python/FunASR-XL.git

			@@ -30,17 +30,17 @@
			BAC009S0002W0124 自六月底呼和浩特市率先宣布取消限购后
			...
			```
			These two files both have two columns, while the first column is the wav ids and the second column is the corresponding wav paths/label tokens.
			These two files both have two columns, while the first column is wav ids and the second column is the corresponding wav paths/label tokens.

			## Stage 1: Feature Generation
			This stage extracts FBank feature from raw wav `wav.scp` and apply speed perturbation as data augmentation according to `speed_perturb`. You can set `nj` to control the number of jobs for feature generation. The output features are saved in `$feats_dir/dump/xxx/ark` and the corresponding `feats.scp` files are saved as `$feats_dir/dump/xxx/feats.scp`. An example of `feats.scp` can be seen as follows:
			This stage extracts FBank features from `wav.scp` and apply speed perturbation as data augmentation according to `speed_perturb`. Users can set `nj` to control the number of jobs for feature generation. The generated features are saved in `$feats_dir/dump/xxx/ark` and the corresponding `feats.scp` files are saved as `$feats_dir/dump/xxx/feats.scp`. An example of `feats.scp` can be seen as follows:
			* `feats.scp`
			```
			...
			BAC009S0002W0122_sp0.9 /nfs/funasr_data/aishell-1/dump/fbank/train/ark/feats.16.ark:592751055
			...
			```
			Note that samples in this file have already been shuffled. This file contains two columns. The first column is the wav-id while the second column is the kaldi-ark feature path. Besides, `speech_shape` and `text_shape` are also generated in this stage, denoting the speech feature shape and text length of each sample. The examples are shown as follows:
			Note that samples in this file have already been shuffled randomly. This file contains two columns. The first column is wav ids while the second column is kaldi-ark feature paths. Besides, `speech_shape` and `text_shape` are also generated in this stage, denoting the speech feature shape and text length of each sample. The examples are shown as follows:
			* `speech_shape`
			```
			...
			@@ -53,10 +53,10 @@
			BAC009S0002W0122_sp0.9 15
			...
			```
			These two files have two columns. The first column is the wav-id and the second column is the corresponding speech feature shape and text length.
			These two files have two columns. The first column is wav ids and the second column is the corresponding speech feature shape and text length.

			## Stage 2: Dictionary Preparation
			This stage prepares a dictionary, which is used as a mapping between label characters and integer indices during ASR training. The output dictionary file is saved as `$feats_dir/data/$lang_toekn_list/$token_type/tokens.txt`. Here we show an example of `tokens.txt` as follows:
			This stage processes the dictionary, which is used as a mapping between label characters and integer indices during ASR training. The processed dictionary file is saved as `$feats_dir/data/$lang_toekn_list/$token_type/tokens.txt`. An example of `tokens.txt` is as follows:
			* `tokens.txt`
			```
			<blank>
			@@ -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