File size: 8,114 Bytes
da8e0c5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 |
from typing import *
import logging
import time
import logging
import sherpa_onnx
import os
import asyncio
import numpy as np
logger = logging.getLogger(__file__)
_asr_engines = {}
class ASRResult:
def __init__(self, text: str, finished: bool, idx: int):
self.text = text
self.finished = finished
self.idx = idx
def to_dict(self):
return {"text": self.text, "finished": self.finished, "idx": self.idx}
class ASRStream:
def __init__(self, recognizer: Union[sherpa_onnx.OnlineRecognizer | sherpa_onnx.OfflineRecognizer], sample_rate: int) -> None:
self.recognizer = recognizer
self.inbuf = asyncio.Queue()
self.outbuf = asyncio.Queue()
self.sample_rate = sample_rate
self.is_closed = False
self.online = isinstance(recognizer, sherpa_onnx.OnlineRecognizer)
async def start(self):
if self.online:
asyncio.create_task(self.run_online())
else:
asyncio.create_task(self.run_offline())
async def run_online(self):
stream = self.recognizer.create_stream()
last_result = ""
segment_id = 0
logger.info('asr: start real-time recognizer')
while not self.is_closed:
samples = await self.inbuf.get()
stream.accept_waveform(self.sample_rate, samples)
while self.recognizer.is_ready(stream):
self.recognizer.decode_stream(stream)
is_endpoint = self.recognizer.is_endpoint(stream)
result = self.recognizer.get_result(stream)
if result and (last_result != result):
last_result = result
logger.info(f' > {segment_id}:{result}')
self.outbuf.put_nowait(
ASRResult(result, False, segment_id))
if is_endpoint:
if result:
logger.info(f'{segment_id}: {result}')
self.outbuf.put_nowait(
ASRResult(result, True, segment_id))
segment_id += 1
self.recognizer.reset(stream)
async def run_offline(self):
vad = _asr_engines['vad']
segment_id = 0
st = None
while not self.is_closed:
samples = await self.inbuf.get()
vad.accept_waveform(samples)
while not vad.empty():
if not st:
st = time.time()
stream = self.recognizer.create_stream()
stream.accept_waveform(self.sample_rate, vad.front.samples)
vad.pop()
self.recognizer.decode_stream(stream)
result = stream.result.text.strip()
if result:
duration = time.time() - st
logger.info(f'{segment_id}:{result} ({duration:.2f}s)')
self.outbuf.put_nowait(ASRResult(result, True, segment_id))
segment_id += 1
st = None
async def close(self):
self.is_closed = True
self.outbuf.put_nowait(None)
async def write(self, pcm_bytes: bytes):
pcm_data = np.frombuffer(pcm_bytes, dtype=np.int16)
samples = pcm_data.astype(np.float32) / 32768.0
self.inbuf.put_nowait(samples)
async def read(self) -> ASRResult:
return await self.outbuf.get()
def create_zipformer(samplerate: int, args) -> sherpa_onnx.OnlineRecognizer:
d = os.path.join(
args.models_root, 'sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20')
if not os.path.exists(d):
raise ValueError(f"asr: model not found {d}")
encoder = os.path.join(d, "encoder-epoch-99-avg-1.onnx")
decoder = os.path.join(d, "decoder-epoch-99-avg-1.onnx")
joiner = os.path.join(d, "joiner-epoch-99-avg-1.onnx")
tokens = os.path.join(d, "tokens.txt")
recognizer = sherpa_onnx.OnlineRecognizer.from_transducer(
tokens=tokens,
encoder=encoder,
decoder=decoder,
joiner=joiner,
provider=args.asr_provider,
num_threads=args.threads,
sample_rate=samplerate,
feature_dim=80,
enable_endpoint_detection=True,
rule1_min_trailing_silence=2.4,
rule2_min_trailing_silence=1.2,
rule3_min_utterance_length=20, # it essentially disables this rule
)
return recognizer
def create_sensevoice(samplerate: int, args) -> sherpa_onnx.OfflineRecognizer:
d = os.path.join(args.models_root,
'sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17')
if not os.path.exists(d):
raise ValueError(f"asr: model not found {d}")
recognizer = sherpa_onnx.OfflineRecognizer.from_sense_voice(
model=os.path.join(d, 'model.onnx'),
tokens=os.path.join(d, 'tokens.txt'),
num_threads=args.threads,
sample_rate=samplerate,
use_itn=True,
debug=0,
language=args.asr_lang,
)
return recognizer
def create_paraformer_trilingual(samplerate: int, args) -> sherpa_onnx.OnlineRecognizer:
d = os.path.join(
args.models_root, 'sherpa-onnx-paraformer-trilingual-zh-cantonese-en')
if not os.path.exists(d):
raise ValueError(f"asr: model not found {d}")
recognizer = sherpa_onnx.OfflineRecognizer.from_paraformer(
paraformer=os.path.join(d, 'model.onnx'),
tokens=os.path.join(d, 'tokens.txt'),
num_threads=args.threads,
sample_rate=samplerate,
debug=0,
provider=args.asr_provider,
)
return recognizer
def create_paraformer_en(samplerate: int, args) -> sherpa_onnx.OnlineRecognizer:
d = os.path.join(
args.models_root, 'sherpa-onnx-paraformer-en')
if not os.path.exists(d):
raise ValueError(f"asr: model not found {d}")
recognizer = sherpa_onnx.OfflineRecognizer.from_paraformer(
paraformer=os.path.join(d, 'model.onnx'),
tokens=os.path.join(d, 'tokens.txt'),
num_threads=args.threads,
sample_rate=samplerate,
use_itn=True,
debug=0,
provider=args.asr_provider,
)
return recognizer
def load_asr_engine(samplerate: int, args) -> sherpa_onnx.OnlineRecognizer:
cache_engine = _asr_engines.get(args.asr_model)
if cache_engine:
return cache_engine
st = time.time()
if args.asr_model == 'zipformer-bilingual':
cache_engine = create_zipformer(samplerate, args)
elif args.asr_model == 'sensevoice':
cache_engine = create_sensevoice(samplerate, args)
_asr_engines['vad'] = load_vad_engine(samplerate, args)
elif args.asr_model == 'paraformer-trilingual':
cache_engine = create_paraformer_trilingual(samplerate, args)
_asr_engines['vad'] = load_vad_engine(samplerate, args)
elif args.asr_model == 'paraformer-en':
cache_engine = create_paraformer_en(samplerate, args)
_asr_engines['vad'] = load_vad_engine(samplerate, args)
else:
raise ValueError(f"asr: unknown model {args.asr_model}")
_asr_engines[args.asr_model] = cache_engine
logger.info(f"asr: engine loaded in {time.time() - st:.2f}s")
return cache_engine
def load_vad_engine(samplerate: int, args, min_silence_duration: float = 0.25, buffer_size_in_seconds: int = 100) -> sherpa_onnx.VoiceActivityDetector:
config = sherpa_onnx.VadModelConfig()
d = os.path.join(args.models_root, 'silero_vad')
if not os.path.exists(d):
raise ValueError(f"vad: model not found {d}")
config.silero_vad.model = os.path.join(d, 'silero_vad.onnx')
config.silero_vad.min_silence_duration = min_silence_duration
config.sample_rate = samplerate
config.provider = args.asr_provider
config.num_threads = args.threads
vad = sherpa_onnx.VoiceActivityDetector(
config,
buffer_size_in_seconds=buffer_size_in_seconds)
return vad
async def start_asr_stream(samplerate: int, args) -> ASRStream:
"""
Start a ASR stream
"""
stream = ASRStream(load_asr_engine(samplerate, args), samplerate)
await stream.start()
return stream
|