Hui Zhang
3 years ago
parent
8432b9b256
commit
1f2b4ab293
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data
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exp
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*.profile
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# MandarinK8
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## Conformer
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| Model | Params | Config | Augmentation| Test set | Decode method | Loss | CER |
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| --- | --- | --- | --- | --- | --- | --- | --- |
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| conformer | 45.73 M | conf/conformer.yaml | spec_aug + shift | test | attention | 2.1794936656951904 | 0.102304 |
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| conformer | 45.73 M | conf/conformer.yaml | spec_aug + shift | test | ctc_greedy_search | 2.1794936656951904 | 0.084295 |
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| conformer | 45.73 M | conf/conformer.yaml | spec_aug + shift | test | ctc_prefix_beam_search | 2.1794936656951904 | 0.084340 |
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| conformer | 45.73 M | conf/conformer.yaml | spec_aug + shift | test | attention_rescoring | 2.1794936656951904 | 0.081675 |
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## Chunk Conformer
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| Model | Params | Config | Augmentation| Test set | Decode method | Chunk Size & Left Chunks | Loss | CER |
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| --- | --- | --- | --- | --- | --- | --- | --- | --- |
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| conformer | 45.73 M | conf/chunk_conformer.yaml | spec_aug + shift | test | attention | 16, -1 | 2.23287845 | 0.087982 |
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| conformer | 45.73 M | conf/chunk_conformer.yaml | spec_aug + shift | test | ctc_greedy_search | 16, -1 | 2.23287845 | 0.086962 |
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| conformer | 45.73 M | conf/chunk_conformer.yaml | spec_aug + shift | test | ctc_prefix_beam_search | 16, -1 | 2.23287845 | 0.086741 |
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| conformer | 45.73 M | conf/chunk_conformer.yaml | spec_aug + shift | test | attention_rescoring | 16, -1 | 2.23287845 | 0.083495 |
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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": "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": "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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# 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 # second
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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: 'char'
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spm_model_prefix: ''
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augmentation_config: conf/augmentation.json
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batch_size: 32
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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: 8000
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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: 4
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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-6
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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: cer
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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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# 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 # second
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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: 0.0
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max_output_input_ratio: .inf
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collator:
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vocab_filepath: data/vocab.txt
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unit_type: 'char'
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spm_model_prefix: ''
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augmentation_config: conf/augmentation.json
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batch_size: 32
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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: 8000
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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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# 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: 100 # 50 will be lowest
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accum_grad: 4
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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: 0.002
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weight_decay: 1e-6
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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: cer
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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: False # simulate streaming inference. Defaults to False.
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#! /usr/bin/env bash
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if [ $# != 2 ];then
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echo "usage: ${0} config_path ckpt_path_prefix"
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exit -1
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fi
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ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
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echo "using $ngpu gpus..."
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device=gpu
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if [ ${ngpu} == 0 ];then
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device=cpu
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fi
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config_path=$1
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ckpt_prefix=$2
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ckpt_name=$(basename ${ckpt_prefxi})
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mkdir -p exp
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batch_size=1
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output_dir=${ckpt_prefix}
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mkdir -p ${output_dir}
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# align dump in `result_file`
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# .tier, .TextGrid dump in `dir of result_file`
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python3 -u ${BIN_DIR}/alignment.py \
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--device ${device} \
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--nproc 1 \
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--config ${config_path} \
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--result_file ${output_dir}/${type}.align \
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--checkpoint_path ${ckpt_prefix} \
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--opts decoding.batch_size ${batch_size}
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if [ $? -ne 0 ]; then
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echo "Failed in ctc alignment!"
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exit 1
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fi
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exit 0
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#! /usr/bin/env bash
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stage=-1
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stop_stage=100
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source ${MAIN_ROOT}/utils/parse_options.sh
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mkdir -p data
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if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
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for dataset in train dev test; do
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mv data/manifest.${dataset} data/manifest.${dataset}.raw
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done
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fi
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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# download data, generate manifests
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# build vocabulary
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python3 ${MAIN_ROOT}/utils/build_vocab.py \
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--unit_type="char" \
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--count_threshold=0 \
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--vocab_path="data/vocab.txt" \
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--manifest_paths "data/manifest.train.raw"
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if [ $? -ne 0 ]; then
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echo "Build vocabulary failed. Terminated."
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exit 1
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fi
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fi
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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# compute mean and stddev for normalizer
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num_workers=$(nproc)
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python3 ${MAIN_ROOT}/utils/compute_mean_std.py \
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--manifest_path="data/manifest.train.raw" \
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--specgram_type="fbank" \
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--feat_dim=80 \
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--delta_delta=false \
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--stride_ms=10.0 \
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--window_ms=25.0 \
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--sample_rate=8000 \
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--use_dB_normalization=False \
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--num_samples=-1 \
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--num_workers=${num_workers} \
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--output_path="data/mean_std.json"
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if [ $? -ne 0 ]; then
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echo "Compute mean and stddev failed. Terminated."
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exit 1
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fi
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fi
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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# format manifest with tokenids, vocab size
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for dataset in train dev test; do
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{
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python3 ${MAIN_ROOT}/utils/format_data.py \
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--feat_type "raw" \
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--cmvn_path "data/mean_std.json" \
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--unit_type "char" \
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--vocab_path="data/vocab.txt" \
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--manifest_path="data/manifest.${dataset}.raw" \
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--output_path="data/manifest.${dataset}"
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if [ $? -ne 0 ]; then
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echo "Formt mnaifest failed. Terminated."
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exit 1
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fi
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} &
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done
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wait
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fi
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echo "data preparation done."
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exit 0
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@ -0,0 +1,21 @@
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#!/bin/bash
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. ${MAIN_ROOT}/utils/utility.sh
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DIR=data/lm
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mkdir -p ${DIR}
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URL='https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm'
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MD5="29e02312deb2e59b3c8686c7966d4fe3"
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TARGET=${DIR}/zh_giga.no_cna_cmn.prune01244.klm
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echo "Download language model ..."
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download $URL $MD5 $TARGET
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if [ $? -ne 0 ]; then
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echo "Fail to download the language model!"
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exit 1
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fi
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exit 0
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#! /usr/bin/env bash
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if [ $# != 3 ];then
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echo "usage: $0 config_path ckpt_prefix jit_model_path"
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exit -1
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fi
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ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
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echo "using $ngpu gpus..."
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config_path=$1
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ckpt_path_prefix=$2
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jit_model_export_path=$3
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device=gpu
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if [ ${ngpu} == 0 ];then
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device=cpu
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fi
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python3 -u ${BIN_DIR}/export.py \
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--device ${device} \
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--nproc ${ngpu} \
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--config ${config_path} \
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--checkpoint_path ${ckpt_path_prefix} \
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--export_path ${jit_model_export_path}
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if [ $? -ne 0 ]; then
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echo "Failed in export!"
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exit 1
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fi
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exit 0
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@ -0,0 +1,67 @@
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#! /usr/bin/env bash
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if [ $# != 2 ];then
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echo "usage: ${0} config_path ckpt_path_prefix"
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exit -1
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fi
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ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
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echo "using $ngpu gpus..."
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device=gpu
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if [ ${ngpu} == 0 ];then
|
||||
device=cpu
|
||||
fi
|
||||
config_path=$1
|
||||
ckpt_prefix=$2
|
||||
|
||||
ckpt_name=$(basename ${ckpt_prefxi})
|
||||
|
||||
mkdir -p exp
|
||||
|
||||
# download language model
|
||||
#bash local/download_lm_ch.sh
|
||||
#if [ $? -ne 0 ]; then
|
||||
# exit 1
|
||||
#fi
|
||||
|
||||
|
||||
for type in attention ctc_greedy_search; do
|
||||
echo "decoding ${type}"
|
||||
batch_size=1
|
||||
output_dir=${ckpt_prefix}
|
||||
mkdir -p ${output_dir}
|
||||
python3 -u ${BIN_DIR}/test.py \
|
||||
--device ${device} \
|
||||
--nproc 1 \
|
||||
--config ${config_path} \
|
||||
--result_file ${output_dir}/${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 ${type}"
|
||||
batch_size=1
|
||||
output_dir=${ckpt_prefix}
|
||||
mkdir -p ${output_dir}
|
||||
python3 -u ${BIN_DIR}/test.py \
|
||||
--device ${device} \
|
||||
--nproc 1 \
|
||||
--config ${config_path} \
|
||||
--result_file ${output_dir}/${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 @@
|
||||
#! /usr/bin/env 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=${MAIN_ROOT}:${MAIN_ROOT}/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 +1,44 @@
|
||||
#!/bin/bash
|
||||
set -e
|
||||
source path.sh
|
||||
|
||||
stage=0
|
||||
stop_stage=100
|
||||
conf_path=conf/conformer.yaml
|
||||
avg_num=20
|
||||
|
||||
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=0,1,2,3 ./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=4 ./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
|
||||
Loading…
Reference in new issue