Deep Speech
free
Model Name: Deep Speech
Docs: Deep Speech
Keywords: Speech-to-text, Open-source, Mozilla-developed
Installation: Docs
Introduction
DeepSpeech is an open-source speech recognition system developed by Mozilla that:
- Converts spoken language into text using deep learning techniques
- Implements an end-to-end neural network architecture
- Processes audio directly to text without intermediate components
- Runs efficiently on various devices from servers to consumer hardware
Built on research published by Baidu's Silicon Valley AI Lab, DeepSpeech has been continuously improved through community contributions, focusing on accuracy, speed, and reduced model size for practical applications.
Instructions
1. Installation Options
- Python Package: Install via pip
- Pre-built Binaries: Available for multiple platforms
- Docker: Containerized deployment
# Python installation
pip install deepspeech
# Download pre-trained English model
curl -LO https://github.com/mozilla/DeepSpeech/releases/download/v0.9.3/deepspeech-0.9.3-models.pbmm
curl -LO https://github.com/mozilla/DeepSpeech/releases/download/v0.9.3/deepspeech-0.9.3-models.scorer
2. Basic Usage
- Load the pre-trained model
- Prepare audio in supported format (16kHz, 16-bit mono)
- Process audio through the model
- Access transcription results
3. Model Configuration
- Beam width: Controls accuracy vs. performance trade-off
- Language model: Optional scorer for improved accuracy
- Advanced parameters: Fine-tune for specific use cases
4. Custom Training
- Prepare training data with transcriptions
- Set up training environment
- Run training process with appropriate parameters
- Evaluate and optimize model performance
Capabilities
Core Features
- • End-to-end speech recognition
- • Language identification
- • Streaming transcription
Technical Aspects
- • Cross-platform support
- • TensorFlow-based architecture
- • CPU & GPU acceleration
Integration Options
- • C, Python, JavaScript APIs
- • Command-line interface
- • Microservice deployment
Practical Applications
- • Transcription services
- • Voice assistants
- • Accessibility tools
Examples
// Basic Python usage
import deepspeech
import numpy as np
import wave
# Load pre-trained model
model = deepspeech.Model('deepspeech-0.9.3-models.pbmm')
model.enableExternalScorer('deepspeech-0.9.3-models.scorer')
# Process audio file
def transcribe_file(audio_file):
w = wave.open(audio_file, 'r')
frames = w.readframes(w.getnframes())
buffer = np.frombuffer(frames, np.int16)
text = model.stt(buffer)
return text
result = transcribe_file('audio.wav')
// Stream processing example
import deepspeech
import numpy as np
model = deepspeech.Model('deepspeech-0.9.3-models.pbmm')
model.enableExternalScorer('deepspeech-0.9.3-models.scorer')
# Create streaming session
stream = model.createStream()
# Process audio in chunks (e.g., from microphone)
def process_audio_chunk(audio_chunk):
buffer = np.frombuffer(audio_chunk, np.int16)
stream.feedAudioContent(buffer)
# Get final result when done
text = stream.finishStream()
Table of Contents
Key Features
- • Open Source: Community-driven development
- • Cross-platform: Works on many devices
- • Lightweight: Optimized for performance
- • Multilingual: Support for various languages
- • Customizable: Train with domain-specific data