python/FunASR-XL.git

			@@ -75,7 +75,7 @@
			* `<unk>`: indicates the out-of-vocabulary token

			## Stage 3: Training
			This stage achieves the training of the specified model. To start training, you should manually set `exp_dir`, `CUDA_VISIBLE_DEVICES` and `gpu_num`, which have already been explained above. By default, the best `$keep_nbest_models` checkpoints on validation dataset will be averaged to generate a better model and adopted for decoding.
			This stage achieves the training of the specified model. To start training, users should manually set `exp_dir`, `CUDA_VISIBLE_DEVICES` and `gpu_num`, which have already been explained above. By default, the best `$keep_nbest_models` checkpoints on validation dataset will be averaged to generate a better model and adopted for decoding.

			* DDP Training

			@@ -83,30 +83,29 @@

			* DataLoader

			[comment]: <> (We support two types of DataLoaders for small and large datasets, respectively. By default, the small DataLoader is used and you can set `dataset_type=large` to enable large DataLoader. For small DataLoader, )
			We support an optional iterable-style DataLoader based on [Pytorch Iterable-style DataPipes](https://pytorch.org/data/beta/torchdata.datapipes.iter.html) for large dataset and you can set `dataset_type=large` to enable it.
			We support an optional iterable-style DataLoader based on [Pytorch Iterable-style DataPipes](https://pytorch.org/data/beta/torchdata.datapipes.iter.html) for large dataset and users can set `dataset_type=large` to enable it.

			* Configuration

			The parameters of the training, including model, optimization, dataset, etc., are specified by a YAML file in `conf` directory. Also, you can directly specify the parameters in `run.sh` recipe. Please avoid to specify the same parameters in both the YAML file and the recipe.
			The parameters of the training, including model, optimization, dataset, etc., can be set by a YAML file in `conf` directory. Also, users can directly set the parameters in `run.sh` recipe. Please avoid to set the same parameters in both the YAML file and the recipe.

			* Training Steps

			We support two parameters to specify the training steps, namely `max_epoch` and `max_update`. `max_epoch` indicates the total training epochs while `max_update` indicates the total training steps. If these two parameters are specified at the same time, once the training reaches any one of the two parameters, the training will be stopped.
			We support two parameters to specify the training steps, namely `max_epoch` and `max_update`. `max_epoch` indicates the total training epochs while `max_update` indicates the total training steps. If these two parameters are specified at the same time, once the training reaches any one of these two parameters, the training will be stopped.

			* Tensorboard

			You can use tensorboard to observe the loss, learning rate, etc. Please run the following command:
			Users can use tensorboard to observe the loss, learning rate, etc. Please run the following command:
			```
			tensorboard --logdir ${exp_dir}/exp/${model_dir}/tensorboard/train
			```

			## Stage 4: Decoding
			This stage generates the recognition results with acoustic features as input and calculate the `CER` to verify the performance of the trained model.
			This stage generates the recognition results and calculates the `CER` to verify the performance of the trained model.

			* Mode Selection

			As we support conformer, paraformer and uniasr in FunASR and they have different inference interfaces, a `mode` param is specified as `asr/paraformer/uniase` according to the trained model.
			As we support paraformer, uniasr, conformer and other models in FunASR, a `mode` parameter should be specified as `asr/paraformer/uniasr` according to the trained model.

			* Configuration