torchaudio - Python wave 读取音频数据对比

深碍√TFBOYSˉ_ 2023-02-16 04:15 197阅读 0赞

# **torchaudio - Python wave 读取音频数据对比** #

## 1. torchaudio: an audio library for PyTorch ##

[https://github.com/pytorch/audio][https_github.com_pytorch_audio]

Data manipulation and transformation for audio signal processing, powered by PyTorch.

**torchaudio: an audio library for PyTorch**  
[https://github.com/pytorch/audio][https_github.com_pytorch_audio]

The following is the corresponding `torchaudio` versions and supported Python versions.

<table> 
 <thead> 
  <tr> 
   <th><code>torch</code></th> 
   <th><code>torchaudio</code></th> 
   <th><code>python</code></th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td><code>master</code> / <code>nightly</code></td> 
   <td><code>master</code> / <code>nightly</code></td> 
   <td><code>&gt;=3.6</code></td> 
  </tr> 
  <tr> 
   <td><code>1.5.0</code></td> 
   <td><code>0.5.0</code></td> 
   <td><code>&gt;=3.5</code></td> 
  </tr> 
  <tr> 
   <td><code>1.4.0</code></td> 
   <td><code>0.4.0</code></td> 
   <td><code>==2.7</code>, <code>&gt;=3.5</code>, <code>&lt;=3.8</code></td> 
  </tr> 
 </tbody> 
</table>

## 2. `torchaudio.load(filepath, out=None, normalization=True, channels_first=True, num_frames=0, offset=0, signalinfo=None, encodinginfo=None, filetype=None)` ##

[https://pytorch.org/audio/][https_pytorch.org_audio]

Loads an audio file from disk into a tensor.  
将音频文件从磁盘加载到张量中。

### 2.1 Parameters ###

`filepath (str or pathlib.Path)` – Path to audio file. - 音频文件的路径。

`out (torch.Tensor, optional)` – An output tensor to use instead of creating one. (Default: `None`) - 使用输出张量 `out` 而不是创建一个张量。

`normalization (bool, number, or callable, optional)` – If boolean True, then output is divided by `1 << 31` (assumes signed 32-bit audio), and normalizes to `[-1, 1]`. If `number`, then output is divided by that `number`. If `callable`, then the output is passed as a parameter to the given function, then the output is divided by the result. (Default: `True`)

`channels_first (bool)` – Set channels first or length first in result. (Default: `True`) - 返回结果中第一维度是 channels or length。

`num_frames (int, optional)` – Number of frames to load. 0 to load everything after the offset. (Default: 0) - 要加载的帧数。

`offset (int, optional)` – Number of frames from the start of the file to begin data loading. (Default: 0) - 从文件开始到开始数据加载的帧数。

`signalinfo (sox_signalinfo_t, optional)` – A `sox_signalinfo_t` type, which could be helpful if the audio type cannot be automatically determined. (Default: `None`) - `sox_signalinfo_t` 类型，如果不能自动确定音频类型，这可能会有所帮助。

`encodinginfo (sox_encodinginfo_t, optional)` – A `sox_encodinginfo_t` type, which could be set if the audio type cannot be automatically determined. (Default: `None`) - `sox_encodinginfo_t` 类型，如果不能自动确定音频类型，则可以设置。

`filetype (str, optional)` – A filetype or extension to be set if sox cannot determine it automatically. (Default: `None`) - 如果 sox 无法自动确定要设置的文件类型或扩展名。

### 2.2 Returns ###

An output tensor of size `[C x L]` or `[L x C]` where `L` is the number of audio frames and `C` is the number of channels. An integer which is the sample rate of the audio (as listed in the metadata of the file)

### 2.3 Return type ###

Tuple`[torch.Tensor, int]`

### 2.4 Example ###

#!/usr/bin/env python
    # -*- coding: utf-8 -*-
    # yongqiang cheng
    
    from __future__ import absolute_import
    from __future__ import division
    from __future__ import print_function
    
    import torchaudio
    
    # WAV file
    audio_file = "/mnt/f/yongqiang_work/ding.wav"
    
    data, sample_rate = torchaudio.load(audio_file)
    print("data.size() =", data.size())
    print("sample_rate =", sample_rate)
    
    data_normalized, sample_rate = torchaudio.load(audio_file, normalization=True)
    print("data_normalized.size() =", data_normalized.size())
    print("sample_rate =", sample_rate)

### 2.5 `data, sample_rate = torchaudio.load(audio_file, normalization=False)` ###

#!/usr/bin/env python
    # -*- coding: utf-8 -*-
    # yongqiang cheng
    
    from __future__ import absolute_import
    from __future__ import division
    from __future__ import print_function
    
    import torchaudio
    
    # WAV file
    audio_file = "/mnt/f/yongqiang_work/ding.wav"
    
    data, sample_rate = torchaudio.load(audio_file, normalization=False)
    print("data.size() =", data.size())
    print("sample_rate =", sample_rate)
    
    data = data.numpy()
    
    data_normalized, sample_rate = torchaudio.load(audio_file, normalization=True)
    print("data_normalized.size() =", data_normalized.size())
    print("sample_rate =", sample_rate)
    
    data_normalized = data_normalized.numpy()

![在这里插入图片描述][20200704231145506.png_pic_center]

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NoZW5neXExMTY_size_16_color_FFFFFF_t_70_pic_center]

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NoZW5neXExMTY_size_16_color_FFFFFF_t_70_pic_center 1]

### 2.6 `data_normalized, sample_rate = torchaudio.load(audio_file, normalization=True)` ###

#!/usr/bin/env python
    # -*- coding: utf-8 -*-
    # yongqiang cheng
    
    from __future__ import absolute_import
    from __future__ import division
    from __future__ import print_function
    
    import torchaudio
    
    # WAV file
    audio_file = "/mnt/f/yongqiang_work/ding.wav"
    
    data, sample_rate = torchaudio.load(audio_file, normalization=False)
    print("data.size() =", data.size())
    print("sample_rate =", sample_rate)
    
    data = data.numpy()
    
    data_normalized, sample_rate = torchaudio.load(audio_file, normalization=True)
    print("data_normalized.size() =", data_normalized.size())
    print("sample_rate =", sample_rate)
    
    data_normalized = data_normalized.numpy()
    
    print("yongqiang cheng")

/home/yongqiang/miniconda3/envs/pt-1.4_py-3.6/bin/python /home/yongqiang/pytorch_work/end2end-asr-pytorch-example/yongqiang.py
    data.size() = torch.Size([2, 17504])
    sample_rate = 44100
    data_normalized.size() = torch.Size([2, 17504])
    sample_rate = 44100
    yongqiang cheng
    
    Process finished with exit code 0

![在这里插入图片描述][20200704234810999.png_pic_center]

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NoZW5neXExMTY_size_16_color_FFFFFF_t_70_pic_center 2]

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NoZW5neXExMTY_size_16_color_FFFFFF_t_70_pic_center 3]

## 3. Python wave 读取音频数据 ##

Python wave 读取音频数据，针对 sample width in bytes = 2 bytes，`short` / `short int` 可以表示的的最大范围是 `[-32768, 32767]`，注意查看读取的数据。Python wave 同 `torchaudio.load()` 读取音频数据表示范围有区别，注意对比。

#!/usr/bin/env python
    # -*- coding: utf-8 -*-
    # yongqiang cheng
    
    from __future__ import absolute_import
    from __future__ import division
    from __future__ import print_function
    
    import wave
    import numpy as np
    import matplotlib.pyplot as plt
    
    # WAV file
    audio_file = "/mnt/f/yongqiang_work/ding.wav"
    object = wave.open(audio_file, "rb")
    
    # (nchannels, sampwidth, framerate, nframes, comptype, compname)
    params = object.getparams()
    nchannels, sampwidth, framerate, nframes, comptype, compname = params[:6]
    print("nchannels =", nchannels)
    print("sampwidth =", sampwidth)
    print("framerate =", framerate)
    print("nframes =", nframes)
    print("comptype =", comptype)
    print("compname =", compname)
    
    # Returns number of audio channels (1 for mono, 2 for stereo).
    print("object.getnchannels() =", object.getnchannels())
    
    # Returns sample width in bytes.
    print("object.getsampwidth() =", object.getsampwidth())
    
    # Returns sampling frequency.
    print("object.getframerate() =", object.getframerate())
    
    # Returns number of audio frames.
    print("object.getnframes() =", object.getnframes())
    
    # Returns compression type ('NONE' is the only supported type).
    print("object.getcomptype() =", object.getcomptype())
    
    # Human-readable version of getcomptype(). Usually 'not compressed' parallels 'NONE'.
    print("object.getcompname() =", object.getcompname())
    
    # Reads and returns at most n frames of audio, as a bytes object.
    str_data = object.readframes(nframes)
    # nframes = 17504,  channels = 2, sampwidth = 2
    # str_data (bytes: 70016) = nframes * channels * sampwidth = 17504 * 2 * 2 = 70016
    num_bytes = len(str_data) # num_bytes = 70016
    print("num_bytes =", num_bytes, "bytes")
    object.close()
    
    wave_data = np.fromstring(str_data, dtype=np.short)
    wave_data.shape = -1, 2
    wave_data = wave_data.T
    time = np.arange(0, nframes) * (1.0 / framerate)
    
    plt.subplot(211)
    plt.plot(time, wave_data[0])
    plt.xlabel("left channel - time (seconds)")
    plt.subplot(212)
    plt.plot(time, wave_data[1], c="g")
    plt.xlabel("right channel - time (seconds)")
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NoZW5neXExMTY_size_16_color_FFFFFF_t_70_pic_center 4]  
`short` / `short int` 可以表示的的最大范围是 `[-32768, 32767]`  
`2^15 = 32768`  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NoZW5neXExMTY_size_16_color_FFFFFF_t_70_pic_center 5]

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NoZW5neXExMTY_size_16_color_FFFFFF_t_70_pic_center 6]

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NoZW5neXExMTY_size_16_color_FFFFFF_t_70_pic_center 7]

## 4. 数据对比 ##

*  torchaudio

#!/usr/bin/env python
    # -*- coding: utf-8 -*-
    # yongqiang cheng
    
    from __future__ import absolute_import
    from __future__ import division
    from __future__ import print_function
    
    import torchaudio
    
    # WAV file
    audio_file = "/mnt/f/yongqiang_work/ding.wav"
    
    data, sample_rate = torchaudio.load(audio_file, normalization=False)
    print("data.size() =", data.size())
    print("sample_rate =", sample_rate)
    
    data = data.numpy()
    print("data[0, 189:205] = ")
    print(data[0, 189:205])
    print("data[1, 189:205] = ")
    print(data[1, 189:205])
    
    data_normalized, sample_rate = torchaudio.load(audio_file, normalization=True)
    print("data_normalized.size() =", data_normalized.size())
    print("sample_rate =", sample_rate)
    
    data_normalized = data_normalized.numpy()
    print("data_normalized[0, 189:205] = ")
    print(data_normalized[0, 189:205])
    print("data_normalized[1, 189:205] = ")
    print(data_normalized[1, 189:205])
    
    print("yongqiang cheng")

/home/yongqiang/miniconda3/envs/pt-1.4_py-3.6/bin/python /home/yongqiang/pytorch_work/end2end-asr-pytorch-example/yongqiang.py
    data.size() = torch.Size([2, 17504])
    sample_rate = 44100
    data[0, 189:205] = 
    [    65536.   -131072.    131072.   -131072.    131072.   -262144.
        196608.   -327680.    327680.   -524288.    524288.  -1179648.
       5308416.   8847360.    196608. -19660800.]
    data[1, 189:205] = 
    [        0.   -131072.     65536.   -131072.    131072.   -131072.
        262144.   -262144.    262144.   -458752.    458752.  -1048576.
       4390912.   7602176.    327680. -16777216.]
    data_normalized.size() = torch.Size([2, 17504])
    sample_rate = 44100
    data_normalized[0, 189:205] = 
    [ 3.0517578e-05 -6.1035156e-05  6.1035156e-05 -6.1035156e-05
      6.1035156e-05 -1.2207031e-04  9.1552734e-05 -1.5258789e-04
      1.5258789e-04 -2.4414062e-04  2.4414062e-04 -5.4931641e-04
      2.4719238e-03  4.1198730e-03  9.1552734e-05 -9.1552734e-03]
    data_normalized[1, 189:205] = 
    [ 0.0000000e+00 -6.1035156e-05  3.0517578e-05 -6.1035156e-05
      6.1035156e-05 -6.1035156e-05  1.2207031e-04 -1.2207031e-04
      1.2207031e-04 -2.1362305e-04  2.1362305e-04 -4.8828125e-04
      2.0446777e-03  3.5400391e-03  1.5258789e-04 -7.8125000e-03]
    yongqiang cheng
    
    Process finished with exit code 0

*  Python wave

#!/usr/bin/env python
    # -*- coding: utf-8 -*-
    # yongqiang cheng
    
    from __future__ import absolute_import
    from __future__ import division
    from __future__ import print_function
    
    import wave
    import numpy as np
    import matplotlib.pyplot as plt
    
    # WAV file
    audio_file = "/mnt/f/yongqiang_work/ding.wav"
    object = wave.open(audio_file, "rb")
    
    # (nchannels, sampwidth, framerate, nframes, comptype, compname)
    params = object.getparams()
    nchannels, sampwidth, framerate, nframes, comptype, compname = params[:6]
    print("nchannels =", nchannels)
    print("sampwidth =", sampwidth)
    print("framerate =", framerate)
    print("nframes =", nframes)
    print("comptype =", comptype)
    print("compname =", compname)
    
    # Returns number of audio channels (1 for mono, 2 for stereo).
    print("object.getnchannels() =", object.getnchannels())
    
    # Returns sample width in bytes.
    print("object.getsampwidth() =", object.getsampwidth())
    
    # Returns sampling frequency.
    print("object.getframerate() =", object.getframerate())
    
    # Returns number of audio frames.
    print("object.getnframes() =", object.getnframes())
    
    # Returns compression type ('NONE' is the only supported type).
    print("object.getcomptype() =", object.getcomptype())
    
    # Human-readable version of getcomptype(). Usually 'not compressed' parallels 'NONE'.
    print("object.getcompname() =", object.getcompname())
    
    # Reads and returns at most n frames of audio, as a bytes object.
    str_data = object.readframes(nframes)
    # nframes = 17504,  channels = 2, sampwidth = 2
    # str_data (bytes: 70016) = nframes * channels * sampwidth = 17504 * 2 * 2 = 70016
    num_bytes = len(str_data) # num_bytes = 70016
    print("num_bytes =", num_bytes, "bytes")
    object.close()
    
    wave_data = np.fromstring(str_data, dtype=np.short)
    wave_data.shape = -1, 2
    wave_data = wave_data.T
    time = np.arange(0, nframes) * (1.0 / framerate)
    
    plt.subplot(211)
    plt.plot(time, wave_data[0])
    plt.xlabel("left channel - time (seconds)")
    plt.subplot(212)
    plt.plot(time, wave_data[1], c="g")
    plt.xlabel("right channel - time (seconds)")
    plt.show()
    
    print("wave_data[0, 189:205] = ")
    print(wave_data[0, 189:205])
    print("wave_data[1, 189:205] = ")
    print(wave_data[1, 189:205])
    
    print("yongqiang cheng")

/home/yongqiang/miniconda3/envs/pt-1.4_py-3.6/bin/python /home/yongqiang/pytorch_work/end2end-asr-pytorch-example/yongqiang.py
    nchannels = 2
    sampwidth = 2
    framerate = 44100
    nframes = 17504
    comptype = NONE
    compname = not compressed
    object.getnchannels() = 2
    object.getsampwidth() = 2
    object.getframerate() = 44100
    object.getnframes() = 17504
    object.getcomptype() = NONE
    object.getcompname() = not compressed
    num_bytes = 70016 bytes
    wave_data[0, 189:205] = 
    [   1   -2    2   -2    2   -4    3   -5    5   -8    8  -18   81  135
        3 -300]
    wave_data[1, 189:205] = 
    [   0   -2    1   -2    2   -2    4   -4    4   -7    7  -16   67  116
        5 -256]
    yongqiang cheng
    
    Process finished with exit code 0

### 4.1 `data, sample_rate = torchaudio.load(audio_file, normalization=False)` ###

针对 sample width in bytes = 2 bytes，`4.1` 中数据为 `4.3` 中对应原始数据乘以 `2^16 = 65536`。

data[0, 189:205] = 
    [    65536.   -131072.    131072.   -131072.
        131072.   -262144.    196608.   -327680.
        327680.   -524288.    524288.  -1179648.
       5308416.   8847360.    196608. -19660800.]
    
    data[1, 189:205] = 
    [        0.   -131072.     65536.   -131072.
        131072.   -131072.    262144.   -262144.
        262144.   -458752.    458752.  -1048576.
       4390912.   7602176.    327680. -16777216.]

`2^31 = 2147483648`  
`2^16 = 65536`  
`2^15 = 32768`

### 4.2 `data_normalized, sample_rate = torchaudio.load(audio_file, normalization=True)` ###

`4.2` 中归一化数据为 `4.1` 中对应数据除以 `2^31 = 2147483648`。针对 sample width in bytes = 2 bytes，`4.2` 中数据为 `4.3` 中对应原始数据除以 `2^(31 - 16) = 2^15 = 32768`。

data_normalized[0, 189:205] = 
    [ 3.0517578e-05 -6.1035156e-05  6.1035156e-05 -6.1035156e-05
      6.1035156e-05 -1.2207031e-04  9.1552734e-05 -1.5258789e-04
      1.5258789e-04 -2.4414062e-04  2.4414062e-04 -5.4931641e-04
      2.4719238e-03  4.1198730e-03  9.1552734e-05 -9.1552734e-03]
    
    data_normalized[1, 189:205] = 
    [ 0.0000000e+00 -6.1035156e-05  3.0517578e-05 -6.1035156e-05
      6.1035156e-05 -6.1035156e-05  1.2207031e-04 -1.2207031e-04
      1.2207031e-04 -2.1362305e-04  2.1362305e-04 -4.8828125e-04
      2.0446777e-03  3.5400391e-03  1.5258789e-04 -7.8125000e-03]

### 4.3 Python wave ###

针对 sample width in bytes = 2 bytes，`4.1` 中数据为 `4.3` 中对应原始数据乘以 `2^16 = 65536`。

wave_data[0, 189:205] = 
    [   1   -2    2   -2
        2   -4    3   -5
        5   -8    8  -18
       81  135    3 -300]
    
    wave_data[1, 189:205] = 
    [   0   -2    1   -2
        2   -2    4   -4
        4   -7    7  -16
       67  116    5 -256]

[https_github.com_pytorch_audio]: https://github.com/pytorch/audio
[https_pytorch.org_audio]: https://pytorch.org/audio/
[20200704231145506.png_pic_center]: https://img-blog.csdnimg.cn/20200704231145506.png#pic_center
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NoZW5neXExMTY_size_16_color_FFFFFF_t_70_pic_center]: https://img-blog.csdnimg.cn/20200704231357873.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NoZW5neXExMTY=,size_16,color_FFFFFF,t_70#pic_center
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