使用 YAMNet 进行声音分类

在 TensorFlow.org 上查看

在 Google Colab 中运行

在 GitHub 上查看

下载笔记本

查看 TF Hub 模型

YAMNet 是一种深度网络，可以从其训练的 AudioSet-YouTube 语料库中预测 521 个音频事件类别。它采用 Mobilenet_v1 深度可分离卷积架构。

import tensorflow as tf
import tensorflow_hub as hub
import numpy as np
import csv

import matplotlib.pyplot as plt
from IPython.display import Audio
from scipy.io import wavfile

从 TensorFlow Hub 加载模型。

# Load the model.
model = hub.load('https://tfhub.dev/google/yamnet/1')

2024-03-09 14:52:27.405707: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:282] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected

标签文件将从模型的资产中加载，位于 model.class_map_path()。您将在 class_names 变量中加载它。

# Find the name of the class with the top score when mean-aggregated across frames.
def class_names_from_csv(class_map_csv_text):
  """Returns list of class names corresponding to score vector."""
  class_names = []
  with tf.io.gfile.GFile(class_map_csv_text) as csvfile:
    reader = csv.DictReader(csvfile)
    for row in reader:
      class_names.append(row['display_name'])

  return class_names

class_map_path = model.class_map_path().numpy()
class_names = class_names_from_csv(class_map_path)

添加一个方法来验证和转换加载的音频是否具有正确的采样率 (16K)，否则会影响模型的结果。

def ensure_sample_rate(original_sample_rate, waveform,
                       desired_sample_rate=16000):
  """Resample waveform if required."""
  if original_sample_rate != desired_sample_rate:
    desired_length = int(round(float(len(waveform)) /
                               original_sample_rate * desired_sample_rate))
    waveform = scipy.signal.resample(waveform, desired_length)
  return desired_sample_rate, waveform

下载和准备声音文件

在这里，您将下载一个 wav 文件并收听它。如果您已经有一个文件可用，只需将其上传到 colab 并使用它即可。

curl -O https://storage.googleapis.com/audioset/speech_whistling2.wav

% Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100  153k  100  153k    0     0  1220k      0 --:--:-- --:--:-- --:--:-- 1220k

curl -O https://storage.googleapis.com/audioset/miaow_16k.wav

% Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100  210k  100  210k    0     0  1913k      0 --:--:-- --:--:-- --:--:-- 1913k

# wav_file_name = 'speech_whistling2.wav'
wav_file_name = 'miaow_16k.wav'
sample_rate, wav_data = wavfile.read(wav_file_name, 'rb')
sample_rate, wav_data = ensure_sample_rate(sample_rate, wav_data)

# Show some basic information about the audio.
duration = len(wav_data)/sample_rate
print(f'Sample rate: {sample_rate} Hz')
print(f'Total duration: {duration:.2f}s')
print(f'Size of the input: {len(wav_data)}')

# Listening to the wav file.
Audio(wav_data, rate=sample_rate)

Sample rate: 16000 Hz
Total duration: 6.73s
Size of the input: 107698
/tmpfs/tmp/ipykernel_101715/2211628228.py:3: WavFileWarning: Chunk (non-data) not understood, skipping it.
  sample_rate, wav_data = wavfile.read(wav_file_name, 'rb')

wav_data 需要规范化为 [-1.0, 1.0] 之间的值（如模型的文档中所述）。

waveform = wav_data / tf.int16.max

执行模型

现在最简单的一步：使用已经准备好的数据，您只需调用模型并获取：分数、嵌入和频谱图。

分数是您将使用的主要结果。频谱图将用于稍后进行一些可视化。

# Run the model, check the output.
scores, embeddings, spectrogram = model(waveform)

scores_np = scores.numpy()
spectrogram_np = spectrogram.numpy()
infered_class = class_names[scores_np.mean(axis=0).argmax()]
print(f'The main sound is: {infered_class}')

The main sound is: Animal

可视化

YAMNet 还返回了一些其他信息，我们可以用它来进行可视化。让我们看看波形、频谱图和推断出的前几个类别。

plt.figure(figsize=(10, 6))

# Plot the waveform.
plt.subplot(3, 1, 1)
plt.plot(waveform)
plt.xlim([0, len(waveform)])

# Plot the log-mel spectrogram (returned by the model).
plt.subplot(3, 1, 2)
plt.imshow(spectrogram_np.T, aspect='auto', interpolation='nearest', origin='lower')

# Plot and label the model output scores for the top-scoring classes.
mean_scores = np.mean(scores, axis=0)
top_n = 10
top_class_indices = np.argsort(mean_scores)[::-1][:top_n]
plt.subplot(3, 1, 3)
plt.imshow(scores_np[:, top_class_indices].T, aspect='auto', interpolation='nearest', cmap='gray_r')

# patch_padding = (PATCH_WINDOW_SECONDS / 2) / PATCH_HOP_SECONDS
# values from the model documentation
patch_padding = (0.025 / 2) / 0.01
plt.xlim([-patch_padding-0.5, scores.shape[0] + patch_padding-0.5])
# Label the top_N classes.
yticks = range(0, top_n, 1)
plt.yticks(yticks, [class_names[top_class_indices[x]] for x in yticks])
_ = plt.ylim(-0.5 + np.array([top_n, 0]))

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