Init commit

.gitignore

+*.onnx
+
+*.pth
+
+# Created by .ignore support plugin (hsz.mobi)
+### Python template
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+.pytest_cache
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+# *.manifest
+# *.spec
+*.res
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
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+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
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+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
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+# Rope project settings
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+
+# mkdocs documentation
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+
+# mypy
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+.dmypy.json
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+
+# Pyre type checker
+.pyre/
+
+#idea
+.vs
+.vscode
+.idea
+
+#models
+
+*.ttf
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+
+
+*.bin
+*.mapping
+*.xml
+
+*.pdiparams
+*.pdiparams.info
+*.pdmodel
+
+.DS_Store
\ No newline at end of file

README.md

+## Rapid paraformer
+- 模型出自阿里达摩院[Paraformer语音识别-中文-通用-16k-离线-large-pytorch](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary)
+- 本分支对模型做了转换，仅采用ONNXRuntime推理引擎
+
+
+#### 使用步骤
+1. 安装环境
+   ```bash
+    pip install -r requirements.txt
+   ```
+2. 下载模型
+   - 由于模型太大，上传到仓库不用下载，提供百度云下载连接：[asr_paraformer.onnx](https://pan.baidu.com/s/1-nEf2eUpkzlcRqiYEwub2A?pwd=dcr3)
+   - 模型下载之后，放在`rapid_paraformer/models`目录下即可，最终目录结构如下：
+        ```text
+        rapid_paraformer
+        ├── config.yaml
+        ├── __init__.py
+        ├── kaldifeat
+        │   ├── feature.py
+        │   ├── __init__.py
+        │   ├── ivector.py
+        │   ├── LICENSE
+        │   └── README.md
+        ├── models
+        │   ├── am.mvn
+        │   ├── asr_paraformer.onnx  # 放在这里
+        │   └── token_list.pkl
+        ├── rapid_paraformer.py
+        └── utils.py
+        ```
+
+3. 运行demo
+   ```bash
+   python demo.py
+   ```
+4. 查看结果
+   ```text
+   [['呃说不配合就不配合的好以上的话呢我们摘取八九十三条因为这三条的话呢比较典型啊一些数字比较明确尤其是时间那么我们要投资者就是了解这一点啊不要轻信这个市场可以快速回来啊这些配市公司啊后期又利好了可
+   以快速快速攻能包括像前一段时间啊有些媒体在二三月份的时候']]
+   ```
\ No newline at end of file

demo.py

+# -*- encoding: utf-8 -*-
+# @Author: SWHL
+# @Contact: liekkaskono@163.com
+from rapid_paraformer import RapidParaformer
+
+
+paraformer = RapidParaformer()
+
+wav_file = 'test_wavs/0478_00017.wav'
+result = paraformer(wav_file)
+print(result)

rapid_paraformer/__init__.py

+# -*- encoding: utf-8 -*-
+# @Author: SWHL
+# @Contact: liekkaskono@163.com
+from .rapid_paraformer import RapidParaformer

rapid_paraformer/config.yaml

+TokenIDConverter:
+  token_path: models/token_list.pkl
+  unk_symbol: <unk>
+
+CharTokenizer:
+  symbol_value:
+  space_symbol: <space>
+  remove_non_linguistic_symbols: false
+
+WavFrontend:
+  cmvn_file: models/am.mvn
+  frontend_conf:
+    fs: 16000
+    window: hamming
+    n_mels: 80
+    frame_length: 25
+    frame_shift: 10
+    lfr_m: 7
+    lfr_n: 6
+    filter_length_max: -.inf
+
+Model:
+  model_path: models/asr_paraformer.onnx
+  use_cuda: false
+  CUDAExecutionProvider:
+      device_id: 0
+      arena_extend_strategy: kNextPowerOfTwo
+      cudnn_conv_algo_search: EXHAUSTIVE
+      do_copy_in_default_stream: true
\ No newline at end of file

rapid_paraformer/kaldifeat/LICENSE

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rapid_paraformer/kaldifeat/README.md

+# KaldiFeat
+
+KaldiFeat is a light-weight Python library for computing Kaldi-style acoustic features based on NumPy. It might be helpful if you want to:
+
+- Test a pre-trained model on new data without writing shell commands and creating a bunch of files.
+- Run a pre-trained model in a new environment without installing Kaldi.
+
+## Example
+
+The following codes calculate MFCCs with the same settings in `kaldi/egs/voxceleb/v2`
+
+```
+import librosa
+
+from kaldifeat import compute_mfcc_feats, compute_vad, apply_cmvn_sliding
+
+# Assume we have a wav file called example.wav whose sample rate is 16000 Hz
+data, _ = librosa.load('example.wav', 16000)
+
+# We adopt 16 bits data, thus we need to transform dtype from float to int16 for librosa
+data = (data * 32768).astype(np.int16)
+
+raw_mfcc = compute_mfcc_feats(data, sample_frequency=16000, frame_length=25, frame_shift=10, low_freq=20, high_freq=-400, num_mel_bins=30, num_ceps=30, snip_edges=False)
+log_energy = raw_mfcc[:, 0]
+vad = compute_vad(log_energy, energy_threshold=5.5, energy_mean_scale=0.5, frames_context=2, proportion_threshold=0.12)
+mfcc = apply_cmvn_sliding(raw_mfcc, window=300, center=True)[vad]
+```
+
+## Supported Functions
+
+### compute_fbank_feats
+
+Compute (log) Mel filter bank energies (FBanks) in the same way as `kaldi/src/featbin/compute_fbank_feats`
+
+| Parameters | Description |
+| :--------- | :---------- |
+|blackman_coeff| Constant coefficient for generalized Blackman window. (float, default = 0.42)|
+|dither| Dithering constant (0.0 means no dither). If you turn this off, you should set the --energy-floor option, e.g. to 1.0 or 0.1 (float, default = 1)|
+|energy_floor| Floor on energy (absolute, not relative) in FBANK computation. Only makes a difference if --use-energy=true; only necessary if --dither=0.0.  Suggested values: 0.1 or 1.0 (float, default = 0)|
+|frame_length| Frame length in milliseconds (float, default = 25)|
+|frame_shift| Frame shift in milliseconds (float, default = 10)|
+|high_freq| High cutoff frequency for mel bins (if <= 0, offset from Nyquist) (float, default = 0)|
+|low_freq| Low cutoff frequency for mel bins (float, default = 20)|
+|num_mel_bins| Number of triangular mel-frequency bins (int, default = 23)|
+|preemphasis_coefficient| Coefficient for use in signal preemphasis (float, default = 0.97)|
+|raw_energy| If true, compute energy before preemphasis and windowing (bool, default = true)|
+|remove_dc_offset| Subtract mean from waveform on each frame (bool, default = true)|
+|round_to_power_of_two| If true, round window size to power of two by zero-padding input to FFT. (bool, default = true)|
+|sample_frequency| Waveform data sample frequency (must match the waveform file, if specified there) (float, default = 16000)|
+|snip_edges| If true, end effects will be handled by outputting only frames that completely fit in the file, and the number of frames depends on the frame-length.  If false, the number of frames depends only on the frame-shift, and we reflect the data at the ends. (bool, default = true)|
+|use_energy| Add an extra energy output. (bool, default = false)|
+|use_log_fbank| If true, produce log-filterbank, else produce linear. (bool, default = true)|
+|use_power| If true, use power, else use magnitude. (bool, default = true)|
+|window_type| Type of window ("hamming"\|"hanning"\|"povey"\|"rectangular"\|"sine"\|"blackmann") (string, default = "povey")|
+|dtype| Type of array (np.float32\|np.float64) (dtype or string, default=np.float32)|
+
+### compute_mfcc_feats
+
+Compute Mel-frequency cepstral coefficients (MFCCs) in the same way as `kaldi/src/featbin/compute_mfcc_feats`
+
+| Parameters | Description |
+| :--------- | :---------- |
+|blackman_coeff| Constant coefficient for generalized Blackman window. (float, default = 0.42)|
+|cepstral_lifter| Constant that controls scaling of MFCCs (float, default = 22)|
+|dither| Dithering constant (0.0 means no dither). If you turn this off, you should set the --energy-floor option, e.g. to 1.0 or 0.1 (float, default = 1)|
+|energy_floor| Floor on energy (absolute, not relative) in MFCC computation. Only makes a difference if --use-energy=true; only necessary if --dither=0.0.  Suggested values: 0.1 or 1.0 (float, default = 0)|
+|frame_length| Frame length in milliseconds (float, default = 25)|
+|frame_shift| Frame shift in milliseconds (float, default = 10)|
+|high_freq| High cutoff frequency for mel bins (if <= 0, offset from Nyquist) (float, default = 0)|
+|low_freq| Low cutoff frequency for mel bins (float, default = 20)|
+|num_ceps| Number of cepstra in MFCC computation (including C0) (int, default = 13)|
+|num_mel_bins| Number of triangular mel-frequency bins (int, default = 23)|
+|preemphasis_coefficient| Coefficient for use in signal preemphasis (float, default = 0.97)|
+|raw_energy| If true, compute energy before preemphasis and windowing (bool, default = true)|
+|remove_dc_offset| Subtract mean from waveform on each frame (bool, default = true)|
+|round_to_power_of_two| If true, round window size to power of two by zero-padding input to FFT. (bool, default = true)|
+|sample_frequency| Waveform data sample frequency (must match the waveform file, if specified there) (float, default = 16000)|
+|snip_edges| If true, end effects will be handled by outputting only frames that completely fit in the file, and the number of frames depends on the frame-length.  If false, the number of frames depends only on the frame-shift, and we reflect the data at the ends. (bool, default = true)|
+|use_energy| Use energy (not C0) in MFCC computation (bool, default = true)|
+|window_type| Type of window ("hamming"\|"hanning"\|"povey"\|"rectangular"\|"sine"\|"blackmann") (string, default = "povey")|
+|dtype| Type of array (np.float32\|np.float64) (dtype or string, default=np.float32)|
+
+### apply_cmvn_sliding
+
+Apply sliding-window cepstral mean (and optionally variance) normalization in the same way as `kaldi/src/featbin/apply_cmvn_sliding`
+
+| Parameters | Description |
+| :--------- | :---------- |
+|center| If true, use a window centered on the current frame (to the extent possible, modulo end effects). If false, window is to the left. (bool, default = false)|
+|window| Window in frames for running average CMN computation (int, default = 600)|
+|min_window| Minimum CMN window used at start of decoding (adds latency only at start). Only applicable if center == false, ignored if center==true (int, default = 100)|
+|norm_vars| If true, normalize variance to one. (bool, default = false)|
+
+### compute_vad
+
+Apply energy-based voice activity detection in the same way as `kaldi/src/ivectorbin/compute_vad`
+
+| Parameters | Description |
+| :--------- | :---------- |
+|energy_mean_scale| If this is set to s, to get the actual threshold we let m be the mean log-energy of the file, and use s\*m + vad-energy-threshold (float, default = 0.5)|
+|energy_threshold| Constant term in energy threshold for VAD (also see energy_mean_scale) (float, default = 5)|
+|frames_context| Number of frames of context on each side of central frame, in window for which energy is monitored (int, default = 0)|
+|proportion_threshold| Parameter controlling the proportion of frames within the window that need to have more energy than the threshold (float, default = 0.6)|
+
+### Related Projects
+
+- [python_speech_features](https://github.com/jameslyons/python_speech_features)
+- [python_kaldi_features](https://github.com/ZitengWang/python_kaldi_features)

rapid_paraformer/kaldifeat/__init__.py

+# -*- encoding: utf-8 -*-
+from .feature import compute_fbank_feats, compute_mfcc_feats, apply_cmvn_sliding
+from .ivector import compute_vad

rapid_paraformer/kaldifeat/feature.py

+import numpy as np
+from scipy.fftpack import dct
+
+
+# ---------- feature-window ----------
+
+def sliding_window(x, window_size, window_shift):
+    shape = x.shape[:-1] + (x.shape[-1] - window_size + 1, window_size)
+    strides = x.strides + (x.strides[-1],)
+    return np.lib.stride_tricks.as_strided(x, shape=shape, strides=strides)[::window_shift]
+
+
+def func_num_frames(num_samples, window_size, window_shift, snip_edges):
+    if snip_edges:
+        if num_samples < window_size:
+            return 0
+        else:
+            return 1 + ((num_samples - window_size) // window_shift)
+    else:
+        return (num_samples + (window_shift // 2)) // window_shift
+
+
+def func_dither(waveform, dither_value):
+    if dither_value == 0.0:
+        return waveform
+    waveform += np.random.normal(size=waveform.shape).astype(waveform.dtype) * dither_value
+    return waveform
+
+
+def func_remove_dc_offset(waveform):
+    return waveform - np.mean(waveform)
+
+
+def func_log_energy(waveform):
+    return np.log(np.dot(waveform, waveform).clip(min=np.finfo(waveform.dtype).eps))
+
+
+def func_preemphasis(waveform, preemph_coeff):
+    if preemph_coeff == 0.0:
+        return waveform
+    assert 0 < preemph_coeff <= 1
+    waveform[1:] -= preemph_coeff * waveform[:-1]
+    waveform[0] -= preemph_coeff * waveform[0]
+    return waveform
+
+
+def sine(M):
+    if M < 1:
+        return np.array([])
+    if M == 1:
+        return np.ones(1, float)
+    n = np.arange(0, M)
+    return np.sin(np.pi*n/(M-1))
+
+
+def povey(M):
+    if M < 1:
+        return np.array([])
+    if M == 1:
+        return np.ones(1, float)
+    n = np.arange(0, M)
+    return (0.5 - 0.5*np.cos(2.0*np.pi*n/(M-1)))**0.85
+
+
+def feature_window_function(window_type, window_size, blackman_coeff):
+    assert window_size > 0
+    if window_type == 'hanning':
+        return np.hanning(window_size)
+    elif window_type == 'sine':
+        return sine(window_size)
+    elif window_type == 'hamming':
+        return np.hamming(window_size)
+    elif window_type == 'povey':
+        return povey(window_size)
+    elif window_type == 'rectangular':
+        return np.ones(window_size)
+    elif window_type == 'blackman':
+        window_func = np.blackman(window_size)
+        if blackman_coeff == 0.42:
+            return window_func
+        else:
+            return window_func - 0.42 + blackman_coeff
+    else:
+        raise ValueError('Invalid window type {}'.format(window_type))
+
+
+def process_window(window, dither, remove_dc_offset, preemphasis_coefficient, window_function, raw_energy):
+    if dither != 0.0:
+        window = func_dither(window, dither)
+    if remove_dc_offset:
+        window = func_remove_dc_offset(window)
+    if raw_energy:
+        log_energy = func_log_energy(window)
+    if preemphasis_coefficient != 0.0:
+        window = func_preemphasis(window, preemphasis_coefficient)
+    window *= window_function
+    if not raw_energy:
+        log_energy = func_log_energy(window)
+    return window, log_energy
+
+
+def extract_window(waveform, blackman_coeff, dither, window_size, window_shift,
+                   preemphasis_coefficient, raw_energy, remove_dc_offset,
+                   snip_edges, window_type, dtype):
+    num_samples = len(waveform)
+    num_frames = func_num_frames(num_samples, window_size, window_shift, snip_edges)
+    num_samples_ = (num_frames - 1) * window_shift + window_size
+    if snip_edges:
+        waveform = waveform[:num_samples_]
+    else:
+        offset = window_shift // 2 - window_size // 2
+        waveform = np.concatenate([
+            waveform[-offset - 1::-1],
+            waveform,
+            waveform[:-(offset + num_samples_ - num_samples + 1):-1]
+        ])
+    frames = sliding_window(waveform, window_size=window_size, window_shift=window_shift)
+    frames = frames.astype(dtype)
+    log_enery = np.empty(frames.shape[0], dtype=dtype)
+    for i in range(frames.shape[0]):
+        frames[i], log_enery[i] = process_window(
+            window=frames[i],
+            dither=dither,
+            remove_dc_offset=remove_dc_offset,
+            preemphasis_coefficient=preemphasis_coefficient,
+            window_function=feature_window_function(
+                window_type=window_type,
+                window_size=window_size,
+                blackman_coeff=blackman_coeff
+            ).astype(dtype),
+            raw_energy=raw_energy
+        )
+    return frames, log_enery
+
+# ---------- feature-window ----------
+
+
+# ---------- feature-functions ----------
+
+def compute_spectrum(frames, n):
+    complex_spec = np.fft.rfft(frames, n)
+    return np.absolute(complex_spec)
+
+
+def compute_power_spectrum(frames, n):
+    return np.square(compute_spectrum(frames, n))
+
+
+def apply_cmvn_sliding_internal(feat, center=False, window=600, min_window=100, norm_vars=False):
+    num_frames, feat_dim = feat.shape
+    std = 1
+    if center:
+        if num_frames <= window:
+            mean = feat.mean(axis=0, keepdims=True).repeat(num_frames, axis=0)
+            if norm_vars:
+                std = feat.std(axis=0, keepdims=True).repeat(num_frames, axis=0)
+        else:
+            feat1 = feat[:window]
+            feat2 = sliding_window(feat.T, window, 1)
+            feat3 = feat[-window:]
+            mean1 = feat1.mean(axis=0, keepdims=True).repeat(window // 2, axis=0)
+            mean2 = feat2.mean(axis=2).T
+            mean3 = feat3.mean(axis=0, keepdims=True).repeat((window - 1) // 2, axis=0)
+            mean = np.concatenate([mean1, mean2, mean3])
+            if norm_vars:
+                std1 = feat1.std(axis=0, keepdims=True).repeat(window // 2, axis=0)
+                std2 = feat2.std(axis=2).T
+                std3 = feat3.mean(axis=0, keepdims=True).repeat((window - 1) // 2, axis=0)
+                std = np.concatenate([std1, std2, std3])
+    else:
+        if num_frames <= min_window:
+            mean = feat.mean(axis=0, keepdims=True).repeat(num_frames, axis=0)
+            if norm_vars:
+                std = feat.std(axis=0, keepdims=True).repeat(num_frames, axis=0)
+        else:
+            feat1 = feat[:min_window]
+            mean1 = feat1.mean(axis=0, keepdims=True).repeat(min_window, axis=0)
+            feat2_cumsum = np.cumsum(feat[:window], axis=0)[min_window:]
+            cumcnt = np.arange(min_window + 1, min(window, num_frames) + 1, dtype=feat.dtype)[:, np.newaxis]
+            mean2 = feat2_cumsum / cumcnt
+            mean = np.concatenate([mean1, mean2])
+            if norm_vars:
+                std1 = feat1.std(axis=0, keepdims=True).repeat(min_window, axis=0)
+                feat2_power_cumsum = np.cumsum(np.square(feat[:window]), axis=0)[min_window:]
+                std2 = np.sqrt(feat2_power_cumsum / cumcnt - np.square(mean2))
+                std = np.concatenate([std1, std2])
+            if num_frames > window:
+                feat3 = sliding_window(feat.T, window, 1)
+                mean3 = feat3.mean(axis=2).T
+                mean = np.concatenate([mean, mean3[1:]])
+                if norm_vars:
+                    std3 = feat3.std(axis=2).T
+                    std = np.concatenate([std, std3[1:]])
+    feat = (feat - mean) / std
+    return feat
+
+# ---------- feature-functions ----------
+
+
+# ---------- mel-computations ----------
+
+def inverse_mel_scale(mel_freq):
+    return 700.0 * (np.exp(mel_freq / 1127.0) - 1.0)
+
+
+def mel_scale(freq):
+    return 1127.0 * np.log(1.0 + freq / 700.0)
+
+
+def compute_mel_banks(num_bins, sample_frequency, low_freq, high_freq, n):
+    """ Compute Mel banks.
+
+    :param num_bins: Number of triangular mel-frequency bins
+    :param sample_frequency: Waveform data sample frequency
+    :param low_freq: Low cutoff frequency for mel bins
+    :param high_freq: High cutoff frequency for mel bins (if <= 0, offset from Nyquist)
+    :param n: Window size
+    :return: Mel banks.
+    """
+    assert num_bins >= 3, 'Must have at least 3 mel bins'
+    num_fft_bins = n // 2
+
+    nyquist = 0.5 * sample_frequency
+    if high_freq <= 0:
+        high_freq = nyquist + high_freq
+    assert 0 <= low_freq < high_freq <= nyquist
+
+    fft_bin_width = sample_frequency / n
+
+    mel_low_freq = mel_scale(low_freq)
+    mel_high_freq = mel_scale(high_freq)
+    mel_freq_delta = (mel_high_freq - mel_low_freq) / (num_bins + 1)
+
+    mel_banks = np.zeros([num_bins, num_fft_bins + 1])
+    for i in range(num_bins):
+        left_mel = mel_low_freq + mel_freq_delta * i
+        center_mel = left_mel + mel_freq_delta
+        right_mel = center_mel + mel_freq_delta
+        for j in range(num_fft_bins):
+            mel = mel_scale(fft_bin_width * j)
+            if left_mel < mel < right_mel:
+                if mel <= center_mel:
+                    mel_banks[i, j] = (mel - left_mel) / (center_mel - left_mel)
+                else:
+                    mel_banks[i, j] = (right_mel - mel) / (right_mel - center_mel)
+    return mel_banks
+
+
+def compute_lifter_coeffs(q, M):
+    """ Compute liftering coefficients (scaling on cepstral coeffs)
+        the zeroth index is C0, which is not affected.
+
+    :param q: Number of lifters
+    :param M: Number of coefficients
+    :return: Lifters.
+    """
+    if M < 1:
+        return np.array([])
+    if M == 1:
+        return np.ones(1, float)
+    n = np.arange(0, M)
+    return 1 + 0.5*np.sin(np.pi*n/q)*q
+
+# ---------- mel-computations ----------
+
+
+# ---------- compute-fbank-feats ----------
+
+def compute_fbank_feats(
+        waveform,
+        blackman_coeff=0.42,
+        dither=1.0,
+        energy_floor=0.0,
+        frame_length=25,
+        frame_shift=10,
+        high_freq=0,
+        low_freq=20,
+        num_mel_bins=23,
+        preemphasis_coefficient=0.97,
+        raw_energy=True,
+        remove_dc_offset=True,
+        round_to_power_of_two=True,
+        sample_frequency=16000,
+        snip_edges=True,
+        use_energy=False,
+        use_log_fbank=True,
+        use_power=True,
+        window_type='povey',
+        dtype=np.float32):
+    """ Compute (log) Mel filter bank energies
+
+    :param waveform: Input waveform.
+    :param blackman_coeff: Constant coefficient for generalized Blackman window. (float, default = 0.42)
+    :param dither: Dithering constant (0.0 means no dither). If you turn this off, you should set the --energy-floor option, e.g. to 1.0 or 0.1 (float, default = 1)
+    :param energy_floor: Floor on energy (absolute, not relative) in FBANK computation. Only makes a difference if --use-energy=true; only necessary if --dither=0.0.  Suggested values: 0.1 or 1.0 (float, default = 0)
+    :param frame_length: Frame length in milliseconds (float, default = 25)
+    :param frame_shift: Frame shift in milliseconds (float, default = 10)
+    :param high_freq: High cutoff frequency for mel bins (if <= 0, offset from Nyquist) (float, default = 0)
+    :param low_freq: Low cutoff frequency for mel bins (float, default = 20)
+    :param num_mel_bins: Number of triangular mel-frequency bins (int, default = 23)
+    :param preemphasis_coefficient: Coefficient for use in signal preemphasis (float, default = 0.97)
+    :param raw_energy: If true, compute energy before preemphasis and windowing (bool, default = true)
+    :param remove_dc_offset: Subtract mean from waveform on each frame (bool, default = true)
+    :param round_to_power_of_two: If true, round window size to power of two by zero-padding input to FFT. (bool, default = true)
+    :param sample_frequency: Waveform data sample frequency (must match the waveform file, if specified there) (float, default = 16000)
+    :param snip_edges: If true, end effects will be handled by outputting only frames that completely fit in the file, and the number of frames depends on the frame-length.  If false, the number of frames depends only on the frame-shift, and we reflect the data at the ends. (bool, default = true)
+    :param use_energy: Add an extra energy output. (bool, default = false)
+    :param use_log_fbank: If true, produce log-filterbank, else produce linear. (bool, default = true)
+    :param use_power: If true, use power, else use magnitude. (bool, default = true)
+    :param window_type: Type of window ("hamming"|"hanning"|"povey"|"rectangular"|"sine"|"blackmann") (string, default = "povey")
+    :param dtype: Type of array (np.float32|np.float64) (dtype or string, default=np.float32)
+    :return: (Log) Mel filter bank energies.
+    """
+    window_size = int(frame_length * sample_frequency * 0.001)
+    window_shift = int(frame_shift * sample_frequency * 0.001)
+    frames, log_energy = extract_window(
+        waveform=waveform,
+        blackman_coeff=blackman_coeff,
+        dither=dither,
+        window_size=window_size,
+        window_shift=window_shift,
+        preemphasis_coefficient=preemphasis_coefficient,
+        raw_energy=raw_energy,
+        remove_dc_offset=remove_dc_offset,
+        snip_edges=snip_edges,
+        window_type=window_type,
+        dtype=dtype
+    )
+    if round_to_power_of_two:
+        n = 1
+        while n < window_size:
+            n *= 2
+    else:
+        n = window_size
+    if use_power:
+        spectrum = compute_power_spectrum(frames, n)
+    else:
+        spectrum = compute_spectrum(frames, n)
+    mel_banks = compute_mel_banks(
+        num_bins=num_mel_bins,
+        sample_frequency=sample_frequency,
+        low_freq=low_freq,
+        high_freq=high_freq,
+        n=n
+    ).astype(dtype)
+    feat = np.dot(spectrum, mel_banks.T)
+    if use_log_fbank:
+        feat = np.log(feat.clip(min=np.finfo(dtype).eps))
+    if use_energy:
+        if energy_floor > 0.0:
+            log_energy.clip(min=np.math.log(energy_floor))
+        return feat, log_energy
+    return feat
+
+# ---------- compute-fbank-feats ----------
+
+
+# ---------- compute-mfcc-feats ----------
+
+def compute_mfcc_feats(
+        waveform,
+        blackman_coeff=0.42,
+        cepstral_lifter=22,
+        dither=1.0,
+        energy_floor=0.0,
+        frame_length=25,
+        frame_shift=10,
+        high_freq=0,
+        low_freq=20,
+        num_ceps=13,
+        num_mel_bins=23,
+        preemphasis_coefficient=0.97,
+        raw_energy=True,
+        remove_dc_offset=True,
+        round_to_power_of_two=True,
+        sample_frequency=16000,
+        snip_edges=True,
+        use_energy=True,
+        window_type='povey',
+        dtype=np.float32):
+    """ Compute mel-frequency cepstral coefficients
+
+    :param waveform: Input waveform.
+    :param blackman_coeff: Constant coefficient for generalized Blackman window. (float, default = 0.42)
+    :param cepstral_lifter: Constant that controls scaling of MFCCs (float, default = 22)
+    :param dither: Dithering constant (0.0 means no dither). If you turn this off, you should set the --energy-floor option, e.g. to 1.0 or 0.1 (float, default = 1)
+    :param energy_floor: Floor on energy (absolute, not relative) in MFCC computation. Only makes a difference if --use-energy=true; only necessary if --dither=0.0.  Suggested values: 0.1 or 1.0 (float, default = 0)
+    :param frame_length: Frame length in milliseconds (float, default = 25)
+    :param frame_shift: Frame shift in milliseconds (float, default = 10)
+    :param high_freq: High cutoff frequency for mel bins (if <= 0, offset from Nyquist) (float, default = 0)
+    :param low_freq: Low cutoff frequency for mel bins (float, default = 20)
+    :param num_ceps: Number of cepstra in MFCC computation (including C0) (int, default = 13)
+    :param num_mel_bins: Number of triangular mel-frequency bins (int, default = 23)
+    :param preemphasis_coefficient: Coefficient for use in signal preemphasis (float, default = 0.97)
+    :param raw_energy: If true, compute energy before preemphasis and windowing (bool, default = true)
+    :param remove_dc_offset: Subtract mean from waveform on each frame (bool, default = true)
+    :param round_to_power_of_two: If true, round window size to power of two by zero-padding input to FFT. (bool, default = true)
+    :param sample_frequency: Waveform data sample frequency (must match the waveform file, if specified there) (float, default = 16000)
+    :param snip_edges: If true, end effects will be handled by outputting only frames that completely fit in the file, and the number of frames depends on the frame-length.  If false, the number of frames depends only on the frame-shift, and we reflect the data at the ends. (bool, default = true)
+    :param use_energy: Use energy (not C0) in MFCC computation (bool, default = true)
+    :param window_type: Type of window ("hamming"|"hanning"|"povey"|"rectangular"|"sine"|"blackmann") (string, default = "povey")
+    :param dtype: Type of array (np.float32|np.float64) (dtype or string, default=np.float32)
+    :return: Mel-frequency cespstral coefficients.
+    """
+    feat, log_energy = compute_fbank_feats(
+        waveform=waveform,
+        blackman_coeff=blackman_coeff,
+        dither=dither,
+        energy_floor=energy_floor,
+        frame_length=frame_length,
+        frame_shift=frame_shift,
+        high_freq=high_freq,
+        low_freq=low_freq,
+        num_mel_bins=num_mel_bins,
+        preemphasis_coefficient=preemphasis_coefficient,
+        raw_energy=raw_energy,
+        remove_dc_offset=remove_dc_offset,
+        round_to_power_of_two=round_to_power_of_two,
+        sample_frequency=sample_frequency,
+        snip_edges=snip_edges,
+        use_energy=use_energy,
+        use_log_fbank=True,
+        use_power=True,
+        window_type=window_type,
+        dtype=dtype
+    )
+    feat = dct(feat, type=2, axis=1, norm='ortho')[:, :num_ceps]
+    lifter_coeffs = compute_lifter_coeffs(cepstral_lifter, num_ceps).astype(dtype)
+    feat = feat * lifter_coeffs
+    if use_energy:
+        feat[:, 0] = log_energy
+    return feat
+
+# ---------- compute-mfcc-feats ----------
+
+
+# ---------- apply-cmvn-sliding ----------
+
+def apply_cmvn_sliding(feat, center=False, window=600, min_window=100, norm_vars=False):
+    """ Apply sliding-window cepstral mean (and optionally variance) normalization
+
+    :param feat: Cepstrum.
+    :param center: If true, use a window centered on the current frame (to the extent possible, modulo end effects). If false, window is to the left. (bool, default = false)
+    :param window: Window in frames for running average CMN computation (int, default = 600)
+    :param min_window: Minimum CMN window used at start of decoding (adds latency only at start). Only applicable if center == false, ignored if center==true (int, default = 100)
+    :param norm_vars: If true, normalize variance to one. (bool, default = false)
+    :return: Normalized cepstrum.
+    """
+    # double-precision
+    feat = apply_cmvn_sliding_internal(
+        feat=feat.astype(np.float64),
+        center=center,
+        window=window,
+        min_window=min_window,
+        norm_vars=norm_vars
+    ).astype(feat.dtype)
+    return feat
+
+# ---------- apply-cmvn-sliding ----------

rapid_paraformer/kaldifeat/ivector.py

+import numpy as np
+
+from .feature import sliding_window
+
+
+# ---------- compute-vad ----------
+
+def compute_vad(log_energy, energy_mean_scale=0.5, energy_threshold=0.5, frames_context=0, proportion_threshold=0.6):
+    """ Apply voice activity detection
+
+    :param log_energy: Log mel energy.
+    :param energy_mean_scale: If this is set to s, to get the actual threshold we let m be the mean log-energy of the file, and use s*m + vad-energy-threshold (float, default = 0.5)
+    :param energy_threshold: Constant term in energy threshold for VAD (also see energy_mean_scale) (float, default = 5)
+    :param frames_context: Number of frames of context on each side of central frame, in window for which energy is monitored (int, default = 0)
+    :param proportion_threshold: Parameter controlling the proportion of frames within the window that need to have more energy than the threshold (float, default = 0.6)
+    :return: A vector of boolean that are True if we judge the frame voiced and False otherwise.
+    """
+    assert len(log_energy.shape) == 1
+    assert energy_mean_scale >= 0
+    assert frames_context >= 0
+    assert 0 < proportion_threshold < 1
+    dtype = log_energy.dtype
+    energy_threshold += energy_mean_scale * log_energy.mean()
+    if frames_context > 0:
+        num_frames = len(log_energy)
+        window_size = frames_context * 2 + 1
+        log_energy_pad = np.concatenate([
+            np.zeros(frames_context, dtype=dtype),
+            log_energy,
+            np.zeros(frames_context, dtype=dtype)
+        ])
+        log_energy_window = sliding_window(log_energy_pad, window_size, 1)
+        num_count = np.count_nonzero(log_energy_window > energy_threshold, axis=1)
+        den_count = np.ones(num_frames, dtype=dtype) * window_size
+        max_den_count = np.arange(frames_context + 1, min(window_size, num_frames) + 1, dtype=dtype)
+        den_count[:-(frames_context + 2):-1] = max_den_count
+        den_count[:frames_context + 1] = np.min([den_count[:frames_context + 1], max_den_count], axis=0)
+        vad = num_count / den_count >= proportion_threshold
+    else:
+        vad = log_energy > energy_threshold
+    return vad
+
+# ---------- compute-vad ----------

rapid_paraformer/models/am.mvn

+<Nnet> 
+<Splice> 560 560
+[ 0 ]
+<AddShift> 560 560 
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+<Rescale> 560 560
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+</Nnet> 

rapid_paraformer/rapid_paraformer.py

+# -*- encoding: utf-8 -*-
+# @Author: SWHL
+# @Contact: liekkaskono@163.com
+from pathlib import Path
+from typing import List
+
+import librosa
+import numpy as np
+
+from .utils import (CharTokenizer, Hypothesis, OrtInferSession,
+                    TokenIDConverter, WavFrontend, read_yaml)
+
+cur_dir = Path(__file__).resolve().parent
+
+
+class RapidParaformer():
+    def __init__(self, config_path: str = None) -> None:
+        config = read_yaml(cur_dir / 'config.yaml')
+        if config_path:
+            config = read_yaml(config_path)
+
+        self.converter = TokenIDConverter(**config['TokenIDConverter'])
+        self.tokenizer = CharTokenizer(**config['CharTokenizer'])
+        self.frontend_asr = WavFrontend(
+            cmvn_file=config['WavFrontend']['cmvn_file'],
+            **config['WavFrontend']['frontend_conf']
+        )
+        self.ort_infer = OrtInferSession(config['Model'])
+
+    def __call__(self, wav_path: str) -> List:
+        waveform = librosa.load(wav_path)[0][None, ...]
+
+        speech, _ = self.frontend_asr.forward_fbank(waveform)
+        feats, feats_len = self.frontend_asr.forward_lfr_cmvn(speech)
+        am_scores = self.ort_infer(input_content=[feats, feats_len])
+
+        results = []
+        for am_score in am_scores:
+            pred_res = self.infer_one_feat(am_score)
+            results.append(pred_res)
+        return results
+
+    def infer_one_feat(self, am_score: np.ndarray) -> 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])
+        nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
+
+        infer_res = []
+        for hyp in nbest_hyps:
+            # 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)
+
+            text = self.tokenizer.tokens2text(token)
+            infer_res.append(text)
+        return infer_res
+
+
+if __name__ == '__main__':
+    paraformer = RapidParaformer()
+
+    wav_file = '0478_00017.wav'
+    for i in range(1000):
+        result = paraformer(wav_file)
+        print(result)

rapid_paraformer/utils.py

+# -*- encoding: utf-8 -*-
+# @Author: SWHL
+# @Contact: liekkaskono@163.com
+import logging
+import pickle
+from pathlib import Path
+from typing import Any, Dict, Iterable, List, NamedTuple, Set, Tuple, Union
+
+import numpy as np
+import yaml
+from onnxruntime import (GraphOptimizationLevel, InferenceSession,
+                         SessionOptions, get_available_providers, get_device)
+from typeguard import check_argument_types
+
+from .kaldifeat import compute_fbank_feats
+
+root_dir = Path(__file__).resolve().parent
+
+
+class TokenIDConverter():
+    def __init__(self, token_path: Union[Path, str],
+                 unk_symbol: str = "<unk>",):
+        check_argument_types()
+
+        self.token_list = self.load_token(root_dir / token_path)
+        self.unk_symbol = unk_symbol
+
+    @staticmethod
+    def load_token(file_path: Union[Path, str]) -> List:
+        if not Path(file_path).exists():
+            raise TokenIDConverterError(f'The {file_path} does not exist.')
+
+        with open(str(file_path), 'rb') as f:
+            token_list = pickle.load(f)
+
+        if len(token_list) != len(set(token_list)):
+            raise TokenIDConverterError('The Token exists duplicated symbol.')
+        return token_list
+
+    def get_num_vocabulary_size(self) -> int:
+        return len(self.token_list)
+
+    def ids2tokens(self,
+                   integers: Union[np.ndarray, Iterable[int]]) -> List[str]:
+        if isinstance(integers, np.ndarray) and integers.ndim != 1:
+            raise TokenIDConverterError(
+                f"Must be 1 dim ndarray, but got {integers.ndim}")
+        return [self.token_list[i] for i in integers]
+
+    def tokens2ids(self, tokens: Iterable[str]) -> List[int]:
+        token2id = {v: i for i, v in enumerate(self.token_list)}
+        if self.unk_symbol not in token2id:
+            raise TokenIDConverterError(
+                f"Unknown symbol '{self.unk_symbol}' doesn't exist in the token_list"
+            )
+        unk_id = token2id[self.unk_symbol]
+        return [token2id.get(i, unk_id) for i in tokens]
+
+
+class CharTokenizer():
+    def __init__(
+        self,
+        symbol_value: Union[Path, str, Iterable[str]] = None,
+        space_symbol: str = "<space>",
+        remove_non_linguistic_symbols: bool = False,
+    ):
+        check_argument_types()
+
+        self.space_symbol = space_symbol
+        self.non_linguistic_symbols = self.load_symbols(symbol_value)
+        self.remove_non_linguistic_symbols = remove_non_linguistic_symbols
+
+    @staticmethod
+    def load_symbols(value: Union[Path, str, Iterable[str]] = None) -> Set:
+        if value is None:
+            return set()
+
+        if isinstance(value, Iterable[str]):
+            return set(value)
+
+        file_path = Path(value)
+        if not file_path.exists():
+            logging.warning("%s doesn't exist.", file_path)
+            return set()
+
+        with file_path.open("r", encoding="utf-8") as f:
+            return set(line.rstrip() for line in f)
+
+    def text2tokens(self, line: Union[str, list]) -> List[str]:
+        tokens = []
+        while len(line) != 0:
+            for w in self.non_linguistic_symbols:
+                if line.startswith(w):
+                    if not self.remove_non_linguistic_symbols:
+                        tokens.append(line[: len(w)])
+                    line = line[len(w):]
+                    break
+            else:
+                t = line[0]
+                if t == " ":
+                    t = "<space>"
+                tokens.append(t)
+                line = line[1:]
+        return tokens
+
+    def tokens2text(self, tokens: Iterable[str]) -> str:
+        tokens = [t if t != self.space_symbol else " " for t in tokens]
+        return "".join(tokens)
+
+    def __repr__(self):
+        return (
+            f"{self.__class__.__name__}("
+            f'space_symbol="{self.space_symbol}"'
+            f'non_linguistic_symbols="{self.non_linguistic_symbols}"'
+            f")"
+        )
+
+
+class WavFrontend():
+    """Conventional frontend structure for ASR.
+    """
+
+    def __init__(
+            self,
+            cmvn_file: str = None,
+            fs: int = 16000,
+            window: str = 'hamming',
+            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
+    ) -> None:
+        check_argument_types()
+
+        self.fs = fs
+        self.window = window
+        self.n_mels = n_mels
+        self.frame_length = frame_length
+        self.frame_shift = frame_shift
+        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 = root_dir / cmvn_file
+        self.dither = dither
+
+    def forward_fbank(self,
+                      input_content: np.ndarray,
+                      ) -> Tuple[np.ndarray, np.ndarray]:
+        feats, feats_lens = [], []
+        batch_size = input_content.shape[0]
+        input_lengths = np.array([input_content.shape[1]])
+        for i in range(batch_size):
+            waveform_length = input_lengths[i]
+            waveform = input_content[i][:waveform_length]
+            waveform = waveform * (1 << 15)
+            mat = compute_fbank_feats(waveform,
+                                      num_mel_bins=self.n_mels,
+                                      frame_length=self.frame_length,
+                                      frame_shift=self.frame_shift,
+                                      dither=self.dither,
+                                      energy_floor=0.0,
+                                      sample_frequency=self.fs)
+            feats.append(mat)
+            feats_lens.append(mat.shape[0])
+
+        feats_pad = np.array(feats).astype(np.float32)
+        feats_lens = np.array(feats_lens).astype(np.int64)
+        return feats_pad, feats_lens
+
+    def forward_lfr_cmvn(self,
+                         input_content: np.ndarray,
+                         ) -> Tuple[np.ndarray, np.ndarray]:
+        feats, feats_lens = [], []
+        batch_size = input_content.shape[0]
+
+        if self.cmvn_file:
+            cmvn = self.load_cmvn()
+
+        input_lengths = np.array([input_content.shape[1]])
+        for i in range(batch_size):
+            mat = input_content[i, :input_lengths[i], :]
+
+            if self.lfr_m != 1 or self.lfr_n != 1:
+                mat = self.apply_lfr(mat, self.lfr_m, self.lfr_n)
+
+            if self.cmvn_file:
+                mat = self.apply_cmvn(mat, cmvn)
+
+            feats.append(mat)
+            feats_lens.append(mat.shape[0])
+
+        feats_pad = np.array(feats).astype(np.float32)
+        feats_lens = np.array(feats_lens).astype(np.int64)
+        return feats_pad, feats_lens
+
+    @staticmethod
+    def apply_lfr(inputs: np.ndarray, lfr_m: int, lfr_n: int) -> np.ndarray:
+        LFR_inputs = []
+
+        T = inputs.shape[0]
+        T_lfr = int(np.ceil(T / lfr_n))
+        left_padding = np.tile(inputs[0], ((lfr_m - 1) // 2, 1))
+        inputs = np.vstack((left_padding, inputs))
+        T = T + (lfr_m - 1) // 2
+        for i in range(T_lfr):
+            if lfr_m <= T - i * lfr_n:
+                LFR_inputs.append(
+                    (inputs[i * lfr_n:i * lfr_n + lfr_m]).reshape(1, -1))
+            else:
+                # process last LFR frame
+                num_padding = lfr_m - (T - i * lfr_n)
+                frame = (inputs[i * lfr_n:]).view(-1)
+                for _ in range(num_padding):
+                    frame = np.hstack((frame, inputs[-1]))
+
+                LFR_inputs.append(frame)
+        LFR_outputs = np.vstack(LFR_inputs).astype(np.float32)
+        return LFR_outputs
+
+    def apply_cmvn(self, inputs: np.ndarray, cmvn: np.ndarray) -> np.ndarray:
+        """
+        Apply CMVN with mvn data
+        """
+        frame, dim = inputs.shape
+        means = np.tile(cmvn[0:1, :dim], (frame, 1))
+        vars = np.tile(cmvn[1:2, :dim], (frame, 1))
+        inputs = (inputs + means) * vars
+        return inputs
+
+    def load_cmvn(self,) -> np.ndarray:
+        with open(self.cmvn_file, 'r', encoding='utf-8') as f:
+            lines = f.readlines()
+
+        means_list = []
+        vars_list = []
+        for i in range(len(lines)):
+            line_item = lines[i].split()
+            if line_item[0] == '<AddShift>':
+                line_item = lines[i + 1].split()
+                if line_item[0] == '<LearnRateCoef>':
+                    add_shift_line = line_item[3:(len(line_item) - 1)]
+                    means_list = list(add_shift_line)
+                    continue
+            elif line_item[0] == '<Rescale>':
+                line_item = lines[i + 1].split()
+                if line_item[0] == '<LearnRateCoef>':
+                    rescale_line = line_item[3:(len(line_item) - 1)]
+                    vars_list = list(rescale_line)
+                    continue
+
+        means = np.array(means_list).astype(np.float64)
+        vars = np.array(vars_list).astype(np.float64)
+        cmvn = np.array([means, vars])
+        return cmvn
+
+
+class Hypothesis(NamedTuple):
+    """Hypothesis data type."""
+
+    yseq: np.ndarray
+    score: Union[float, np.ndarray] = 0
+    scores: Dict[str, Union[float, np.ndarray]] = dict()
+    states: Dict[str, Any] = dict()
+
+    def asdict(self) -> dict:
+        """Convert data to JSON-friendly dict."""
+        return self._replace(
+            yseq=self.yseq.tolist(),
+            score=float(self.score),
+            scores={k: float(v) for k, v in self.scores.items()},
+        )._asdict()
+
+
+class TokenIDConverterError(Exception):
+    pass
+
+
+class OrtInferSession():
+    def __init__(self, config):
+        sess_opt = SessionOptions()
+        sess_opt.log_severity_level = 4
+        sess_opt.enable_cpu_mem_arena = False
+        sess_opt.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL
+
+        cuda_ep = 'CUDAExecutionProvider'
+        cpu_ep = 'CPUExecutionProvider'
+        cpu_provider_options = {
+            "arena_extend_strategy": "kSameAsRequested",
+        }
+
+        EP_list = []
+        if config['use_cuda'] and get_device() == 'GPU' \
+                and cuda_ep in get_available_providers():
+            EP_list = [(cuda_ep, config[cuda_ep])]
+        EP_list.append((cpu_ep, cpu_provider_options))
+
+        config['model_path'] = str(root_dir / config['model_path'])
+        self._verify_model(config['model_path'])
+        self.session = InferenceSession(config['model_path'],
+                                        sess_options=sess_opt,
+                                        providers=EP_list)
+
+        if config['use_cuda'] and cuda_ep not in self.session.get_providers():
+            warnings.warn(f'{cuda_ep} is not avaiable for current env, the inference part is automatically shifted to be executed under {cpu_ep}.\n'
+                          'Please ensure the installed onnxruntime-gpu version matches your cuda and cudnn version, '
+                          'you can check their relations from the offical web site: '
+                          'https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html',
+                          RuntimeWarning)
+
+    def __call__(self,
+                 input_content: List[Union[np.ndarray, np.ndarray]]) -> np.ndarray:
+        input_dict = dict(zip(self.get_input_names(), input_content))
+        return self.session.run(None, input_dict)[0]
+
+    def get_input_names(self, ):
+        return [v.name for v in self.session.get_inputs()]
+
+    def get_output_name(self, output_idx=0):
+        return self.session.get_outputs()[output_idx].name
+
+    def get_character_list(self, key: str = 'character'):
+        return self.meta_dict[key].splitlines()
+
+    def have_key(self, key: str = 'character') -> bool:
+        self.meta_dict = self.session.get_modelmeta().custom_metadata_map
+        if key in self.meta_dict.keys():
+            return True
+        return False
+
+    @staticmethod
+    def _verify_model(model_path):
+        model_path = Path(model_path)
+        if not model_path.exists():
+            raise FileNotFoundError(f'{model_path} does not exists.')
+        if not model_path.is_file():
+            raise FileExistsError(f'{model_path} is not a file.')
+
+
+def read_yaml(yaml_path: Union[str, Path]) -> Dict:
+    if not Path(yaml_path).exists():
+        raise FileExistsError(f'The {yaml_path} does not exist.')
+
+    with open(str(yaml_path), 'rb') as f:
+        data = yaml.load(f, Loader=yaml.Loader)
+    return data

requirements.txt

+librosa==0.9.2
+numpy==1.21.6
+onnxruntime==1.13.1
+scipy==1.7.3
+typeguard==2.13.3

tests/test_infer.py

+# -*- encoding: utf-8 -*-
+# @Author: SWHL
+# @Contact: liekkaskono@163.com
+import os
+from pathlib import Path
+
+os.sys.path.append(str(Path(__file__).resolve().parent.parent))
+
+from rapid_paraformer import RapidParaformer
+
+paraformer = RapidParaformer()
+
+
+def test_normal():
+    wav_file = 'test_wavs/0478_00017.wav'
+    result = paraformer(wav_file)
+    assert result[0][0][:5] == '呃说不配合'