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/utils/timestamp_utils.py | 58 +++++++++++++++++++++++++++++
funasr/runtime/python/onnxruntime/demo.py | 3 +
funasr/utils/timestamp_tools.py | 1
funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py | 13 ++++--
4 files changed, 70 insertions(+), 5 deletions(-)
diff --git a/funasr/runtime/python/onnxruntime/demo.py b/funasr/runtime/python/onnxruntime/demo.py
index 48ad6ed..5209f31 100644
--- a/funasr/runtime/python/onnxruntime/demo.py
+++ b/funasr/runtime/python/onnxruntime/demo.py
@@ -2,7 +2,8 @@
from rapid_paraformer import Paraformer
model_dir = "/Users/shixian/code/funasr2/export/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
-model_dir = "/Users/shixian/code/funasr2/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
+# model_dir = "/Users/shixian/code/funasr2/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
+
model = Paraformer(model_dir, batch_size=1)
wav_path = ['/Users/shixian/code/funasr2/export/damo/speech_paraformer-tiny-commandword_asr_nat-zh-cn-16k-vocab544-pytorch/example/asr_example.wav']
diff --git a/funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py b/funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py
index 8af3474..9b8a67b 100644
--- a/funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py
+++ b/funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py
@@ -14,7 +14,7 @@
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()
@@ -41,17 +41,16 @@
)
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:
outputs = self.infer(feats, feats_len)
am_scores, valid_token_lens = outputs[0], outputs[1]
@@ -68,11 +67,17 @@
preds, raw_token = self.decode(am_scores, valid_token_lens)[0]
res['preds'] = preds
if us_cif_peak is not None:
- timestamp = time_stamp_lfr6_pl(us_alphas, us_cif_peak, copy.copy(raw_token), log=False)
+ 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:
def load_wav(path: str) -> np.ndarray:
diff --git a/funasr/runtime/python/onnxruntime/rapid_paraformer/utils/timestamp_utils.py b/funasr/runtime/python/onnxruntime/rapid_paraformer/utils/timestamp_utils.py
new file mode 100644
index 0000000..767e864
--- /dev/null
+++ b/funasr/runtime/python/onnxruntime/rapid_paraformer/utils/timestamp_utils.py
@@ -0,0 +1,58 @@
+import numpy as np
+
+
+def time_stamp_lfr6_onnx(us_cif_peak, char_list, begin_time=0.0):
+ if not len(char_list):
+ return []
+ START_END_THRESHOLD = 5
+ MAX_TOKEN_DURATION = 14
+ TIME_RATE = 10.0 * 6 / 1000 / 3 # 3 times upsampled
+ cif_peak = us_cif_peak.reshape(-1)
+ num_frames = cif_peak.shape[-1]
+ if char_list[-1] == '</s>':
+ char_list = char_list[:-1]
+ # char_list = [i for i in text]
+ timestamp_list = []
+ new_char_list = []
+ # for bicif model trained with large data, cif2 actually fires when a character starts
+ # so treat the frames between two peaks as the duration of the former token
+ fire_place = np.where(cif_peak>1.0-1e-4)[0] - 1.5 # np format
+ num_peak = len(fire_place)
+ assert num_peak == len(char_list) + 1 # number of peaks is supposed to be number of tokens + 1
+ # begin silence
+ if fire_place[0] > START_END_THRESHOLD:
+ # char_list.insert(0, '<sil>')
+ timestamp_list.append([0.0, fire_place[0]*TIME_RATE])
+ new_char_list.append('<sil>')
+ # tokens timestamp
+ for i in range(len(fire_place)-1):
+ new_char_list.append(char_list[i])
+ if MAX_TOKEN_DURATION < 0 or fire_place[i+1] - fire_place[i] < MAX_TOKEN_DURATION:
+ timestamp_list.append([fire_place[i]*TIME_RATE, fire_place[i+1]*TIME_RATE])
+ else:
+ # cut the duration to token and sil of the 0-weight frames last long
+ _split = fire_place[i] + MAX_TOKEN_DURATION
+ timestamp_list.append([fire_place[i]*TIME_RATE, _split*TIME_RATE])
+ timestamp_list.append([_split*TIME_RATE, fire_place[i+1]*TIME_RATE])
+ new_char_list.append('<sil>')
+ # tail token and end silence
+ if num_frames - fire_place[-1] > START_END_THRESHOLD:
+ _end = (num_frames + fire_place[-1]) / 2
+ timestamp_list[-1][1] = _end*TIME_RATE
+ timestamp_list.append([_end*TIME_RATE, num_frames*TIME_RATE])
+ new_char_list.append("<sil>")
+ else:
+ timestamp_list[-1][1] = num_frames*TIME_RATE
+ if begin_time: # add offset time in model with vad
+ for i in range(len(timestamp_list)):
+ timestamp_list[i][0] = timestamp_list[i][0] + begin_time / 1000.0
+ timestamp_list[i][1] = timestamp_list[i][1] + begin_time / 1000.0
+ assert len(new_char_list) == len(timestamp_list)
+ res_total = []
+ for char, timestamp in zip(new_char_list, timestamp_list):
+ res_total.append([char, timestamp[0], timestamp[1]]) # += "{} {} {};".format(char, timestamp[0], timestamp[1])
+ res = []
+ for char, timestamp in zip(new_char_list, timestamp_list):
+ if char != '<sil>':
+ res.append([int(timestamp[0] * 1000), int(timestamp[1] * 1000)])
+ return res, res_total
\ No newline at end of file
diff --git a/funasr/utils/timestamp_tools.py b/funasr/utils/timestamp_tools.py
index b82c74a..4a367f8 100644
--- a/funasr/utils/timestamp_tools.py
+++ b/funasr/utils/timestamp_tools.py
@@ -55,6 +55,7 @@
res.append([int(timestamp[0] * 1000), int(timestamp[1] * 1000)])
return res
+
def time_stamp_sentence(punc_id_list, time_stamp_postprocessed, text_postprocessed):
res = []
if text_postprocessed is None:
--
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