94d77fa52e
* fix garbled console output with chinese characters * use print to instead sherpa_onnx.Display
128 lines
3.3 KiB
Python
Executable File
128 lines
3.3 KiB
Python
Executable File
#!/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 argparse
|
|
import sys
|
|
from pathlib import Path
|
|
|
|
try:
|
|
import sounddevice as sd
|
|
except ImportError:
|
|
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 assert_file_exists(filename: str):
|
|
assert Path(filename).is_file(), (
|
|
f"{filename} does not exist!\n"
|
|
"Please refer to "
|
|
"https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html to download it"
|
|
)
|
|
|
|
|
|
def get_args():
|
|
parser = argparse.ArgumentParser(
|
|
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--tokens",
|
|
type=str,
|
|
help="Path to tokens.txt",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--encoder",
|
|
type=str,
|
|
help="Path to the encoder model",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--decoder",
|
|
type=str,
|
|
help="Path to the decoder model",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--joiner",
|
|
type=str,
|
|
help="Path to the joiner model",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--decoding-method",
|
|
type=str,
|
|
default="greedy_search",
|
|
help="Valid values are greedy_search and modified_beam_search",
|
|
)
|
|
|
|
return parser.parse_args()
|
|
|
|
|
|
def create_recognizer():
|
|
args = get_args()
|
|
assert_file_exists(args.encoder)
|
|
assert_file_exists(args.decoder)
|
|
assert_file_exists(args.joiner)
|
|
assert_file_exists(args.tokens)
|
|
# 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=args.tokens,
|
|
encoder=args.encoder,
|
|
decoder=args.decoder,
|
|
joiner=args.joiner,
|
|
num_threads=1,
|
|
sample_rate=16000,
|
|
feature_dim=80,
|
|
decoding_method=args.decoding_method,
|
|
)
|
|
return recognizer
|
|
|
|
|
|
def main():
|
|
recognizer = create_recognizer()
|
|
print("Started! Please speak")
|
|
|
|
# The model is using 16 kHz, we use 48 kHz here to demonstrate that
|
|
# sherpa-onnx will do resampling inside.
|
|
sample_rate = 48000
|
|
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("\r{}".format(result), end="", flush=True)
|
|
|
|
|
|
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")
|