From 9be8a443d74d68f179de88fff13b4e8424579d7b Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 10 三月 2023 18:24:39 +0800
Subject: [PATCH] Merge pull request #207 from alibaba-damo-academy/dev_dzh
---
funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py | 122 +++++++++++++++++-----------------------
1 files changed, 51 insertions(+), 71 deletions(-)
diff --git a/funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py b/funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py
index d77bcf7..850f007 100644
--- a/funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py
+++ b/funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py
@@ -1,6 +1,7 @@
# -*- encoding: utf-8 -*-
# @Author: SWHL
# @Contact: liekkaskono@163.com
+from cgitb import text
import os.path
from pathlib import Path
from typing import List, Union, Tuple
@@ -14,7 +15,7 @@
read_yaml)
from .utils.postprocess_utils import sentence_postprocess
from .utils.frontend import WavFrontend
-from funasr.utils.timestamp_tools import time_stamp_lfr6_pl
+from .utils.timestamp_utils import time_stamp_lfr6_onnx
logging = get_logger()
@@ -23,6 +24,8 @@
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():
@@ -41,28 +44,65 @@
)
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:
- am_scores, valid_token_lens = self.infer(feats, feats_len)
+ 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)
-
- asr_res.extend(preds)
+ 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:
@@ -108,8 +148,8 @@
def infer(self, feats: np.ndarray,
feats_len: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
- am_scores, token_nums = self.ort_infer([feats, feats_len])
- return am_scores, token_nums
+ 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)
@@ -136,67 +176,7 @@
# Change integer-ids to tokens
token = self.converter.ids2tokens(token_int)
- # token = token[:valid_token_num-1]
+ token = token[:valid_token_num-self.pred_bias]
texts = sentence_postprocess(token)
- text = texts[0]
- # text = self.tokenizer.tokens2text(token)
- return text
+ return texts
-
-class BiCifParaformer(Paraformer):
- def infer(self, feats: np.ndarray,
- feats_len: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
- am_scores, token_nums, us_alphas, us_cif_peak = self.ort_infer([feats, feats_len])
- return am_scores, token_nums, us_alphas, us_cif_peak
- 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):
- res = {}
- end_idx = min(waveform_nums, beg_idx + self.batch_size)
- feats, feats_len = self.extract_feat(waveform_list[beg_idx:end_idx])
- am_scores, valid_token_lens, us_alphas, us_cif_peak = self.infer(feats, feats_len)
-
- try:
- am_scores, valid_token_lens, us_alphas, us_cif_peak = self.infer(feats, feats_len)
- except ONNXRuntimeError:
- #logging.warning(traceback.format_exc())
- logging.warning("input wav is silence or noise")
- preds = ['']
- else:
- token = self.decode(am_scores, valid_token_lens)
- timestamp = time_stamp_lfr6_pl(us_alphas, us_cif_peak, copy.copy(token[0]), log=False)
- texts = sentence_postprocess(token[0], timestamp)
- # texts = sentence_postprocess(token[0])
- text = texts[0]
- res['text'] = text
- res['timestamp'] = timestamp
- asr_res.append(res)
-
- return asr_res
-
- 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-1]
- return token
\ No newline at end of file
--
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