| | |
|---|
| | | |
|---|
| | | if self.cmvn_file: |
|---|
| | | self.cmvn = self.load_cmvn() |
|---|
| | | self.fbank_fn = None |
|---|
| | | self.fbank_beg_idx = 0 |
|---|
| | | |
|---|
| | | 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: |
|---|
| | |
|---|
| | | 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 |
|---|
