FunASR/examples/industrial_data_pretraining/llm_asr/conf/template.yaml

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1.9 KiB
YAML

# This is an example that demonstrates how to configure a model file.
# You can modify the configuration according to your own requirements.
# to print the register_table:
# from funasr.register import tables
# tables.print()
# network architecture
model: LLMASR
model_conf:
lsm_weight: 0.1 # label smoothing option
length_normalized_loss: true
# encoder
encoder: WhisperWarp
encoder_conf:
hub: funasr
init_param_path: "/nfs/maziyang.mzy/models/Whisper-large-v2"
freeze: true
llm: Vicuna
llm_conf:
hub: hf
init_param_path: "/nfs/maziyang.mzy/models/vicuna-7b-v1.5"
freeze: true
adaptor: Linear
adaptor_conf:
downsample_rate: 5
llm_dim: 4096
encoder_dim: 512
# frontend related
frontend: WhisperFrontend
frontend_conf:
fs: 16000
whisper_model: large
do_pad_trim: true
specaug: SpecAugLFR
specaug_conf:
apply_time_warp: false
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 30
lfr_rate: 6
num_freq_mask: 1
apply_time_mask: true
time_mask_width_range:
- 0
- 12
num_time_mask: 1
train_conf:
accum_grad: 1
grad_clip: 5
max_epoch: 150
keep_nbest_models: 10
log_interval: 10
optim: adamw
optim_conf:
lr: 0.0001
weight_decay: 0.000001
scheduler: warmuplr
scheduler_conf:
warmup_steps: 1500
dataset: AudioLLMDataset
dataset_conf:
index_ds: IndexDSJsonl
batch_sampler: BatchSampler
batch_type: example # example or length
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;
max_token_length: 2048 # filter samples if source_token_len+target_token_len > max_token_length,
buffer_size: 500
shuffle: True
num_workers: 4
preprocessor_text: TextPreprocessRemovePunctuation
tokenizer: HuggingfaceTokenizer
tokenizer_conf:
unk_symbol: <unk>
init_param_path: "/nfs/maziyang.mzy/models/vicuna-7b-v1.5"