# 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: LLMASR2
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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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audio_encoder: "/nfs/zhifu.gzf/init_model/SenseVoiceModelscope"
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audio_encoder_conf:
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hub: ms
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freeze: true
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llm: Qwen1.5-7b-chat
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llm_conf:
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hub: hf
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freeze: true
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init_param_path: "/nfs/zhifu.gzf/init_model/qwen/Qwen1___5-7B-Chat_raw"
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audio_adaptor: Transformer
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audio_adaptor_conf:
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downsample_rate: 2
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llm_dim: 4096
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encoder_dim: 1280
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n_layer: 2
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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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whisper_model: large-v3
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do_pad_trim: true
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permute: false # true: [bs, frames, dims]; false: [bs, dims, frames]
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filters_path: "/nfs/zhifu.gzf/init_model/SenseVoiceModelscope/assets/mel_filters.npz"
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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: 15
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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: 0.000000
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scheduler: warmuplr
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scheduler_conf:
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warmup_steps: 1500
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dataset: OpenAIDataset
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dataset_conf:
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index_ds: OpenAIIndexDSJsonl
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batch_sampler: CustomDistributedBatchSampler
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batch_type: example # example or length
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batch_size: 4 # 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: 3000 # filter samples if source_token_len+target_token_len > max_token_length,
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shuffle: True
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num_workers: 0
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audio_adaptor_downsample_rate: ${audio_adaptor_conf.downsample_rate}
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audio_encoder_downsample_rate: 2
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# prompt: "<|startoftranscription|><|zh|><|transcribe|><|zh|><|notimestamps|><|wo_itn|>"
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tokenizer: HuggingfaceTokenizer
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tokenizer_conf:
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init_param_path: "/nfs/zhifu.gzf/init_model/qwen/Qwen1___5-7B-Chat_raw"
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