Sound classification with YAMNet

Stay organized with collections Save and categorize content based on your preferences.

View on Run in Google Colab View on GitHub Download notebook See TF Hub model

YAMNet is a deep net that predicts 521 audio event classes from the AudioSet-YouTube corpus it was trained on. It employs the Mobilenet_v1 depthwise-separable convolution architecture.

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 import wavfile

Load the Model from TensorFlow Hub.

# Load the model.
model = hub.load('')

The labels file will be loaded from the models assets and is present at model.class_map_path(). You will load it on the class_names variable.

# 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 as csvfile:
    reader = csv.DictReader(csvfile)
    for row in reader:

  return class_names

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

Add a method to verify and convert a loaded audio is on the proper sample_rate (16K), otherwise it would affect the model's results.

def ensure_sample_rate(original_sample_rate, waveform,
  """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

Downloading and preparing the sound file

Here you will download a wav file and listen to it. If you have a file already available, just upload it to colab and use it instead.

curl -O
% Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100  153k  100  153k    0     0   955k      0 --:--:-- --:--:-- --:--:--  961k
curl -O
% Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100  210k  100  210k    0     0  2923k      0 --:--:-- --:--:-- --:--:-- 2923k
# wav_file_name = 'speech_whistling2.wav'
wav_file_name = 'miaow_16k.wav'
sample_rate, wav_data =, '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_111157/ WavFileWarning: Chunk (non-data) not understood, skipping it.
  sample_rate, wav_data =, 'rb')

The wav_data needs to be normalized to values in [-1.0, 1.0] (as stated in the model's documentation).

waveform = wav_data / tf.int16.max

Executing the Model

Now the easy part: using the data already prepared, you just call the model and get the: scores, embedding and the spectrogram.

The score is the main result you will use. The spectrogram you will use to do some visualizations later.

# 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


YAMNet also returns some additional information that we can use for visualization. Let's take a look on the Waveform, spectrogram and the top classes inferred.

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

# Plot the waveform.
plt.subplot(3, 1, 1)
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')

# 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]))