Merge pull request #70 from alibaba-damo-academy/dev_lhn

zhifu gao

2023-02-07 6cba7f633d93f33368afc20dc74b1bd493e14165

Merge pull request #70 from alibaba-damo-academy/dev_lhn

Dev lhn

 egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/README.md

@@ -1,13 +1,14 @@
# ModelScope Model

## How to finetune and infer using a pretrained Paraformer-large Model
## How to finetune and infer using a pretrained UniASR Model

### Finetune

- Modify finetune training related parameters in `finetune.py`
    - <strong>output_dir:</strong> # result dir
    - <strong>data_dir:</strong> # the dataset dir needs to include files: train/wav.scp, train/text; validation/wav.scp, validation/text.
    - <strong>batch_bins:</strong> # batch size
    - <strong>data_dir:</strong> # the dataset dir needs to include files: `train/wav.scp`, `train/text`; `validation/wav.scp`, `validation/text`
    - <strong>dataset_type:</strong> # for dataset larger than 1000 hours, set as `large`, otherwise set as `small`
    - <strong>batch_bins:</strong> # batch size. For dataset_type is `small`, `batch_bins` indicates the feature frames. For dataset_type is `large`, `batch_bins` indicates the duration in ms
    - <strong>max_epoch:</strong> # number of training epoch
    - <strong>lr:</strong> # learning rate

@@ -21,10 +22,32 @@
Or you can use the finetuned model for inference directly.

- Setting parameters in `infer.py`
    - <strong>audio_in:</strong> # support wav, url, bytes, and parsed audio format.
    - <strong>output_dir:</strong> # If the input format is wav.scp, it needs to be set.
    - <strong>data_dir:</strong> # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed
    - <strong>output_dir:</strong> # result dir
    - <strong>ngpu:</strong> # the number of GPUs for decoding
    - <strong>njob:</strong> # the number of jobs for each GPU

- Then you can run the pipeline to infer with:
```python
    python infer.py
```

- Results

The decoding results can be found in `$output_dir/1best_recog/text.cer`, which includes recognition results of each sample and the CER metric of the whole test set.

### Inference using local finetuned model

- Modify inference related parameters in `infer_after_finetune.py`
    - <strong>output_dir:</strong> # result dir
    - <strong>data_dir:</strong> # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed
    - <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pth`

- Then you can run the pipeline to finetune with:
```python
    python infer_after_finetune.py
```

- Results

The decoding results can be found in `$output_dir/decoding_results/text.cer`, which includes recognition results of each sample and the CER metric of the whole test set.

 egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/finetune.py

@@ -1,7 +1,10 @@
import os

from modelscope.metainfo import Trainers
from modelscope.trainers import build_trainer

from funasr.datasets.ms_dataset import MsDataset
from funasr.utils.modelscope_param import modelscope_args


def modelscope_finetune(params):
@@ -11,7 +14,6 @@
    ds_dict = MsDataset.load(params.data_path)
    kwargs = dict(
        model=params.model,
        model_revision=params.model_revision,
        data_dir=ds_dict,
        dataset_type=params.dataset_type,
        work_dir=params.output_dir,
@@ -23,7 +25,6 @@


if __name__ == '__main__':
    from funasr.utils.modelscope_param import modelscope_args
    params = modelscope_args(model="damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline", data_path="./data")
    params.output_dir = "./checkpoint"              # m模型保存路径
    params.data_path = "./example_data/"            # 数据路径

 egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/infer.py

@@ -1,14 +1,88 @@
import os
import shutil
from multiprocessing import Pool

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

if __name__ == '__main__':
    audio_in = 'https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav'
    output_dir = None
from funasr.utils.compute_wer import compute_wer


def modelscope_infer_core(output_dir, split_dir, njob, idx):
    output_dir_job = os.path.join(output_dir, "output.{}".format(idx))
    gpu_id = (int(idx) - 1) // njob
    if "CUDA_VISIBLE_DEVICES" in os.environ.keys():
        gpu_list = os.environ['CUDA_VISIBLE_DEVICES'].split(",")
        os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_list[gpu_id])
    else:
        os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id)
    inference_pipline = pipeline(
        task=Tasks.auto_speech_recognition,
        model="damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline",
        output_dir=output_dir,
        output_dir=output_dir_job,
        batch_size=1
    )
    rec_result = inference_pipline(audio_in=audio_in)
    print(rec_result)
    audio_in = os.path.join(split_dir, "wav.{}.scp".format(idx))
    inference_pipline(audio_in=audio_in)


def modelscope_infer(params):
    # prepare for multi-GPU decoding
    ngpu = params["ngpu"]
    njob = params["njob"]
    output_dir = params["output_dir"]
    if os.path.exists(output_dir):
        shutil.rmtree(output_dir)
    os.mkdir(output_dir)
    split_dir = os.path.join(output_dir, "split")
    os.mkdir(split_dir)
    nj = ngpu * njob
    wav_scp_file = os.path.join(params["data_dir"], "wav.scp")
    with open(wav_scp_file) as f:
        lines = f.readlines()
        num_lines = len(lines)
        num_job_lines = num_lines // nj
    start = 0
    for i in range(nj):
        end = start + num_job_lines
        file = os.path.join(split_dir, "wav.{}.scp".format(str(i + 1)))
        with open(file, "w") as f:
            if i == nj - 1:
                f.writelines(lines[start:])
            else:
                f.writelines(lines[start:end])
        start = end

    p = Pool(nj)
    for i in range(nj):
        p.apply_async(modelscope_infer_core,
                      args=(output_dir, split_dir, njob, str(i + 1)))
    p.close()
    p.join()

    # combine decoding results
    best_recog_path = os.path.join(output_dir, "1best_recog")
    os.mkdir(best_recog_path)
    files = ["text", "token", "score"]
    for file in files:
        with open(os.path.join(best_recog_path, file), "w") as f:
            for i in range(nj):
                job_file = os.path.join(output_dir, "output.{}/1best_recog".format(str(i + 1)), file)
                with open(job_file) as f_job:
                    lines = f_job.readlines()
                f.writelines(lines)

    # If text exists, compute CER
    text_in = os.path.join(params["data_dir"], "text")
    if os.path.exists(text_in):
        text_proc_file = os.path.join(best_recog_path, "token")
        compute_wer(text_in, text_proc_file, os.path.join(best_recog_path, "text.cer"))


if __name__ == "__main__":
    params = {}
    params["data_dir"] = "./data/test"
    params["output_dir"] = "./results"
    params["ngpu"] = 1
    params["njob"] = 1
    modelscope_infer(params)

 egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/infer_after_finetune.py

New file
@@ -0,0 +1,53 @@
import json
import os
import shutil

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

from funasr.utils.compute_wer import compute_wer


def modelscope_infer_after_finetune(params):
    # prepare for decoding
    pretrained_model_path = os.path.join(os.environ["HOME"], ".cache/modelscope/hub", params["modelscope_model_name"])
    for file_name in params["required_files"]:
        if file_name == "configuration.json":
            with open(os.path.join(pretrained_model_path, file_name)) as f:
                config_dict = json.load(f)
                config_dict["model"]["am_model_name"] = params["decoding_model_name"]
            with open(os.path.join(params["output_dir"], "configuration.json"), "w") as f:
                json.dump(config_dict, f, indent=4, separators=(',', ': '))
        else:
            shutil.copy(os.path.join(pretrained_model_path, file_name),
                        os.path.join(params["output_dir"], file_name))
    decoding_path = os.path.join(params["output_dir"], "decode_results")
    if os.path.exists(decoding_path):
        shutil.rmtree(decoding_path)
    os.mkdir(decoding_path)

    # decoding
    inference_pipeline = pipeline(
        task=Tasks.auto_speech_recognition,
        model=params["output_dir"],
        output_dir=decoding_path,
        batch_size=1
    )
    audio_in = os.path.join(params["data_dir"], "wav.scp")
    inference_pipeline(audio_in=audio_in)

    # computer CER if GT text is set
    text_in = os.path.join(params["data_dir"], "text")
    if os.path.exists(text_in):
        text_proc_file = os.path.join(decoding_path, "1best_recog/token")
        compute_wer(text_in, text_proc_file, os.path.join(decoding_path, "text.cer"))


if __name__ == '__main__':
    params = {}
    params["modelscope_model_name"] = "damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline"
    params["required_files"] = ["am.mvn", "decoding.yaml", "configuration.json"]
    params["output_dir"] = "./checkpoint"
    params["data_dir"] = "./data/test"
    params["decoding_model_name"] = "20epoch.pth"
    modelscope_infer_after_finetune(params)

 egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/README.md

@@ -1,13 +1,14 @@
# ModelScope Model

## How to finetune and infer using a pretrained Paraformer-large Model
## How to finetune and infer using a pretrained UniASR Model

### Finetune

- Modify finetune training related parameters in `finetune.py`
    - <strong>output_dir:</strong> # result dir
    - <strong>data_dir:</strong> # the dataset dir needs to include files: train/wav.scp, train/text; validation/wav.scp, validation/text.
    - <strong>batch_bins:</strong> # batch size
    - <strong>data_dir:</strong> # the dataset dir needs to include files: `train/wav.scp`, `train/text`; `validation/wav.scp`, `validation/text`
    - <strong>dataset_type:</strong> # for dataset larger than 1000 hours, set as `large`, otherwise set as `small`
    - <strong>batch_bins:</strong> # batch size. For dataset_type is `small`, `batch_bins` indicates the feature frames. For dataset_type is `large`, `batch_bins` indicates the duration in ms
    - <strong>max_epoch:</strong> # number of training epoch
    - <strong>lr:</strong> # learning rate

@@ -21,10 +22,32 @@
Or you can use the finetuned model for inference directly.

- Setting parameters in `infer.py`
    - <strong>audio_in:</strong> # support wav, url, bytes, and parsed audio format.
    - <strong>output_dir:</strong> # If the input format is wav.scp, it needs to be set.
    - <strong>data_dir:</strong> # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed
    - <strong>output_dir:</strong> # result dir
    - <strong>ngpu:</strong> # the number of GPUs for decoding
    - <strong>njob:</strong> # the number of jobs for each GPU

- Then you can run the pipeline to infer with:
```python
    python infer.py
```

- Results

The decoding results can be found in `$output_dir/1best_recog/text.cer`, which includes recognition results of each sample and the CER metric of the whole test set.

### Inference using local finetuned model

- Modify inference related parameters in `infer_after_finetune.py`
    - <strong>output_dir:</strong> # result dir
    - <strong>data_dir:</strong> # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed
    - <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pth`

- Then you can run the pipeline to finetune with:
```python
    python infer_after_finetune.py
```

- Results

The decoding results can be found in `$output_dir/decoding_results/text.cer`, which includes recognition results of each sample and the CER metric of the whole test set.

 egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/finetune.py

@@ -1,7 +1,10 @@
import os

from modelscope.metainfo import Trainers
from modelscope.trainers import build_trainer

from funasr.datasets.ms_dataset import MsDataset
from funasr.utils.modelscope_param import modelscope_args


def modelscope_finetune(params):
@@ -11,7 +14,6 @@
    ds_dict = MsDataset.load(params.data_path)
    kwargs = dict(
        model=params.model,
        model_revision=params.model_revision,
        data_dir=ds_dict,
        dataset_type=params.dataset_type,
        work_dir=params.output_dir,
@@ -23,7 +25,6 @@


if __name__ == '__main__':
    from funasr.utils.modelscope_param import modelscope_args
    params = modelscope_args(model="damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online", data_path="./data")
    params.output_dir = "./checkpoint"              # m模型保存路径
    params.data_path = "./example_data/"            # 数据路径

 egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/infer.py

@@ -1,14 +1,88 @@
import os
import shutil
from multiprocessing import Pool

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

if __name__ == '__main__':
    audio_in = 'https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav'
    output_dir = None
from funasr.utils.compute_wer import compute_wer


def modelscope_infer_core(output_dir, split_dir, njob, idx):
    output_dir_job = os.path.join(output_dir, "output.{}".format(idx))
    gpu_id = (int(idx) - 1) // njob
    if "CUDA_VISIBLE_DEVICES" in os.environ.keys():
        gpu_list = os.environ['CUDA_VISIBLE_DEVICES'].split(",")
        os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_list[gpu_id])
    else:
        os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id)
    inference_pipline = pipeline(
        task=Tasks.auto_speech_recognition,
        model="damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online",
        output_dir=output_dir,
        output_dir=output_dir_job,
        batch_size=1
    )
    rec_result = inference_pipline(audio_in=audio_in)
    print(rec_result)
    audio_in = os.path.join(split_dir, "wav.{}.scp".format(idx))
    inference_pipline(audio_in=audio_in)


def modelscope_infer(params):
    # prepare for multi-GPU decoding
    ngpu = params["ngpu"]
    njob = params["njob"]
    output_dir = params["output_dir"]
    if os.path.exists(output_dir):
        shutil.rmtree(output_dir)
    os.mkdir(output_dir)
    split_dir = os.path.join(output_dir, "split")
    os.mkdir(split_dir)
    nj = ngpu * njob
    wav_scp_file = os.path.join(params["data_dir"], "wav.scp")
    with open(wav_scp_file) as f:
        lines = f.readlines()
        num_lines = len(lines)
        num_job_lines = num_lines // nj
    start = 0
    for i in range(nj):
        end = start + num_job_lines
        file = os.path.join(split_dir, "wav.{}.scp".format(str(i + 1)))
        with open(file, "w") as f:
            if i == nj - 1:
                f.writelines(lines[start:])
            else:
                f.writelines(lines[start:end])
        start = end

    p = Pool(nj)
    for i in range(nj):
        p.apply_async(modelscope_infer_core,
                      args=(output_dir, split_dir, njob, str(i + 1)))
    p.close()
    p.join()

    # combine decoding results
    best_recog_path = os.path.join(output_dir, "1best_recog")
    os.mkdir(best_recog_path)
    files = ["text", "token", "score"]
    for file in files:
        with open(os.path.join(best_recog_path, file), "w") as f:
            for i in range(nj):
                job_file = os.path.join(output_dir, "output.{}/1best_recog".format(str(i + 1)), file)
                with open(job_file) as f_job:
                    lines = f_job.readlines()
                f.writelines(lines)

    # If text exists, compute CER
    text_in = os.path.join(params["data_dir"], "text")
    if os.path.exists(text_in):
        text_proc_file = os.path.join(best_recog_path, "token")
        compute_wer(text_in, text_proc_file, os.path.join(best_recog_path, "text.cer"))


if __name__ == "__main__":
    params = {}
    params["data_dir"] = "./data/test"
    params["output_dir"] = "./results"
    params["ngpu"] = 1
    params["njob"] = 1
    modelscope_infer(params)

 egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/infer_after_finetune.py

New file
@@ -0,0 +1,53 @@
import json
import os
import shutil

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

from funasr.utils.compute_wer import compute_wer


def modelscope_infer_after_finetune(params):
    # prepare for decoding
    pretrained_model_path = os.path.join(os.environ["HOME"], ".cache/modelscope/hub", params["modelscope_model_name"])
    for file_name in params["required_files"]:
        if file_name == "configuration.json":
            with open(os.path.join(pretrained_model_path, file_name)) as f:
                config_dict = json.load(f)
                config_dict["model"]["am_model_name"] = params["decoding_model_name"]
            with open(os.path.join(params["output_dir"], "configuration.json"), "w") as f:
                json.dump(config_dict, f, indent=4, separators=(',', ': '))
        else:
            shutil.copy(os.path.join(pretrained_model_path, file_name),
                        os.path.join(params["output_dir"], file_name))
    decoding_path = os.path.join(params["output_dir"], "decode_results")
    if os.path.exists(decoding_path):
        shutil.rmtree(decoding_path)
    os.mkdir(decoding_path)

    # decoding
    inference_pipeline = pipeline(
        task=Tasks.auto_speech_recognition,
        model=params["output_dir"],
        output_dir=decoding_path,
        batch_size=1
    )
    audio_in = os.path.join(params["data_dir"], "wav.scp")
    inference_pipeline(audio_in=audio_in)

    # computer CER if GT text is set
    text_in = os.path.join(params["data_dir"], "text")
    if os.path.exists(text_in):
        text_proc_file = os.path.join(decoding_path, "1best_recog/token")
        compute_wer(text_in, text_proc_file, os.path.join(decoding_path, "text.cer"))


if __name__ == '__main__':
    params = {}
    params["modelscope_model_name"] = "damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online"
    params["required_files"] = ["am.mvn", "decoding.yaml", "configuration.json"]
    params["output_dir"] = "./checkpoint"
    params["data_dir"] = "./data/test"
    params["decoding_model_name"] = "20epoch.pth"
    modelscope_infer_after_finetune(params)

 setup.py

@@ -36,7 +36,7 @@
        "ci_sdr",
        "pytorch_wpe",
        "editdistance==0.5.2",
        "tensorboard>=1.14",
        "tensorboard==1.15",
        "g2p",
        # PAI
        "oss2"