From fc08b62d05723cdc1ce021bb8ba044ca014fb1f7 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 13 三月 2023 18:38:41 +0800
Subject: [PATCH] readme
---
funasr/runtime/python/onnxruntime/rapid_paraformer/utils/frontend.py | 73 ++++++++++++++++++++++++++++++++----
1 files changed, 64 insertions(+), 9 deletions(-)
diff --git a/funasr/runtime/python/onnxruntime/rapid_paraformer/utils/frontend.py b/funasr/runtime/python/onnxruntime/rapid_paraformer/utils/frontend.py
index eb8a7c8..11a8644 100644
--- a/funasr/runtime/python/onnxruntime/rapid_paraformer/utils/frontend.py
+++ b/funasr/runtime/python/onnxruntime/rapid_paraformer/utils/frontend.py
@@ -23,11 +23,10 @@
n_mels: int = 80,
frame_length: int = 25,
frame_shift: int = 10,
- filter_length_min: int = -1,
- filter_length_max: float = -1,
lfr_m: int = 1,
lfr_n: int = 1,
- dither: float = 1.0
+ dither: float = 1.0,
+ **kwargs,
) -> None:
check_argument_types()
@@ -43,27 +42,46 @@
opts.mel_opts.debug_mel = False
self.opts = opts
- self.filter_length_min = filter_length_min
- self.filter_length_max = filter_length_max
self.lfr_m = lfr_m
self.lfr_n = lfr_n
self.cmvn_file = cmvn_file
if self.cmvn_file:
self.cmvn = self.load_cmvn()
+ self.fbank_fn = None
+ self.fbank_beg_idx = 0
+ self.reset_status()
def fbank(self,
waveform: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
waveform = waveform * (1 << 15)
- fbank_fn = knf.OnlineFbank(self.opts)
- fbank_fn.accept_waveform(self.opts.frame_opts.samp_freq, waveform.tolist())
- frames = fbank_fn.num_frames_ready
+ self.fbank_fn = knf.OnlineFbank(self.opts)
+ self.fbank_fn.accept_waveform(self.opts.frame_opts.samp_freq, waveform.tolist())
+ frames = self.fbank_fn.num_frames_ready
mat = np.empty([frames, self.opts.mel_opts.num_bins])
for i in range(frames):
- mat[i, :] = fbank_fn.get_frame(i)
+ mat[i, :] = self.fbank_fn.get_frame(i)
feat = mat.astype(np.float32)
feat_len = np.array(mat.shape[0]).astype(np.int32)
return feat, feat_len
+
+ def fbank_online(self,
+ waveform: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+ waveform = waveform * (1 << 15)
+ # self.fbank_fn = knf.OnlineFbank(self.opts)
+ self.fbank_fn.accept_waveform(self.opts.frame_opts.samp_freq, waveform.tolist())
+ frames = self.fbank_fn.num_frames_ready
+ mat = np.empty([frames, self.opts.mel_opts.num_bins])
+ for i in range(self.fbank_beg_idx, frames):
+ mat[i, :] = self.fbank_fn.get_frame(i)
+ # self.fbank_beg_idx += (frames-self.fbank_beg_idx)
+ feat = mat.astype(np.float32)
+ feat_len = np.array(mat.shape[0]).astype(np.int32)
+ return feat, feat_len
+
+ def reset_status(self):
+ self.fbank_fn = knf.OnlineFbank(self.opts)
+ self.fbank_beg_idx = 0
def lfr_cmvn(self, feat: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
if self.lfr_m != 1 or self.lfr_n != 1:
@@ -134,3 +152,40 @@
vars = np.array(vars_list).astype(np.float64)
cmvn = np.array([means, vars])
return cmvn
+
+def load_bytes(input):
+ middle_data = np.frombuffer(input, dtype=np.int16)
+ middle_data = np.asarray(middle_data)
+ if middle_data.dtype.kind not in 'iu':
+ raise TypeError("'middle_data' must be an array of integers")
+ dtype = np.dtype('float32')
+ if dtype.kind != 'f':
+ raise TypeError("'dtype' must be a floating point type")
+
+ i = np.iinfo(middle_data.dtype)
+ abs_max = 2 ** (i.bits - 1)
+ offset = i.min + abs_max
+ array = np.frombuffer((middle_data.astype(dtype) - offset) / abs_max, dtype=np.float32)
+ return array
+
+
+def test():
+ path = "/nfs/zhifu.gzf/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav"
+ import librosa
+ cmvn_file = "/nfs/zhifu.gzf/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/am.mvn"
+ config_file = "/nfs/zhifu.gzf/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/config.yaml"
+ from funasr.runtime.python.onnxruntime.rapid_paraformer.utils.utils import read_yaml
+ config = read_yaml(config_file)
+ waveform, _ = librosa.load(path, sr=None)
+ frontend = WavFrontend(
+ cmvn_file=cmvn_file,
+ **config['frontend_conf'],
+ )
+ speech, _ = frontend.fbank_online(waveform) #1d, (sample,), numpy
+ feat, feat_len = frontend.lfr_cmvn(speech) # 2d, (frame, 450), np.float32 -> torch, torch.from_numpy(), dtype, (1, frame, 450)
+
+ frontend.reset_status() # clear cache
+ return feat, feat_len
+
+if __name__ == '__main__':
+ test()
\ No newline at end of file
--
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