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# from logging import getLogger
import numpy as np
from funasr import AutoModel
import config
# logger = getLogger(__name__)
class FunASR:
def __init__(self, source_lange: str = 'en', warmup=True) -> None:
self.source_lange = source_lange
model_dir = config.MODEL_DIR
asr_model_path = model_dir / 'speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'
vad_model_path = model_dir / 'speech_fsmn_vad_zh-cn-16k-common-pytorch'
punc_model_path = model_dir / 'punc_ct-transformer_cn-en-common-vocab471067-large'
self.model = AutoModel(
model=asr_model_path.as_posix(),
vad_model=vad_model_path.as_posix(),
punc_model=punc_model_path.as_posix(),
log_level="ERROR",
disable_update=True
)
if warmup:
self.warmup()
def warmup(self, warmup_steps=1):
warmup_soundfile = f"{config.ASSERT_DIR}/jfk.flac"
for _ in range(warmup_steps):
self.model.generate(input=warmup_soundfile, disable_pbar=True)
def transcribe(self, audio_buffer: bytes, language):
audio_frames = np.frombuffer(audio_buffer, dtype=np.float32)
# sf.write(f'{config.ASSERT_DIR}/{time.time()}.wav', audio_frames, samplerate=16000)
try:
output = self.model.generate(input=audio_frames, disable_pbar=True, hotword=config.hotwords_file.as_posix())
return output
except Exception as e:
print(f"Error during transcription: {e}")
return []
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