Hands-on with DeepSpeech

By Bill Sharlow

Day 7: Developing a Voice Recognition System:

Welcome back to our DeepSpeech journey! Today, we’re diving into hands-on examples and tutorials to demonstrate how you can leverage Mozilla’s DeepSpeech to perform speech recognition tasks. Get ready to explore real-world applications and unlock the full potential of DeepSpeech in your projects.

Example 1: Transcribing Audio Files

In this example, we’ll transcribe an audio file using DeepSpeech. Let’s get started:

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.enableExternalScorer(scorer_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 = audio_file.read()
text = model.stt(audio_data)
print("Transcript:", text)

Example 2: Real-time Speech Recognition

In this example, we’ll perform real-time speech recognition using DeepSpeech and the microphone:

import deepspeech
import pyaudio
import wave

# 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.enableExternalScorer(scorer_path)
model.setScorerAlphaBeta(0.75, 1.85)

# Configure microphone settings
sample_rate = 16000
chunk_size = 1024

# Open microphone stream
audio = pyaudio.PyAudio()
stream = audio.open(format=pyaudio.paInt16, channels=1, rate=sample_rate, input=True, frames_per_buffer=chunk_size)

# Perform real-time speech recognition
print("Listening...")
frames = []
while True:
    data = stream.read(chunk_size)
    frames.append(data)
    if len(data) == 0:
        break
audio_data = b"".join(frames)
text = model.stt(audio_data)
print("Transcript:", text)

# Close microphone stream
stream.stop_stream()
stream.close()
audio.terminate()

Conclusion

In today’s blog post, we’ve explored hands-on examples and tutorials for using Mozilla’s DeepSpeech to perform speech recognition tasks. We’ve covered transcribing audio files and performing real-time speech recognition using the microphone. With these examples, you can now start integrating DeepSpeech into your projects and building powerful voice recognition applications.

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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