/
speech-recognition-from-url.py
executable file
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/
speech-recognition-from-url.py
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#!/usr/bin/env python3
#
# Real-time speech recognition from a URL with sherpa-onnx Python API
#
# Supported URLs are those supported by ffmpeg.
#
# For instance:
# (1) RTMP
# rtmp://localhost/live/livestream
#
# (2) A file
# https://huggingface.co/spaces/k2-fsa/automatic-speech-recognition/resolve/main/test_wavs/wenetspeech/DEV_T0000000000.opus
# https://huggingface.co/spaces/k2-fsa/automatic-speech-recognition/resolve/main/test_wavs/aishell2/ID0012W0030.wav
# file:///Users/fangjun/open-source/sherpa-onnx/a.wav
#
# Note that it supports all file formats supported by ffmpeg
#
# Please refer to
# https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html
# to download pre-trained models
import argparse
import shutil
import subprocess
import sys
from pathlib import Path
import numpy as np
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,
required=True,
help="Path to tokens.txt",
)
parser.add_argument(
"--encoder",
type=str,
required=True,
help="Path to the encoder model",
)
parser.add_argument(
"--decoder",
type=str,
required=True,
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",
)
parser.add_argument(
"--url",
type=str,
required=True,
help="""Example values:
rtmp://localhost/live/livestream
https://huggingface.co/spaces/k2-fsa/automatic-speech-recognition/resolve/main/test_wavs/wenetspeech/DEV_T0000000000.opus
https://huggingface.co/spaces/k2-fsa/automatic-speech-recognition/resolve/main/test_wavs/aishell2/ID0012W0030.wav
""",
)
parser.add_argument(
"--hotwords-file",
type=str,
default="",
help="""
The file containing hotwords, one words/phrases per line, and for each
phrase the bpe/cjkchar are separated by a space. For example:
▁HE LL O ▁WORLD
你 好 世 界
""",
)
parser.add_argument(
"--hotwords-score",
type=float,
default=1.5,
help="""
The hotword score of each token for biasing word/phrase. Used only if
--hotwords-file is given.
""",
)
return parser.parse_args()
def create_recognizer(args):
# 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.from_transducer(
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,
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
hotwords_file=args.hotwords_file,
hotwords_score=args.hotwords_score,
)
return recognizer
def main():
args = get_args()
assert_file_exists(args.encoder)
assert_file_exists(args.decoder)
assert_file_exists(args.joiner)
assert_file_exists(args.tokens)
recognizer = create_recognizer(args)
ffmpeg_cmd = [
"ffmpeg",
"-i",
args.url,
"-f",
"s16le",
"-acodec",
"pcm_s16le",
"-ac",
"1",
"-ar",
"16000",
"-",
]
process = subprocess.Popen(
ffmpeg_cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL
)
frames_per_read = 1600 # 0.1 second
stream = recognizer.create_stream()
last_result = ""
segment_id = 0
print("Started!")
while True:
# *2 because int16_t has two bytes
data = process.stdout.read(frames_per_read * 2)
if not data:
break
samples = np.frombuffer(data, dtype=np.int16)
samples = samples.astype(np.float32) / 32768
stream.accept_waveform(16000, 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
print("\r{}:{}".format(segment_id, result), end="", flush=True)
if is_endpoint:
if result:
print("\r{}:{}".format(segment_id, result), flush=True)
segment_id += 1
recognizer.reset(stream)
if __name__ == "__main__":
if shutil.which("ffmpeg") is None:
sys.exit("Please install ffmpeg first!")
main()