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Merge pull request #84 from alibaba-damo-academy/dev_lhn
add minnan uniasr model recipe
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hnluo
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# ModelScope Model
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## How to finetune and infer using a pretrained Paraformer-large Model
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### Finetune
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- Modify finetune training related parameters in `finetune.py`
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- <strong>output_dir:</strong> # result dir
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- <strong>data_dir:</strong> # the dataset dir needs to include files: `train/wav.scp`, `train/text`; `validation/wav.scp`, `validation/text`
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- <strong>dataset_type:</strong> # for dataset larger than 1000 hours, set as `large`, otherwise set as `small`
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- <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
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- <strong>max_epoch:</strong> # number of training epoch
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- <strong>lr:</strong> # learning rate
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- Then you can run the pipeline to finetune with:
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```python
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python finetune.py
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```
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### Inference
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Or you can use the finetuned model for inference directly.
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- Setting parameters in `infer.py`
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- <strong>data_dir:</strong> # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed
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- <strong>output_dir:</strong> # result dir
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- <strong>ngpu:</strong> # the number of GPUs for decoding
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- <strong>njob:</strong> # the number of jobs for each GPU
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- Then you can run the pipeline to infer with:
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```python
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python infer.py
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```
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- Results
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The decoding results can be found in `$output_dir/1best_recog/text.cer`, which includes recognition results of each sample and the CER metric of the whole test set.
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### Inference using local finetuned model
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- Modify inference related parameters in `infer_after_finetune.py`
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- <strong>output_dir:</strong> # result dir
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- <strong>data_dir:</strong> # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed
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- <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pth`
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- Then you can run the pipeline to finetune with:
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```python
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python infer_after_finetune.py
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```
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- Results
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The decoding results can be found in `$output_dir/decoding_results/text.cer`, which includes recognition results of each sample and the CER metric of the whole test set.
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import os
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from modelscope.metainfo import Trainers
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from modelscope.trainers import build_trainer
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from funasr.datasets.ms_dataset import MsDataset
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from funasr.utils.modelscope_param import modelscope_args
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def modelscope_finetune(params):
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if not os.path.exists(params.output_dir):
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os.makedirs(params.output_dir, exist_ok=True)
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# dataset split ["train", "validation"]
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ds_dict = MsDataset.load(params.data_path)
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kwargs = dict(
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model=params.model,
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data_dir=ds_dict,
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dataset_type=params.dataset_type,
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work_dir=params.output_dir,
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batch_bins=params.batch_bins,
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max_epoch=params.max_epoch,
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lr=params.lr)
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trainer = build_trainer(Trainers.speech_asr_trainer, default_args=kwargs)
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trainer.train()
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if __name__ == '__main__':
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params = modelscope_args(model="damo/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825", data_path="./data")
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params.output_dir = "./checkpoint" # m模型保存路径
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params.data_path = "./example_data/" # 数据路径
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params.dataset_type = "small" # 小数据量设置small，若数据量大于1000小时，请使用large
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params.batch_bins = 2000 # batch size，如果dataset_type="small"，batch_bins单位为fbank特征帧数，如果dataset_type="large"，batch_bins单位为毫秒，
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params.max_epoch = 20 # 最大训练轮数
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params.lr = 0.00005 # 设置学习率
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modelscope_finetune(params)
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import os
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import shutil
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from multiprocessing import Pool
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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from funasr.utils.compute_wer import compute_wer
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def modelscope_infer_core(output_dir, split_dir, njob, idx):
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output_dir_job = os.path.join(output_dir, "output.{}".format(idx))
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gpu_id = (int(idx) - 1) // njob
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if "CUDA_VISIBLE_DEVICES" in os.environ.keys():
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gpu_list = os.environ['CUDA_VISIBLE_DEVICES'].split(",")
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os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_list[gpu_id])
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else:
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os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id)
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inference_pipline = pipeline(
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task=Tasks.auto_speech_recognition,
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model="damo/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825",
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output_dir=output_dir_job,
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batch_size=1
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)
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audio_in = os.path.join(split_dir, "wav.{}.scp".format(idx))
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inference_pipline(audio_in=audio_in)
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def modelscope_infer(params):
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# prepare for multi-GPU decoding
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ngpu = params["ngpu"]
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njob = params["njob"]
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output_dir = params["output_dir"]
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if os.path.exists(output_dir):
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shutil.rmtree(output_dir)
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os.mkdir(output_dir)
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split_dir = os.path.join(output_dir, "split")
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os.mkdir(split_dir)
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nj = ngpu * njob
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wav_scp_file = os.path.join(params["data_dir"], "wav.scp")
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with open(wav_scp_file) as f:
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lines = f.readlines()
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num_lines = len(lines)
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num_job_lines = num_lines // nj
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start = 0
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for i in range(nj):
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end = start + num_job_lines
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file = os.path.join(split_dir, "wav.{}.scp".format(str(i + 1)))
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with open(file, "w") as f:
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if i == nj - 1:
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f.writelines(lines[start:])
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else:
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f.writelines(lines[start:end])
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start = end
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p = Pool(nj)
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for i in range(nj):
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p.apply_async(modelscope_infer_core,
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args=(output_dir, split_dir, njob, str(i + 1)))
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p.close()
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p.join()
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# combine decoding results
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best_recog_path = os.path.join(output_dir, "1best_recog")
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os.mkdir(best_recog_path)
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files = ["text", "token", "score"]
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for file in files:
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with open(os.path.join(best_recog_path, file), "w") as f:
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for i in range(nj):
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job_file = os.path.join(output_dir, "output.{}/1best_recog".format(str(i + 1)), file)
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with open(job_file) as f_job:
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lines = f_job.readlines()
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f.writelines(lines)
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# If text exists, compute CER
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text_in = os.path.join(params["data_dir"], "text")
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if os.path.exists(text_in):
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text_proc_file = os.path.join(best_recog_path, "token")
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compute_wer(text_in, text_proc_file, os.path.join(best_recog_path, "text.cer"))
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os.system("tail -n 3 {}".format(os.path.join(best_recog_path, "text.cer")))
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if __name__ == "__main__":
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params = {}
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params["data_dir"] = "./data/test"
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params["output_dir"] = "./results"
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params["ngpu"] = 1
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params["njob"] = 8
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modelscope_infer(params)
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import json
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import os
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import shutil
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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from funasr.utils.compute_wer import compute_wer
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def modelscope_infer_after_finetune(params):
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# prepare for decoding
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pretrained_model_path = os.path.join(os.environ["HOME"], ".cache/modelscope/hub", params["modelscope_model_name"])
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for file_name in params["required_files"]:
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if file_name == "configuration.json":
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with open(os.path.join(pretrained_model_path, file_name)) as f:
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config_dict = json.load(f)
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config_dict["model"]["am_model_name"] = params["decoding_model_name"]
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with open(os.path.join(params["output_dir"], "configuration.json"), "w") as f:
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json.dump(config_dict, f, indent=4, separators=(',', ': '))
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else:
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shutil.copy(os.path.join(pretrained_model_path, file_name),
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os.path.join(params["output_dir"], file_name))
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decoding_path = os.path.join(params["output_dir"], "decode_results")
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if os.path.exists(decoding_path):
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shutil.rmtree(decoding_path)
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os.mkdir(decoding_path)
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# decoding
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inference_pipeline = pipeline(
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task=Tasks.auto_speech_recognition,
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model=params["output_dir"],
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output_dir=decoding_path,
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batch_size=1
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)
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audio_in = os.path.join(params["data_dir"], "wav.scp")
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inference_pipeline(audio_in=audio_in)
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# computer CER if GT text is set
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text_in = os.path.join(params["data_dir"], "text")
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if os.path.exists(text_in):
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text_proc_file = os.path.join(decoding_path, "1best_recog/token")
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compute_wer(text_in, text_proc_file, os.path.join(decoding_path, "text.cer"))
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os.system("tail -n 3 {}".format(os.path.join(decoding_path, "text.cer")))
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if __name__ == '__main__':
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params = {}
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params["modelscope_model_name"] = "damo/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825"
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params["required_files"] = ["am.mvn", "decoding.yaml", "configuration.json"]
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params["output_dir"] = "./checkpoint"
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params["data_dir"] = "./data/test"
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params["decoding_model_name"] = "20epoch.pth"
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modelscope_infer_after_finetune(params)
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