From c5f132a451876a49a7b81d30add64be51668cb4d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=BB=81=E8=BF=B7?= Date: Tue, 7 Feb 2023 10:43:38 +0800 Subject: [PATCH] update 8k uniasr recipe --- .../README.md | 33 +++++-- .../finetune.py | 7 +- .../infer.py | 88 +++++++++++++++++-- .../infer_after_finetune.py | 53 +++++++++++ .../README.md | 33 +++++-- .../finetune.py | 7 +- .../infer.py | 88 +++++++++++++++++-- .../infer_after_finetune.py | 53 +++++++++++ 8 files changed, 332 insertions(+), 30 deletions(-) create mode 100644 egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/infer_after_finetune.py create mode 100644 egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/infer_after_finetune.py diff --git a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/README.md b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/README.md index c68a8cd4f..dd947d329 100644 --- a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/README.md +++ b/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` - output_dir: # result dir - - data_dir: # the dataset dir needs to include files: train/wav.scp, train/text; validation/wav.scp, validation/text. - - batch_bins: # batch size + - data_dir: # the dataset dir needs to include files: `train/wav.scp`, `train/text`; `validation/wav.scp`, `validation/text` + - dataset_type: # for dataset larger than 1000 hours, set as `large`, otherwise set as `small` + - batch_bins: # 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 - max_epoch: # number of training epoch - lr: # learning rate @@ -21,10 +22,32 @@ Or you can use the finetuned model for inference directly. - Setting parameters in `infer.py` - - audio_in: # support wav, url, bytes, and parsed audio format. - - output_dir: # If the input format is wav.scp, it needs to be set. + - data_dir: # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed + - output_dir: # result dir + - ngpu: # the number of GPUs for decoding + - njob: # 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 `$output_dir/1best_recog/text.cer`, which includes recognition results of each sample and the CER metric of the whole test set. + +### Inference using local finetuned model + +- Modify inference related parameters in `infer_after_finetune.py` + - output_dir: # result dir + - data_dir: # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed + - decoding_model_name: # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pth` + +- Then you can run the pipeline to finetune with: +```python + python infer_after_finetune.py +``` + +- Results + +The decoding results can be found in `$output_dir/decoding_results/text.cer`, which includes recognition results of each sample and the CER metric of the whole test set. diff --git a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/finetune.py b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/finetune.py index fe88cdf34..65df8cbba 100644 --- a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/finetune.py +++ b/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 @@ def modelscope_finetune(params): 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, @@ -23,8 +25,7 @@ def modelscope_finetune(params): 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 = 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 params.dataset_type = "small" # 小数据量设置small，若数据量大于1000小时，请使用large diff --git a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/infer.py b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/infer.py index 27d7903a5..8fd751344 100644 --- a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/infer.py +++ b/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, + model="damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online", + 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) diff --git a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/infer_after_finetune.py b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/infer_after_finetune.py new file mode 100644 index 000000000..b4dde6048 --- /dev/null +++ b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/infer_after_finetune.py @@ -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) diff --git a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/README.md b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/README.md index c68a8cd4f..dd947d329 100644 --- a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/README.md +++ b/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` - output_dir: # result dir - - data_dir: # the dataset dir needs to include files: train/wav.scp, train/text; validation/wav.scp, validation/text. - - batch_bins: # batch size + - data_dir: # the dataset dir needs to include files: `train/wav.scp`, `train/text`; `validation/wav.scp`, `validation/text` + - dataset_type: # for dataset larger than 1000 hours, set as `large`, otherwise set as `small` + - batch_bins: # 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 - max_epoch: # number of training epoch - lr: # learning rate @@ -21,10 +22,32 @@ Or you can use the finetuned model for inference directly. - Setting parameters in `infer.py` - - audio_in: # support wav, url, bytes, and parsed audio format. - - output_dir: # If the input format is wav.scp, it needs to be set. + - data_dir: # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed + - output_dir: # result dir + - ngpu: # the number of GPUs for decoding + - njob: # 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 `$output_dir/1best_recog/text.cer`, which includes recognition results of each sample and the CER metric of the whole test set. + +### Inference using local finetuned model + +- Modify inference related parameters in `infer_after_finetune.py` + - output_dir: # result dir + - data_dir: # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed + - decoding_model_name: # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pth` + +- Then you can run the pipeline to finetune with: +```python + python infer_after_finetune.py +``` + +- Results + +The decoding results can be found in `$output_dir/decoding_results/text.cer`, which includes recognition results of each sample and the CER metric of the whole test set. diff --git a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/finetune.py b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/finetune.py index 6341caff7..b2325b2bb 100644 --- a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/finetune.py +++ b/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 @@ def modelscope_finetune(params): 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, @@ -23,8 +25,7 @@ def modelscope_finetune(params): 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 = 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 params.dataset_type = "small" # 小数据量设置small，若数据量大于1000小时，请使用large diff --git a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/infer.py b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/infer.py index 3b2964cc3..e855032de 100644 --- a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/infer.py +++ b/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, + model="damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline", + 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) diff --git a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/infer_after_finetune.py b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/infer_after_finetune.py new file mode 100644 index 000000000..6664c3d02 --- /dev/null +++ b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/infer_after_finetune.py @@ -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)