[ASR] Support Hubert, fintuned on the librispeech dataset (#3088)

* librispeech hubert, test=asr

* librispeech hubert, test=asr

* hubert decode

* review

* copyright, notes, example related

* hubert cli

* pre-commit format

* fix conflicts

* fix conflicts

* doc related

* doc and train config

* librispeech.py

* support hubert cli

demos/speech_ssl/README.md

@@ -36,7 +36,7 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav
   ```
   Arguments:
   - `input`(required): Audio file to recognize.
-  - `model`: Model type of asr task. Default: `wav2vec2ASR_librispeech`.
+  - `model`: Model type of asr task. Default: `wav2vec2`, choices: [wav2vec2, hubert].
   - `task`: Output type. Default: `asr`.
   - `lang`: Model language. Default: `en`.
   - `sample_rate`: Sample rate of the model. Default: `16000`.
@@ -56,7 +56,7 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav
 
   # to recognize text 
   text = ssl_executor(
-      model='wav2vec2ASR_librispeech',
+      model='wav2vec2',
       task='asr',
       lang='en',
       sample_rate=16000,

demos/speech_ssl/README_cn.md

@@ -36,7 +36,7 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav
   ```
   参数：
   - `input`(必须输入)：用于识别的音频文件。
-  - `model`：ASR 任务的模型，默认值：`wav2vec2ASR_librispeech`。
+  - `model`：ASR 任务的模型，默认值：`wav2vec2`, 可选项：[wav2vec2, hubert]。
   - `task`：输出类别，默认值：`asr`。
   - `lang`：模型语言，默认值：`en`。
   - `sample_rate`：音频采样率，默认值：`16000`。
@@ -56,7 +56,7 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav
 
   # 识别文本
   text = ssl_executor(
-      model='wav2vec2ASR_librispeech',
+      model='wav2vec2,
       task='asr',
       lang='en',
       sample_rate=16000,

docs/source/released_model.md

@@ -26,6 +26,8 @@ Model | Pre-Train Method | Pre-Train Data | Finetune Data | Size | Descriptions
 [Wav2vec2ASR-large-960h-librispeech Model](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr3/wav2vec2ASR-large-960h-librispeech_ckpt_1.3.1.model.tar.gz) | wav2vec2 | Librispeech and LV-60k Dataset (5.3w h) | Librispeech (960 h) | 718 MB |Encoder: Wav2vec2.0, Decoder: CTC, Decoding method: Greedy search | - | 0.0189 | [Wav2vecASR Librispeech ASR3](../../examples/librispeech/asr3) |
 [Wav2vec2-large-wenetspeech-self Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr3/wav2vec2-large-wenetspeech-self_ckpt_1.3.0.model.tar.gz) | wav2vec2 | Wenetspeech Dataset (1w h) | - | 714 MB |Pre-trained Wav2vec2.0 Model | - | - | - | 
 [Wav2vec2ASR-large-aishell1 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr3/wav2vec2ASR-large-aishell1_ckpt_1.4.0.model.tar.gz) | wav2vec2 | Wenetspeech Dataset (1w h) | aishell1 (train set) | 1.18 GB |Encoder: Wav2vec2.0, Decoder: CTC, Decoding method: Greedy search | 0.0510 | - | - |
+[Hubert-large-lv60 Model](https://paddlespeech.bj.bcebos.com/hubert/hubert-large-lv60.pdparams) | hubert | LV-60k Dataset | - | 1.18 GB |Pre-trained hubert Model | - | - | - | 
+[Hubert-large-100h-librispeech Model](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr4/hubertASR-large-100h-librispeech_ckpt_1.4.0.model.tar.gz) | hubert | LV-60k Dataset | librispeech train-clean-100 | 1.27 GB |Encoder: Hubert, Decoder: Linear + CTC, Decoding method: Greedy search | - | 0.0587 | [HubertASR Librispeech ASR4](../../examples/librispeech/asr4) |
 
 ### Whisper Model
 Demo Link | Training Data | Size | Descriptions | CER | Model 

examples/librispeech/asr3/path.sh

@@ -10,6 +10,4 @@ export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
 
 export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib/
 
-
+export BIN_DIR=${MAIN_ROOT}/paddlespeech/s2t/exps/wav2vec2/bin
-MODEL=wav2vec2
-export BIN_DIR=${MAIN_ROOT}/paddlespeech/s2t/exps/${MODEL}/bin

examples/librispeech/asr4/README.md

@@ -0,0 +1,197 @@
+# Hubert2ASR with Librispeech
+This example contains code used to finetune [hubert](https://arxiv.org/abs/2106.07447) model with [Librispeech dataset](http://www.openslr.org/resources/12)
+## Overview
+All the scripts you need are in `run.sh`. There are several stages in `run.sh`, and each stage has its function.
+| Stage | Function                                                     |
+|:---- |:----------------------------------------------------------- |
+| 0     | Process data. It includes: <br>       (1) Download the dataset <br>       (2) Calculate the CMVN of the train dataset <br>       (3) Get the vocabulary file <br>       (4) Get the manifest files of the train, development and test dataset<br>       (5) Download the pretrained wav2vec2 model |
+| 1     | Train the model                                              |
+| 2     | Get the final model by averaging the top-k models, set k = 1 means to choose the best model |
+| 3     | Test the final model performance                             |
+| 4     | Infer the single audio file                                  |
+
+
+You can choose to run a range of stages by setting `stage` and `stop_stage `. 
+
+For example, if you want to execute the code in stage 2 and stage 3, you can run this script:
+```bash
+bash run.sh --stage 2 --stop_stage 3
+```
+Or you can set `stage` equal to `stop-stage` to only run one stage.
+For example, if you only want to run `stage 0`, you can use the script below:
+```bash
+bash run.sh --stage 0 --stop_stage 0
+```
+The document below will describe the scripts in `run.sh` in detail.
+## The Environment Variables
+The path.sh contains the environment variables. 
+```bash
+. ./path.sh
+. ./cmd.sh
+```
+This script needs to be run first. And another script is also needed:
+```bash
+source ${MAIN_ROOT}/utils/parse_options.sh
+```
+It will support the way of using `--variable value` in the shell scripts.
+## The Local Variables
+Some local variables are set in `run.sh`. 
+`gpus` denotes the GPU number you want to use. If you set `gpus=`, it means you only use CPU. 
+`stage` denotes the number of stages you want to start from in the experiments.
+`stop stage` denotes the number of the stage you want to end at in the experiments. 
+`conf_path` denotes the config path of the model.
+`avg_num` denotes the number K of top-K models you want to average to get the final model.
+`audio file` denotes the file path of the single file you want to infer in stage 5
+`ckpt` denotes the checkpoint prefix of the model, e.g. "hubertASR"
+
+You can set the local variables (except `ckpt`) when you use `run.sh`
+
+For example, you can set the `gpus` and `avg_num` when you use the command line:
+```bash
+bash run.sh --gpus 0,1 --avg_num 20
+```
+## Stage 0: Data Processing
+To use this example, you need to process data firstly and you can use stage 0 in `run.sh` to do this. The code is shown below:
+```bash
+ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+     # prepare data
+     bash ./local/data.sh || exit -1
+ fi
+```
+Stage 0 is for processing the data.
+
+If you only want to process the data. You can run
+```bash
+bash run.sh --stage 0 --stop_stage 0
+```
+You can also just run these scripts in your command line.
+```bash
+. ./path.sh
+. ./cmd.sh
+bash ./local/data.sh
+```
+After processing the data, the `data` directory will look like this:
+```bash
+data/
+|-- dev.meta
+|-- lang_char
+|   `-- bpe_unigram_5000.model
+|   `-- bpe_unigram_5000.vocab
+|   `-- vocab.txt
+|-- manifest.dev
+|-- manifest.dev.raw
+|-- manifest.test
+|-- manifest.test.raw
+|-- manifest.train
+|-- manifest.train.raw
+|-- mean_std.json
+|-- test.meta
+`-- train.meta
+```
+
+Stage 0 also downloads the pre-trained [hubert](https://paddlespeech.bj.bcebos.com/hubert/hubert-large-lv60.pdparams) model.
+```bash
+mkdir -p exp/hubert
+wget -P exp/hubert https://paddlespeech.bj.bcebos.com/hubert/hubert-large-lv60.pdparams
+```
+## Stage 1: Model Training
+If you want to train the model. you can use stage 1 in `run.sh`. The code is shown below. 
+```bash
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+     # train model, all `ckpt` under `exp` dir
+     CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${ckpt}
+ fi
+```
+If you want to train the model, you can use the script below to execute stage 0 and stage 1:
+```bash
+bash run.sh --stage 0 --stop_stage 1
+```
+or you can run these scripts in the command line (only use CPU).
+```bash
+. ./path.sh
+. ./cmd.sh
+bash ./local/data.sh
+CUDA_VISIBLE_DEVICES= ./local/train.sh conf/hubertASR.yaml hubertASR
+```
+## Stage 2: Top-k Models Averaging
+After training the model, we need to get the final model for testing and inference. In every epoch, the model checkpoint is saved, so we can choose the best model from them based on the validation loss or we can sort them and average the parameters of the top-k models to get the final model. We can use stage 2 to do this, and the code is shown below. Note: We only train one epoch for hubertASR, thus the `avg_num` is set to 1.
+```bash
+ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+     # avg n best model
+     avg.sh best exp/${ckpt}/checkpoints ${avg_num}
+ fi
+```
+The `avg.sh` is in the `../../../utils/` which is define in the `path.sh`.
+If you want to get the final model, you can use the script below to execute stage 0, stage 1, and stage 2:
+```bash
+bash run.sh --stage 0 --stop_stage 2
+```
+or you can run these scripts in the command line (only use CPU).
+
+```bash
+. ./path.sh
+. ./cmd.sh
+bash ./local/data.sh
+CUDA_VISIBLE_DEVICES= ./local/train.sh conf/hubertASR.yaml hubertASR
+avg.sh best exp/hubertASR/checkpoints 1
+```
+## Stage 3: Model Testing
+The test stage is to evaluate the model performance. The code of test stage is shown below:
+```bash
+ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+     # test ckpt avg_n
+     CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
+ fi
+```
+If you want to train a model and test it, you can use the script below to execute stage 0, stage 1, stage 2, and stage 3 :
+```bash
+bash run.sh --stage 0 --stop_stage 3
+```
+or you can run these scripts in the command line (only use CPU).
+```bash
+. ./path.sh
+. ./cmd.sh
+bash ./local/data.sh
+CUDA_VISIBLE_DEVICES= ./local/train.sh conf/hubertASR.yaml hubertASR
+avg.sh best exp/hubertASR/checkpoints 1
+CUDA_VISIBLE_DEVICES= ./local/test.sh conf/hubertASR.yaml conf/tuning/decode.yaml exp/hubertASR/checkpoints/avg_1
+```
+## Pretrained Model
+You can get the pretrained hubertASR from [this](../../../docs/source/released_model.md).
+
+using the `tar` scripts to unpack the model and then you can use the script to test the model.
+
+For example:
+```bash
+wget https://paddlespeech.bj.bcebos.com/hubert/hubertASR-large-100h-librispeech_ckpt_1.4.0.model.tar.gz
+tar xzvf hubertASR-large-100h-librispeech_ckpt_1.4.0.model.tar.gz
+source path.sh
+# If you have process the data and get the manifest file， you can skip the following 2 steps
+bash local/data.sh --stage -1 --stop_stage -1
+bash local/data.sh --stage 2 --stop_stage 2
+CUDA_VISIBLE_DEVICES= ./local/test.sh conf/hubertASR.yaml conf/tuning/decode.yaml exp/hubertASR/checkpoints/avg_1
+```
+The performance of the released models are shown in [here](./RESULTS.md).
+
+
+## Stage 4: Single Audio File Inference
+In some situations, you want to use the trained model to do the inference for the single audio file. You can use stage 5. The code is shown below
+```bash
+ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
+     # test a single .wav file
+     CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1
+ fi
+```
+you can train the model by yourself using ```bash run.sh --stage 0 --stop_stage 3```, or you can download the pretrained model through the script below:
+```bash
+wget https://paddlespeech.bj.bcebos.com/hubert/hubertASR-large-100h-librispeech_ckpt_1.4.0.model.tar.gz
+tar xzvf hubertASR-large-100h-librispeech_ckpt_1.4.0.model.tar.gz
+```
+You can download the audio demo:
+```bash
+wget -nc https://paddlespeech.bj.bcebos.com/datasets/single_wav/en/demo_002_en.wav -P data/
+```
+You need to prepare an audio file or use the audio demo above, please confirm the sample rate of the audio is 16K. You can get the result of the audio demo by running the script below.
+```bash
+CUDA_VISIBLE_DEVICES= ./local/test_wav.sh conf/hubertASR.yaml conf/tuning/decode.yaml exp/hubertASR/checkpoints/avg_1 data/demo_002_en.wav
+```

examples/librispeech/asr4/RESULTS.md

@@ -0,0 +1,9 @@
+# LibriSpeech
+
+## hubertASR
+Fintuning on train-clean-100
+train: Epoch 3, 1*V100-32G, batchsize: 4, accum_grad: 8
+
+| Model | Params | Config | Augmentation| Test set | Decode method | WER |  
+| --- | --- | --- | --- | --- | --- | --- |
+| hubertASR | 326.16M | conf/hubertASR.yaml | spec_aug | test-clean | greedy search | 0.05868 |  

examples/librispeech/asr4/cmd.sh

@@ -0,0 +1,89 @@
+# ====== About run.pl, queue.pl, slurm.pl, and ssh.pl ======
+# Usage: <cmd>.pl [options] JOB=1:<nj> <log> <command...>
+# e.g.
+#   run.pl --mem 4G JOB=1:10 echo.JOB.log echo JOB
+#
+# Options:
+#   --time <time>: Limit the maximum time to execute.
+#   --mem <mem>: Limit the maximum memory usage.
+#   -–max-jobs-run <njob>: Limit the number parallel jobs. This is ignored for non-array jobs.
+#   --num-threads <ngpu>: Specify the number of CPU core.
+#   --gpu <ngpu>: Specify the number of GPU devices.
+#   --config: Change the configuration file from default.
+#
+# "JOB=1:10" is used for "array jobs" and it can control the number of parallel jobs.
+# The left string of "=", i.e. "JOB", is replaced by <N>(Nth job) in the command and the log file name,
+# e.g. "echo JOB" is changed to "echo 3" for the 3rd job and "echo 8" for 8th job respectively.
+# Note that the number must start with a positive number, so you can't use "JOB=0:10" for example.
+#
+# run.pl, queue.pl, slurm.pl, and ssh.pl have unified interface, not depending on its backend.
+# These options are mapping to specific options for each backend and
+# it is configured by "conf/queue.conf" and "conf/slurm.conf" by default.
+# If jobs failed, your configuration might be wrong for your environment.
+#
+#
+# The official documentation for run.pl, queue.pl, slurm.pl, and ssh.pl:
+#   "Parallelization in Kaldi": http://kaldi-asr.org/doc/queue.html
+# =========================================================~
+
+
+# Select the backend used by run.sh from "local", "sge", "slurm", or "ssh"
+cmd_backend='local'
+
+# Local machine, without any Job scheduling system
+if [ "${cmd_backend}" = local ]; then
+
+    # The other usage
+    export train_cmd="run.pl"
+    # Used for "*_train.py": "--gpu" is appended optionally by run.sh
+    export cuda_cmd="run.pl"
+    # Used for "*_recog.py"
+    export decode_cmd="run.pl"
+
+# "qsub" (SGE, Torque, PBS, etc.)
+elif [ "${cmd_backend}" = sge ]; then
+    # The default setting is written in conf/queue.conf.
+    # You must change "-q g.q" for the "queue" for your environment.
+    # To know the "queue" names, type "qhost -q"
+    # Note that to use "--gpu *", you have to setup "complex_value" for the system scheduler.
+
+    export train_cmd="queue.pl"
+    export cuda_cmd="queue.pl"
+    export decode_cmd="queue.pl"
+
+# "sbatch" (Slurm)
+elif [ "${cmd_backend}" = slurm ]; then
+    # The default setting is written in conf/slurm.conf.
+    # You must change "-p cpu" and "-p gpu" for the "partion" for your environment.
+    # To know the "partion" names, type "sinfo".
+    # You can use "--gpu * " by default for slurm and it is interpreted as "--gres gpu:*"
+    # The devices are allocated exclusively using "${CUDA_VISIBLE_DEVICES}".
+
+    export train_cmd="slurm.pl"
+    export cuda_cmd="slurm.pl"
+    export decode_cmd="slurm.pl"
+
+elif [ "${cmd_backend}" = ssh ]; then
+    # You have to create ".queue/machines" to specify the host to execute jobs.
+    # e.g. .queue/machines
+    #   host1
+    #   host2
+    #   host3
+    # Assuming you can login them without any password, i.e. You have to set ssh keys.
+
+    export train_cmd="ssh.pl"
+    export cuda_cmd="ssh.pl"
+    export decode_cmd="ssh.pl"
+
+# This is an example of specifying several unique options in the JHU CLSP cluster setup.
+# Users can modify/add their own command options according to their cluster environments.
+elif [ "${cmd_backend}" = jhu ]; then
+
+    export train_cmd="queue.pl --mem 2G"
+    export cuda_cmd="queue-freegpu.pl --mem 2G --gpu 1 --config conf/gpu.conf"
+    export decode_cmd="queue.pl --mem 4G"
+
+else
+    echo "$0: Error: Unknown cmd_backend=${cmd_backend}" 1>&2
+    return 1
+fi

examples/librispeech/asr4/conf/config.json

@@ -0,0 +1,77 @@
+{
+  "_name_or_path": "facebook/hubert-large-ll60k",
+  "activation_dropout": 0.0,
+  "apply_spec_augment": true,
+  "architectures": [
+    "HubertModel"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": 1,
+  "conv_bias": true,
+  "conv_dim": [
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512
+  ],
+  "conv_kernel": [
+    10,
+    3,
+    3,
+    3,
+    3,
+    2,
+    2
+  ],
+  "conv_stride": [
+    5,
+    2,
+    2,
+    2,
+    2,
+    2,
+    2
+  ],
+  "ctc_loss_reduction": "sum",
+  "ctc_zero_infinity": false,
+  "do_stable_layer_norm": true,
+  "eos_token_id": 2,
+  "feat_extract_activation": "gelu",
+  "feat_extract_dropout": 0.0,
+  "feat_extract_norm": "layer",
+  "feat_proj_dropout": 0.1,
+  "final_dropout": 0.0,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "layerdrop": 0.1,
+  "mask_channel_length": 10,
+  "mask_channel_min_space": 1,
+  "mask_channel_other": 0.0,
+  "mask_channel_prob": 0.0,
+  "mask_channel_selection": "static",
+  "mask_feature_length": 10,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_space": 1,
+  "mask_time_other": 0.0,
+  "mask_time_prob": 0.075,
+  "mask_time_selection": "static",
+  "model_type": "hubert",
+  "num_attention_heads": 16,
+  "num_conv_pos_embedding_groups": 16,
+  "num_conv_pos_embeddings": 128,
+  "num_feat_extract_layers": 7,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "transformers_version": "4.10.0.dev0",
+  "vocab_size": 32,
+  "tokenizer_class": "Wav2Vec2CTCTokenizer"
+}

examples/librispeech/asr4/conf/hubertASR.yaml

@@ -0,0 +1,142 @@
+############################################
+#          Network Architecture           #
+############################################
+freeze_hubert: False
+normalize_wav: True
+output_norm: True
+init_type: kaiming_uniform # !Warning: need to convergence
+enc:
+  input_shape: 1024
+  dnn_blocks: 2
+  dnn_neurons: 1024
+  activation: True
+ctc:
+  enc_n_units: 1024
+  blank_id: 0
+  dropout_rate: 0.0
+hubert_params_path: "exp/hubert/hubert-large-lv60.pdparams"
+
+
+task_cfg:
+  label_rate: 50.0
+  sample_rate: 16000
+  normalize: True
+  enable_padding: False
+  max_keep_size: None
+  max_sample_size: 250000
+  min_sample_size: 32000
+  single_target: False
+  random_crop: True
+  pad_audio: False
+
+model_cfg:
+  dropout_input: 0.0
+  final_dropout: 0.0
+  dropout: 0.0
+  attention_dropout: 0.0
+  activation_dropout: 0.1
+  apply_mask: True
+  mask_length: 10
+  mask_prob: 0.5
+  mask_selection: static
+  mask_other: 0.0
+  no_mask_overlap: False
+  mask_channel_length: 64
+  mask_channel_prob: 0.25
+  mask_channel_selection: static
+  mask_channel_other: 0.0
+  no_mask_channel_overlap: False
+  feature_grad_mult: 0.0
+  layerdrop: 0.1
+  normalize: True
+  fp16: True
+  label_rate: 50
+  extractor_mode: layer_norm
+  encoder_layers: 24
+  encoder_embed_dim: 1024
+  encoder_ffn_embed_dim: 4096
+  encoder_attention_heads: 16
+  activation_fn: gelu
+  encoder_layerdrop: 0.1
+  dropout_features: 0.0
+  final_dim: 768
+  untie_final_proj: True
+  layer_norm_first: True
+  conv_feature_layers: "[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2"
+  conv_bias: False
+  logit_temp: 0.1
+  target_glu: False
+  mask_min_space: 1
+  mask_channel_min_space: 1
+  conv_pos: 128
+  conv_pos_groups: 16
+  latent_temp: [2.0, 0.5, 0.999995]
+  skip_masked: False
+  skip_nomask: True
+
+###########################################
+#                   Data                  #
+###########################################
+train_manifest: data/manifest.train-clean-100
+dev_manifest: data/manifest.dev
+test_manifest: data/manifest.test-clean
+
+###########################################
+#              Dataloader                 #
+###########################################
+vocab_filepath: data/lang_char/vocab.txt 
+unit_type: char
+mean_std_filepath: ""
+preprocess_config: conf/preprocess.yaml
+sortagrad: -1 # Feed samples from shortest to longest ; -1: enabled for all epochs 0: disabled other: enabled for other epochs 
+batch_size: 4  # Different batch_size may cause large differences in results
+maxlen_in: 1500  # if input length  > maxlen-in batchsize is automatically reduced
+maxlen_out: 150  # if output length > maxlen-out batchsize is automatically reduced
+minibatches: 0 # for debug
+batch_count: auto
+batch_bins: 0 
+batch_frames_in: 0
+batch_frames_out: 0
+batch_frames_inout: 0
+num_workers: 0
+subsampling_factor: 1
+num_encs: 1
+dist_sampler: True
+shortest_first: True
+return_lens_rate: True
+  
+############################################
+#             Data Augmentation            #
+############################################
+audio_augment:  # for raw audio 
+  sample_rate: 16000
+  speeds: [95, 100, 105]
+
+###########################################
+#                 Training                #
+###########################################
+n_epoch: 3
+accum_grad: 8
+global_grad_clip: 5.0
+model_optim: adadelta
+model_optim_conf:
+  lr: 1.0
+  epsilon: 1.0e-6
+  rho: 0.95
+model_scheduler: constantlr    
+model_scheduler_conf:
+  warmup_steps: 25000
+  lr_decay: 1.0
+hubert_optim: adadelta
+hubert_optim_conf:
+  lr: 0.95
+  epsilon: 1.0e-6
+  rho: 0.95
+hubert_scheduler: constantlr    
+hubert_scheduler_conf:
+  warmup_steps: 25000
+  lr_decay: 1.0
+log_interval: 1
+checkpoint:
+  kbest_n: 50
+  latest_n: 5

examples/librispeech/asr4/conf/preprocess.yaml

@@ -0,0 +1,3 @@
+process:
+    # use raw audio
+  - type: wav_process

examples/librispeech/asr4/conf/preprocessor_config.json

@@ -0,0 +1,9 @@
+{
+  "do_normalize": true,
+  "feature_extractor_type": "Wav2Vec2FeatureExtractor",
+  "feature_size": 1,
+  "padding_side": "right",
+  "padding_value": 0,
+  "return_attention_mask": true,
+  "sampling_rate": 16000
+}

examples/librispeech/asr4/conf/tuning/decode.yaml

@@ -0,0 +1,4 @@
+decode_batch_size: 1
+error_rate_type: wer
+decoding_method: ctc_greedy_search  # 'ctc_greedy_search', 'ctc_prefix_beam_search'
+beam_size: 10

examples/librispeech/asr4/local/data.sh

@@ -0,0 +1,110 @@
+#!/bin/bash
+
+stage=-1
+stop_stage=100
+
+unit_type=char
+dict_dir=data/lang_char
+
+source ${MAIN_ROOT}/utils/parse_options.sh
+
+mkdir -p data
+mkdir -p ${dict_dir}
+TARGET_DIR=${MAIN_ROOT}/dataset
+mkdir -p ${TARGET_DIR}
+
+if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
+    # download data, generate manifests
+    python3 ${TARGET_DIR}/librispeech/librispeech.py \
+    --manifest_prefix="data/manifest" \
+    --target_dir="${TARGET_DIR}/librispeech" \
+    --full_download="True"
+
+    if [ $? -ne 0 ]; then
+        echo "Prepare LibriSpeech failed. Terminated."
+        exit 1
+    fi
+
+    for set in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do
+        mv data/manifest.${set} data/manifest.${set}.raw
+    done
+
+    rm -rf data/manifest.train.raw data/manifest.dev.raw  data/manifest.test.raw
+    for set in train-clean-100 train-clean-360 train-other-500; do
+        cat data/manifest.${set}.raw >> data/manifest.train.raw
+    done
+
+    for set in dev-clean dev-other; do
+        cat data/manifest.${set}.raw >> data/manifest.dev.raw
+    done
+
+    for set in test-clean test-other; do
+        cat data/manifest.${set}.raw >> data/manifest.test.raw
+    done
+fi
+
+if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+    # compute mean and stddev for normalizer
+    num_workers=$(nproc)
+    python3 ${MAIN_ROOT}/utils/compute_mean_std.py \
+    --manifest_path="data/manifest.train.raw" \
+    --num_samples=2000 \
+    --spectrum_type="fbank" \
+    --feat_dim=161 \
+    --delta_delta=false \
+    --sample_rate=16000 \
+    --stride_ms=10 \
+    --window_ms=25 \
+    --use_dB_normalization=False \
+    --num_workers=${num_workers} \
+    --output_path="data/mean_std.json"
+
+    if [ $? -ne 0 ]; then
+        echo "Compute mean and stddev failed. Terminated."
+        exit 1
+    fi
+fi
+
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+    # build vocabulary
+    python3 ${MAIN_ROOT}/utils/build_vocab.py \
+    --unit_type ${unit_type} \
+    --count_threshold=0 \
+    --vocab_path="${dict_dir}/vocab.txt" \
+    --manifest_paths="data/manifest.train.raw"
+
+    if [ $? -ne 0 ]; then
+        echo "Build vocabulary failed. Terminated."
+        exit 1
+    fi
+fi
+
+if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+    # format manifest with tokenids, vocab size
+    for set in train dev test dev-clean dev-other test-clean test-other; do
+    {
+        python3 ${MAIN_ROOT}/utils/format_data.py \
+        --cmvn_path "data/mean_std.json" \
+        --unit_type ${unit_type} \
+        --vocab_path="${dict_dir}/vocab.txt" \
+        --manifest_path="data/manifest.${set}.raw" \
+        --output_path="data/manifest.${set}"
+
+        if [ $? -ne 0 ]; then
+            echo "Formt mnaifest.${set} failed. Terminated."
+            exit 1
+        fi
+    }&
+    done
+    wait
+fi
+
+echo "LibriSpeech Data preparation done."
+
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+    mkdir -p exp/hubert
+    echo "Pretrained hubert model download"
+    wget -P exp/hubert https://paddlespeech.bj.bcebos.com/hubert/hubert-large-lv60.pdparams
+fi
+
+exit 0

examples/librispeech/asr4/local/test.sh

@@ -0,0 +1,83 @@
+#!/bin/bash
+
+set -e
+
+ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+echo "using $ngpu gpus..."
+
+expdir=exp
+datadir=data
+
+recog_set="test-clean test-other dev-clean dev-other"
+recog_set="test-clean"
+
+config_path=$1
+decode_config_path=$2
+ckpt_prefix=$3
+
+source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
+
+# download language model
+#bash local/download_lm_en.sh
+#if [ $? -ne 0 ]; then
+#    exit 1
+#fi
+
+python3 utils/format_rsl.py \
+    --origin_ref data/manifest.test-clean.raw \
+    --trans_ref data/manifest.test-clean.text
+
+
+for type in ctc_greedy_search; do
+    echo "decoding ${type}"
+    batch_size=16
+    python3 -u ${BIN_DIR}/test.py \
+        --ngpu ${ngpu} \
+        --config ${config_path} \
+        --decode_cfg ${decode_config_path} \
+        --result_file ${ckpt_prefix}.${type}.rsl \
+        --checkpoint_path ${ckpt_prefix} \
+        --opts decode.decoding_method ${type} \
+        --opts decode.decode_batch_size ${batch_size}
+
+    if [ $? -ne 0 ]; then
+        echo "Failed in evaluation!"
+        exit 1
+    fi
+    python3 utils/format_rsl.py \
+        --origin_hyp ${ckpt_prefix}.${type}.rsl \
+        --trans_hyp ${ckpt_prefix}.${type}.rsl.text
+
+    python3 utils/compute-wer.py --char=1 --v=1 \
+        data/manifest.test-clean.text ${ckpt_prefix}.${type}.rsl.text > ${ckpt_prefix}.${type}.error
+    echo "decoding ${type} done."
+done
+
+for type in ctc_prefix_beam_search; do
+    echo "decoding ${type}"
+    batch_size=1
+    python3 -u ${BIN_DIR}/test.py \
+        --ngpu ${ngpu} \
+        --config ${config_path} \
+        --decode_cfg ${decode_config_path} \
+        --result_file ${ckpt_prefix}.${type}.rsl \
+        --checkpoint_path ${ckpt_prefix} \
+        --opts decode.decoding_method ${type} \
+        --opts decode.decode_batch_size ${batch_size}
+
+    if [ $? -ne 0 ]; then
+        echo "Failed in evaluation!"
+        exit 1
+    fi
+    python3 utils/format_rsl.py \
+        --origin_hyp ${ckpt_prefix}.${type}.rsl \
+        --trans_hyp ${ckpt_prefix}.${type}.rsl.text
+
+    python3 utils/compute-wer.py --char=1 --v=1 \
+        data/manifest.test-clean.text ${ckpt_prefix}.${type}.rsl.text > ${ckpt_prefix}.${type}.error
+    echo "decoding ${type} done."
+done
+
+echo "Finished"
+
+exit 0

examples/librispeech/asr4/local/test_wav.sh

@@ -0,0 +1,58 @@
+#!/bin/bash
+
+if [ $# != 4 ];then
+    echo "usage: ${0} config_path decode_config_path ckpt_path_prefix audio_file"
+    exit -1
+fi
+
+ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+echo "using $ngpu gpus..."
+
+config_path=$1
+decode_config_path=$2
+ckpt_prefix=$3
+audio_file=$4
+
+mkdir -p data
+wget -nc https://paddlespeech.bj.bcebos.com/datasets/single_wav/en/demo_002_en.wav -P data/
+if [ $? -ne 0 ]; then
+   exit 1
+fi
+
+if [ ! -f ${audio_file} ]; then
+    echo "Plase input the right audio_file path"
+    exit 1
+fi
+
+chunk_mode=false
+if [[ ${config_path} =~ ^.*chunk_.*yaml$ ]];then
+    chunk_mode=true
+fi
+
+# download language model
+#bash local/download_lm_ch.sh
+#if [ $? -ne 0 ]; then
+#    exit 1
+#fi
+
+for type in ctc_greedy_search; do
+    echo "decoding ${type}"
+    batch_size=1
+    output_dir=${ckpt_prefix}
+    mkdir -p ${output_dir}
+    python3 -u ${BIN_DIR}/test_wav.py \
+    --ngpu ${ngpu} \
+    --config ${config_path} \
+    --decode_cfg ${decode_config_path} \
+    --result_file ${output_dir}/${type}.rsl \
+    --checkpoint_path ${ckpt_prefix} \
+    --opts decode.decoding_method ${type} \
+    --opts decode.decode_batch_size ${batch_size} \
+    --audio_file ${audio_file}
+
+    if [ $? -ne 0 ]; then
+        echo "Failed in evaluation!"
+        exit 1
+    fi
+done
+exit 0

examples/librispeech/asr4/local/train.sh

@@ -0,0 +1,58 @@
+#!/bin/bash
+
+if [ $# -lt 2 ] && [ $# -gt 3 ];then
+    echo "usage: CUDA_VISIBLE_DEVICES=0 ${0} config_path ckpt_name ips(optional)"
+    exit -1
+fi
+
+ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+echo "using $ngpu gpus..."
+
+config_path=$1
+ckpt_name=$2
+resume=$3
+ips=$4
+
+if [ ! $ips ];then
+  ips_config=
+else
+  ips_config="--ips="${ips}
+fi
+
+mkdir -p exp
+
+# seed may break model convergence
+seed=1988
+if [ ${seed} != 0 ]; then
+    export FLAGS_cudnn_deterministic=True
+fi
+
+# export FLAGS_cudnn_exhaustive_search=true
+# export FLAGS_conv_workspace_size_limit=4000
+export FLAGS_allocator_strategy=naive_best_fit
+if [ ${ngpu} == 0 ]; then
+python3 -u ${BIN_DIR}/train.py \
+--ngpu ${ngpu} \
+--config ${config_path} \
+--output exp/${ckpt_name} \
+--seed ${seed} \
+--resume ${resume}
+else
+python3 -m paddle.distributed.launch --gpus=${CUDA_VISIBLE_DEVICES} ${ips_config} ${BIN_DIR}/train.py \
+--ngpu ${ngpu} \
+--config ${config_path} \
+--output exp/${ckpt_name} \
+--seed ${seed} \
+--resume ${resume}
+fi
+
+if [ ${seed} != 0 ]; then
+    unset FLAGS_cudnn_deterministic
+fi
+
+if [ $? -ne 0 ]; then
+    echo "Failed in training!"
+    exit 1
+fi
+
+exit 0

examples/librispeech/asr4/path.sh

@@ -0,0 +1,13 @@
+export MAIN_ROOT=`realpath ${PWD}/../../../`
+
+export PATH=${MAIN_ROOT}:${MAIN_ROOT}/tools/sctk/bin:${PWD}/utils:${PATH}
+export LC_ALL=C
+
+export PYTHONDONTWRITEBYTECODE=1
+# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
+export PYTHONIOENCODING=UTF-8
+export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
+
+export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib/
+
+export BIN_DIR=${MAIN_ROOT}/paddlespeech/s2t/exps/hubert/bin

examples/librispeech/asr4/run.sh

@@ -0,0 +1,47 @@
+#!/bin/bash
+set -e
+
+. ./path.sh || exit 1;
+. ./cmd.sh || exit 1;
+
+gpus=0
+stage=0
+stop_stage=0
+conf_path=conf/hubertASR.yaml
+ips=            #xx.xx.xx.xx,xx.xx.xx.xx
+decode_conf_path=conf/tuning/decode.yaml
+avg_num=1
+resume=         # xx e.g. 30
+
+. ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
+
+audio_file=data/demo_002_en.wav
+
+avg_ckpt=avg_${avg_num}
+ckpt=$(basename ${conf_path} | awk -F'.' '{print $1}')
+echo "checkpoint name ${ckpt}"
+
+if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+    # prepare data
+    bash ./local/data.sh || exit -1
+fi
+
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+    # train model, all `ckpt` under `exp` dir
+    CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${ckpt} ${resume} ${ips}
+fi
+
+if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+    # avg n best model
+    avg.sh best exp/${ckpt}/checkpoints ${avg_num}
+fi
+
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+    # greedy search decoder
+    CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
+fi
+
+if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
+    # test a single .wav file
+    CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1
+fi

examples/librispeech/asr4/utils

@@ -0,0 +1 @@
+../../../utils

paddlespeech/cli/asr/infer.py

@@ -25,9 +25,6 @@ import librosa
 import numpy as np
 import paddle
 import soundfile
-from paddlespeech.audio.transform.transformation import Transformation
-from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
-from paddlespeech.s2t.utils.utility import UpdateConfig
 from yacs.config import CfgNode
 
 from ...utils.env import MODEL_HOME
@@ -37,6 +34,9 @@ from ..log import logger
 from ..utils import CLI_TIMER
 from ..utils import stats_wrapper
 from ..utils import timer_register
+from paddlespeech.audio.transform.transformation import Transformation
+from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
+from paddlespeech.s2t.utils.utility import UpdateConfig
 
 __all__ = ['ASRExecutor']
 

paddlespeech/cli/ssl/infer.py

@@ -51,11 +51,8 @@ class SSLExecutor(BaseExecutor):
         self.parser.add_argument(
             '--model',
             type=str,
-            default=None,
+            default='wav2vec2',
-            choices=[
+            choices=['wav2vec2', 'hubert'],
-                tag[:tag.index('-')]
-                for tag in self.task_resource.pretrained_models.keys()
-            ],
             help='Choose model type of asr task.')
         self.parser.add_argument(
             '--task',
@@ -67,7 +64,7 @@ class SSLExecutor(BaseExecutor):
             '--lang',
             type=str,
             default='en',
-            help='Choose model language. zh or en, zh:[wav2vec2ASR_aishell1-zh-16k], en:[wav2vec2ASR_librispeech-en-16k]'
+            help='Choose model language. zh or en, zh:[wav2vec2ASR_aishell1-zh-16k], en:[wav2vec2ASR_librispeech-en-16k, hubertASR_librispeech_100-en-16k]'
         )
         self.parser.add_argument(
             "--sample_rate",
@@ -137,13 +134,6 @@ class SSLExecutor(BaseExecutor):
         logger.debug("start to init the model")
 
         if model_type is None:
-            if lang == 'en':
-                model_type = 'wav2vec2ASR_librispeech'
-            elif lang == 'zh':
-                model_type = 'wav2vec2ASR_aishell1'
-            else:
-                logger.error(
-                    "invalid lang, please input --lang en or --lang zh")
             logger.debug(
                 "Model type had not been specified, default {} was used.".
                 format(model_type))
@@ -155,9 +145,20 @@ class SSLExecutor(BaseExecutor):
         if cfg_path is None or ckpt_path is None:
             sample_rate_str = '16k' if sample_rate == 16000 else '8k'
             if task == 'asr':
-                tag = model_type + '-' + lang + '-' + sample_rate_str
+                if model_type == 'wav2vec2':
+                    if lang == 'en':
+                        model_prefix = 'wav2vec2ASR_librispeech'
+                    elif lang == 'zh':
+                        model_prefix = 'wav2vec2ASR_aishell1'
+                    tag = model_prefix + '-' + lang + '-' + sample_rate_str
+                elif model_type == 'hubert':
+                    if lang == 'en':
+                        model_prefix = 'hubertASR_librispeech-100h'
+                    elif lang == 'zh':
+                        logger.error("zh hubertASR is not supported yet")
+                    tag = model_prefix + '-' + lang + '-' + sample_rate_str
             else:
-                tag = 'wav2vec2' + '-' + lang + '-' + sample_rate_str
+                tag = model_type + '-' + lang + '-' + sample_rate_str
             self.task_resource.set_task_model(tag, version=None)
             self.res_path = self.task_resource.res_dir
 
@@ -184,16 +185,17 @@ class SSLExecutor(BaseExecutor):
                     self.text_feature = TextFeaturizer(
                         unit_type=self.config.unit_type,
                         vocab=self.config.vocab_filepath)
+                    self.config.output_dim = len(self.config.vocab_filepath)
                 elif lang == 'zh':
                     self.text_feature = AutoTokenizer.from_pretrained(
                         self.config.tokenizer)
+                    self.config.output_dim = self.text_feature.vocab_size
                 self.config.decode.decoding_method = decode_method
-            model_name = model_type[:model_type.rindex(
+            model_name = model_prefix[:model_prefix.rindex(
                 '_')]  # model_type: {model_name}_{dataset}
         else:
-            model_name = 'wav2vec2'
+            model_name = model_type
         model_class = self.task_resource.get_model_class(model_name)
-
         model_conf = self.config
         model = model_class.from_config(model_conf)
         self.model = model
@@ -204,9 +206,9 @@ class SSLExecutor(BaseExecutor):
         if task == 'asr':
             self.model.set_state_dict(model_dict)
         else:
-            self.model.wav2vec2.set_state_dict(model_dict)
+            getattr(self.model, model_type).set_state_dict(model_dict)
 
-    def preprocess(self, model_type: str, input: Union[str, os.PathLike]):
+    def preprocess(self, input: Union[str, os.PathLike]):
         """
         Input preprocess and return paddle.Tensor stored in self.input.
         Input content can be a text(tts), a file(asr, cls) or a streaming(not supported yet).
@@ -263,8 +265,7 @@ class SSLExecutor(BaseExecutor):
         audio = self._inputs["audio"]
         if task == 'asr':
             cfg = self.config.decode
-            logger.debug(
+            logger.debug(f"we will use the {model_type}ASR like model.")
-                f"we will use the wav2vec2ASR like model : {model_type}")
             try:
                 result_transcripts = self.model.decode(
                     audio,
@@ -277,7 +278,8 @@ class SSLExecutor(BaseExecutor):
                 logger.exception(e)
         else:
             logger.debug(
-                "we will use the wav2vec2 like model to extract audio feature")
+                f"we will use the {model_type} like model to extract audio feature."
+            )
             try:
                 out_feature = self.model(audio[:, :, 0])
                 self._outputs["result"] = out_feature[0]
@@ -454,7 +456,7 @@ class SSLExecutor(BaseExecutor):
         if rtf:
             k = self.__class__.__name__
             CLI_TIMER[k]['start'].append(time.time())
-        self.preprocess(model, audio_file)
+        self.preprocess(audio_file)
         self.infer(model, task)
         res = self.postprocess()  # Retrieve result of asr.
 

paddlespeech/resource/model_alias.py

@@ -23,6 +23,8 @@ model_alias = {
     # ---------------------------------
     "wav2vec2ASR": ["paddlespeech.s2t.models.wav2vec2:Wav2vec2ASR"],
     "wav2vec2": ["paddlespeech.s2t.models.wav2vec2:Wav2vec2Base"],
+    "hubertASR": ["paddlespeech.s2t.models.hubert:HubertASR"],
+    "hubert": ["paddlespeech.s2t.models.hubert:HubertBase"],
 
     # ---------------------------------
     # -------------- ASR --------------

paddlespeech/resource/pretrained_models.py

@@ -117,6 +117,38 @@ ssl_dynamic_pretrained_models = {
             'exp/wav2vec2ASR/checkpoints/avg_1.pdparams',
         },
     },
+    "hubert-en-16k": {
+        '1.4': {
+            'url':
+            'https://paddlespeech.bj.bcebos.com/hubert/hubert-large-lv60_ckpt_1.4.0.model.tar.gz',
+            'md5':
+            'efecfb87a8718aa9253b7459c1fe9b54',
+            'cfg_path':
+            'model.yaml',
+            'ckpt_path':
+            'hubert-large-lv60',
+            'model':
+            'hubert-large-lv60.pdparams',
+            'params':
+            'hubert-large-lv60.pdparams',
+        },
+    },
+    "hubertASR_librispeech-100h-en-16k": {
+        '1.4': {
+            'url':
+            'https://paddlespeech.bj.bcebos.com/hubert/hubertASR-large-100h-librispeech_ckpt_1.4.0.model.tar.gz',
+            'md5':
+            '574cefd11aaef5737969ce22a7f33ea2',
+            'cfg_path':
+            'model.yaml',
+            'ckpt_path':
+            'exp/hubertASR/checkpoints/avg_1',
+            'model':
+            'exp/hubertASR/checkpoints/avg_1.pdparams',
+            'params':
+            'exp/hubertASR/checkpoints/avg_1.pdparams',
+        },
+    },
 }
 
 # ---------------------------------

paddlespeech/s2t/exps/hubert/__init__.py

@@ -0,0 +1,13 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

paddlespeech/s2t/exps/hubert/bin/__init__.py

@@ -0,0 +1,13 @@
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

paddlespeech/s2t/exps/hubert/bin/test.py

@@ -0,0 +1,64 @@
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Evaluation for hubert model."""
+import cProfile
+
+from yacs.config import CfgNode
+
+from paddlespeech.s2t.exps.hubert.model import HubertASRTester as Tester
+from paddlespeech.s2t.training.cli import default_argument_parser
+from paddlespeech.s2t.utils.utility import print_arguments
+
+
+def main_sp(config, args):
+    exp = Tester(config, args)
+    with exp.eval():
+        exp.setup()
+        exp.run_test()
+
+
+def main(config, args):
+    main_sp(config, args)
+
+
+if __name__ == "__main__":
+    parser = default_argument_parser()
+    # save asr result to
+    parser.add_argument(
+        '--dict-path', type=str, default=None, help='dict path.')
+    parser.add_argument(
+        "--result_file", type=str, help="path of save the asr result")
+    args = parser.parse_args()
+    print_arguments(args, globals())
+
+    # https://yaml.org/type/float.html
+    config = CfgNode(new_allowed=True)
+    if args.config:
+        config.merge_from_file(args.config)
+    if args.decode_cfg:
+        decode_confs = CfgNode(new_allowed=True)
+        decode_confs.merge_from_file(args.decode_cfg)
+        config.decode = decode_confs
+    if args.opts:
+        config.merge_from_list(args.opts)
+    config.freeze()
+    print(config)
+    if args.dump_config:
+        with open(args.dump_config, 'w') as f:
+            print(config, file=f)
+
+    # Setting for profiling
+    pr = cProfile.Profile()
+    pr.runcall(main, config, args)
+    pr.dump_stats('test.profile')

paddlespeech/s2t/exps/hubert/bin/test_wav.py

@@ -0,0 +1,118 @@
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Evaluation for hubert model."""
+import os
+import sys
+from pathlib import Path
+
+import paddle
+import soundfile
+from yacs.config import CfgNode
+
+from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
+from paddlespeech.s2t.models.hubert.hubert_ASR import HubertASR
+from paddlespeech.s2t.training.cli import default_argument_parser
+from paddlespeech.s2t.utils.log import Log
+from paddlespeech.s2t.utils.utility import UpdateConfig
+logger = Log(__name__).getlog()
+
+
+class HubertInfer():
+    def __init__(self, config, args):
+        self.args = args
+        self.config = config
+        self.audio_file = args.audio_file
+
+        self.text_feature = TextFeaturizer(
+            unit_type=config.unit_type, vocab=config.vocab_filepath)
+        paddle.set_device('gpu' if self.args.ngpu > 0 else 'cpu')
+
+        # model
+        model_conf = config
+        with UpdateConfig(model_conf):
+            model_conf.output_dim = self.text_feature.vocab_size
+        model = HubertASR.from_config(model_conf)
+        self.model = model
+        self.model.eval()
+
+        # load model
+        params_path = self.args.checkpoint_path + ".pdparams"
+        model_dict = paddle.load(params_path)
+        self.model.set_state_dict(model_dict)
+
+    def run(self):
+        check(args.audio_file)
+
+        with paddle.no_grad():
+            # read
+            audio, _ = soundfile.read(
+                self.audio_file, dtype="int16", always_2d=True)
+            logger.info(f"audio shape: {audio.shape}")
+
+            xs = paddle.to_tensor(audio, dtype='float32').unsqueeze(axis=0)
+            decode_config = self.config.decode
+            result_transcripts, result_tokenids = self.model.decode(
+                xs,
+                text_feature=self.text_feature,
+                decoding_method=decode_config.decoding_method,
+                beam_size=decode_config.beam_size)
+            rsl = result_transcripts[0]
+            utt = Path(self.audio_file).name
+            logger.info(f"hyp: {utt} {rsl}")
+            return rsl
+
+
+def check(audio_file):
+    if not os.path.isfile(audio_file):
+        print("Please input the right audio file path")
+        sys.exit(-1)
+
+    logger.info("checking the audio file format......")
+    try:
+        sig, sample_rate = soundfile.read(audio_file)
+    except Exception as e:
+        logger.error(str(e))
+        logger.error(
+            "can not open the wav file, please check the audio file format")
+        sys.exit(-1)
+    logger.info("The sample rate is %d" % sample_rate)
+    assert (sample_rate == 16000)
+    logger.info("The audio file format is right")
+
+
+def main(config, args):
+    HubertInfer(config, args).run()
+
+
+if __name__ == "__main__":
+    parser = default_argument_parser()
+    # save asr result to
+    parser.add_argument(
+        "--result_file", type=str, help="path of save the asr result")
+    parser.add_argument(
+        "--audio_file", type=str, help="path of the input audio file")
+    args = parser.parse_args()
+
+    config = CfgNode(new_allowed=True)
+
+    if args.config:
+        config.merge_from_file(args.config)
+    if args.decode_cfg:
+        decode_confs = CfgNode(new_allowed=True)
+        decode_confs.merge_from_file(args.decode_cfg)
+        config.decode = decode_confs
+    if args.opts:
+        config.merge_from_list(args.opts)
+    config.freeze()
+    main(config, args)

paddlespeech/s2t/exps/hubert/bin/train.py

@@ -0,0 +1,55 @@
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Trainer for hubert model."""
+import cProfile
+import os
+
+from yacs.config import CfgNode
+
+from paddlespeech.s2t.exps.hubert.model import HubertASRTrainer as Trainer
+from paddlespeech.s2t.training.cli import default_argument_parser
+from paddlespeech.s2t.utils.utility import print_arguments
+
+
+def main_sp(config, args):
+    exp = Trainer(config, args)
+    exp.setup()
+    exp.run()
+
+
+def main(config, args):
+    main_sp(config, args)
+
+
+if __name__ == "__main__":
+    parser = default_argument_parser()
+    parser.add_argument(
+        '--resume', type=str, default="", nargs="?", help='resume ckpt path.')
+    args = parser.parse_args()
+    print_arguments(args, globals())
+    # https://yaml.org/type/float.html
+    config = CfgNode(new_allowed=True)
+    if args.config:
+        config.merge_from_file(args.config)
+    if args.opts:
+        config.merge_from_list(args.opts)
+    config.freeze()
+    if args.dump_config:
+        with open(args.dump_config, 'w') as f:
+            print(config, file=f)
+
+    # Setting for profiling
+    pr = cProfile.Profile()
+    pr.runcall(main, config, args)
+    pr.dump_stats(os.path.join(args.output, 'train.profile'))

paddlespeech/s2t/exps/hubert/model.py

@@ -0,0 +1,918 @@
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains hubert model."""
+import json
+import math
+import os
+import re
+import time
+from collections import OrderedDict
+from contextlib import nullcontext
+
+import jsonlines
+import numpy as np
+import paddle
+from hyperpyyaml import load_hyperpyyaml
+from paddle import distributed as dist
+from paddlenlp.transformers import AutoTokenizer
+
+from paddlespeech.s2t.frontend.featurizer import TextFeaturizer
+from paddlespeech.s2t.io.dataloader import DataLoaderFactory
+from paddlespeech.s2t.io.speechbrain import data_pipeline
+from paddlespeech.s2t.io.speechbrain import dataio
+from paddlespeech.s2t.io.speechbrain import dataset
+from paddlespeech.s2t.io.speechbrain.dataloader import make_dataloader
+from paddlespeech.s2t.models.hubert.hubert_ASR import HubertASR
+from paddlespeech.s2t.models.wav2vec2.processing.speech_augmentation import TimeDomainSpecAugment
+from paddlespeech.s2t.training.optimizer import OptimizerFactory
+from paddlespeech.s2t.training.reporter import ObsScope
+from paddlespeech.s2t.training.reporter import report
+from paddlespeech.s2t.training.scheduler import LRSchedulerFactory
+from paddlespeech.s2t.training.timer import Timer
+from paddlespeech.s2t.training.trainer import Trainer
+from paddlespeech.s2t.utils import error_rate
+from paddlespeech.s2t.utils import layer_tools
+from paddlespeech.s2t.utils import mp_tools
+from paddlespeech.s2t.utils.log import Log
+from paddlespeech.s2t.utils.utility import UpdateConfig
+
+logger = Log(__name__).getlog()
+
+
+# Todo: change this when paddle supports this api
+def clip_grad_norm_(
+        parameters,
+        max_norm,
+        norm_type=2.0,
+        error_if_nonfinite=False, ):
+    r"""Clips gradient norm of the iteratable parameters.
+
+    Norms are calculated together on all gradients, just as they are
+    connected into one vector. The gradient will be modified in place.
+
+    This API can only run in dynamic graph mode, not static graph mode.
+
+    Args:
+        parameters (Iterable[paddle.Tensor] or paddle.Tensor): Tensors or a single Tensor
+            that will be normalized gradients
+        max_norm (float or int): max norm of the gradients
+        norm_type (float or int): type of the used p-norm. Can be `inf` for
+            infinity norm.
+        error_if_nonfinite (bool): if True, throw an error if the total
+            norm of the gradients from :attr:`parameters` is `nan`,
+            `inf`, or `-inf`.
+
+    Returns:
+        Total norm of the parameter gradients (treated as a single vector).
+    Example:
+        .. code-block:: python
+            import paddle
+
+            x = paddle.uniform([10, 10], min=-1.0, max=1.0, dtype='float32')
+            max_norm = float(5.0)
+            linear = paddle.nn.Linear(in_features=10, out_features=10)
+            out = linear(x)
+            loss = paddle.mean(out)
+            loss.backward()
+
+            paddle.nn.utils.clip_grad_norm_(linear.parameters(), max_norm)
+
+            sdg = paddle.optimizer.SGD(learning_rate=0.1, parameters=linear.parameters())
+            sdg.step()
+    """
+    if not paddle.in_dynamic_mode():
+        raise RuntimeError('this API can only run in dynamic mode.')
+
+    if isinstance(parameters, paddle.Tensor):
+        parameters = [parameters]
+
+    support_norm_type = [float("inf"), 0, 1, 2]
+    if norm_type not in support_norm_type:
+        raise ValueError(f'norm_type only support {support_norm_type}')
+
+    grads = [p.grad for p in parameters if p.grad is not None]
+    max_norm = float(max_norm)
+    norm_type = float(norm_type)
+    if len(grads) == 0:
+        return paddle.to_tensor(0.0)
+    if norm_type == float("inf"):
+        norms = [g.detach().abs().max() for g in grads]
+        total_norm = (norms[0]
+                      if len(norms) == 1 else paddle.max(paddle.stack(norms)))
+    else:
+        total_norm = paddle.linalg.norm(
+            paddle.stack(
+                [paddle.linalg.norm(g.detach(), norm_type) for g in grads]),
+            norm_type, )
+
+    if error_if_nonfinite and paddle.logical_or(total_norm.isnan(),
+                                                total_norm.isinf()):
+        raise RuntimeError(
+            f'The total norm of {norm_type} order of the gradients from '
+            '`parameters` is non-finite, so it cannot be clipped. In any case, '
+            'disable this error and scale the gradient by non-finite norm, '
+            'set `error_if_nonfinite=False`')
+    clip_coef = max_norm / (total_norm + 1e-6)
+    # Note: when the coef is clamped to 1, it is redundant to multiply the clamped coef, but this
+    # avoids the `if clip_coef < 1:` condition.
+    clip_coef_clamped = paddle.clip(clip_coef, max=1.0)
+    with paddle.no_grad():
+        for _, p in enumerate(parameters):
+            g = p.grad
+            if g is not None:
+                p.grad = paddle.multiply(x=g, y=clip_coef_clamped)
+    return total_norm
+
+
+class HubertASRTrainer(Trainer):
+    def __init__(self, config, args):
+        super().__init__(config, args)
+        self.avg_train_loss = 0.0
+        self.loss_isfinite = True  # while flag is 'False', loss in Nan or inf, and can not be avg
+        self.use_sb = True  # whether use speech brain dataloader
+
+    def update_average(self, batch_index, loss):
+        """Update running average of the loss.
+        Arguments
+        ---------
+        batch_index : int
+            current batch index
+        loss : paddle.tensor
+            detached loss, a single float value.
+        """
+        if math.isfinite(loss):
+            self.avg_train_loss -= self.avg_train_loss / (batch_index + 1)
+            self.avg_train_loss += loss / (batch_index + 1)
+        else:
+            self.loss_isfinite = False
+            logger.info('loss:{} in Nan or inf, error'.format(loss))
+
+    def before_train(self):
+        from_scratch = self.resume_or_scratch()
+        if from_scratch:
+            # scratch: save init model, i.e. 0 epoch
+            self.save(tag='init', infos=None)
+        else:
+            # resume: train next_epoch and next_iteration
+            self.epoch += 1
+            logger.info(
+                f"Resume train: epoch {self.epoch }, step {self.iteration}!")
+
+        self.maybe_batch_sampler_step()
+
+    def train_batch(self, batch_index, batch, msg):
+        train_conf = self.config
+        start = time.time()
+
+        # forward
+        ## sb data pipeline
+        if self.use_sb:
+            wav, wavs_lens_rate = batch['sig']
+            target, target_lens_rate = batch['tokens']
+            target_lens = (target_lens_rate *
+                           target.shape[1]).round().astype(paddle.int64)
+        else:
+            utt, wav, wavs_lens, target, target_lens = batch
+            wavs_lens_rate = wavs_lens / wav.shape[1]
+            wav = wav[:, :, 0]
+        logger.info('training utt ids: {}'.format(utt))
+        if hasattr(train_conf, 'audio_augment'):
+            wav = self.speech_augmentation(wav, wavs_lens_rate)
+
+        loss = self.model(wav, wavs_lens_rate, target, target_lens)
+
+        # loss div by `batch_size * accum_grad`
+        loss /= train_conf.accum_grad
+        # update self.avg_train_loss
+        self.update_average(batch_index, float(loss))
+
+        # loss backward
+        if (batch_index + 1) % train_conf.accum_grad != 0:
+            # Disable gradient synchronizations across DDP processes.
+            # Within this context, gradients will be accumulated on module
+            # variables, which will later be synchronized.
+            # When using cpu w/o DDP, model does not have `no_sync`
+            context = self.model.no_sync if (hasattr(self.model, "no_sync") and
+                                             self.parallel) else nullcontext
+        else:
+            # Used for single gpu training and DDP gradient synchronization
+            # processes.
+            context = nullcontext
+        with context():
+            loss.backward()
+
+            layer_tools.print_grads(self.model, print_func=None)
+
+        # optimizer step old
+        if (batch_index + 1) % train_conf.accum_grad == 0:
+            #do global grad clip
+            if train_conf.global_grad_clip != 0:
+                clip_grad_norm_(self.model.parameters(),
+                                train_conf.global_grad_clip)
+            self.model_optimizer.step()
+            self.model_optimizer.clear_grad()
+            if not train_conf.freeze_hubert:
+                self.hubert_optimizer.step()
+                self.hubert_optimizer.clear_grad()
+            if self.config.model_scheduler != 'newbobscheduler':
+                self.model_lr_scheduler.step()
+            if self.config.hubert_scheduler != 'newbobscheduler':
+                if not train_conf.freeze_hubert:
+                    self.hubert_lr_scheduler.step()
+            self.iteration += 1
+
+        losses_np = {'loss': self.avg_train_loss * train_conf.accum_grad}
+        iteration_time = time.time() - start
+        for k, v in losses_np.items():
+            report(k, v)
+        report("loss_whitoutavg", float(loss))
+        report("batch_size", self.config.batch_size)
+        report("accum", train_conf.accum_grad)
+        report("step_cost", iteration_time)
+
+        if (batch_index + 1) % train_conf.accum_grad == 0:
+            if dist.get_rank() == 0 and self.visualizer:
+                losses_np_v = losses_np.copy()
+                losses_np_v.update({
+                    "model_lr": self.model_lr_scheduler(),
+                    "hubert_lr": self.hubert_lr_scheduler()
+                })
+                for key, val in losses_np_v.items():
+                    self.visualizer.add_scalar(
+                        tag='train/' + key, value=val, step=self.iteration - 1)
+
+    @paddle.no_grad()
+    def valid(self):
+        self.model.eval()
+        if not self.use_streamdata:
+            logger.info(
+                f"Valid Total Examples: {len(self.valid_loader.dataset)}")
+        valid_losses = {}
+        step = 0
+        total_loss = 0.0
+        num_seen_utts = 1  # use update_average and no need for num_seen_utts here
+        for i, batch in enumerate(self.valid_loader):
+            if self.use_sb:
+                wav, wavs_lens_rate = batch['sig']
+                target, target_lens_rate = batch['tokens']
+                target_lens = (target_lens_rate *
+                               target.shape[1]).round().astype(paddle.int64)
+            else:
+                utt, wav, wavs_lens, target, target_lens = batch
+                wavs_lens_rate = wavs_lens / wav.shape[1]
+                wav = wav[:, :, 0]
+
+            loss = self.model(wav, wavs_lens_rate, target, target_lens)
+            # use update_average
+            total_loss -= total_loss / (step + 1)
+            total_loss += loss / (step + 1)
+
+            if math.isfinite(float(loss)):
+                step += 1
+                valid_losses['val_loss'] = float(loss)
+            else:
+                logger.info('loss:{} in Nan or inf, error'.format(float(loss)))
+
+            if (i + 1) % self.config.log_interval == 0:
+                valid_losses['val_history_loss'] = float(total_loss)
+
+                # logging
+                msg = f"Valid: Rank: {dist.get_rank()}, "
+                msg += "epoch: {}, ".format(self.epoch)
+                msg += "step: {}, ".format(self.iteration)
+                if not self.use_streamdata:
+                    msg += "batch: {}/{}, ".format(i + 1,
+                                                   len(self.valid_loader))
+                msg += ', '.join('{}: {:>.6f}'.format(k, v)
+                                 for k, v in valid_losses.items())
+                logger.info(msg)
+
+        logger.info(
+            'Rank {} Val info val_loss {}'.format(dist.get_rank(), total_loss))
+        return total_loss, num_seen_utts
+
+    @mp_tools.rank_zero_only
+    def save(self, tag=None, infos: dict=None):
+        """Save checkpoint (model parameters and optimizer states).
+
+        Args:
+            tag (int or str, optional): None for step, else using tag, e.g epoch. Defaults to None.
+            infos (dict, optional): meta data to save. Defaults to None.
+        """
+
+        infos = infos if infos else dict()
+        infos.update({
+            "epoch": self.epoch,
+            "model_lr": self.model_optimizer.get_lr(),
+            "hubert_lr": self.hubert_optimizer.get_lr()
+        })
+
+        checkpoint_path = os.path.join(
+            self.checkpoint_dir,
+            "{}".format(self.iteration if tag is None else tag))
+
+        model_dict = self.model.state_dict()
+        params_path = checkpoint_path + ".pdparams"
+        paddle.save(model_dict, params_path)
+        logger.info("Saved model to {}".format(params_path))
+
+        model_opt_dict = self.model_optimizer.state_dict()
+        hubert_opt_dict = self.hubert_optimizer.state_dict()
+
+        opt_dict = {'model': model_opt_dict, 'hubert': hubert_opt_dict}
+
+        optimizer_path = checkpoint_path + ".pdopt"
+        paddle.save(opt_dict, optimizer_path)
+        logger.info("Saved optimzier state to {}".format(optimizer_path))
+
+        scheduler_dict = {}
+
+        if self.config.model_scheduler == 'newbobscheduler':
+            scheduler_dict['model'] = self.model_lr_scheduler.save()
+        if self.config.hubert_scheduler == 'newbobscheduler':
+            scheduler_dict['hubert'] = self.hubert_lr_scheduler.save()
+        if scheduler_dict:
+            scheduler_path = checkpoint_path + ".pdlrs"
+            paddle.save(scheduler_dict, scheduler_path)
+            logger.info("Saved scheduler state to {}".format(scheduler_path))
+        info_path = re.sub('.pdparams$', '.json', params_path)
+        infos = {} if infos is None else infos
+        with open(info_path, 'w', encoding='utf8') as fout:
+            data = json.dumps(infos)
+            fout.write(data)
+
+    def resume_or_scratch(self):
+        """Resume from latest checkpoint at checkpoints in the output
+        directory or load a specified checkpoint.
+
+        If ``args.checkpoint_path`` is not None, load the checkpoint, else
+        resume training.
+        """
+        scratch = None
+        if self.args.resume:
+            # just restore ckpt
+            # lr will resotre from optimizer ckpt
+            resume_json_path = os.path.join(self.checkpoint_dir,
+                                            self.args.resume + '.json')
+            with open(resume_json_path, 'r', encoding='utf8') as f:
+                resume_json = json.load(f)
+            self.iteration = 0
+            self.epoch = resume_json["epoch"]
+
+            # resotre model from *.pdparams
+            params_path = os.path.join(self.checkpoint_dir,
+                                       "{}".format(self.epoch)) + '.pdparams'
+            model_dict = paddle.load(params_path)
+            self.model.set_state_dict(model_dict)
+
+            # resotre optimizer from *.pdopt
+            optimizer_path = os.path.join(self.checkpoint_dir,
+                                          "{}".format(self.epoch)) + '.pdopt'
+            optimizer_dict = paddle.load(optimizer_path)
+            self.model_optimizer.set_state_dict(optimizer_dict['model'])
+            self.hubert_optimizer.set_state_dict(optimizer_dict['hubert'])
+
+            # resotre lr_scheduler from *.pdlrs
+            scheduler_path = os.path.join(self.checkpoint_dir,
+                                          "{}".format(self.epoch)) + '.pdlrs'
+            if os.path.isfile(os.path.join(scheduler_path)):
+                scheduler_dict = paddle.load(scheduler_path)
+                if self.config.model_scheduler == 'newbobscheduler':
+                    self.model_lr_scheduler.load(scheduler_dict['model'])
+                if self.config.hubert_scheduler == 'newbobscheduler':
+                    self.hubert_lr_scheduler.load(scheduler_dict['hubert'])
+            logger.info(
+                f"Restore ckpt: epoch {self.epoch }, step {self.iteration}!")
+            scratch = False
+        else:
+            self.iteration = 0
+            self.epoch = 0
+            scratch = True
+            logger.info("Init from scratch!")
+        return scratch
+
+    def do_train(self):
+        """The training process control by step."""
+        # !!!IMPORTANT!!!
+        # Try to export the model by script, if fails, we should refine
+        # the code to satisfy the script export requirements
+        # script_model = paddle.jit.to_static(self.model)
+        # script_model_path = str(self.checkpoint_dir / 'init')
+        # paddle.jit.save(script_model, script_model_path)
+
+        self.before_train()
+        if not self.use_streamdata:
+            logger.info(
+                f"Train Total Examples: {len(self.train_loader.dataset)}")
+        while self.epoch < self.config.n_epoch:
+            with Timer("Epoch-Train Time Cost: {}"):
+                self.model.train()
+                try:
+                    data_start_time = time.time()
+                    for batch_index, batch in enumerate(self.train_loader):
+                        dataload_time = time.time() - data_start_time
+                        msg = "Train:"
+                        observation = OrderedDict()
+                        with ObsScope(observation):
+                            report("Rank", dist.get_rank())
+                            report("epoch", self.epoch)
+                            report('step', self.iteration)
+                            report("model_lr", self.model_optimizer.get_lr())
+                            report("hubert_lr", self.hubert_optimizer.get_lr())
+                            self.train_batch(batch_index, batch, msg)
+                            self.after_train_batch()
+                            report('iter', batch_index + 1)
+                            if not self.use_streamdata:
+                                report('total', len(self.train_loader))
+                            report('reader_cost', dataload_time)
+                        observation['batch_cost'] = observation[
+                            'reader_cost'] + observation['step_cost']
+                        observation['samples'] = observation['batch_size']
+                        observation['ips,samples/s'] = observation[
+                            'batch_size'] / observation['batch_cost']
+                        for k, v in observation.items():
+                            msg += f" {k.split(',')[0]}: "
+                            msg += f"{v:>.8f}" if isinstance(v,
+                                                             float) else f"{v}"
+                            msg += f" {k.split(',')[1]}" if len(
+                                k.split(',')) == 2 else ""
+                            msg += ","
+                        msg = msg[:-1]  # remove the last ","
+                        if (batch_index + 1) % self.config.log_interval == 0:
+                            logger.info(msg)
+                        data_start_time = time.time()
+                except Exception as e:
+                    logger.error(e)
+                    raise e
+            with Timer("Eval Time Cost: {}"):
+                total_loss, num_seen_utts = self.valid()
+                if dist.get_world_size() > 1:
+                    num_seen_utts = paddle.to_tensor(num_seen_utts)
+                    dist.all_reduce(num_seen_utts)
+                    total_loss = paddle.to_tensor(total_loss)
+                    dist.all_reduce(total_loss)
+                    cv_loss = total_loss / num_seen_utts
+                    cv_loss = float(cv_loss)
+                else:
+                    cv_loss = float(total_loss)
+            logger.info(
+                'Epoch {} Val info val_loss {}'.format(self.epoch, cv_loss))
+            if self.visualizer:
+                self.visualizer.add_scalar(
+                    tag='eval/cv_loss', value=cv_loss, step=self.epoch)
+                self.visualizer.add_scalar(
+                    tag='eval/model_lr',
+                    value=self.model_lr_scheduler(),
+                    step=self.epoch)
+                self.visualizer.add_scalar(
+                    tag='eval/hubert_lr',
+                    value=self.hubert_lr_scheduler(),
+                    step=self.epoch)
+
+            if self.config.model_scheduler == 'newbobscheduler':
+                self.model_lr_scheduler.step(cv_loss)
+            if self.config.hubert_scheduler == 'newbobscheduler':
+                if not self.config.freeze_hubert:
+                    self.hubert_lr_scheduler.step(cv_loss)
+            self.save(tag=self.epoch, infos={'val_loss': cv_loss})
+            self.avg_train_loss = 0.0
+            self.new_epoch()
+
+    def dataio_prepare(self, hparams):
+        """This function prepares the datasets to be used in the brain class.
+        It also defines the data processing pipeline through user-defined functions."""
+        data_folder = hparams["data_folder"]
+
+        train_data = dataset.DynamicItemDataset.from_csv(
+            csv_path=hparams["train_data"],
+            replacements={"data_root": data_folder}, )
+
+        if hparams["sorting"] == "ascending":
+            # we sort training data to speed up training and get better results.
+            train_data = train_data.filtered_sorted(sort_key="duration")
+            # when sorting do not shuffle in dataloader ! otherwise is pointless
+            hparams["train_dataloader_opts"]["shuffle"] = False
+
+        elif hparams["sorting"] == "descending":
+            train_data = train_data.filtered_sorted(
+                sort_key="duration", reverse=True)
+            # when sorting do not shuffle in dataloader ! otherwise is pointless
+            hparams["train_dataloader_opts"]["shuffle"] = False
+
+        elif hparams["sorting"] == "random":
+            pass
+
+        else:
+            raise NotImplementedError(
+                "sorting must be random, ascending or descending")
+
+        valid_data = dataset.DynamicItemDataset.from_csv(
+            csv_path=hparams["valid_data"],
+            replacements={"data_root": data_folder}, )
+        valid_data = valid_data.filtered_sorted(sort_key="duration")
+
+        test_data = dataset.DynamicItemDataset.from_csv(
+            csv_path=hparams["test_data"],
+            replacements={"data_root": data_folder}, )
+        test_data = test_data.filtered_sorted(sort_key="duration")
+
+        datasets = [train_data, valid_data, test_data]
+
+        # Defining tokenizer and loading it
+        tokenizer = AutoTokenizer.from_pretrained('bert-base-chinese')
+        self.tokenizer = tokenizer
+
+        # 2. Define audio pipeline:
+        @data_pipeline.takes("wav")
+        @data_pipeline.provides("sig")
+        def audio_pipeline(wav):
+            sig = dataio.read_audio(wav)
+            return sig
+
+        dataset.add_dynamic_item(datasets, audio_pipeline)
+
+        # 3. Define text pipeline:
+        @data_pipeline.takes("transcript")
+        @data_pipeline.provides("wrd", "tokens_list", "tokens")
+        def text_pipeline(wrd):
+            wrd = "".join(wrd.split(" "))
+            yield wrd
+            tokens_list = tokenizer(wrd)["input_ids"]
+            yield tokens_list
+            tokens = np.array(tokens_list, dtype="int64")
+            # tokens = paddle.to_tensor(tokens_list, dtype="int64")
+            yield tokens
+
+        dataset.add_dynamic_item(datasets, text_pipeline)
+
+        # 4. Set output:
+        dataset.set_output_keys(
+            datasets,
+            ["id", "sig", "wrd", "tokens"], )
+
+        # 5. If Dynamic Batching is used, we instantiate the needed samplers.
+        train_batch_sampler = None
+        valid_batch_sampler = None
+        if hparams["dynamic_batching"]:
+            from sampler import DynamicBatchSampler  # noqa
+
+            dynamic_hparams = hparams["dynamic_batch_sampler"]
+            num_buckets = dynamic_hparams["num_buckets"]
+
+            train_batch_sampler = DynamicBatchSampler(
+                train_data,
+                dynamic_hparams["max_batch_len"],
+                num_buckets=num_buckets,
+                length_func=lambda x: x["duration"],
+                shuffle=dynamic_hparams["shuffle_ex"],
+                batch_ordering=dynamic_hparams["batch_ordering"], )
+
+            valid_batch_sampler = DynamicBatchSampler(
+                valid_data,
+                dynamic_hparams["max_batch_len"],
+                num_buckets=num_buckets,
+                length_func=lambda x: x["duration"],
+                shuffle=dynamic_hparams["shuffle_ex"],
+                batch_ordering=dynamic_hparams["batch_ordering"], )
+
+        return (train_data, valid_data, test_data, tokenizer,
+                train_batch_sampler, valid_batch_sampler, )
+
+    def setup_dataloader(self):
+        config = self.config.clone()
+        self.use_streamdata = config.get("use_stream_data", False)
+        self.use_sb = config.get("use_sb_pipeline", False)
+        if self.use_sb:
+            hparams_file = config.sb_pipeline_conf
+            with open(hparams_file, 'r', encoding='utf8') as fin:
+                hparams = load_hyperpyyaml(fin, None)
+
+            (train_data, valid_data, test_data, tokenizer, train_bsampler,
+             valid_bsampler, ) = self.dataio_prepare(hparams)
+
+            train_dataloader_opts = hparams["train_dataloader_opts"]
+            valid_dataloader_opts = hparams["valid_dataloader_opts"]
+
+            if train_bsampler is not None:
+                train_dataloader_opts = {
+                    "batch_sampler": train_bsampler,
+                    "num_workers": hparams["num_workers"],
+                }
+
+            if valid_bsampler is not None:
+                valid_dataloader_opts = {"batch_sampler": valid_bsampler}
+
+            if self.train:
+                self.train_loader = make_dataloader(
+                    train_data, stage='train', **train_dataloader_opts)
+                self.valid_loader = make_dataloader(
+                    valid_data,
+                    stage='val',
+                    **valid_dataloader_opts, )
+                logger.info("Setup train/valid Dataloader!")
+            else:
+                self.test_loader = make_dataloader(
+                    test_data, stage='test', **hparams["test_dataloader_opts"])
+        else:
+            if self.train:
+                self.train_loader = DataLoaderFactory.get_dataloader(
+                    'train', config, self.args)
+                self.valid_loader = DataLoaderFactory.get_dataloader(
+                    'valid', config, self.args)
+                logger.info("Setup train/valid Dataloader!")
+            else:
+                decode_batch_size = config.get('decode', dict()).get(
+                    'decode_batch_size', 1)
+                self.test_loader = DataLoaderFactory.get_dataloader(
+                    'test', config, self.args)
+                self.align_loader = DataLoaderFactory.get_dataloader(
+                    'align', config, self.args)
+                logger.info("Setup test/align Dataloader!")
+
+    def setup_model(self):
+        config = self.config
+        model_conf = config
+
+        with UpdateConfig(model_conf):
+            if self.use_sb:
+                model_conf.output_dim = self.tokenizer.vocab_size
+            else:
+                if self.train:
+                    model_conf.input_dim = self.train_loader.feat_dim
+                    model_conf.output_dim = self.train_loader.vocab_size
+                else:
+                    model_conf.input_dim = self.test_loader.feat_dim
+                    model_conf.output_dim = self.test_loader.vocab_size
+
+        model = HubertASR.from_config(model_conf)
+
+        model_dict = paddle.load(config.hubert_params_path)
+        model.set_state_dict(model_dict)
+
+        if self.parallel:
+            model = paddle.DataParallel(model, find_unused_parameters=True)
+
+        layer_tools.print_params(model, logger.info)
+        self.model = model
+        logger.info("Setup model!")
+
+        # setup speech augmentation for hubert
+        if hasattr(config, 'audio_augment') and self.train:
+            self.speech_augmentation = TimeDomainSpecAugment(
+                **config.audio_augment)
+
+        if not self.train:
+            return
+
+        train_config = config
+        model_optim_type = train_config.model_optim
+        model_optim_conf = train_config.model_optim_conf
+        logger.info("optim_model:{},{}", model_optim_type, model_optim_conf)
+        hubert_optim_type = train_config.hubert_optim
+        hubert_optim_conf = train_config.hubert_optim_conf
+        logger.info("optim_model:{},{}", hubert_optim_type, hubert_optim_conf)
+
+        model_scheduler_type = train_config.model_scheduler
+        model_scheduler_conf = train_config.model_scheduler_conf
+        hubert_scheduler_type = train_config.hubert_scheduler
+        hubert_scheduler_conf = train_config.hubert_scheduler_conf
+
+        model_scheduler_args = dict(
+            **{"learning_rate": model_optim_conf.lr,
+               "verbose": False}, **(dict(model_scheduler_conf)))
+
+        hubert_scheduler_args = dict(
+            **{"learning_rate": hubert_optim_conf.lr,
+               "verbose": False}, **(dict(hubert_scheduler_conf)))
+
+        model_lr_scheduler = LRSchedulerFactory.from_args(model_scheduler_type,
+                                                          model_scheduler_args)
+        hubert_lr_scheduler = LRSchedulerFactory.from_args(
+            hubert_scheduler_type, hubert_scheduler_args)
+
+        def optimizer_args(
+                config,
+                optim_type,
+                optim_conf,
+                parameters,
+                lr_scheduler=None, ):
+            optim_arg = dict(optim_conf)
+            optim_arg.update({
+                "learning_rate":
+                lr_scheduler if lr_scheduler else optim_conf.lr,
+                "parameters":
+                parameters
+            })
+            return optim_arg
+
+        model_optimizer_args = optimizer_args(config, model_optim_type,
+                                              model_optim_conf, [{
+                                                  'params':
+                                                  model._layers.enc.parameters()
+                                              }, {
+                                                  'params':
+                                                  model._layers.ctc.parameters()
+                                              }] if self.parallel else [{
+                                                  'params':
+                                                  model.enc.parameters()
+                                              }, {
+                                                  'params':
+                                                  model.ctc.parameters()
+                                              }], model_lr_scheduler)
+
+        hubert_optimizer_args = optimizer_args(
+            config, hubert_optim_type, hubert_optim_conf,
+            model._layers.hubert.parameters() if self.parallel else
+            model.hubert.parameters(), hubert_lr_scheduler)
+
+        model_optimizer = OptimizerFactory.from_args(model_optim_type,
+                                                     model_optimizer_args)
+        hubert_optimizer = OptimizerFactory.from_args(hubert_optim_type,
+                                                      hubert_optimizer_args)
+
+        self.model_optimizer = model_optimizer
+        self.hubert_optimizer = hubert_optimizer
+        self.model_lr_scheduler = model_lr_scheduler
+        self.hubert_lr_scheduler = hubert_lr_scheduler
+        logger.info("Setup optimizer/lr_scheduler!")
+
+
+class HubertASRTester(HubertASRTrainer):
+    def __init__(self, config, args):
+        super().__init__(config, args)
+        self.text_featurizer = TextFeaturizer(
+            unit_type=config.unit_type, vocab=config.vocab_filepath)
+        self.vocab_list = self.text_featurizer.vocab_list
+
+    def id2token(self, texts, texts_len):
+        """ ord() id to chr() chr """
+        trans = []
+        for text, n in zip(texts, texts_len):
+            n = n.numpy().item()
+            ids = text[:n]
+            trans.append(self.text_featurizer.defeaturize(ids.numpy().tolist()))
+        return trans
+
+    def compute_metrics(self, id, audio, audio_len, texts, texts_len,
+                        fout=None):
+        decode_cfg = self.config.decode
+        errors_sum, len_refs, num_ins = 0.0, 0, 0
+        errors_func = error_rate.char_errors if decode_cfg.error_rate_type == 'cer' else error_rate.word_errors
+        error_rate_func = error_rate.cer if decode_cfg.error_rate_type == 'cer' else error_rate.wer
+
+        start_time = time.time()
+        target_transcripts = self.id2token(texts, texts_len)
+        result_transcripts, result_tokenids = self.model.decode(
+            audio,
+            text_feature=self.text_featurizer,
+            decoding_method=decode_cfg.decoding_method,
+            beam_size=decode_cfg.beam_size)
+        decode_time = time.time() - start_time
+
+        for utt, target, result, rec_tids in zip(
+                id, target_transcripts, result_transcripts, result_tokenids):
+            errors, len_ref = errors_func(target, result)
+            errors_sum += errors
+            len_refs += len_ref
+            num_ins += 1
+            if fout:
+                fout.write({
+                    "utt": utt,
+                    "refs": [target],
+                    "hyps": [result],
+                    "hyps_tokenid": [rec_tids],
+                })
+            logger.info(f"Utt: {utt}")
+            logger.info(f"Ref: {target}")
+            logger.info(f"Hyp: {result}")
+            logger.info("One example error rate [%s] = %f" % (
+                decode_cfg.error_rate_type, error_rate_func(target, result)))
+
+        return dict(
+            errors_sum=errors_sum,
+            len_refs=len_refs,
+            num_ins=num_ins,  # num examples
+            error_rate=errors_sum / len_refs,
+            error_rate_type=decode_cfg.error_rate_type,
+            num_frames=audio_len.sum().numpy().item(),
+            decode_time=decode_time)
+
+    def sb_compute_metrics(self, id, sig, wrd, tokens, fout=None):
+        decode_cfg = self.config.decode
+        errors_sum, len_refs, num_ins = 0.0, 0, 0
+        errors_func = error_rate.char_errors if decode_cfg.error_rate_type == 'cer' else error_rate.word_errors
+        error_rate_func = error_rate.cer if decode_cfg.error_rate_type == 'cer' else error_rate.wer
+        start_time = time.time()
+        target_transcripts = wrd
+        result_transcripts, result_tokenids = self.model.decode(
+            sig[0],
+            text_feature=self.tokenizer,
+            decoding_method=decode_cfg.decoding_method,
+            beam_size=decode_cfg.beam_size,
+            sb_pipeline=True)
+        decode_time = time.time() - start_time
+
+        for utt, target, result, rec_tids in zip(
+                id, target_transcripts, result_transcripts, result_tokenids):
+            errors, len_ref = errors_func(target, result)
+            errors_sum += errors
+            len_refs += len_ref
+            num_ins += 1
+            if fout:
+                fout.write({
+                    "utt": utt,
+                    "refs": [target],
+                    "hyps": [result],
+                    "hyps_tokenid": [rec_tids],
+                })
+            logger.info(f"Utt: {utt}")
+            logger.info(f"Ref: {target}")
+            logger.info(f"Hyp: {result}")
+            logger.info("One example error rate [%s] = %f" % (
+                decode_cfg.error_rate_type, error_rate_func(target, result)))
+
+        return dict(
+            errors_sum=errors_sum,
+            len_refs=len_refs,
+            num_ins=num_ins,  # num examples
+            error_rate=errors_sum / len_refs,
+            error_rate_type=decode_cfg.error_rate_type,
+            num_frames=sig[1].sum().numpy().item(),
+            decode_time=decode_time)
+
+    @mp_tools.rank_zero_only
+    @paddle.no_grad()
+    def test(self):
+        logger.info(f"Test Total Examples: {len(self.test_loader.dataset)}")
+        self.model.eval()
+
+        error_rate_type = None
+        errors_sum, len_refs, num_ins = 0.0, 0, 0
+        num_frames = 0.0
+        num_time = 0.0
+        # Initialized the decoder in model
+        decode_cfg = self.config.decode
+        vocab_list = self.vocab_list
+        decode_batch_size = decode_cfg.decode_batch_size
+
+        with jsonlines.open(self.args.result_file, 'w') as fout:
+            for i, batch in enumerate(self.test_loader):
+                if self.use_sb:
+                    metrics = self.sb_compute_metrics(**batch, fout=fout)
+                else:
+                    metrics = self.compute_metrics(*batch, fout=fout)
+                num_frames += metrics['num_frames']
+                num_time += metrics["decode_time"]
+                errors_sum += metrics['errors_sum']
+                len_refs += metrics['len_refs']
+                num_ins += metrics['num_ins']
+                error_rate_type = metrics['error_rate_type']
+                rtf = num_time / (num_frames)
+                logger.info(
+                    "RTF: %f, Error rate [%s] (%d/?) = %f" %
+                    (rtf, error_rate_type, num_ins, errors_sum / len_refs))
+
+        # logging
+        msg = "Test: "
+        msg += "epoch: {}, ".format(self.epoch)
+        msg += "step: {}, ".format(self.iteration)
+        msg += "Final error rate [%s] (%d/%d) = %f" % (
+            error_rate_type, num_ins, num_ins, errors_sum / len_refs)
+        logger.info(msg)
+
+        err_meta_path = os.path.splitext(self.args.result_file)[0] + '.err'
+        err_type_str = "{}".format(error_rate_type)
+        with open(err_meta_path, 'w', encoding='utf8') as f:
+            data = json.dumps({
+                "epoch":
+                self.epoch,
+                "step":
+                self.iteration,
+                "rtf":
+                rtf,
+                error_rate_type:
+                errors_sum / len_refs,
+                "dataset_hour": (num_frames) / 1000.0 / 3600.0,
+                "process_hour":
+                num_time / 1000.0 / 3600.0,
+                "num_examples":
+                num_ins,
+                "err_sum":
+                errors_sum,
+                "ref_len":
+                len_refs,
+                "decode_method":
+                self.config.decode.decoding_method,
+            })
+            f.write(data + '\n')

paddlespeech/s2t/models/hubert/__init__.py

@@ -0,0 +1,17 @@
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from .hubert_ASR import HubertASR
+from .hubert_ASR import HubertBase
+
+__all__ = ["HubertASR", "HubertBase"]

paddlespeech/s2t/models/hubert/hubert_ASR.py

@@ -0,0 +1,368 @@
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""HubertASR model."""
+from collections import defaultdict
+from copy import deepcopy
+from dataclasses import dataclass
+from dataclasses import is_dataclass
+from typing import Dict
+from typing import List
+from typing import Tuple
+
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+from paddlespeech.s2t.models.hubert.modules.hubert_model import HubertConfig
+from paddlespeech.s2t.models.hubert.modules.hubert_model import HubertModel
+from paddlespeech.s2t.models.hubert.modules.hubert_model import HubertPretrainingConfig
+from paddlespeech.s2t.models.wav2vec2.modules.VanillaNN import VanillaNN
+from paddlespeech.s2t.models.wav2vec2.processing.speech_augmentation import SpecAugment
+from paddlespeech.s2t.modules.ctc import CTCDecoderBase as CTC
+from paddlespeech.s2t.modules.initializer import DefaultInitializerContext
+from paddlespeech.s2t.utils.ctc_utils import remove_duplicates_and_blank
+from paddlespeech.s2t.utils.log import Log
+from paddlespeech.s2t.utils.utility import log_add
+
+logger = Log(__name__).getlog()
+
+
+class HubertASR(nn.Layer):
+    def __init__(self, config: dict):
+        super().__init__()
+        init_type = config.get("init_type", None)
+        with DefaultInitializerContext(init_type):
+            self.config = config
+            task_cfg = self.merge_with_parent(HubertPretrainingConfig,
+                                              dict(self.config.task_cfg))
+            model_cfg = self.merge_with_parent(HubertConfig,
+                                               dict(self.config.model_cfg))
+            hubert = HubertModel(model_cfg, task_cfg, [None])
+
+            self.normalize_wav = config.normalize_wav
+            self.output_norm = config.output_norm
+            if hasattr(config, 'spec_augment'):
+                self.spec_augment = SpecAugment(**config.spec_augment)
+
+            if config.freeze_hubert:
+                hubert.eval()
+                for parm in hubert.parameters():
+                    parm.trainable = False
+            self.hubert = hubert
+            self.enc = VanillaNN(**config.enc)
+            self.ctc = CTC(**config.ctc,
+                           odim=config.output_dim,
+                           batch_average=False,
+                           reduction='mean')
+
+    def merge_with_parent(self, dc: dataclass, cfg: dict):
+        assert is_dataclass(dc)
+        assert type(cfg) == dict
+        cfg = deepcopy(cfg)
+
+        def fix_cfg(cfg):
+            target_keys = set(dc.__dataclass_fields__.keys())
+            for k in list(cfg.keys()):
+                if k not in target_keys:
+                    del cfg[k]
+
+        fix_cfg(cfg)
+        assert len(cfg) > 0
+        return dc(**cfg)
+
+    def forward(self, wav, wavs_lens_rate, target, target_lens):
+
+        if self.normalize_wav:
+            wav = F.layer_norm(wav, wav.shape)
+
+        # Extract wav2vec output
+        out = self.hubert.extract_features(wav)[0]
+        # We normalize the output if required
+        if self.output_norm:
+            out = F.layer_norm(out, out.shape)
+
+        if self.training and hasattr(self.config, 'spec_augment'):
+            feats = self.spec_augment(out)
+        else:
+            feats = out
+
+        x = self.enc(feats)
+
+        x_lens = (wavs_lens_rate * x.shape[1]).round().astype(paddle.int64)
+
+        ctc_loss = self.ctc(x, x_lens, target, target_lens)
+
+        return ctc_loss
+
+    @paddle.no_grad()
+    def decode(self,
+               feats: paddle.Tensor,
+               text_feature: Dict[str, int],
+               decoding_method: str,
+               beam_size: int,
+               tokenizer: str=None,
+               sb_pipeline=False):
+        batch_size = feats.shape[0]
+
+        if decoding_method == 'ctc_prefix_beam_search' and batch_size > 1:
+            logger.error(
+                f"decoding mode {decoding_method} must be running with batch_size == 1"
+            )
+            logger.error(f"current batch_size is {batch_size}")
+
+        if decoding_method == 'ctc_greedy_search':
+            if tokenizer is None and sb_pipeline is False:
+                hyps = self.ctc_greedy_search(feats)
+                res = [text_feature.defeaturize(hyp) for hyp in hyps]
+                res_tokenids = [hyp for hyp in hyps]
+            else:
+                if sb_pipeline is True:
+                    hyps = self.ctc_greedy_search(feats.unsqueeze(-1))
+                else:
+                    hyps = self.ctc_greedy_search(feats)
+                res = []
+                res_tokenids = []
+                for sequence in hyps:
+                    # Decode token terms to words 
+                    predicted_tokens = text_feature.convert_ids_to_tokens(
+                        sequence)
+                tmp_res = []
+                tmp_res_tokenids = []
+                for c in predicted_tokens:
+                    if c == "[CLS]":
+                        continue
+                    elif c == "[SEP]" or c == "[PAD]":
+                        break
+                    else:
+                        tmp_res.append(c)
+                        tmp_res_tokenids.append(text_feature.vocab[c])
+                res.append(''.join(tmp_res))
+                res_tokenids.append(tmp_res_tokenids)
+
+        # ctc_prefix_beam_search and attention_rescoring only return one
+        # result in List[int], change it to List[List[int]] for compatible
+        # with other batch decoding mode
+        elif decoding_method == 'ctc_prefix_beam_search':
+            assert feats.shape[0] == 1
+            if tokenizer is None and sb_pipeline is False:
+                hyp = self.ctc_prefix_beam_search(feats, beam_size)
+                res = [text_feature.defeaturize(hyp)]
+                res_tokenids = [hyp]
+            else:
+                if sb_pipeline is True:
+                    hyp = self.ctc_prefix_beam_search(
+                        feats.unsqueeze(-1), beam_size)
+                else:
+                    hyp = self.ctc_prefix_beam_search(feats, beam_size)
+                res = []
+                res_tokenids = []
+                predicted_tokens = text_feature.convert_ids_to_tokens(hyp)
+                tmp_res = []
+                tmp_res_tokenids = []
+                for c in predicted_tokens:
+                    if c == "[CLS]":
+                        continue
+                    elif c == "[SEP]" or c == "[PAD]":
+                        break
+                    else:
+                        tmp_res.append(c)
+                        tmp_res_tokenids.append(text_feature.vocab[c])
+                res.append(''.join(tmp_res))
+                res_tokenids.append(tmp_res_tokenids)
+        else:
+            raise ValueError(
+                f"wav2vec2 not support decoding method: {decoding_method}")
+
+        return res, res_tokenids
+
+    @classmethod
+    def from_config(cls, config):
+        model = cls(config)
+        return model
+
+    def ctc_greedy_search(self, wav) -> List[List[int]]:
+        """ Apply CTC greedy search
+        Args:
+            speech (paddle.Tensor): (batch, max_len)
+            speech_length (paddle.Tensor): (batch, )
+        Returns:
+            List[List[int]]: best path result
+        """
+        batch_size = wav.shape[0]
+        wav = wav[:, :, 0]
+        if self.normalize_wav:
+            wav = F.layer_norm(wav, wav.shape[1:])
+        # Extract wav2vec output
+        out = self.hubert.extract_features(wav)[0]
+        # We normalize the output if required
+        if self.output_norm:
+            out = F.layer_norm(out, out.shape[1:])
+        feats = out
+        x = self.enc(feats)
+        x_lens = x.shape[1]
+        ctc_probs = self.ctc.log_softmax(x)  # (B, maxlen, vocab_size)
+        topk_prob, topk_index = ctc_probs.topk(1, axis=2)  # (B, maxlen, 1)
+        topk_index = topk_index.view(batch_size, x_lens)  # (B, maxlen)
+
+        hyps = [hyp.tolist() for hyp in topk_index]
+        hyps = [remove_duplicates_and_blank(hyp) for hyp in hyps]
+        return hyps
+
+    def _ctc_prefix_beam_search(
+            self,
+            wav,
+            beam_size,
+            blank_id: int=0, ) -> Tuple[List[Tuple[int, float]], paddle.Tensor]:
+        """ CTC prefix beam search inner implementation
+        Args:
+            speech (paddle.Tensor): (batch, max_len, feat_dim)
+            speech_length (paddle.Tensor): (batch, )
+            beam_size (int): beam size for beam search
+            decoding_chunk_size (int): decoding chunk for dynamic chunk
+                trained model.
+                <0: for decoding, use full chunk.
+                >0: for decoding, use fixed chunk size as set.
+                0: used for training, it's prohibited here
+            simulate_streaming (bool): whether do encoder forward in a
+                streaming fashion
+        Returns:
+            List[Tuple[int, float]]: nbest results, (N,1), (text, likelihood)
+            paddle.Tensor: encoder output, (1, max_len, encoder_dim),
+                it will be used for rescoring in attention rescoring mode
+        """
+        wav = wav[:, :, 0]
+
+        if self.normalize_wav:
+            wav = F.layer_norm(wav, wav.shape[1:])
+        # Extract wav2vec output
+        out = self.hubert.extract_features(wav)[0]
+        # We normalize the output if required
+        if self.output_norm:
+            out = F.layer_norm(out, out.shape[1:])
+        feats = out
+
+        x = self.enc(feats)
+        maxlen = x.shape[1]
+        ctc_probs = self.ctc.log_softmax(x)  # (1, maxlen, vocab_size)
+        ctc_probs = ctc_probs.squeeze(0)
+
+        # cur_hyps: (prefix, (blank_ending_score, none_blank_ending_score))
+        # blank_ending_score and  none_blank_ending_score in ln domain
+        cur_hyps = [(tuple(), (0.0, -float('inf')))]
+        # 2. CTC beam search step by step
+        for t in range(0, maxlen):
+            logp = ctc_probs[t]  # (vocab_size,)
+            # key: prefix, value (pb, pnb), default value(-inf, -inf)
+            next_hyps = defaultdict(lambda: (-float('inf'), -float('inf')))
+            # 2.1 First beam prune: select topk best
+            top_k_logp, top_k_index = logp.topk(beam_size)  # (beam_size,)
+            for s in top_k_index:
+                s = s.item()
+                ps = logp[s].item()
+                for prefix, (pb, pnb) in cur_hyps:
+                    last = prefix[-1] if len(prefix) > 0 else None
+                    if s == blank_id:  # blank
+                        n_pb, n_pnb = next_hyps[prefix]
+                        n_pb = log_add([n_pb, pb + ps, pnb + ps])
+                        next_hyps[prefix] = (n_pb, n_pnb)
+                    elif s == last:
+                        #  Update *ss -> *s;
+                        n_pb, n_pnb = next_hyps[prefix]
+                        n_pnb = log_add([n_pnb, pnb + ps])
+                        next_hyps[prefix] = (n_pb, n_pnb)
+                        # Update *s-s -> *ss, - is for blank
+                        n_prefix = prefix + (s, )
+                        n_pb, n_pnb = next_hyps[n_prefix]
+                        n_pnb = log_add([n_pnb, pb + ps])
+                        next_hyps[n_prefix] = (n_pb, n_pnb)
+                    else:
+                        n_prefix = prefix + (s, )
+                        n_pb, n_pnb = next_hyps[n_prefix]
+                        n_pnb = log_add([n_pnb, pb + ps, pnb + ps])
+                        next_hyps[n_prefix] = (n_pb, n_pnb)
+
+            # 2.2 Second beam prune
+            next_hyps = sorted(
+                next_hyps.items(),
+                key=lambda x: log_add(list(x[1])),
+                reverse=True)
+            cur_hyps = next_hyps[:beam_size]
+
+        hyps = [(y[0], log_add([y[1][0], y[1][1]])) for y in cur_hyps]
+        return hyps
+
+    def ctc_prefix_beam_search(self, wav, beam_size) -> List[int]:
+        """ Apply CTC prefix beam search
+        Args:
+            speech (paddle.Tensor): (batch, max_len, feat_dim)
+            speech_length (paddle.Tensor): (batch, )
+            beam_size (int): beam size for beam search
+            decoding_chunk_size (int): decoding chunk for dynamic chunk
+                trained model.
+                <0: for decoding, use full chunk.
+                >0: for decoding, use fixed chunk size as set.
+                0: used for training, it's prohibited here
+            simulate_streaming (bool): whether do encoder forward in a
+                streaming fashion
+        Returns:
+            List[int]: CTC prefix beam search nbest results
+        """
+        hyps = self._ctc_prefix_beam_search(wav, beam_size)
+        return hyps[0][0]
+
+
+class HubertBase(nn.Layer):
+    """Hubert model"""
+
+    def __init__(self, config: dict):
+        super().__init__()
+        self.config = config
+        task_cfg = self.merge_with_parent(HubertPretrainingConfig,
+                                          dict(self.config.task_cfg))
+        model_cfg = self.merge_with_parent(HubertConfig,
+                                           dict(self.config.model_cfg))
+        hubert = HubertModel(model_cfg, task_cfg, [None])
+        self.hubert = hubert
+
+    @classmethod
+    def from_config(cls, configs: dict):
+        """init model.
+        Args:
+            configs (dict): config dict.
+        Raises:
+            ValueError: raise when using not support encoder type.
+        Returns:
+            nn.Layer: HubertBase
+        """
+        model = cls(configs)
+        return model
+
+    def merge_with_parent(self, dc: dataclass, cfg: dict):
+        assert is_dataclass(dc)
+        assert type(cfg) == dict
+        cfg = deepcopy(cfg)
+
+        def fix_cfg(cfg):
+            target_keys = set(dc.__dataclass_fields__.keys())
+            for k in list(cfg.keys()):
+                if k not in target_keys:
+                    del cfg[k]
+
+        fix_cfg(cfg)
+        assert len(cfg) > 0
+        return dc(**cfg)
+
+    def forward(self, wav):
+        out = self.hubert.extract_features(wav)
+        return out

paddlespeech/s2t/models/hubert/modules/__init__.py

@@ -0,0 +1,13 @@
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

paddlespeech/s2t/models/hubert/modules/hubert_model.py

@@ -0,0 +1,586 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Paddle Hubert model."""
+from dataclasses import dataclass
+from dataclasses import field
+from typing import Any
+from typing import Dict
+from typing import List
+from typing import Optional
+from typing import Tuple
+
+import numpy as np
+import paddle
+import paddle.nn as nn
+
+from paddlespeech.s2t.models.wav2vec2.modules.wav2vec2_model import ChoiceEnum
+from paddlespeech.s2t.models.wav2vec2.modules.wav2vec2_model import compute_mask_indices
+from paddlespeech.s2t.models.wav2vec2.modules.wav2vec2_model import ConvFeatureExtractionModel
+from paddlespeech.s2t.models.wav2vec2.modules.wav2vec2_model import EXTRACTOR_MODE_CHOICES
+from paddlespeech.s2t.models.wav2vec2.modules.wav2vec2_model import get_available_activation_fns
+from paddlespeech.s2t.models.wav2vec2.modules.wav2vec2_model import GLU
+from paddlespeech.s2t.models.wav2vec2.modules.wav2vec2_model import GradMultiply
+from paddlespeech.s2t.models.wav2vec2.modules.wav2vec2_model import LAYER_TYPE_CHOICES
+from paddlespeech.s2t.models.wav2vec2.modules.wav2vec2_model import MASKING_DISTRIBUTION_CHOICES
+from paddlespeech.s2t.models.wav2vec2.modules.wav2vec2_model import TransformerEncoder
+from paddlespeech.s2t.modules.align import LayerNorm
+from paddlespeech.s2t.modules.align import Linear
+from paddlespeech.s2t.utils.log import Log
+
+logger = Log(__name__).getlog()
+
+
+@dataclass
+class HubertPretrainingConfig:
+    label_rate: float = field(
+        default=-1.0,
+        metadata={"help": "label frame rate. -1.0 for sequence label"}, )
+    sample_rate: int = field(
+        default=16_000,
+        metadata={
+            "help":
+            "target sample rate. audio files will be up/down "
+            "sampled to this rate"
+        }, )
+    normalize: bool = field(
+        default=False,
+        metadata={
+            "help": "if set, normalizes input to have 0 mean and unit variance"
+        }, )
+    enable_padding: bool = field(
+        default=False,
+        metadata={"help": "pad shorter samples instead of cropping"}, )
+    max_keep_size: Optional[int] = field(
+        default=None,
+        metadata={"help": "exclude sample longer than this"}, )
+    max_sample_size: Optional[int] = field(
+        default=None,
+        metadata={"help": "max sample size to crop to for batching"}, )
+    min_sample_size: Optional[int] = field(
+        default=None,
+        metadata={"help": "min sample size to crop to for batching"}, )
+    random_crop: Optional[bool] = field(
+        default=True,
+        metadata={"help": "always crop from the beginning if false"}, )
+    pad_audio: Optional[bool] = field(
+        default=False,
+        metadata={"help": "pad audio to the longest one in the batch if true"},
+    )
+
+
+@dataclass
+class HubertConfig:
+    label_rate: float
+
+    extractor_mode: EXTRACTOR_MODE_CHOICES = field(
+        default="default",
+        metadata={
+            "help":
+            "mode for feature extractor. default has a single group "
+            "norm with d groups in the first conv block, whereas layer_norm "
+            "has layer norms in every block (meant to use with normalize=True)"
+        }, )
+    encoder_layers: int = field(
+        default=12, metadata={"help": "num encoder layers in the transformer"})
+    encoder_embed_dim: int = field(
+        default=768, metadata={"help": "encoder embedding dimension"})
+    encoder_ffn_embed_dim: int = field(
+        default=3072, metadata={"help": "encoder embedding dimension for FFN"})
+    encoder_attention_heads: int = field(
+        default=12, metadata={"help": "num encoder attention heads"})
+    activation_fn: ChoiceEnum(get_available_activation_fns()) = field(
+        default="gelu", metadata={"help": "activation function to use"})
+    layer_type: LAYER_TYPE_CHOICES = field(
+        default="transformer", metadata={"help": "layer type in encoder"})
+
+    # dropouts
+    dropout: float = field(
+        default=0.1,
+        metadata={"help": "dropout probability for the transformer"}, )
+    attention_dropout: float = field(
+        default=0.1,
+        metadata={"help": "dropout probability for attention weights"}, )
+    activation_dropout: float = field(
+        default=0.0,
+        metadata={"help": "dropout probability after activation in FFN"}, )
+    encoder_layerdrop: float = field(
+        default=0.0,
+        metadata={"help": "probability of dropping a tarnsformer layer"}, )
+    dropout_input: float = field(
+        default=0.0,
+        metadata={"help": "dropout to apply to the input (after feat extr)"}, )
+    dropout_features: float = field(
+        default=0.0,
+        metadata={"help": "dropout to apply to the features (after feat extr)"},
+    )
+
+    final_dim: int = field(
+        default=0,
+        metadata={
+            "help":
+            "project final representations and targets to this many "
+            "dimensions. set to encoder_embed_dim is <= 0"
+        }, )
+    untie_final_proj: bool = field(
+        default=False,
+        metadata={"help": "use separate projection for each target"}, )
+    layer_norm_first: bool = field(
+        default=False,
+        metadata={"help": "apply layernorm first in the transformer"}, )
+    conv_feature_layers: str = field(
+        default="[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2",
+        metadata={
+            "help":
+            "string describing convolutional feature extraction "
+            "layers in form of a python list that contains "
+            "[(dim, kernel_size, stride), ...]"
+        }, )
+    conv_bias: bool = field(
+        default=False, metadata={"help": "include bias in conv encoder"})
+    logit_temp: float = field(
+        default=0.1, metadata={"help": "temperature to divide logits by"})
+    target_glu: bool = field(
+        default=False, metadata={"help": "adds projection + glu to targets"})
+    feature_grad_mult: float = field(
+        default=1.0,
+        metadata={"help": "multiply feature extractor var grads by this"}, )
+
+    # masking
+    mask_length: int = field(default=10, metadata={"help": "mask length"})
+    mask_prob: float = field(
+        default=0.65,
+        metadata={"help": "probability of replacing a token with mask"}, )
+    mask_selection: MASKING_DISTRIBUTION_CHOICES = field(
+        default="static", metadata={"help": "how to choose mask length"})
+    mask_other: float = field(
+        default=0,
+        metadata={
+            "help":
+            "secondary mask argument "
+            "(used for more complex distributions), "
+            "see help in compute_mask_indicesh"
+        }, )
+    no_mask_overlap: bool = field(
+        default=False, metadata={"help": "whether to allow masks to overlap"})
+    mask_min_space: int = field(
+        default=1,
+        metadata={"help": "min space between spans (if no overlap is enabled)"},
+    )
+
+    # channel masking
+    mask_channel_length: int = field(
+        default=10,
+        metadata={"help": "length of the mask for features (channels)"}, )
+    mask_channel_prob: float = field(
+        default=0.0,
+        metadata={"help": "probability of replacing a feature with 0"}, )
+    mask_channel_selection: MASKING_DISTRIBUTION_CHOICES = field(
+        default="static",
+        metadata={"help": "how to choose mask length for channel masking"}, )
+    mask_channel_other: float = field(
+        default=0,
+        metadata={
+            "help":
+            "secondary mask argument "
+            "(used for more complex distributions), "
+            "see help in compute_mask_indicesh"
+        }, )
+    no_mask_channel_overlap: bool = field(
+        default=False,
+        metadata={"help": "whether to allow channel masks to overlap"}, )
+    mask_channel_min_space: int = field(
+        default=1,
+        metadata={"help": "min space between spans (if no overlap is enabled)"},
+    )
+
+    # positional embeddings
+    conv_pos: int = field(
+        default=128,
+        metadata={
+            "help": "number of filters for convolutional positional embeddings"
+        }, )
+    conv_pos_groups: int = field(
+        default=16,
+        metadata={
+            "help": "number of groups for convolutional positional embedding"
+        }, )
+
+    latent_temp: Tuple[float, float, float] = field(
+        default=(2, 0.5, 0.999995),
+        metadata={"help": "legacy (to be removed)"}, )
+
+    # loss computation
+    skip_masked: bool = field(
+        default=False,
+        metadata={"help": "skip computing losses over masked frames"}, )
+    skip_nomask: bool = field(
+        default=False,
+        metadata={"help": "skip computing losses over unmasked frames"}, )
+
+    checkpoint_activations: bool = field(
+        default=False,
+        metadata={
+            "help": "recompute activations and save memory for extra compute"
+        }, )
+
+    # FP16 optimization
+    required_seq_len_multiple: int = field(
+        default=2,
+        metadata={
+            "help":
+            "pad the input to encoder such that the sequence length is divisible by multiple"
+        }, )
+
+    # Conformer
+    depthwise_conv_kernel_size: int = field(
+        default=31,
+        metadata={
+            "help":
+            "depthwise-conv-kernel-size for convolution in conformer layer"
+        }, )
+    attn_type: str = field(
+        default="",
+        metadata={"help": "if espnet use ESPNET MHA"}, )
+    pos_enc_type: str = field(
+        default="abs",
+        metadata={"help": "Positional encoding type to use in conformer"}, )
+    fp16: bool = field(
+        default=False, metadata={"help": "If fp16 is being used"})
+
+
+class HubertModel(nn.Layer):
+    def __init__(
+            self,
+            cfg: HubertConfig,
+            task_cfg: HubertPretrainingConfig,
+            dictionaries: List[Any], ) -> None:
+        super().__init__()
+        logger.info(f"HubertModel Config: {cfg}")
+
+        feature_enc_layers = eval(cfg.conv_feature_layers)  # noqa
+        self.embed = feature_enc_layers[-1][0]
+
+        self.feature_extractor = ConvFeatureExtractionModel(
+            conv_layers=feature_enc_layers,
+            dropout=0.0,
+            mode=cfg.extractor_mode,
+            conv_bias=cfg.conv_bias, )
+        feature_ds_rate = np.prod([s for _, _, s in feature_enc_layers])
+        self.feat2tar_ratio = cfg.label_rate * feature_ds_rate / task_cfg.sample_rate
+
+        self.post_extract_proj = (Linear(self.embed, cfg.encoder_embed_dim) if
+                                  self.embed != cfg.encoder_embed_dim else None)
+
+        self.mask_prob = cfg.mask_prob
+        self.mask_selection = cfg.mask_selection
+        self.mask_other = cfg.mask_other
+        self.mask_length = cfg.mask_length
+        self.no_mask_overlap = cfg.no_mask_overlap
+        self.mask_min_space = cfg.mask_min_space
+
+        self.mask_channel_prob = cfg.mask_channel_prob
+        self.mask_channel_selection = cfg.mask_channel_selection
+        self.mask_channel_other = cfg.mask_channel_other
+        self.mask_channel_length = cfg.mask_channel_length
+        self.no_mask_channel_overlap = cfg.no_mask_channel_overlap
+        self.mask_channel_min_space = cfg.mask_channel_min_space
+
+        self.dropout_input = nn.Dropout(cfg.dropout_input)
+        self.dropout_features = nn.Dropout(cfg.dropout_features)
+
+        self.feature_grad_mult = cfg.feature_grad_mult
+        self.logit_temp = cfg.logit_temp
+        self.skip_masked = cfg.skip_masked
+        self.skip_nomask = cfg.skip_nomask
+
+        final_dim = cfg.final_dim if cfg.final_dim > 0 else cfg.encoder_embed_dim
+
+        self.mask_emb = paddle.create_parameter(
+            shape=[cfg.encoder_embed_dim],
+            dtype='float32',
+            default_initializer=paddle.nn.initializer.Uniform(low=0), )
+
+        self.encoder = TransformerEncoder(cfg)
+        self.layer_norm = LayerNorm(self.embed)
+
+        self.target_glu = None
+        if cfg.target_glu:
+            self.target_glu = nn.Sequential(
+                Linear(final_dim, final_dim * 2), GLU())
+
+        self.untie_final_proj = cfg.untie_final_proj
+        if self.untie_final_proj:
+            self.final_proj = Linear(cfg.encoder_embed_dim,
+                                     final_dim * len(dictionaries))
+        else:
+            self.final_proj = Linear(cfg.encoder_embed_dim, final_dim)
+
+        # modules below are not needed during fine-tuning
+        if any([d is None for d in dictionaries]):
+            logger.info(
+                "cannot find dictionary. assume will be used for fine-tuning")
+        else:
+            self.num_classes = [len(d) for d in dictionaries]
+            self.label_embs_concat = paddle.create_parameter(
+                shape=[sum(self.num_classes), final_dim],
+                dtype='float32',
+                default_initializer=paddle.nn.initializer.Uniform(low=0), )
+
+    @classmethod
+    def build_model(cls, cfg: HubertConfig, task):
+        """Build a new model instance."""
+
+        model = HubertModel(cfg, task.cfg, task.dictionaries)
+        return model
+
+    def apply_mask(self, x, padding_mask, target_list):
+        B, T, C = x.shape
+        if self.mask_prob > 0:
+            mask_indices = compute_mask_indices(
+                (B, T),
+                padding_mask,
+                self.mask_prob,
+                self.mask_length,
+                self.mask_selection,
+                self.mask_other,
+                min_masks=2,
+                no_overlap=self.no_mask_overlap,
+                min_space=self.mask_min_space, )
+
+            mask_indices = paddle.to_tensor(
+                mask_indices, dtype='int64', place=x.place)
+            x[mask_indices] = self.mask_emb
+        else:
+            mask_indices = None
+
+        if self.mask_channel_prob > 0:
+            mask_channel_indices = compute_mask_indices(
+                (B, C),
+                None,
+                self.mask_channel_prob,
+                self.mask_channel_length,
+                self.mask_channel_selection,
+                self.mask_channel_other,
+                no_overlap=self.no_mask_channel_overlap,
+                min_space=self.mask_channel_min_space, )
+            mask_channel_indices = (paddle.to_tensor(
+                mask_channel_indices, dtype='int64', place=x.place).unsqueeze(1)
+                                    .expand([-1, T, -1]))
+            x[mask_channel_indices] = 0
+
+        return x, mask_indices
+
+    def compute_nce(self, x, pos, negs):
+        neg_is_pos = (pos == negs).all(-1)
+        pos = pos.unsqueeze(0)
+        targets = paddle.concat([pos, negs], axis=0)
+
+        logits = paddle.nn.functional.cosine_similarity(
+            x.astype('float32'), targets.astype('float32'), axis=-1)
+        logits /= self.logit_temp
+        if paddle.any(neg_is_pos):
+            logits[1:][neg_is_pos] = float("-inf")
+        logits = logits.transpose([1, 0])  # (num_x, num_cls+1)
+        return logits
+
+    def forward_features(self, source: paddle.Tensor) -> paddle.Tensor:
+        if self.feature_grad_mult > 0:
+            features = self.feature_extractor(source)
+            if self.feature_grad_mult != 1.0:
+                features = GradMultiply.apply(features, self.feature_grad_mult)
+        else:
+            with paddle.no_grad():
+                features = self.feature_extractor(source)
+        return features
+
+    def forward_targets(
+            self,
+            features: paddle.Tensor,
+            target_list: List[paddle.Tensor],
+    ) -> Tuple[paddle.Tensor, paddle.Tensor]:
+        # Trim features to ensure labels exist and then get aligned labels
+        feat_tsz = features.shape[2]
+        targ_tsz = min([t.shape[1] for t in target_list])
+        if self.feat2tar_ratio * feat_tsz > targ_tsz:
+            feat_tsz = int(targ_tsz / self.feat2tar_ratio)
+            features = features[:, :, :feat_tsz]
+        target_inds = paddle.arange(feat_tsz).astype(
+            'float32') * self.feat2tar_ratio
+        target_list = [t[:, target_inds.astype('int64')] for t in target_list]
+        return features, target_list
+
+    def forward_padding_mask(
+            self,
+            features: paddle.Tensor,
+            padding_mask: paddle.Tensor, ) -> paddle.Tensor:
+        extra = padding_mask.shape[1] % features.shape[1]
+        if extra > 0:
+            padding_mask = padding_mask[:, :-extra]
+        padding_mask = paddle.reshape(
+            padding_mask, [padding_mask.shape[0], features.shape[1], -1])
+        padding_mask = paddle.all(padding_mask, axis=-1)
+        return padding_mask
+
+    def forward(
+            self,
+            source: paddle.Tensor,
+            target_list: Optional[List[paddle.Tensor]]=None,
+            padding_mask: Optional[paddle.Tensor]=None,
+            mask: bool=True,
+            features_only: bool=False,
+            output_layer: Optional[int]=None, ) -> Dict[str, paddle.Tensor]:
+        """output layer is 1-based"""
+        features = self.forward_features(source)
+        if target_list is not None:
+            features, target_list = self.forward_targets(features, target_list)
+
+        features_pen = features.pow(2).mean()
+
+        features = features.transpose([0, 2, 1])
+        features = self.layer_norm(features)
+        unmasked_features = features.clone()
+
+        if padding_mask is not None:
+            padding_mask = self.forward_padding_mask(features, padding_mask)
+
+        if self.post_extract_proj is not None:
+            features = self.post_extract_proj(features)
+
+        features = self.dropout_input(features)
+        unmasked_features = self.dropout_features(unmasked_features)
+
+        if mask:
+            x, mask_indices = self.apply_mask(features, padding_mask,
+                                              target_list)
+        else:
+            x = features
+            mask_indices = None
+
+        # feature: (B, T, D), float
+        # target: (B, T), long
+        # x: (B, T, D), float
+        # padding_mask: (B, T), bool
+        # mask_indices: (B, T), bool
+        x, _ = self.encoder(
+            x,
+            padding_mask=padding_mask,
+            layer=None if output_layer is None else output_layer - 1, )
+
+        if features_only:
+            return {"x": x, "padding_mask": padding_mask, "features": features}
+
+        def compute_pred(self, proj_x, target, label_embs):
+            # compute logits for the i-th label set
+            y = paddle.index_select(
+                label_embs, index=target.astype('int64'), axis=0)
+            negs = paddle.expand(
+                label_embs.unsqueeze(1),
+                [label_embs.shape[0], proj_x.shape[0], label_embs.shape[-1]])
+            if self.target_glu:
+                y = self.target_glu(y)
+                negs = self.target_glu(negs)
+            # proj_x: (S, D)
+            # y: (S, D)
+            # negs: (Neg, S, D)
+            return self.compute_nce(proj_x, y, negs)
+
+        label_embs_list = self.label_embs_concat.split(self.num_classes, 0)
+
+        if not self.skip_masked:
+            masked_indices = paddle.logical_and(~padding_mask, mask_indices)
+            proj_x_m = self.final_proj(x[masked_indices])
+            if self.untie_final_proj:
+                proj_x_m_list = proj_x_m.chunk(len(target_list), dim=-1)
+            else:
+                proj_x_m_list = [proj_x_m for _ in range(len(target_list))]
+            logit_m_list = [
+                compute_pred(proj_x_m, t[masked_indices], label_embs_list[i])
+                for i, (proj_x_m, t
+                        ) in enumerate(zip(proj_x_m_list, target_list))
+            ]
+        else:
+            logit_m_list = [None for _ in target_list]
+
+        if not self.skip_nomask:
+            nomask_indices = paddle.logical_and(~padding_mask, ~mask_indices)
+            proj_x_u = self.final_proj(x[nomask_indices])
+            if self.untie_final_proj:
+                proj_x_u_list = proj_x_u.chunk(len(target_list), dim=-1)
+            else:
+                proj_x_u_list = [proj_x_u for _ in range(len(target_list))]
+
+            logit_u_list = [
+                compute_pred(proj_x_u, t[nomask_indices], label_embs_list[i])
+                for i, (proj_x_u, t
+                        ) in enumerate(zip(proj_x_u_list, target_list))
+            ]
+        else:
+            logit_u_list = [None for _ in target_list]
+
+        result = {
+            "logit_m_list": logit_m_list,
+            "logit_u_list": logit_u_list,
+            "padding_mask": padding_mask,
+            "features_pen": features_pen,
+        }
+        return result
+
+    def extract_features(
+            self,
+            source: paddle.Tensor,
+            padding_mask: Optional[paddle.Tensor]=None,
+            mask: bool=False,
+            ret_conv: bool=False,
+            output_layer: Optional[int]=None,
+    ) -> Tuple[paddle.Tensor, paddle.Tensor]:
+        res = self.forward(
+            source,
+            padding_mask=padding_mask,
+            mask=mask,
+            features_only=True,
+            output_layer=output_layer, )
+        feature = res["features"] if ret_conv else res["x"]
+        return feature, res["padding_mask"]
+
+    def get_logits(self, net_output, is_masked=True):
+        if is_masked:
+            logits_list = net_output["logit_m_list"]
+        else:
+            logits_list = net_output["logit_u_list"]
+        logits_list = [
+            paddle.cast(x, 'float32') for x in logits_list if x is not None
+        ]
+        return logits_list
+
+    def get_targets(self, net_output, is_masked=True):
+        logits_list = self.get_logits(net_output, is_masked)
+        targets_list = [
+            paddle.zeros_like(x, dtype='int64') for x in logits_list
+        ]
+        return targets_list
+
+    def get_extra_losses(self, net_output):
+        extra_losses = []
+        names = []
+
+        if "features_pen" in net_output:
+            extra_losses.append(net_output["features_pen"])
+            names.append("features_pen")
+
+        return extra_losses, names
+
+    def remove_pretraining_modules(self):
+        self.target_glu = None
+        self.final_proj = None

paddlespeech/s2t/models/wav2vec2/wav2vec2_ASR.py

@@ -27,8 +27,11 @@ from paddlespeech.s2t.models.wav2vec2.processing.speech_augmentation import Spec
 from paddlespeech.s2t.modules.ctc import CTCDecoderBase as CTC
 from paddlespeech.s2t.modules.initializer import DefaultInitializerContext
 from paddlespeech.s2t.utils.ctc_utils import remove_duplicates_and_blank
+from paddlespeech.s2t.utils.log import Log
 from paddlespeech.s2t.utils.utility import log_add
 
+logger = Log(__name__).getlog()
+
 
 class Wav2vec2ASR(nn.Layer):
     def __init__(self, config: dict):