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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
@hydra.main(config_name=None, version_base=None)
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()
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