def upload_file():
UPLOAD_FOLDER = './audio_sources/'
#app = Flask(__name__)
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
sourceDir = "./uploads/"
destDir = "./Speakers_models/"
wavSourceDir = "./audio_sources/"
chunk_length_ms = 3000
if request.method == 'POST':
file = request.files['file']
personname = request.form['personname']
if file and allowed_file(file.filename):
filename = secure_filename(file.filename)
guid = str(uuid.uuid1()).replace("-", "")
replace_filename = str(personname).replace(
" ", "") + '-' + str(guid) + '.wav'
isMP3 = False
print("@@@@@@@@", replace_filename)
if filename.endswith(".mp3"):
isMP3 = True
file.save(
os.path.join('audio_sources',
secure_filename(file.filename)))
#Convert mp3 to wav and save to audio_sources with appended guid
getWavfile(8000, 1, filename, replace_filename,
"./audio_sources/", "./audio_sources/")
else:
file.save(
os.path.join('audio_sources',
secure_filename(file.filename)))
#Save the uploaded wav file to audio_sources with appended guid
os.rename('./audio_sources/' + filename,
'./audio_sources/' + replace_filename)
#rename file name
#os.rename('./audio_sources/' + filename, './audio_sources/' + replace_filename)
audio_split(replace_filename, isMP3, wavSourceDir, chunk_length_ms)
training_result = model_train(replace_filename, sourceDir, destDir)
responseJson = {}
appurl = request.url.split("/upload")
if training_result == "Modelling completed":
responseJson = jsonify(status=200,
message="Enrollment Successful",
guid=str(guid),
name=personname,
link=appurl[0] + "/audiofile/" +
replace_filename)
else:
responseJson = jsonify(status=500,
message="Enrollment Failed",
guid=str(guid),
name=personname)
return responseJson
return '''
def compare_urls():
UPLOAD_FOLDER = './Unknown/'
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
sourceDir = "./Unknown/"
destDir = "./Unknown/"
trainSourceDir="./uploads/"
chunk_length_ms = 1000
if request.method == 'POST':
uploaded_files=[]
urlnames = []
urlnames.append(request.form['filelink1'])
convertedFileName = convertURLToFile(urlnames[0])
uploaded_files.append(convertedFileName)
urlnames.append(request.form['filelink2'])
convertedFileName = convertURLToFile(urlnames[1])
uploaded_files.append(convertedFileName)
for filename in uploaded_files:
isMP3 = False
if filename.endswith(".mp3"):
isMP3 = True
#file.save(os.path.join('Unknown',secure_filename(file.filename)))
#filenames.append(replace_filename)
#Convert mp3 to wav and save to audio_sources with appended guid
getWavfile(8000,1,filename,replace_filename,"./Unknown/","./Unknown/")
audio_split(uploaded_files[0], isMP3 , sourceDir, chunk_length_ms)
training_result = model_train(uploaded_files[0],trainSourceDir,destDir)
audio_split(uploaded_files[1], isMP3 , sourceDir, chunk_length_ms)
training_result = model_train(uploaded_files[1],trainSourceDir,destDir)
responseJson = {}
appurl = request.url.split("/compare")
if training_result == "Modelling completed":
print("*********",uploaded_files[1])
flag, _similarityProbScore, _compareMatch = compare_test(uploaded_files,sourceDir,destDir)
responseJson = {}
confidenceThreshold = 0.75
if(_similarityProbScore == 1):
responseJson = jsonify(
status = 200,
message = _compareMatch
)
else:
responseJson = jsonify(
status = 200,
message = _compareMatch,
similarityProbScore =format(_similarityProbScore, '.8f')
)
return responseJson
return '''
def upload_testfile():
sourceDir = "test_samples/"
destDir = "Speakers_models/"
UPLOAD_FOLDER = './test_samples/'
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
if request.method == 'POST':
file = request.files['file']
if file and allowed_file(file.filename):
filename = secure_filename(file.filename)
if filename.endswith(".mp3"):
file.save(
os.path.join('test_samples',
secure_filename(file.filename)))
replace_filename = filename.split(".")[0] + ".wav"
getWavfile(8000, 1, filename, replace_filename,
"./test_samples/", "./test_samples/")
else:
file.save(
os.path.join('test_samples',
secure_filename(file.filename)))
flag, _speakerMatch, _confidence = test_sample(
filename, sourceDir, destDir)
responseJson = {}
_speakerName = ""
_guid = ""
confidenceThreshold = 0.75
htmlStr = ""
if (_speakerMatch != ""):
filenNameArr = _speakerMatch.split(
"-") #get the name of the speaker
_speakerName = filenNameArr[0]
_guid = filenNameArr[1]
if (_confidence > confidenceThreshold):
responseJson = jsonify(status=200,
message="Match found",
name=_speakerName,
guid=_guid,
confidence=_confidence)
elif (_confidence < confidenceThreshold):
responseJson = jsonify(status=200,
message="Match not found",
guid=_guid,
speaker=_speakerName,
confidence=_confidence)
else:
responseJson = jsonify(status=500,
message="Internal server error")
return responseJson
return '''
def compare_files():
UPLOAD_FOLDER = './Unknown/'
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
sourceDir = "./Unknown/"
destDir = "./Unknown/"
trainSourceDir="./uploads/"
chunk_length_ms = 1000
if request.method == 'POST':
uploaded_files = request.files.getlist("file[]")
filenames = []
for file in uploaded_files:
if file and allowed_file(file.filename):
filename = secure_filename(file.filename)
guid = str(uuid.uuid1()).replace("-", "")
replace_filename = str(filename.split(".wav")[0]).replace(" ", "") + '-' + str(guid) + '.wav'
isMP3 = False
if filename.endswith(".mp3"):
isMP3 = True
file.save(os.path.join('Unknown',secure_filename(file.filename)))
filenames.append(replace_filename)
#Convert mp3 to wav and save to audio_sources with appended guid
getWavfile(8000,1,filename,replace_filename,"./Unknown/","./Unknown/")
else:
file.save(os.path.join('Unknown',secure_filename(file.filename)))
filenames.append(replace_filename)
#Save the uploaded wav file to audio_sources with appended guid
os.rename('./Unknown/' + filename, './Unknown/' + replace_filename)
#rename file name
#os.rename('./audio_sources/' + filename, './audio_sources/' + replace_filename)
print("*******",filenames)
audio_split(filenames[0], isMP3 , sourceDir, chunk_length_ms)
training_result = model_train(filenames[0],trainSourceDir,destDir)
audio_split(filenames[1], isMP3 , sourceDir, chunk_length_ms)
training_result = model_train(filenames[1],trainSourceDir,destDir)
responseJson = {}
appurl = request.url.split("/compare")
if training_result == "Modelling completed":
print("*********",filenames[1])
flag, _similarityProbScore, _compareMatch = compare_test(filenames,sourceDir,destDir)
responseJson = {}
confidenceThreshold = 0.75
if(_similarityProbScore == 1):
responseJson = jsonify(
status = 200,
message = _compareMatch
)
else:
responseJson = jsonify(
status = 200,
message = _compareMatch,
similarityProbScore =format(_similarityProbScore, '.8f')
)
return responseJson
return '''