# This is an example that demonstrates how to configure a model file.
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# You can modify the configuration according to your own requirements.
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# to print the register_table:
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# from funasr.register import tables
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# tables.print()
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# network architecture
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model: SenseVoice
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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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# 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: IndexDSJsonl
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batch_sampler: EspnetStyleBatchSampler
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batch_type: length # example or length
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batch_size: 7000 # 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: 2000 # filter samples if source_token_len+target_token_len > max_token_length,
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min_token_length: 60
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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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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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