Dev gzf (#1379)

zhifu gao

2024-02-22 20c35cdbc7ab0962629c9d88fd0ca07ad3968050

Dev gzf (#1379)

* update train recipe

* v1.0.8

* llm

* update trainer

* update trainer

* update trainer

* train finetune demo

* train finetune demo

 .gitignore

@@ -24,3 +24,4 @@
outputs*
emotion2vec*
GPT-SoVITS*
modelscope_models

 README.md

@@ -95,7 +95,7 @@
### Command-line usage

```shell
funasr +model=paraformer-zh +vad_model="fsmn-vad" +punc_model="ct-punc" +input=asr_example_zh.wav
funasr ++model=paraformer-zh ++vad_model="fsmn-vad" ++punc_model="ct-punc" ++input=asr_example_zh.wav
```

Notes: Support recognition of single audio file, as well as file list in Kaldi-style wav.scp format: `wav_id wav_pat`

 README_zh.md

@@ -91,7 +91,7 @@
### 可执行命令行

```shell
funasr +model=paraformer-zh +vad_model="fsmn-vad" +punc_model="ct-punc" +input=asr_example_zh.wav
funasr ++model=paraformer-zh ++vad_model="fsmn-vad" ++punc_model="ct-punc" ++input=asr_example_zh.wav
```

注：支持单条音频文件识别，也支持文件列表，列表为kaldi风格wav.scp：`wav_id   wav_path`

 examples/aishell/branchformer/demo_infer.sh

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@@ -0,0 +1 @@
../paraformer/demo_infer.sh

 examples/aishell/branchformer/demo_train_or_finetune.sh

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../paraformer/demo_train_or_finetune.sh

 examples/aishell/conformer/demo_infer.sh

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../paraformer/demo_infer.sh

 examples/aishell/conformer/demo_train_or_finetune.sh

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../paraformer/demo_train_or_finetune.sh

 examples/aishell/e_branchformer/demo_infer.sh

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../paraformer/demo_infer.sh

 examples/aishell/e_branchformer/demo_train_or_finetune.sh

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../paraformer/demo_train_or_finetune.sh

 examples/aishell/e_branchformer/infer.sh

File was deleted

 examples/aishell/paraformer/demo_infer.sh

File was renamed from examples/aishell/conformer/infer.sh
@@ -1,3 +1,6 @@
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
#  MIT License  (https://opensource.org/licenses/MIT)



python -m funasr.bin.inference \

 examples/aishell/paraformer/demo_train_or_finetune.sh

New file
@@ -0,0 +1,51 @@
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
#  MIT License  (https://opensource.org/licenses/MIT)


# which gpu to train or finetune
export CUDA_VISIBLE_DEVICES="0,1"
gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')

# data dir, which contains: train.json, val.json, tokens.jsonl/tokens.txt, am.mvn
data_dir="/Users/zhifu/funasr1.0/data/list"

## generate jsonl from wav.scp and text.txt
#python -m funasr.datasets.audio_datasets.scp2jsonl \
#++scp_file_list='["/Users/zhifu/funasr1.0/test_local/wav.scp", "/Users/zhifu/funasr1.0/test_local/text.txt"]' \
#++data_type_list='["source", "target"]' \
#++jsonl_file_out=/Users/zhifu/funasr1.0/test_local/audio_datasets.jsonl

train_data="${data_dir}/train.jsonl"
val_data="${data_dir}/val.jsonl"
tokens="${data_dir}/tokens.json"
cmvn_file="${data_dir}/am.mvn"

# exp output dir
output_dir="/Users/zhifu/exp"
log_file="${output_dir}/log.txt"

workspace=`pwd`
config="paraformer_conformer_12e_6d_2048_256.yaml"

init_param="${output_dir}/model.pt"

mkdir -p ${output_dir}
echo "log_file: ${log_file}"

torchrun \
--nnodes 1 \
--nproc_per_node ${gpu_num} \
../../../funasr/bin/train.py \
--config-path "${workspace}/conf" \
--config-name "${config}" \
++train_data_set_list="${train_data}" \
++valid_data_set_list="${val_data}" \
++tokenizer_conf.token_list="${tokens}" \
++frontend_conf.cmvn_file="${cmvn_file}" \
++dataset_conf.batch_size=32 \
++dataset_conf.batch_type="example" \
++dataset_conf.num_workers=4 \
++train_conf.max_epoch=150 \
++optim_conf.lr=0.0002 \
++init_param="${init_param}" \
++output_dir="${output_dir}" &> ${log_file}

 examples/aishell/paraformer/infer.sh

File was deleted

 examples/aishell/transformer/demo_infer.sh

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@@ -0,0 +1 @@
../paraformer/demo_infer.sh

 examples/aishell/transformer/demo_train_or_finetune.sh

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@@ -0,0 +1 @@
../paraformer/demo_train_or_finetune.sh

 examples/aishell/transformer/infer.sh

File was deleted

 examples/industrial_data_pretraining/bicif_paraformer/demo.sh

 examples/industrial_data_pretraining/conformer/demo.sh

 examples/industrial_data_pretraining/contextual_paraformer/demo.sh

 examples/industrial_data_pretraining/ct_transformer/demo.sh

 examples/industrial_data_pretraining/ct_transformer_streaming/demo.sh

 examples/industrial_data_pretraining/emotion2vec/infer.sh

File was deleted

 examples/industrial_data_pretraining/fsmn_vad_streaming/demo.sh

 examples/industrial_data_pretraining/monotonic_aligner/demo.sh

 examples/industrial_data_pretraining/paraformer-zh-spk/demo.sh

 examples/industrial_data_pretraining/paraformer/demo.sh

File was deleted

 examples/industrial_data_pretraining/paraformer/finetune.sh

@@ -1,9 +1,14 @@
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
#  MIT License  (https://opensource.org/licenses/MIT)

## download model
#local_path_root=../modelscope_models
#mkdir -p ${local_path_root}
#local_path=${local_path_root}/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch
#git clone https://www.modelscope.cn/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch.git ${local_path}
# method1, finetune from model hub

# which gpu to train or finetune
export CUDA_VISIBLE_DEVICES="0,1"
gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')

# data dir, which contains: train.json, val.json
data_dir="/Users/zhifu/funasr1.0/data/list"

## generate jsonl from wav.scp and text.txt
#python -m funasr.datasets.audio_datasets.scp2jsonl \
@@ -11,17 +16,29 @@
#++data_type_list='["source", "target"]' \
#++jsonl_file_out=/Users/zhifu/funasr1.0/test_local/audio_datasets.jsonl

train_data="${data_dir}/train.jsonl"
val_data="${data_dir}/val.jsonl"

# torchrun \
# --nnodes 1 \
# --nproc_per_node 1 \
python funasr/bin/train.py \
+model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \
+model_revision="v2.0.4" \
+train_data_set_list="/Users/zhifu/funasr_github/test_local/aishell2_dev_ios/asr_task_debug_len_10.jsonl" \
+valid_data_set_list="/Users/zhifu/funasr_github/test_local/aishell2_dev_ios/asr_task_debug_len_10.jsonl" \
++dataset_conf.batch_size=64 \

# exp output dir
output_dir="/Users/zhifu/exp"
log_file="${output_dir}/log.txt"


mkdir -p ${output_dir}
echo "log_file: ${log_file}"

torchrun \
--nnodes 1 \
--nproc_per_node ${gpu_num} \
funasr/bin/train.py \
++model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \
++model_revision="v2.0.4" \
++train_data_set_list="${train_data}" \
++valid_data_set_list="${val_data}" \
++dataset_conf.batch_size=32 \
++dataset_conf.batch_type="example" \
++train_conf.max_epoch=2 \
++dataset_conf.num_workers=4 \
+output_dir="outputs/debug/ckpt/funasr2/exp2"
++train_conf.max_epoch=20 \
++optim_conf.lr=0.0002 \
++output_dir="${output_dir}" &> ${log_file}

 examples/industrial_data_pretraining/paraformer/finetune_from_local.sh

New file
@@ -0,0 +1,61 @@
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
#  MIT License  (https://opensource.org/licenses/MIT)

# method2, finetune from local model

workspace=`pwd`

# download model
local_path_root=${workspace}/modelscope_models
mkdir -p ${local_path_root}
local_path=${local_path_root}/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch
git clone https://www.modelscope.cn/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch.git ${local_path}


# which gpu to train or finetune
export CUDA_VISIBLE_DEVICES="0,1"
gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')

# data dir, which contains: train.json, val.json
data_dir="/Users/zhifu/funasr1.0/data/list"

## generate jsonl from wav.scp and text.txt
#python -m funasr.datasets.audio_datasets.scp2jsonl \
#++scp_file_list='["/Users/zhifu/funasr1.0/test_local/wav.scp", "/Users/zhifu/funasr1.0/test_local/text.txt"]' \
#++data_type_list='["source", "target"]' \
#++jsonl_file_out=/Users/zhifu/funasr1.0/test_local/audio_datasets.jsonl

train_data="${data_dir}/train.jsonl"
val_data="${data_dir}/val.jsonl"

tokens="${local_path}/tokens.json"
cmvn_file="${local_path}/am.mvn"

# exp output dir
output_dir="/Users/zhifu/exp"
log_file="${output_dir}/log.txt"

config="config.yaml"

init_param="${local_path}/model.pt"

mkdir -p ${output_dir}
echo "log_file: ${log_file}"

torchrun \
--nnodes 1 \
--nproc_per_node ${gpu_num} \
../../../funasr/bin/train.py \
--config-path "${local_path}" \
--config-name "${config}" \
++train_data_set_list="${train_data}" \
++valid_data_set_list="${val_data}" \
++tokenizer_conf.token_list="${tokens}" \
++frontend_conf.cmvn_file="${cmvn_file}" \
++dataset_conf.batch_size=32 \
++dataset_conf.batch_type="example" \
++dataset_conf.num_workers=4 \
++train_conf.max_epoch=20 \
++optim_conf.lr=0.0002 \
++init_param="${init_param}" \
++output_dir="${output_dir}" &> ${log_file}

 examples/industrial_data_pretraining/paraformer/infer.sh

New file
@@ -0,0 +1,21 @@
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
#  MIT License  (https://opensource.org/licenses/MIT)

# method1, inference from model hub

# for more input type, please ref to readme.md
input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav"

output_dir="./outputs/debug"

model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
model_revision="v2.0.4"

device="cuda:0" # "cuda:0" for gpu0, "cuda:1" for gpu1, "cpu"

python -m funasr.bin.inference \
++model=${model} \
++model_revision=${model_revision} \
++input="${input}" \
++output_dir="${output_dir}" \
++device="${device}" \

 examples/industrial_data_pretraining/paraformer/infer_after_finetune.sh

File was deleted

 examples/industrial_data_pretraining/paraformer/infer_from_local.sh

New file
@@ -0,0 +1,39 @@
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
#  MIT License  (https://opensource.org/licenses/MIT)

# method2, inference from local model

# for more input type, please ref to readme.md
input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav"

output_dir="./outputs/debug"

workspace=`pwd`

# download model
local_path_root=${workspace}/modelscope_models
mkdir -p ${local_path_root}
local_path=${local_path_root}/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch
git clone https://www.modelscope.cn/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch.git ${local_path}

device="cuda:0" # "cuda:0" for gpu0, "cuda:1" for gpu1, "cpu"

tokens="${local_path}/tokens.json"
cmvn_file="${local_path}/am.mvn"

config="config.yaml"
init_param="${local_path}/model.pt"

python -m funasr.bin.inference \
--config-path "${local_path}" \
--config-name "${config}" \
++init_param="${init_param}" \
++tokenizer_conf.token_list="${tokens}" \
++frontend_conf.cmvn_file="${cmvn_file}" \
++input="${input}" \
++output_dir="${output_dir}" \
++device="${device}" \





 examples/industrial_data_pretraining/paraformer_streaming/demo.sh

 examples/industrial_data_pretraining/scama/demo.sh

 examples/industrial_data_pretraining/seaco_paraformer/demo.sh

 examples/industrial_data_pretraining/uniasr/demo.sh

 funasr/bin/train.py

@@ -96,6 +96,7 @@
            init_param = (init_param,)
        logging.info("init_param is not None: %s", init_param)
        for p in init_param:
            if os.path.exists(p):
            logging.info(f"Loading pretrained params from {p}")
            load_pretrained_model(
                model=model,
@@ -106,6 +107,8 @@
                excludes=kwargs.get("excludes", None),
            )
    else:
                logging.info(f"Checkpoint does not exist, init randomly: {p}")
    else:
        initialize(model, kwargs.get("init", "kaiming_normal"))



 funasr/train_utils/trainer.py

@@ -181,7 +181,7 @@

            time2 = time.perf_counter()
            time_escaped = (time2 - time1)/3600.0
            print(f"\nrank: {self.local_rank}, time_escaped_epoch: {time_escaped:.3f} hours, estimated to finish {self.max_epoch} epoch: {(self.max_epoch-epoch)*time_escaped:.3f}\n")
            print(f"\nrank: {self.local_rank}, time_escaped_epoch: {time_escaped:.3f} hours, estimated to finish {self.max_epoch} epoch: {(self.max_epoch-epoch)*time_escaped:.3f} hours\n")

        if self.rank == 0:
            average_checkpoints(self.output_dir, self.avg_nbest_model)
@@ -293,7 +293,7 @@
                    f"{time_now}, "
                    f"rank: {self.local_rank}, "
                    f"epoch: {epoch}/{self.max_epoch}, "
                    f"step: {batch_idx+1}/{len(self.dataloader_train)}, total: {self.batch_total}, "
                    f"step: {batch_idx+1}/{len(self.dataloader_train)}, total step: {self.batch_total}, "
                    f"(loss: {loss.detach().cpu().item():.3f}), "
                    f"(lr: {lr:.3e}), "
                    f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}, "