def getres(wp, m, d, save_path):
res = predict(wp, m, d)
# preds.append(res)
if res in ["", " "]:
wp = wp.replace("IOS", "Android")
res = predict(wp, m, d)
if res in ["", " "]:
wp = wp.replace("Android", "Recorder")
res = predict(wp, m, d)
with open(save_path, "a+") as fw:
fw.write(",".join([wp, res + "\r\n"]))
return res
def masr_recognize(audio_path):
audio_files=os.listdir(audio_path)
list_texts={}
for path in audio_files:
audio_file=os.path.join(audio_path,path)
# text = model.predict(audio_file)
text=beamdecode.predict(audio_file)
list_texts[path]=text
return list_texts
def recognize():
try:
f = request.files["audio"]
file_name = f.filename.replace('"', '').strip()
fpath = os.path.join('save_audio', file_name)
f.save(fpath)
text = beamdecode.predict(fpath)
return jsonify({'recognize text': text, 'code': 200, 'message': '成功'})
except Exception:
return jsonify({
'recognize text': '',
'code': 600,
'message': '识别过程有误！'
})
def recognize():
f = request.files["file"]
f.save("test.wav")
return beamdecode.predict("test.wav")
import _init_path
from models.conv import GatedConv
import beamdecode
model = GatedConv.load("../pretrained/gated-conv.pth")
while 1:
text = beamdecode.predict("test/123.wav")
print("")
print("识别结果:")
print(text)