speech-recognition-from-microphone-with-endpoint-detection.py
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#!/usr/bin/env python3
# Real-time speech recognition from a microphone with sherpa-onnx Python API
# with endpoint detection.
#
# 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 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 assert_file_exists(filename: str):
assert Path(
filename
).is_file(), f"{filename} does not exist!\nPlease 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,
enable_endpoint_detection=True,
rule1_min_trailing_silence=2.4,
rule2_min_trailing_silence=1.2,
rule3_min_utterance_length=300, # it essentially disables this rule
decoding_method=args.decoding_method,
max_feature_vectors=100, # 1 second
)
return recognizer
def main():
recognizer = create_recognizer()
print("Started! Please speak")
sample_rate = 16000
samples_per_read = int(0.1 * sample_rate) # 0.1 second = 100 ms
last_result = ""
stream = recognizer.create_stream()
last_result = ""
segment_id = 0
display = sherpa_onnx.Display(max_word_per_line=30)
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)
is_endpoint = recognizer.is_endpoint(stream)
result = recognizer.get_result(stream)
if result and (last_result != result):
last_result = result
display.print(segment_id, result)
if is_endpoint:
if result:
segment_id += 1
recognizer.reset(stream)
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")