model_prefix: 'data/bpe_unigram_5000'
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mean_std_filepath: ""
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augmentation_config: conf/augmentation.json
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batch_size: 16
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raw_wav: True # use raw_wav or kaldi feature
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specgram_type: fbank #linear, mfcc, fbank
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feat_dim: 80
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delta_delta: False
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dither: 1.0
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target_sample_rate: 16000
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max_freq: None
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n_fft: None
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stride_ms: 10.0
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window_ms: 25.0
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use_dB_normalization: True
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target_dB: -20
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random_seed: 0
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keep_transcription_text: False
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sortagrad: True
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shuffle_method: batch_shuffle
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num_workers: 2
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|
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|
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# network architecture
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model:
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cmvn_file: "data/mean_std.json"
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cmvn_file_type: "json"
|
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# encoder related
|
||||
encoder: conformer
|
||||
encoder_conf:
|
||||
output_size: 256 # dimension of attention
|
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attention_heads: 4
|
||||
linear_units: 2048 # the number of units of position-wise feed forward
|
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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'
|
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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.
|
||||
|
||||
|
||||
@ -0,0 +1,111 @@
|
||||
# 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.
|
||||
|
||||
|
||||
@ -0,0 +1,37 @@
|
||||
#!/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 [ $? -ne 0 ]; then
|
||||
echo "Failed in ctc alignment!"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
exit 0
|
||||
@ -0,0 +1,111 @@
|
||||
#!/bin/bash
|
||||
|
||||
stage=-1
|
||||
stop_stage=100
|
||||
|
||||
# bpemode (unigram or bpe)
|
||||
nbpe=5000
|
||||
bpemode=unigram
|
||||
bpeprefix="data/bpe_${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
|
||||
@ -0,0 +1,20 @@
|
||||
#!/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
|
||||
@ -0,0 +1,34 @@
|
||||
#!/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
|
||||
@ -0,0 +1,72 @@
|
||||
#!/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
|
||||
@ -0,0 +1,33 @@
|
||||
#!/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 [ $? -ne 0 ]; then
|
||||
echo "Failed in training!"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
exit 0
|
||||
@ -0,0 +1,14 @@
|
||||
export MAIN_ROOT=${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
|
||||
@ -0,0 +1,43 @@
|
||||
#!/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
|
||||
@ -0,0 +1 @@
|
||||
../../../utils/
|
||||
Loading…
Reference in new issue