# network architecture
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model: SenseVoiceRWKV
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model_conf:
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lsm_weight: 0.1
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length_normalized_loss: true
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activation_checkpoint: true
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sos: "<|startoftranscript|>"
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eos: "<|endoftext|>"
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downsample_rate: 4
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use_padmask: true
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dims:
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n_mels: 128
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n_vocab: 60515
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n_audio_ctx: 1500
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n_audio_state: 1280
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n_audio_head: 20
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n_audio_layer: 32
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n_text_ctx: 448
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n_text_state: 1280
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n_text_head: 20
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n_text_layer: 32
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# decoder
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decoder: SenseVoiceDecoder
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decoder_conf:
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rwkv_cfg:
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n_embd: 1280
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dropout: 0
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head_size_a: 64
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ctx_len: 1280
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dim_att: 1280 #${model_conf.rwkv_cfg.n_embd}
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dim_ffn: null
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head_size_divisor: 8
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n_layer: 32
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pre_ffn: 0
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ln0: false
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ln1: false
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init_rwkv: false
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datatype: bf16
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# frontend related
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frontend: WhisperFrontend
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frontend_conf:
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fs: 16000
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n_mels: ${model_conf.dims.n_mels}
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do_pad_trim: false
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tokenizer: SenseVoiceTokenizer
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tokenizer_conf:
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vocab_path: null
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is_multilingual: true
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num_languages: 8749
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dataset: SenseVoiceDataset
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dataset_conf:
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index_ds: IndexDSJsonlRankSplit
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batch_sampler: EspnetStyleBatchSampler
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rank_split: true
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batch_type: token # example or length
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batch_size: 3500 # if batch_type is example, batch_size is the numbers of samples; if length, batch_size is source_token_len+target_token_len;
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max_token_length: 2200
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min_token_length: 60
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max_source_length: 2000
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min_source_length: 60
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max_target_length: 150
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min_target_length: 0
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shuffle: True
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num_workers: 4
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sos: ${model_conf.sos}
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eos: ${model_conf.eos}
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train_conf:
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accum_grad: 2
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grad_clip: 5
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max_epoch: 20
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keep_nbest_models: 20
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avg_nbest_model: ${train_conf.keep_nbest_models}
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log_interval: 50
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reset_gpu_cache: true
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optim: adamw
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optim_conf:
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lr: 0.00002
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scheduler: warmuplr
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scheduler_conf:
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warmup_steps: 10000
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specaug: SpecAug
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specaug_conf:
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apply_time_warp: true
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time_warp_window: 5
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time_warp_mode: bicubic
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apply_freq_mask: true
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freq_mask_width_range:
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- 0
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- 40
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num_freq_mask: 2
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apply_time_mask: true
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time_mask_width_ratio_range:
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- 0.0
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- 0.12
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num_time_mask: 2
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scope_map: ['encoder.encoders', 'model.encoder', 'decoder.decoders', 'model.decoder']
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