# -*- encoding: utf-8 -*-
|
# @Author: SWHL
|
# @Contact: liekkaskono@163.com
|
import os.path
|
from pathlib import Path
|
from typing import List, Union, Tuple
|
|
import copy
|
import librosa
|
import numpy as np
|
|
from .utils.utils import (CharTokenizer, Hypothesis, ONNXRuntimeError,
|
OrtInferSession, TokenIDConverter, get_logger,
|
read_yaml)
|
from .utils.postprocess_utils import sentence_postprocess
|
from .utils.frontend import WavFrontend
|
from .utils.timestamp_utils import time_stamp_lfr6_onnx
|
|
logging = get_logger()
|
|
|
class Paraformer():
|
def __init__(self, model_dir: Union[str, Path] = None,
|
batch_size: int = 1,
|
device_id: Union[str, int] = "-1",
|
plot_timestamp_to: str = "",
|
pred_bias: int = 1,
|
):
|
|
if not Path(model_dir).exists():
|
raise FileNotFoundError(f'{model_dir} does not exist.')
|
|
model_file = os.path.join(model_dir, 'model.onnx')
|
config_file = os.path.join(model_dir, 'config.yaml')
|
cmvn_file = os.path.join(model_dir, 'am.mvn')
|
config = read_yaml(config_file)
|
|
self.converter = TokenIDConverter(config['token_list'])
|
self.tokenizer = CharTokenizer()
|
self.frontend = WavFrontend(
|
cmvn_file=cmvn_file,
|
**config['frontend_conf']
|
)
|
self.ort_infer = OrtInferSession(model_file, device_id)
|
self.batch_size = batch_size
|
self.plot_timestamp_to = plot_timestamp_to
|
self.pred_bias = pred_bias
|
|
def __call__(self, wav_content: Union[str, np.ndarray, List[str]], **kwargs) -> List:
|
waveform_list = self.load_data(wav_content, self.frontend.opts.frame_opts.samp_freq)
|
waveform_nums = len(waveform_list)
|
asr_res = []
|
for beg_idx in range(0, waveform_nums, self.batch_size):
|
|
end_idx = min(waveform_nums, beg_idx + self.batch_size)
|
feats, feats_len = self.extract_feat(waveform_list[beg_idx:end_idx])
|
try:
|
outputs = self.infer(feats, feats_len)
|
am_scores, valid_token_lens = outputs[0], outputs[1]
|
if len(outputs) == 4:
|
# for BiCifParaformer Inference
|
us_alphas, us_cif_peak = outputs[2], outputs[3]
|
else:
|
us_alphas, us_cif_peak = None, None
|
except ONNXRuntimeError:
|
#logging.warning(traceback.format_exc())
|
logging.warning("input wav is silence or noise")
|
preds = ['']
|
else:
|
preds = self.decode(am_scores, valid_token_lens)
|
if us_cif_peak is None:
|
for pred in preds:
|
asr_res.append({'preds': pred})
|
else:
|
for pred, us_cif_peak_ in zip(preds, us_cif_peak):
|
text, tokens = pred
|
timestamp, timestamp_total = time_stamp_lfr6_onnx(us_cif_peak_, copy.copy(tokens))
|
if len(self.plot_timestamp_to):
|
self.plot_wave_timestamp(waveform_list[0], timestamp_total, self.plot_timestamp_to)
|
asr_res.append({'preds': text, 'timestamp': timestamp})
|
return asr_res
|
|
def plot_wave_timestamp(self, wav, text_timestamp, dest):
|
# TODO: Plot the wav and timestamp results with matplotlib
|
import matplotlib
|
matplotlib.use('Agg')
|
matplotlib.rc("font", family='Alibaba PuHuiTi') # set it to a font that your system supports
|
import matplotlib.pyplot as plt
|
fig, ax1 = plt.subplots(figsize=(11, 3.5), dpi=320)
|
ax2 = ax1.twinx()
|
ax2.set_ylim([0, 2.0])
|
# plot waveform
|
ax1.set_ylim([-0.3, 0.3])
|
time = np.arange(wav.shape[0]) / 16000
|
ax1.plot(time, wav/wav.max()*0.3, color='gray', alpha=0.4)
|
# plot lines and text
|
for (char, start, end) in text_timestamp:
|
ax1.vlines(start, -0.3, 0.3, ls='--')
|
ax1.vlines(end, -0.3, 0.3, ls='--')
|
x_adj = 0.045 if char != '<sil>' else 0.12
|
ax1.text((start + end) * 0.5 - x_adj, 0, char)
|
# plt.legend()
|
plotname = "{}/timestamp.png".format(dest)
|
plt.savefig(plotname, bbox_inches='tight')
|
|
def load_data(self,
|
wav_content: Union[str, np.ndarray, List[str]], fs: int = None) -> List:
|
def load_wav(path: str) -> np.ndarray:
|
waveform, _ = librosa.load(path, sr=fs)
|
return waveform
|
|
if isinstance(wav_content, np.ndarray):
|
return [wav_content]
|
|
if isinstance(wav_content, str):
|
return [load_wav(wav_content)]
|
|
if isinstance(wav_content, list):
|
return [load_wav(path) for path in wav_content]
|
|
raise TypeError(
|
f'The type of {wav_content} is not in [str, np.ndarray, list]')
|
|
def extract_feat(self,
|
waveform_list: List[np.ndarray]
|
) -> Tuple[np.ndarray, np.ndarray]:
|
feats, feats_len = [], []
|
for waveform in waveform_list:
|
speech, _ = self.frontend.fbank(waveform)
|
feat, feat_len = self.frontend.lfr_cmvn(speech)
|
feats.append(feat)
|
feats_len.append(feat_len)
|
|
feats = self.pad_feats(feats, np.max(feats_len))
|
feats_len = np.array(feats_len).astype(np.int32)
|
return feats, feats_len
|
|
@staticmethod
|
def pad_feats(feats: List[np.ndarray], max_feat_len: int) -> np.ndarray:
|
def pad_feat(feat: np.ndarray, cur_len: int) -> np.ndarray:
|
pad_width = ((0, max_feat_len - cur_len), (0, 0))
|
return np.pad(feat, pad_width, 'constant', constant_values=0)
|
|
feat_res = [pad_feat(feat, feat.shape[0]) for feat in feats]
|
feats = np.array(feat_res).astype(np.float32)
|
return feats
|
|
def infer(self, feats: np.ndarray,
|
feats_len: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
|
outputs = self.ort_infer([feats, feats_len])
|
return outputs
|
|
def decode(self, am_scores: np.ndarray, token_nums: int) -> List[str]:
|
return [self.decode_one(am_score, token_num)
|
for am_score, token_num in zip(am_scores, token_nums)]
|
|
def decode_one(self,
|
am_score: np.ndarray,
|
valid_token_num: int) -> List[str]:
|
yseq = am_score.argmax(axis=-1)
|
score = am_score.max(axis=-1)
|
score = np.sum(score, axis=-1)
|
|
# pad with mask tokens to ensure compatibility with sos/eos tokens
|
# asr_model.sos:1 asr_model.eos:2
|
yseq = np.array([1] + yseq.tolist() + [2])
|
hyp = Hypothesis(yseq=yseq, score=score)
|
|
# remove sos/eos and get results
|
last_pos = -1
|
token_int = hyp.yseq[1:last_pos].tolist()
|
|
# remove blank symbol id, which is assumed to be 0
|
token_int = list(filter(lambda x: x not in (0, 2), token_int))
|
|
# Change integer-ids to tokens
|
token = self.converter.ids2tokens(token_int)
|
token = token[:valid_token_num-self.pred_bias]
|
texts = sentence_postprocess(token)
|
return texts
|
