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| import os | |
| import json | |
| import numpy as np | |
| import torch | |
| import hydra | |
| import logging | |
| from omegaconf import DictConfig, OmegaConf | |
| from funasr_detach.register import tables | |
| from funasr_detach.download.download_from_hub import download_model | |
| from funasr_detach.train_utils.set_all_random_seed import set_all_random_seed | |
| def main_hydra(kwargs: DictConfig): | |
| if kwargs.get("debug", False): | |
| import pdb | |
| pdb.set_trace() | |
| assert "model" in kwargs | |
| if "model_conf" not in kwargs: | |
| logging.info( | |
| "download models from model hub: {}".format(kwargs.get("model_hub", "ms")) | |
| ) | |
| kwargs = download_model(is_training=kwargs.get("is_training", True), **kwargs) | |
| main(**kwargs) | |
| def main(**kwargs): | |
| print(kwargs) | |
| # set random seed | |
| tables.print() | |
| set_all_random_seed(kwargs.get("seed", 0)) | |
| torch.backends.cudnn.enabled = kwargs.get( | |
| "cudnn_enabled", torch.backends.cudnn.enabled | |
| ) | |
| torch.backends.cudnn.benchmark = kwargs.get( | |
| "cudnn_benchmark", torch.backends.cudnn.benchmark | |
| ) | |
| torch.backends.cudnn.deterministic = kwargs.get("cudnn_deterministic", True) | |
| tokenizer = kwargs.get("tokenizer", None) | |
| # build frontend if frontend is none None | |
| frontend = kwargs.get("frontend", None) | |
| if frontend is not None: | |
| frontend_class = tables.frontend_classes.get(frontend) | |
| frontend = frontend_class(**kwargs["frontend_conf"]) | |
| kwargs["frontend"] = frontend | |
| kwargs["input_size"] = frontend.output_size() | |
| # dataset | |
| dataset_class = tables.dataset_classes.get(kwargs.get("dataset", "AudioDataset")) | |
| dataset_train = dataset_class( | |
| kwargs.get("train_data_set_list"), | |
| frontend=frontend, | |
| tokenizer=None, | |
| is_training=False, | |
| **kwargs.get("dataset_conf") | |
| ) | |
| # dataloader | |
| batch_sampler = kwargs["dataset_conf"].get( | |
| "batch_sampler", "DynamicBatchLocalShuffleSampler" | |
| ) | |
| batch_sampler_train = None | |
| if batch_sampler is not None: | |
| batch_sampler_class = tables.batch_sampler_classes.get(batch_sampler) | |
| dataset_conf = kwargs.get("dataset_conf") | |
| dataset_conf["batch_type"] = "example" | |
| dataset_conf["batch_size"] = 1 | |
| batch_sampler_train = batch_sampler_class( | |
| dataset_train, is_training=False, **dataset_conf | |
| ) | |
| dataloader_train = torch.utils.data.DataLoader( | |
| dataset_train, | |
| collate_fn=dataset_train.collator, | |
| batch_sampler=batch_sampler_train, | |
| num_workers=int(kwargs.get("dataset_conf").get("num_workers", 4)), | |
| pin_memory=True, | |
| ) | |
| iter_stop = int(kwargs.get("scale", 1.0) * len(dataloader_train)) | |
| total_frames = 0 | |
| for batch_idx, batch in enumerate(dataloader_train): | |
| if batch_idx >= iter_stop: | |
| break | |
| fbank = batch["speech"].numpy()[0, :, :] | |
| if total_frames == 0: | |
| mean_stats = np.sum(fbank, axis=0) | |
| var_stats = np.sum(np.square(fbank), axis=0) | |
| else: | |
| mean_stats += np.sum(fbank, axis=0) | |
| var_stats += np.sum(np.square(fbank), axis=0) | |
| total_frames += fbank.shape[0] | |
| cmvn_info = { | |
| "mean_stats": list(mean_stats.tolist()), | |
| "var_stats": list(var_stats.tolist()), | |
| "total_frames": total_frames, | |
| } | |
| cmvn_file = kwargs.get("cmvn_file", "cmvn.json") | |
| # import pdb;pdb.set_trace() | |
| with open(cmvn_file, "w") as fout: | |
| fout.write(json.dumps(cmvn_info)) | |
| mean = -1.0 * mean_stats / total_frames | |
| var = 1.0 / np.sqrt(var_stats / total_frames - mean * mean) | |
| dims = mean.shape[0] | |
| am_mvn = os.path.dirname(cmvn_file) + "/am.mvn" | |
| with open(am_mvn, "w") as fout: | |
| fout.write( | |
| "<Nnet>" | |
| + "\n" | |
| + "<Splice> " | |
| + str(dims) | |
| + " " | |
| + str(dims) | |
| + "\n" | |
| + "[ 0 ]" | |
| + "\n" | |
| + "<AddShift> " | |
| + str(dims) | |
| + " " | |
| + str(dims) | |
| + "\n" | |
| ) | |
| mean_str = ( | |
| str(list(mean)).replace(",", "").replace("[", "[ ").replace("]", " ]") | |
| ) | |
| fout.write("<LearnRateCoef> 0 " + mean_str + "\n") | |
| fout.write("<Rescale> " + str(dims) + " " + str(dims) + "\n") | |
| var_str = str(list(var)).replace(",", "").replace("[", "[ ").replace("]", " ]") | |
| fout.write("<LearnRateCoef> 0 " + var_str + "\n") | |
| fout.write("</Nnet>" + "\n") | |
| """ | |
| python funasr/bin/compute_audio_cmvn.py \ | |
| --config-path "/Users/zhifu/funasr1.0/examples/aishell/paraformer/conf" \ | |
| --config-name "train_asr_paraformer_conformer_12e_6d_2048_256.yaml" \ | |
| ++train_data_set_list="/Users/zhifu/funasr1.0/data/list/audio_datasets.jsonl" \ | |
| ++cmvn_file="/Users/zhifu/funasr1.0/data/list/cmvn.json" \ | |
| ++dataset_conf.num_workers=0 | |
| """ | |
| if __name__ == "__main__": | |
| main_hydra() | |