# 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: LLMASRNAR
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model_conf:
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lsm_weight: 0.1 # label smoothing option
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length_normalized_loss: true
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# encoder
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encoder: Paraformer
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encoder_conf:
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hub: funasr
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init_param_path: "iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
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freeze: false
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llm: Vicuna
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llm_conf:
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hub: hf
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init_param_path: "/nfs/maziyang.mzy/models/vicuna-7b-v1.5"
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freeze: true
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adaptor: Linear
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adaptor_conf:
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downsample_rate: 1
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llm_dim: 4096
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encoder_dim: 512
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# frontend related
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frontend: WavFrontend
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frontend_conf:
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fs: 16000
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window: hamming
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n_mels: 80
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frame_length: 25
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frame_shift: 10
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lfr_m: 7
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lfr_n: 6
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cmvn_file: "/root/.cache/modelscope/hub/iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/am.mvn"
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specaug: SpecAugLFR
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specaug_conf:
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apply_time_warp: false
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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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- 30
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lfr_rate: 6
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num_freq_mask: 1
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apply_time_mask: true
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time_mask_width_range:
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- 0
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- 12
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num_time_mask: 1
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train_conf:
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accum_grad: 1
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grad_clip: 5
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max_epoch: 150
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keep_nbest_models: 10
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log_interval: 10
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optim: adamw
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optim_conf:
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lr: 0.0001
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weight_decay: 0.000001
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scheduler: warmuplr
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scheduler_conf:
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warmup_steps: 1500
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dataset: AudioLLMDataset
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dataset_conf:
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index_ds: IndexDSJsonl
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batch_sampler: RankFullLocalShuffleBatchSampler
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batch_type: example # example or length
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batch_size: 8 # 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: 2048 # filter samples if source_token_len+target_token_len > max_token_length,
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buffer_size: 500
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shuffle: True
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num_workers: 4
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preprocessor_text: TextPreprocessRemovePunctuation
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tokenizer: HuggingfaceTokenizer
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tokenizer_conf:
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unk_symbol: <unk>
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init_param_path: "/nfs/maziyang.mzy/models/vicuna-7b-v1.5"
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