From 94cb66dbb9ae12e044a41fb8a3d84e1835ee7e7b Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 02 三月 2023 20:20:10 +0800
Subject: [PATCH] Merge pull request #177 from alibaba-damo-academy/dev_timestamp
---
funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py | 56 +++++++++++++++++++++++++++++++++++---------------------
1 files changed, 35 insertions(+), 21 deletions(-)
diff --git a/funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py b/funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py
index e0c622c..9b8a67b 100644
--- a/funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py
+++ b/funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py
@@ -2,28 +2,29 @@
# @Author: SWHL
# @Contact: liekkaskono@163.com
import os.path
-import traceback
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.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,
+ def __init__(self, model_dir: Union[str, Path] = None,
batch_size: int = 1,
- device_id: Union[str, int]="-1",
+ device_id: Union[str, int] = "-1",
):
-
+
if not Path(model_dir).exists():
raise FileNotFoundError(f'{model_dir} does not exist.')
@@ -40,28 +41,42 @@
)
self.ort_infer = OrtInferSession(model_file, device_id)
self.batch_size = batch_size
+ self.plot = True
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])
-
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)
+ preds, raw_token = self.decode(am_scores, valid_token_lens)[0]
+ res['preds'] = preds
+ if us_cif_peak is not None:
+ timestamp, timestamp_total = time_stamp_lfr6_onnx(us_cif_peak, copy.copy(raw_token))
+ res['timestamp'] = timestamp
+ if self.plot:
+ self.plot_wave_timestamp(waveform_list[0], timestamp_total)
+ asr_res.append(res)
return asr_res
+
+ def plot_wave_timestamp(self, wav, text_timestamp):
+ # TODO: Plot the wav and timestamp results with matplotlib
+ import pdb; pdb.set_trace()
def load_data(self,
wav_content: Union[str, np.ndarray, List[str]], fs: int = None) -> List:
@@ -107,8 +122,8 @@
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)
@@ -135,10 +150,9 @@
# Change integer-ids to tokens
token = self.converter.ids2tokens(token_int)
- token = token[:valid_token_num-1]
+ # token = token[:valid_token_num-1]
texts = sentence_postprocess(token)
text = texts[0]
# text = self.tokenizer.tokens2text(token)
- return text
-
+ return text, token
--
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