I am now working on Hubert model
I have pretrained a new model from Youtube dataset and finetuned the model using some animal sound files. The labels for each file are animal names, so I have created new dictionary and ltr, wrd. I set ltr and wrd to be same, the animal name labels.
I run 50 iterations for pretrain and 50 iterations for finetune
but when I decode test dataset using the checkpoint_best.pt , I have trouble with the following error.
What is your question?
File "/export/home/chu/fairseq/fairseq/models/hubert/hubert_asr.py", line 374, in init model = pretrain_task.build_model(w2v_args.model, from_checkpoint=True)
File "/export/home/chu/fairseq/fairseq/tasks/fairseq_task.py", line 355, in build_model model = models.build_model(cfg, self, from_checkpoint)
File "/export/home/chu/fairseq/fairseq/models/init.py", line 106, in build_model return model.build_model(cfg, task)
File "/export/home/chu/fairseq/fairseq/models/hubert/hubert_asr.py", line 170, in build_model w2v_encoder = HubertEncoder(cfg, task)
File "/export/home/chu/fairseq/fairseq/models/hubert/hubert_asr.py", line 349, in init state = checkpoint_utils.load_checkpoint_to_cpu(cfg.w2v_path, arg_overrides)
File "/export/home/chu/fairseq/fairseq/checkpoint_utils.py", line 358, in load_checkpoint_to_cpu state["cfg"] = OmegaConf.create(state["cfg"])
RecursionError: maximum recursion depth exceeded in comparison full_key: job_logging_cfg.formatters.simple.format
reference_type=Any object_type=dict
full_key: job_logging_cfg.formatters.simple reference_type=Any
object_type=dict full_key: job_logging_cfg.formatters
reference_type=Any object_type=dict
full_key: job_logging_cfg reference_type=Optional[Dict[Union[str, Enum], Any]]
object_type=dict
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
❓ Questions and Help
I am now working on Hubert model I have pretrained a new model from Youtube dataset and finetuned the model using some animal sound files. The labels for each file are animal names, so I have created new dictionary and ltr, wrd. I set ltr and wrd to be same, the animal name labels.
I run 50 iterations for pretrain and 50 iterations for finetune
but when I decode test dataset using the checkpoint_best.pt , I have trouble with the following error.
What is your question?
File "/export/home/chu/fairseq/fairseq/models/hubert/hubert_asr.py", line 374, in init model = pretrain_task.build_model(w2v_args.model, from_checkpoint=True) File "/export/home/chu/fairseq/fairseq/tasks/fairseq_task.py", line 355, in build_model model = models.build_model(cfg, self, from_checkpoint) File "/export/home/chu/fairseq/fairseq/models/init.py", line 106, in build_model return model.build_model(cfg, task) File "/export/home/chu/fairseq/fairseq/models/hubert/hubert_asr.py", line 170, in build_model w2v_encoder = HubertEncoder(cfg, task) File "/export/home/chu/fairseq/fairseq/models/hubert/hubert_asr.py", line 349, in init state = checkpoint_utils.load_checkpoint_to_cpu(cfg.w2v_path, arg_overrides) File "/export/home/chu/fairseq/fairseq/checkpoint_utils.py", line 358, in load_checkpoint_to_cpu state["cfg"] = OmegaConf.create(state["cfg"]) RecursionError: maximum recursion depth exceeded in comparison full_key: job_logging_cfg.formatters.simple.format reference_type=Any object_type=dict full_key: job_logging_cfg.formatters.simple reference_type=Any object_type=dict full_key: job_logging_cfg.formatters reference_type=Any object_type=dict full_key: job_logging_cfg reference_type=Optional[Dict[Union[str, Enum], Any]] object_type=dict
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
Code
python fairseq_cli/hydra_train.py \ --config-dir ~/fairseq/examples/hubert/config/finetune \ --config-name base_10h \ task.data=/.../data/finetune_data \ task.label_dir=/.../trans/finetune_trans \ model.w2v_path=/.../fairseq/None/checkpoints/checkpoint_best.pt \ dataset.skip_invalid_size_inputs_valid_test=true \ checkpoint.save_interval=1 \ checkpoint.reset_optimizer=true
python examples/speech_recognition/new/infer.py \ --config-dir ~/.../config/decode \ --config-name infer_viterbi \ task.data=/.../data \ task.normalize=false \ dataset.gen_subset=test \ common_eval.path=/.../checkpoints/checkpoint_best.pt
Config file
defaults:
hydra: run: dir: ${common_eval.results_path}/viterbi sweep: dir: ${common_eval.results_path} subdir: viterbi
task: _name: hubert_pretraining single_target: true fine_tuning: true data: ??? normalize: ???
decoding: type: viterbi unique_wer_file: true common_eval: results_path: ??? path: ??? post_process: letter dataset: max_tokens: 1100000 gen_subset: ???
What's your environment?