A PyPI package that wraps Whisper.cpp for speech-to-text (STT) transcription, compatible with the FastRTC STTModel protocol. This package provides efficient, CPU-based speech recognition using the optimized Whisper.cpp implementation.
pip install fastrtc-whisper-cppFor audio file loading capabilities, install with the audio extras:
pip install "fastrtc-whisper-cpp[audio]"For development:
pip install "fastrtc-whisper-cpp[dev]"from fastrtc_whisper_cpp import get_stt_model
import numpy as np
# Create the model (downloads from HF if not cached)
model = get_stt_model()
# Example: Create a sample audio array (actual audio would come from a file or mic)
sample_rate = 16000
audio_data = np.zeros(16000, dtype=np.float32) # 1 second of silence
# Transcribe
text = model.stt((sample_rate, audio_data))
print(f"Transcription: {text}")If you've installed with the audio extras:
from fastrtc_whisper_cpp import get_stt_mo
8000
del, load_audio
# Load model
model = get_stt_model()
# Load audio file (automatically resamples to 16kHz)
audio = load_audio("path/to/audio.wav")
# Transcribe
text = model.stt(audio)
print(f"Transcription: {text}")from fastrtc_whisper_cpp import get_stt_model
# Create the model
whisper_model = get_stt_model()
# Use within FastRTC applications
# (Follow FastRTC documentation for integration details)The package supports various Whisper.cpp models with different sizes and quantization levels:
-
English-only models (faster, smaller):
tiny.en,tiny.en-q5_1,tiny.en-q8_0base.en,base.en-q5_1,base.en-q8_0small.en,small.en-q5_1,small.en-q8_0medium.en,medium.en-q5_0,medium.en-q8_0
-
Multilingual models:
tiny,tiny-q5_1,tiny-q8_0base,base-q5_1,base-q8_0small,small-q5_1,small-q8_0medium,medium-q5_0,medium-q8_0large-v1large-v2,large-v2-q5_0,large-v2-q8_0large-v3,large-v3-q5_0large-v3-turbo,large-v3-turbo-q5_0,large-v3-turbo-q8_0
Example:
from fastrtc_whisper_cpp import get_stt_model
# Choose a specific model
model = get_stt_model("medium.en-q8_0")You can configure the model with specific parameters:
from fastrtc_whisper_cpp import WhisperCppSTT
# Configure with specific model and models directory
model = WhisperCppSTT(
model="medium.en",
models_dir="/path/to/models" # Optional custom models directory
)- Python 3.10+
- numpy
- pywhispercpp
- librosa (optional, for audio file loading)
- click (for CLI features)
Clone the repository and install in development mode:
git clone https://github.com/mahimairaja/fastrtc-whisper-cpp.git
cd fastrtc-whisper-cpp
pip install -e ".[dev,audio]"MIT