speech-recognition-from-microphone.py
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#!/usr/bin/env python3
# Real-time speech recognition from a microphone with sherpa-onnx Python API
#
# Please refer to
# https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html
# to download pre-trained models
import sys
try:
import sounddevice as sd
except ImportError as e:
print("Please install sounddevice first. You can use")
print()
print(" pip install sounddevice")
print()
print("to install it")
sys.exit(-1)
import sherpa_onnx
def create_recognizer():
# Please replace the model files if needed.
# See https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html
# for download links.
recognizer = sherpa_onnx.OnlineRecognizer(
tokens="./sherpa-onnx-lstm-en-2023-02-17/tokens.txt",
encoder="./sherpa-onnx-lstm-en-2023-02-17/encoder-epoch-99-avg-1.onnx",
decoder="./sherpa-onnx-lstm-en-2023-02-17/decoder-epoch-99-avg-1.onnx",
joiner="./sherpa-onnx-lstm-en-2023-02-17/joiner-epoch-99-avg-1.onnx",
num_threads=4,
sample_rate=16000,
feature_dim=80,
)
return recognizer
def main():
print("Started! Please speak")
recognizer = create_recognizer()
sample_rate = 16000
samples_per_read = int(0.1 * sample_rate) # 0.1 second = 100 ms
last_result = ""
stream = recognizer.create_stream()
with sd.InputStream(channels=1, dtype="float32", samplerate=sample_rate) as s:
while True:
samples, _ = s.read(samples_per_read) # a blocking read
samples = samples.reshape(-1)
stream.accept_waveform(sample_rate, samples)
while recognizer.is_ready(stream):
recognizer.decode_stream(stream)
result = recognizer.get_result(stream)
if last_result != result:
last_result = result
print(result)
if __name__ == "__main__":
devices = sd.query_devices()
print(devices)
default_input_device_idx = sd.default.device[0]
print(f'Use default device: {devices[default_input_device_idx]["name"]}')
try:
main()
except KeyboardInterrupt:
print("\nCaught Ctrl + C. Exiting")