PyTorch for Speech Recognition

Speech recognition is a transformative technology that enables computers to understand and interpret spoken language, fostering seamless interaction between humans and machines. By implementing algorithms and machine learning techniques, speech recognition systems transcribe spoken words into text, facilitating a diverse array of applications. In this article, we will see how to use Pytorch for speech recognition.

Using PyTorch For Speech Recognition

In this section, we will delve into the process of using PyTorch for speech recognition, covering essential steps from loading and preprocessing audio data to leveraging state-of-the-art models like Wav2Vec2 for transcription. Whether you’re a beginner exploring the field of speech recognition or an experienced developer looking to implement advanced models, this guide will provide you with practical insights and code examples to get started with PyTorch for speech recognition tasks.

We will use PyTorch for audio processing by following these steps:

Installing Required Packages

We will be importing following libraries:

• PyTorch for building and training deep learning models.
• Torchaudio is an extension library for PyTorch offering audio processing functionalities, simplifying audio data integration for tasks like speech recognition.
• Librosa for audio and music analysis, offering tools for loading audio, computing features, and visualizing audio data.

pip install torch torchaudio
pip install matplotlib
pip install librosa

Loading and Preprocessing Audio Data

• Loading Audio Data: First load the audio file from the storage location into memory. The audio file can be in various formats like WAV, MP3, or FLAC. Libraries like torchaudio in Python provide functions to load audio files efficiently. The loaded audio data is represented as a waveform, which is essentially a time-series of audio samples.

import torchaudio

# Load audio file
waveform, sample_rate = torchaudio.load(' Your audio_file.wav')

• Resampling (Optional): Audio data may have different sampling rates, which represent the number of samples per second. Some models may require a specific sampling rate for processing. In such cases, resampling is performed to convert the audio data to the desired sampling rate. Resampling ensures uniformity in the sampling rate across different audio files.

resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=target_sample_rate)
waveform = resampler(waveform)

• Preprocessing: Preprocessing techniques may include normalization, feature extraction, or augmentation, depending on the specific task and requirements. Common preprocessing steps for speech recognition tasks include removing silence, applying noise reduction techniques, and extracting features like Mel-frequency cepstral coefficients (MFCCs) or spectrograms.

mfcc_transform = torchaudio.transforms.MFCC(sample_rate=sample_rate)
mfcc = mfcc_transform(waveform)

• Loading and Preprocessing Audio Data with librosa (alternative): The librosa.load function is used to load an audio file ('audio_file.wav') into memory, returning the audio waveform as a one-dimensional NumPy array (waveform) and the sample rate of the audio (sample_rate).

import librosa

# Load audio file
waveform, sample_rate = librosa.load('audio_file.wav', sr=None)

# Resampling (if needed)
waveform = librosa.resample(waveform, orig_sr=sample_rate, target_sr=target_sample_rate)

# Feature Extraction (e.g., MFCCs)
mfcc = librosa.feature.mfcc(waveform, sr=target_sample_rate)

Using Wav2Vec2 Model for Speech Recognition

• Using a pre-trained Wav2Vec2 model for speech recognition or feature extraction is straightforward with the torchaudio library in PyTorch.
• Wav2Vec2 model is a deep learning architecture designed for speech processing tasks, particularly for automatic speech recognition (ASR). It is an extension of the original Wav2Vec model and utilizes a self-supervised pre-training approach to learn speech representations directly from raw audio waveforms.

We will use the following audio file.

Python3

import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import matplotlib.pyplot as plt

# Load pre-trained model
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")

# Load audio data
waveform, sample_rate = torchaudio.load("your_audio.wav")
waveform_resampled = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)(waveform)

# Plot waveform and spectrogram
plt.figure(figsize=(12, 6))
plt.subplot(2, 1, 1)
plt.plot(waveform.t().numpy())
plt.title('Waveform')
plt.xlabel('Sample')
plt.ylabel('Amplitude')

plt.subplot(2, 1, 2)
spectrogram = torchaudio.transforms.Spectrogram()(waveform_resampled)
# Extract the first channel of the spectrogram for visualization
spectrogram_channel1 = spectrogram[0, :, :]
plt.imshow(spectrogram_channel1.log2().numpy(), aspect='auto', cmap='inferno')
plt.title('Spectrogram')
plt.xlabel('Time')
plt.ylabel('Frequency')

plt.tight_layout()
plt.show()

# Perform inference
with torch.no_grad():
    logits = model(waveform_resampled).logits

# Decode logits to text
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)

# Print transcription
print("Transcription:", transcription)

Output:

Transcription: ['THE STALE SMELL OF OLD BEER LINGERS IT TAKES HEAT TO BRING OUT THE ODOUR A COLD DIP RESTORES HEALTH AND ZEST A SALT PICKLE TASTES FINE WITH HAM TAKOZAL PASTOR ARE MY FAVORITE A ZESTFUL FOOD IS THE HOT CROSS BUN', 'THE STALE SMELL OF OLD BEER LINGERS IT TAKES HEAT TO BRING OUT THE ODOR A COLD DIP RESTORES HEALTH AND ZEST A SALT PICKLE TASTES FINE WITH HAM TAKOZAL PASTOR ARE MY FAVORITE A ZESTFUL FOOD IS THE HOT CROSS BUN']

The transcribed text represents the spoken words in the audio file. It is the result of passing the audio waveform through the Wav2Vec2 model and decoding the output logits into text. The transcription provides a textual representation of the audio content, allowing for further analysis or processing of the spoken words.The transcription is printed to the console using the print("Transcription:", transcription) statement. This allows the user to easily view the transcribed text and use it for various purposes, such as text analysis, captioning, or indexing audio content.

Overall, the output of the code provides the transcribed text of the input audio file, demonstrating the ability of the Wav2Vec2 model to perform automatic speech recognition tasks.