import torch
|
import torchaudio
|
from modelscope.pipelines import pipeline
|
from modelscope.utils.constant import Tasks
|
|
from modelscope.utils.logger import get_logger
|
import logging
|
logger = get_logger(log_level=logging.CRITICAL)
|
logger.setLevel(logging.CRITICAL)
|
|
inference_pipeline = pipeline(
|
task=Tasks.auto_speech_recognition,
|
model='damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online',
|
model_revision='v1.0.2')
|
|
waveform, sample_rate = torchaudio.load("waihu.wav")
|
speech_length = waveform.shape[1]
|
speech = waveform[0]
|
|
cache_en = {"start_idx": 0, "pad_left": 0, "stride": 10, "pad_right": 5, "cif_hidden": None, "cif_alphas": None}
|
cache_de = {"decode_fsmn": None}
|
cache = {"encoder": cache_en, "decoder": cache_de}
|
param_dict = {}
|
param_dict["cache"] = cache
|
|
first_chunk = True
|
speech_buffer = speech
|
speech_cache = []
|
final_result = ""
|
|
while len(speech_buffer) >= 960:
|
if first_chunk:
|
if len(speech_buffer) >= 14400:
|
rec_result = inference_pipeline(audio_in=speech_buffer[0:14400], param_dict=param_dict)
|
speech_buffer = speech_buffer[4800:]
|
else:
|
cache_en["stride"] = len(speech_buffer) // 960
|
cache_en["pad_right"] = 0
|
rec_result = inference_pipeline(audio_in=speech_buffer, param_dict=param_dict)
|
speech_buffer = []
|
cache_en["start_idx"] = -5
|
first_chunk = False
|
else:
|
cache_en["start_idx"] += 10
|
if len(speech_buffer) >= 4800:
|
cache_en["pad_left"] = 5
|
rec_result = inference_pipeline(audio_in=speech_buffer[:19200], param_dict=param_dict)
|
speech_buffer = speech_buffer[9600:]
|
else:
|
cache_en["stride"] = len(speech_buffer) // 960
|
cache_en["pad_right"] = 0
|
rec_result = inference_pipeline(audio_in=speech_buffer, param_dict=param_dict)
|
speech_buffer = []
|
if len(rec_result) !=0 and rec_result['text'] != "sil":
|
final_result += rec_result['text']
|
print(rec_result)
|
print(final_result)
|
