Exploring Offline Speech-to-Text Transcription Tools in Python

Speech-to-text transcription tools have become an integral part of modern applications. Whether it’s for automating transcription in media production, building voice assistants, or assisting with accessibility, developers now have multiple options to incorporate speech recognition in their Python applications. One of the key challenges, however, is finding offline transcription tools. Online tools often raise concerns about privacy, data security, and internet connectivity. In this post, we’ll explore the best offline speech-to-text transcription libraries available in Python.

Why Offline Transcription?

While there are a number of powerful online APIs for speech-to-text (like Google Cloud Speech, IBM Watson, or Amazon Transcribe), offline tools offer several advantages:

• Privacy: Since data doesn’t leave your system, there are no privacy concerns.
• No internet dependency: Applications can work in environments where internet connectivity is unreliable or unavailable.
• Cost: You avoid subscription or usage fees for cloud-based APIs.

Let’s look at some of the popular offline transcription tools you can use in Python:

1. CMU Sphinx (PocketSphinx)

CMU Sphinx is one of the oldest speech recognition systems, developed at Carnegie Mellon University. The library is widely used for offline speech recognition and includes a Python wrapper called PocketSphinx.

Key Features:

• Language Model Flexibility: You can train it on custom language models to recognize domain-specific vocabulary.
• Lightweight: PocketSphinx is relatively lightweight and works efficiently on low-resource devices.
• Multilingual support: Available in multiple languages beyond just English.

How to Use:

First, install the library:

pip install pocketsphinx

Next, you can transcribe an audio file in Python:

import speech_recognition as sr

recognizer = sr.Recognizer()
with sr.AudioFile('audio.wav') as source:
    audio = recognizer.record(source)
    
# Transcribing audio using PocketSphinx
try:
    print(recognizer.recognize_sphinx(audio))
except sr.UnknownValueError:
    print("Sphinx could not understand audio")
except sr.RequestError as e:
    print(f"Sphinx error: {e}")

Pros:

• Works offline and is highly customizable.
• Lightweight and efficient.

Cons:

• Not as accurate as modern machine-learning-based models.
• Can require significant tuning to achieve acceptable accuracy.

2. Vosk

Vosk is a modern offline speech recognition toolkit built with cutting-edge machine learning models. It supports multiple languages and can be easily integrated with Python applications.

Key Features:

• High accuracy: Vosk uses deep learning techniques to deliver state-of-the-art transcription accuracy.
• Low resource consumption: Works on smaller devices like Raspberry Pi and Android phones.
• Real-time transcription: Supports real-time speech recognition.
• Supports multiple languages: Over 20 languages are supported, including English, Spanish, Russian, and Chinese.

How to Use:

To get started, install the vosk Python package:

pip install vosk

You also need to download a Vosk language model:

wget https://alphacephei.com/vosk/models/vosk-model-en-us-0.22.zip
unzip vosk-model-en-us-0.22.zip

Here’s how you can transcribe an audio file:

import wave
import json
from vosk import Model, KaldiRecognizer

# Load Vosk model
model = Model("vosk-model-en-us-0.22")
rec = KaldiRecognizer(model, 16000)

# Open audio file
wf = wave.open("audio.wav", "rb")

# Read audio and transcribe
while True:
    data = wf.readframes(4000)
    if len(data) == 0:
        break
    if rec.AcceptWaveform(data):
        result = rec.Result()
        print(json.loads(result))
    else:
        print(rec.PartialResult())

Pros:

• High transcription accuracy.
• Works offline and is compatible with low-resource devices.
• Supports multiple languages.

Cons:

• Requires downloading relatively large language models.

3. DeepSpeech

DeepSpeech is an open-source speech-to-text engine created by Mozilla. It is based on a deep learning model and offers high accuracy, even in noisy environments.

Key Features:

• State-of-the-art accuracy: Uses a recurrent neural network (RNN) architecture trained on large datasets.
• Supports pre-trained models: You can use Mozilla’s pre-trained models or fine-tune the system with your own data.
• Streaming support: Can be used for real-time speech recognition applications.

How to Use:

You can install DeepSpeech using the following command:

pip install deepspeech

You’ll also need to download the DeepSpeech pre-trained model:

wget https://github.com/mozilla/DeepSpeech/releases/download/v0.9.3/deepspeech-0.9.3-models.pbmm
wget https://github.com/mozilla/DeepSpeech/releases/download/v0.9.3/deepspeech-0.9.3-models.scorer

Now, you can transcribe an audio file with DeepSpeech:

import deepspeech
import wave
import numpy as np

model_file_path = 'deepspeech-0.9.3-models.pbmm'
scorer_file_path = 'deepspeech-0.9.3-models.scorer'
audio_file_path = 'audio.wav'

# Load DeepSpeech model
model = deepspeech.Model(model_file_path)
model.enableExternalScorer(scorer_file_path)

# Open the audio file
wf = wave.open(audio_file_path, 'rb')

# Read audio data
audio = np.frombuffer(wf.readframes(wf.getnframes()), np.int16)

# Transcribe
text = model.stt(audio)
print(text)

Pros:

• High accuracy and robust performance in noisy conditions.
• Easy to fine-tune for custom applications.

Cons:

• Relatively large model size.
• Higher resource consumption compared to lightweight tools like PocketSphinx.

4. Wav2Vec 2.0 (Fairseq)

Wav2Vec 2.0 is an innovative speech recognition model developed by Facebook AI. It leverages self-supervised learning to achieve remarkable transcription accuracy.

Key Features:

• High accuracy: Comparable to some of the best online services.
• Self-supervised learning: Requires fewer labeled datasets to fine-tune.
• Custom training: Can be trained on specific data for domain-specific tasks.

How to Use:

Wav2Vec 2.0 models can be accessed through Hugging Face Transformers, making it easy to use in Python:

pip install transformers datasets

Here’s a basic transcription example:

from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import torch
import librosa

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

# Load audio
audio, rate = librosa.load("audio.wav", sr=16000)

# Process the audio for transcription
input_values = processor(audio, return_tensors="pt", sampling_rate=rate).input_values
logits = model(input_values).logits

# Get the predicted transcription
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)[0]
print(transcription)

Pros:

• Cutting-edge performance.
• Uses modern deep learning techniques to achieve exceptional transcription quality.

Cons:

• Requires significant computational resources.
• May need GPU support for real-time applications.

Conclusion

Offline speech-to-text transcription in Python has come a long way, thanks to the advancement of machine learning models and open-source communities. Whether you need a lightweight, customizable solution like PocketSphinx or a highly accurate modern tool like Vosk or Wav2Vec 2.0, there’s an option to suit your project’s needs.

Each tool comes with its own set of trade-offs between accuracy, model size, and resource consumption. Experiment with these libraries to find the best fit for your offline transcription requirements.

Happy coding!