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.. _sphx_glr_intermediate_speech_command_classification_with_torchaudio_tutorial.py:
Speech Command Classification with torchaudio
*********************************************
This tutorial will show you how to correctly format an audio dataset and
then train/test an audio classifier network on the dataset.
Colab has GPU option available. In the menu tabs, select “Runtime” then
“Change runtime type”. In the pop-up that follows, you can choose GPU.
After the change, your runtime should automatically restart (which means
information from executed cells disappear).
First, let’s import the common torch packages such as
`torchaudio <https://github.com/pytorch/audio>`__ that can be installed
by following the instructions on the website.
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.. code-block:: default
# Uncomment the line corresponding to your "runtime type" to run in Google Colab
# CPU:
# !pip install pydub torch==1.7.0+cpu torchvision==0.8.1+cpu torchaudio==0.7.0 -f https://download.pytorch.org/whl/torch_stable.html
# GPU:
# !pip install pydub torch==1.7.0+cu101 torchvision==0.8.1+cu101 torchaudio==0.7.0 -f https://download.pytorch.org/whl/torch_stable.html
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchaudio
import sys
import matplotlib.pyplot as plt
import IPython.display as ipd
from tqdm import tqdm
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Let’s check if a CUDA GPU is available and select our device. Running
the network on a GPU will greatly decrease the training/testing runtime.
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.. code-block:: default
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
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Importing the Dataset
---------------------
We use torchaudio to download and represent the dataset. Here we use
`SpeechCommands <https://arxiv.org/abs/1804.03209>`__, which is a
datasets of 35 commands spoken by different people. The dataset
``SPEECHCOMMANDS`` is a ``torch.utils.data.Dataset`` version of the
dataset. In this dataset, all audio files are about 1 second long (and
so about 16000 time frames long).
The actual loading and formatting steps happen when a data point is
being accessed, and torchaudio takes care of converting the audio files
to tensors. If one wants to load an audio file directly instead,
``torchaudio.load()`` can be used. It returns a tuple containing the
newly created tensor along with the sampling frequency of the audio file
(16kHz for SpeechCommands).
Going back to the dataset, here we create a subclass that splits it into
standard training, validation, testing subsets.
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.. code-block:: default
from torchaudio.datasets import SPEECHCOMMANDS
import os
class SubsetSC(SPEECHCOMMANDS):
def __init__(self, subset: str = None):
super().__init__("./", download=True)
def load_list(filename):
filepath = os.path.join(self._path, filename)
with open(filepath) as fileobj:
return [os.path.normpath(os.path.join(self._path, line.strip())) for line in fileobj]
if subset == "validation":
self._walker = load_list("validation_list.txt")
elif subset == "testing":
self._walker = load_list("testing_list.txt")
elif subset == "training":
excludes = load_list("validation_list.txt") + load_list("testing_list.txt")
excludes = set(excludes)
self._walker = [w for w in self._walker if w not in excludes]
# Create training and testing split of the data. We do not use validation in this tutorial.
train_set = SubsetSC("training")
test_set = SubsetSC("testing")
waveform, sample_rate, label, speaker_id, utterance_number = train_set[0]
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A data point in the SPEECHCOMMANDS dataset is a tuple made of a waveform
(the audio signal), the sample rate, the utterance (label), the ID of
the speaker, the number of the utterance.
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.. code-block:: default
print("Shape of waveform: {}".format(waveform.size()))
print("Sample rate of waveform: {}".format(sample_rate))
plt.plot(waveform.t().numpy());
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Let’s find the list of labels available in the dataset.
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.. code-block:: default
labels = sorted(list(set(datapoint[2] for datapoint in train_set)))
labels
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The 35 audio labels are commands that are said by users. The first few
files are people saying “marvin”.
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.. code-block:: default
waveform_first, *_ = train_set[0]
ipd.Audio(waveform_first.numpy(), rate=sample_rate)
waveform_second, *_ = train_set[1]
ipd.Audio(waveform_second.numpy(), rate=sample_rate)
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The last file is someone saying “visual”.
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.. code-block:: default
waveform_last, *_ = train_set[-1]
ipd.Audio(waveform_last.numpy(), rate=sample_rate)
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Formatting the Data
-------------------
This is a good place to apply transformations to the data. For the
waveform, we downsample the audio for faster processing without losing
too much of the classification power.
We don’t need to apply other transformations here. It is common for some
datasets though to have to reduce the number of channels (say from
stereo to mono) by either taking the mean along the channel dimension,
or simply keeping only one of the channels. Since SpeechCommands uses a
single channel for audio, this is not needed here.
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.. code-block:: default
new_sample_rate = 8000
transform = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=new_sample_rate)
transformed = transform(waveform)
ipd.Audio(transformed.numpy(), rate=new_sample_rate)
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We are encoding each word using its index in the list of labels.
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.. code-block:: default
def label_to_index(word):
# Return the position of the word in labels
return torch.tensor(labels.index(word))
def index_to_label(index):
# Return the word corresponding to the index in labels
# This is the inverse of label_to_index
return labels[index]
word_start = "yes"
index = label_to_index(word_start)
word_recovered = index_to_label(index)
print(word_start, "-->", index, "-->", word_recovered)
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To turn a list of data point made of audio recordings and utterances
into two batched tensors for the model, we implement a collate function
which is used by the PyTorch DataLoader that allows us to iterate over a
dataset by batches. Please see `the
documentation <https://pytorch.org/docs/stable/data.html#working-with-collate-fn>`__
for more information about working with a collate function.
In the collate function, we also apply the resampling, and the text
encoding.
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.. code-block:: default
def pad_sequence(batch):
# Make all tensor in a batch the same length by padding with zeros
batch = [item.t() for item in batch]
batch = torch.nn.utils.rnn.pad_sequence(batch, batch_first=True, padding_value=0.)
return batch.permute(0, 2, 1)
def collate_fn(batch):
# A data tuple has the form:
# waveform, sample_rate, label, speaker_id, utterance_number
tensors, targets = [], []
# Gather in lists, and encode labels as indices
for waveform, _, label, *_ in batch:
tensors += [waveform]
targets += [label_to_index(label)]
# Group the list of tensors into a batched tensor
tensors = pad_sequence(tensors)
targets = torch.stack(targets)
return tensors, targets
batch_size = 256
if device == "cuda":
num_workers = 1
pin_memory = True
else:
num_workers = 0
pin_memory = False
train_loader = torch.utils.data.DataLoader(
train_set,
batch_size=batch_size,
shuffle=True,
collate_fn=collate_fn,
num_workers=num_workers,
pin_memory=pin_memory,
)
test_loader = torch.utils.data.DataLoader(
test_set,
batch_size=batch_size,
shuffle=False,
drop_last=False,
collate_fn=collate_fn,
num_workers=num_workers,
pin_memory=pin_memory,
)
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Define the Network
------------------
For this tutorial we will use a convolutional neural network to process
the raw audio data. Usually more advanced transforms are applied to the
audio data, however CNNs can be used to accurately process the raw data.
The specific architecture is modeled after the M5 network architecture
described in `this paper <https://arxiv.org/pdf/1610.00087.pdf>`__. An
important aspect of models processing raw audio data is the receptive
field of their first layer’s filters. Our model’s first filter is length
80 so when processing audio sampled at 8kHz the receptive field is
around 10ms (and at 4kHz, around 20 ms). This size is similar to speech
processing applications that often use receptive fields ranging from
20ms to 40ms.
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.. code-block:: default
class M5(nn.Module):
def __init__(self, n_input=1, n_output=35, stride=16, n_channel=32):
super().__init__()
self.conv1 = nn.Conv1d(n_input, n_channel, kernel_size=80, stride=stride)
self.bn1 = nn.BatchNorm1d(n_channel)
self.pool1 = nn.MaxPool1d(4)
self.conv2 = nn.Conv1d(n_channel, n_channel, kernel_size=3)
self.bn2 = nn.BatchNorm1d(n_channel)
self.pool2 = nn.MaxPool1d(4)
self.conv3 = nn.Conv1d(n_channel, 2 * n_channel, kernel_size=3)
self.bn3 = nn.BatchNorm1d(2 * n_channel)
self.pool3 = nn.MaxPool1d(4)
self.conv4 = nn.Conv1d(2 * n_channel, 2 * n_channel, kernel_size=3)
self.bn4 = nn.BatchNorm1d(2 * n_channel)
self.pool4 = nn.MaxPool1d(4)
self.fc1 = nn.Linear(2 * n_channel, n_output)
def forward(self, x):
x = self.conv1(x)
x = F.relu(self.bn1(x))
x = self.pool1(x)
x = self.conv2(x)
x = F.relu(self.bn2(x))
x = self.pool2(x)
x = self.conv3(x)
x = F.relu(self.bn3(x))
x = self.pool3(x)
x = self.conv4(x)
x = F.relu(self.bn4(x))
x = self.pool4(x)
x = F.avg_pool1d(x, x.shape[-1])
x = x.permute(0, 2, 1)
x = self.fc1(x)
return F.log_softmax(x, dim=2)
model = M5(n_input=transformed.shape[0], n_output=len(labels))
model.to(device)
print(model)
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
n = count_parameters(model)
print("Number of parameters: %s" % n)
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We will use the same optimization technique used in the paper, an Adam
optimizer with weight decay set to 0.0001. At first, we will train with
a learning rate of 0.01, but we will use a ``scheduler`` to decrease it
to 0.001 during training after 20 epochs.
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.. code-block:: default
optimizer = optim.Adam(model.parameters(), lr=0.01, weight_decay=0.0001)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.1) # reduce the learning after 20 epochs by a factor of 10
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Training and Testing the Network
--------------------------------
Now let’s define a training function that will feed our training data
into the model and perform the backward pass and optimization steps. For
training, the loss we will use is the negative log-likelihood. The
network will then be tested after each epoch to see how the accuracy
varies during the training.
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.. code-block:: default
def train(model, epoch, log_interval):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data = data.to(device)
target = target.to(device)
# apply transform and model on whole batch directly on device
data = transform(data)
output = model(data)
# negative log-likelihood for a tensor of size (batch x 1 x n_output)
loss = F.nll_loss(output.squeeze(), target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# print training stats
if batch_idx % log_interval == 0:
print(f"Train Epoch: {epoch} [{batch_idx * len(data)}/{len(train_loader.dataset)} ({100. * batch_idx / len(train_loader):.0f}%)]\tLoss: {loss.item():.6f}")
# update progress bar
pbar.update(pbar_update)
# record loss
losses.append(loss.item())
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Now that we have a training function, we need to make one for testing
the networks accuracy. We will set the model to ``eval()`` mode and then
run inference on the test dataset. Calling ``eval()`` sets the training
variable in all modules in the network to false. Certain layers like
batch normalization and dropout layers behave differently during
training so this step is crucial for getting correct results.
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.. code-block:: default
def number_of_correct(pred, target):
# count number of correct predictions
return pred.squeeze().eq(target).sum().item()
def get_likely_index(tensor):
# find most likely label index for each element in the batch
return tensor.argmax(dim=-1)
def test(model, epoch):
model.eval()
correct = 0
for data, target in test_loader:
data = data.to(device)
target = target.to(device)
# apply transform and model on whole batch directly on device
data = transform(data)
output = model(data)
pred = get_likely_index(output)
correct += number_of_correct(pred, target)
# update progress bar
pbar.update(pbar_update)
print(f"\nTest Epoch: {epoch}\tAccuracy: {correct}/{len(test_loader.dataset)} ({100. * correct / len(test_loader.dataset):.0f}%)\n")
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Finally, we can train and test the network. We will train the network
for ten epochs then reduce the learn rate and train for ten more epochs.
The network will be tested after each epoch to see how the accuracy
varies during the training.
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.. code-block:: default
log_interval = 20
n_epoch = 2
pbar_update = 1 / (len(train_loader) + len(test_loader))
losses = []
# The transform needs to live on the same device as the model and the data.
transform = transform.to(device)
with tqdm(total=n_epoch) as pbar:
for epoch in range(1, n_epoch + 1):
train(model, epoch, log_interval)
test(model, epoch)
scheduler.step()
# Let's plot the training loss versus the number of iteration.
# plt.plot(losses);
# plt.title("training loss");
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The network should be more than 65% accurate on the test set after 2
epochs, and 85% after 21 epochs. Let’s look at the last words in the
train set, and see how the model did on it.
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.. code-block:: default
def predict(tensor):
# Use the model to predict the label of the waveform
tensor = tensor.to(device)
tensor = transform(tensor)
tensor = model(tensor.unsqueeze(0))
tensor = get_likely_index(tensor)
tensor = index_to_label(tensor.squeeze())
return tensor
waveform, sample_rate, utterance, *_ = train_set[-1]
ipd.Audio(waveform.numpy(), rate=sample_rate)
print(f"Expected: {utterance}. Predicted: {predict(waveform)}.")
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Let’s find an example that isn’t classified correctly, if there is one.
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.. code-block:: default
for i, (waveform, sample_rate, utterance, *_) in enumerate(test_set):
output = predict(waveform)
if output != utterance:
ipd.Audio(waveform.numpy(), rate=sample_rate)
print(f"Data point #{i}. Expected: {utterance}. Predicted: {output}.")
break
else:
print("All examples in this dataset were correctly classified!")
print("In this case, let's just look at the last data point")
ipd.Audio(waveform.numpy(), rate=sample_rate)
print(f"Data point #{i}. Expected: {utterance}. Predicted: {output}.")
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Feel free to try with one of your own recordings of one of the labels!
For example, using Colab, say “Go” while executing the cell below. This
will record one second of audio and try to classify it.
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.. code-block:: default
def record(seconds=1):
from google.colab import output as colab_output
from base64 import b64decode
from io import BytesIO
from pydub import AudioSegment
RECORD = (
b"const sleep = time => new Promise(resolve => setTimeout(resolve, time))\n"
b"const b2text = blob => new Promise(resolve => {\n"
b" const reader = new FileReader()\n"
b" reader.onloadend = e => resolve(e.srcElement.result)\n"
b" reader.readAsDataURL(blob)\n"
b"})\n"
b"var record = time => new Promise(async resolve => {\n"
b" stream = await navigator.mediaDevices.getUserMedia({ audio: true })\n"
b" recorder = new MediaRecorder(stream)\n"
b" chunks = []\n"
b" recorder.ondataavailable = e => chunks.push(e.data)\n"
b" recorder.start()\n"
b" await sleep(time)\n"
b" recorder.onstop = async ()=>{\n"
b" blob = new Blob(chunks)\n"
b" text = await b2text(blob)\n"
b" resolve(text)\n"
b" }\n"
b" recorder.stop()\n"
b"})"
)
RECORD = RECORD.decode("ascii")
print(f"Recording started for {seconds} seconds.")
display(ipd.Javascript(RECORD))
s = colab_output.eval_js("record(%d)" % (seconds * 1000))
print("Recording ended.")
b = b64decode(s.split(",")[1])
fileformat = "wav"
filename = f"_audio.{fileformat}"
AudioSegment.from_file(BytesIO(b)).export(filename, format=fileformat)
return torchaudio.load(filename)
# Detect whether notebook runs in google colab
if "google.colab" in sys.modules:
waveform, sample_rate = record()
print(f"Predicted: {predict(waveform)}.")
ipd.Audio(waveform.numpy(), rate=sample_rate)
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Conclusion
----------
In this tutorial, we used torchaudio to load a dataset and resample the
signal. We have then defined a neural network that we trained to
recognize a given command. There are also other data preprocessing
methods, such as finding the mel frequency cepstral coefficients (MFCC),
that can reduce the size of the dataset. This transform is also
available in torchaudio as ``torchaudio.transforms.MFCC``.
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