python/FunASR-XL.git

			@@ -1,21 +1,25 @@
			# Speech Recognition
			Here we take "Training a paraformer model from scratch using the AISHELL-1 dataset" as an example to introduce how to use FunASR. According to this example, users can similarly employ other datasets (such as AISHELL-2 dataset, etc.) to train other models (such as conformer, transformer, etc.).

			## Overall Introduction
			## Quick Start



			## Introduction
			We provide a recipe `egs/aishell/paraformer/run.sh` for training a paraformer model on AISHELL-1 dataset. This recipe consists of five stages, supporting training on multiple GPUs and decoding by CPU or GPU. Before introducing each stage in detail, we first explain several parameters which should be set by users.
			- `CUDA_VISIBLE_DEVICES`: visible gpu list
			- `gpu_num`: the number of GPUs used for training
			- `gpu_inference`: whether to use GPUs for decoding
			- `njob`: for CPU decoding, indicating the total number of CPU jobs; for GPU decoding, indicating the number of jobs on each GPU
			- `data_aishell`: the raw path of AISHELL-1 dataset
			- `raw_data`: the raw path of AISHELL-1 dataset
			- `feats_dir`: the path for saving processed data
			- `nj`: the number of jobs for data preparation
			- `speed_perturb`: the range of speech perturbed
			- `exp_dir`: the path for saving experimental results
			- `tag`: the suffix of experimental result directory

			## Stage 0: Data preparation
			This stage processes raw AISHELL-1 dataset `$data_aishell` and generates the corresponding `wav.scp` and `text` in `$feats_dir/data/xxx`. `xxx` means `train/dev/test`. Here we assume users have already downloaded AISHELL-1 dataset. If not, users can download data [here](https://www.openslr.org/33/) and set the path for `$data_aishell`. The examples of `wav.scp` and `text` are as follows:
			### Stage 0: Data preparation
			This stage processes raw AISHELL-1 dataset `$raw_data` and generates the corresponding `wav.scp` and `text` in `$feats_dir/data/xxx`. `xxx` means `train/dev/test`. Here we assume users have already downloaded AISHELL-1 dataset. If not, users can download data [here](https://www.openslr.org/33/) and set the path for `$raw_data`. The examples of `wav.scp` and `text` are as follows:
			* `wav.scp`
			```
			BAC009S0002W0122 /nfs/ASR_DATA/AISHELL-1/data_aishell/wav/train/S0002/BAC009S0002W0122.wav
			@@ -32,30 +36,10 @@
			```
			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 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 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`
			```
			...
			BAC009S0002W0122_sp0.9 665,80
			...
			```
			* `text_shape`
			```
			...
			BAC009S0002W0122_sp0.9 15
			...
			```
			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 1: Feature and CMVN Generation
			This stage computes CMVN based on `train` dataset, which is used in the following stages. Users can set `nj` to control the number of jobs for computing CMVN. The generated CMVN file is saved as `$feats_dir/data/train/cmvn/cmvn.mvn`.

			## Stage 2: Dictionary Preparation
			### Stage 2: Dictionary Preparation
			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`
			```
			@@ -74,7 +58,7 @@
			* `</s>`: indicates the end-of-sentence token
			* `<unk>`: indicates the out-of-vocabulary token

			## Stage 3: Training
			### Stage 3: Training
			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
			@@ -100,7 +84,7 @@
			tensorboard --logdir ${exp_dir}/exp/${model_dir}/tensorboard/train
			```

			## Stage 4: Decoding
			### Stage 4: Decoding
			This stage generates the recognition results and calculates the `CER` to verify the performance of the trained model.

			* Mode Selection
			@@ -117,7 +101,7 @@

			* Performance

			We adopt `CER` to verify the performance. The results are in `$exp_dir/exp/$model_dir/$decoding_yaml_name/$average_model_name/$dset`, namely `text.cer` and `text.cer.txt`. `text.cer` saves the comparison between the recognized text and the reference text while `text.cer.txt` saves the final `CER` result. The following is an example of `text.cer`:
			We adopt `CER` to verify the performance. The results are in `$exp_dir/exp/$model_dir/$decoding_yaml_name/$average_model_name/$dset`, namely `text.cer` and `text.cer.txt`. `text.cer` saves the comparison between the recognized text and the reference text while `text.cer.txt` saves the final `CER` results. The following is an example of `text.cer`:
			* `text.cer`
			```
			...
			@@ -127,3 +111,4 @@
			...
			```

			## Change settings