Installing and Configuring DeepSpeech

By Bill Sharlow

Day 6: Developing a Voice Recognition System

Welcome back! Today, we’re diving into the practical aspects of using Mozilla’s DeepSpeech. In this blog post, we’ll walk you through the process of installing and configuring DeepSpeech on your local machine or server environment, so you can start harnessing its powerful speech recognition capabilities.

Step 1: Installing DeepSpeech

Before you can start using DeepSpeech, you’ll need to install the DeepSpeech library and its dependencies. Here’s how you can do it:

  1. Using pip:
   pip install deepspeech
  1. From source:
  • Clone the DeepSpeech repository from GitHub:
    bash git clone
  • Navigate to the DeepSpeech directory and run the installation script:
    bash cd DeepSpeech pip install .

Step 2: Downloading Pre-Trained Models

Next, you’ll need to download pre-trained DeepSpeech models and language models. These models are trained on large datasets and provide the necessary parameters for performing speech recognition tasks. You can download pre-trained models from the official DeepSpeech releases page:

Step 3: Performing Speech Recognition

Once you have DeepSpeech installed and the pre-trained models downloaded, you can start performing speech recognition on audio files or real-time audio streams. Here’s a basic example of how to transcribe an audio file using DeepSpeech:

import deepspeech

# Initialize DeepSpeech model
model_path = "path/to/deepspeech-0.9.3-models.pbmm"
scorer_path = "path/to/deepspeech-0.9.3-models.scorer"
beam_width = 500
model = deepspeech.Model(model_path)
model.setScorerAlphaBeta(0.75, 1.85)

# Transcribe audio file
audio_path = "path/to/audio/file.wav"
with open(audio_path, "rb") as audio_file:
    audio_data =
text = model.stt(audio_data)
print("Transcript:", text)


In today’s blog post, we’ve walked you through the process of installing and configuring Mozilla’s DeepSpeech on your local machine or server environment. We’ve also provided instructions for downloading pre-trained models and performing speech recognition tasks using DeepSpeech. With DeepSpeech, you can now start building powerful voice recognition applications and unlock new possibilities for interaction and accessibility.

Stay tuned for tomorrow’s post, where we’ll delve into more advanced features and techniques for leveraging DeepSpeech in your projects.

If you have any questions or thoughts, feel free to share them in the comments section below!

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