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reformat
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@WisamAbbasi
WisamAbbasi committed Aug 22, 2023
1 parent abb57a4 commit cd95bab
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Showing 10 changed files with 309 additions and 225 deletions.
4 changes: 2 additions & 2 deletions api/metadata.py
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Original file line number Diff line number Diff line change
@@ -1,9 +1,9 @@
from maxfw.core import MAX_API, METADATA_SCHEMA, MetadataAPI

from core.model import ModelWrapper
from maxfw.core import MAX_API, MetadataAPI, METADATA_SCHEMA


class ModelMetadataAPI(MetadataAPI):

@MAX_API.marshal_with(METADATA_SCHEMA)
def get(self):
"""Return the metadata associated with the model"""
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211 changes: 122 additions & 89 deletions api/predict.py
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@@ -1,73 +1,102 @@
import re
from core.model import ModelWrapper
from flask_restplus import fields
from werkzeug.datastructures import FileStorage
from werkzeug.exceptions import BadRequest
from maxfw.core import MAX_API, PredictAPI

import os
import datetime
import hashlib
import io
import platform

import random
from pydub import AudioSegment
import io
import re

import numpy as np
from scipy import signal
import soundfile as sf
import io
from flask_restplus import fields
from maxfw.core import MAX_API, PredictAPI
from pydub import AudioSegment
from scipy import signal
from werkzeug.datastructures import FileStorage
from werkzeug.exceptions import BadRequest

from core.model import ModelWrapper

# set up parser for audio input data
input_parser = MAX_API.parser()
input_parser.add_argument('audio', type=FileStorage, location='files', required=True,
help="signed 16-bit PCM WAV audio file")
input_parser.add_argument('start_time', type=float, default=0,
help='The number of seconds into the audio file the prediction should start at.')
input_parser.add_argument('filter', required=False, action='split', help='List of labels to filter (optional)')

label_prediction = MAX_API.model('LabelPrediction', {
'label_id': fields.String(required=False, description='Label identifier'),
'label': fields.String(required=True, description='Audio class label'),
'probability': fields.Float(required=True)
})

predict_response = MAX_API.model('ModelPredictResponse', {
'status': fields.String(required=True, description='Response status message'),
'predictions': fields.List(fields.Nested(label_prediction), description='Predicted audio classes and probabilities'),
'audio_file': fields.String(required=True, description='The passed audio file.'),
'method': fields.String(required=True, description='The passed privacy method.'),
'requestor_id': fields.String(required=True, description='The passed requestor_id.'),
'requestor_type': fields.String(required=True, description='The passed requestor_type.'),
'request_id': fields.String(required=True, description='The passed request_id.'),
'analyzer_id': fields.String(required=True, description='The analyzer_id.'),
'analysis_id': fields.String(required=True, description='The analysis_id.')
})
input_parser.add_argument(
"audio",
type=FileStorage,
location="files",
required=True,
help="signed 16-bit PCM WAV audio file",
)
input_parser.add_argument(
"start_time",
type=float,
default=0,
help="The number of seconds into the audio file the prediction should start at.",
)
input_parser.add_argument(
"filter", required=False, action="split", help="List of labels to filter (optional)"
)

label_prediction = MAX_API.model(
"LabelPrediction",
{
"label_id": fields.String(required=False, description="Label identifier"),
"label": fields.String(required=True, description="Audio class label"),
"probability": fields.Float(required=True),
},
)

predict_response = MAX_API.model(
"ModelPredictResponse",
{
"status": fields.String(required=True, description="Response status message"),
"predictions": fields.List(
fields.Nested(label_prediction),
description="Predicted audio classes and probabilities",
),
"audio_file": fields.String(
required=True, description="The passed audio file."
),
"method": fields.String(
required=True, description="The passed privacy method."
),
"requestor_id": fields.String(
required=True, description="The passed requestor_id."
),
"requestor_type": fields.String(
required=True, description="The passed requestor_type."
),
"request_id": fields.String(
required=True, description="The passed request_id."
),
"analyzer_id": fields.String(required=True, description="The analyzer_id."),
"analysis_id": fields.String(required=True, description="The analysis_id."),
},
)


def scrample(audio_file_data):

audio_data = AudioSegment.from_wav(audio_file_data)

# Shuffle the audio segments
segment_duration = 1000 # Duration of each segment in milliseconds
segments = [audio_data[i:i+segment_duration] for i in range(0, len(audio_data), segment_duration)]
segments = [
audio_data[i : i + segment_duration]
for i in range(0, len(audio_data), segment_duration)
]
random.shuffle(segments)
scrambled_audio = AudioSegment.empty()
for segment in segments:
scrambled_audio += segment

# Convert audio to raw data
audio_buffer = io.BytesIO()
scrambled_audio.export(audio_buffer, format='wav')
scrambled_audio.export(audio_buffer, format="wav")
audio_buffer.seek(0)
audio_raw = audio_buffer.read()

return audio_raw

def dp_noise(audio_file_data):

def dp_noise(audio_file_data):
# define the privacy budget and sensitivity
epsilon = 0.8
sensitivity = 0.05
Expand All @@ -84,23 +113,22 @@ def dp_noise(audio_file_data):
# noisy_audio = audio_data + noise[:, np.newaxis]

# apply a low-pass filter to smooth the noise
b, a = signal.butter(4, 4000/(sample_rate/2), 'lowpass')
smoothed_audio = signal.filtfilt(b, a, noisy_audio)
b, a = signal.butter(4, 4000 / (sample_rate / 2), "lowpass")
signal.filtfilt(b, a, noisy_audio)

# convert audio to raw data
audio_buffer = io.BytesIO()
sf.write(audio_buffer, noisy_audio, sample_rate, format='WAV', subtype='PCM_16')
sf.write(audio_buffer, noisy_audio, sample_rate, format="WAV", subtype="PCM_16")
audio_buffer.seek(0)
audio_raw = audio_buffer.read()

return audio_raw


class ModelPredictAPI(PredictAPI):

model_wrapper = ModelWrapper()

@MAX_API.doc('predict')
@MAX_API.doc("predict")
@MAX_API.expect(input_parser)
@MAX_API.marshal_with(predict_response)
def post(self, audio_file, method, requestor_id, requestor_type, request_id):
Expand All @@ -111,79 +139,84 @@ def post(self, audio_file, method, requestor_id, requestor_type, request_id):

# Generate a random hash using SHA-256 algorithm
hash_object = hashlib.sha256()
hash_object.update(bytes(str(now), 'utf-8'))
hash_object.update(bytes(str(now), "utf-8"))
hash_value = hash_object.hexdigest()

# Concatenate the time and the hash
analysis_id = str(analyzer_id) + str(now) + hash_value

# def post(self):
"""Predict audio classes from input data"""
result = {'status': 'error'}
result = {"status": "error"}

args = input_parser.parse_args()

if not re.match("audio/.*wav", str(args['audio'].mimetype)):
if not re.match("audio/.*wav", str(args["audio"].mimetype)):
e = BadRequest()
e.data = {'status': 'error', 'message': 'Invalid file type/extension: ' + str(args['audio'].mimetype)}
e.data = {
"status": "error",
"message": "Invalid file type/extension: "
+ str(args["audio"].mimetype),
}
raise e

# audio_data = args['audio'].read()


audio_file = args['audio']
audio_file = args["audio"]

if method == "scrample":
audio_data = scrample(audio_file)
elif method == "dp_noise":
audio_data = dp_noise(audio_file)
else:
audio_data = args['audio'].read()


audio_data = args["audio"].read()

# Getting the predictions
try:
preds = self.model_wrapper._predict(audio_data, args['start_time'])
preds = self.model_wrapper._predict(audio_data, args["start_time"])
except ValueError:
e = BadRequest()
e.data = {'status': 'error', 'message': 'Invalid start time: value outside audio clip'}
e.data = {
"status": "error",
"message": "Invalid start time: value outside audio clip",
}
raise e

# Aligning the predictions to the required API format
label_preds = [{'label_id': p[0], 'label': p[1], 'probability': p[2]} for p in preds]
label_preds = [
{"label_id": p[0], "label": p[1], "probability": p[2]} for p in preds
]

# Filter list
if args['filter'] is not None and any(x.strip() != '' for x in args['filter']):
label_preds = [x for x in label_preds if x['label'] in args['filter']]

result['predictions'] = label_preds
result['status'] = 'ok'
result['audio_file'] = audio_file
result['method'] = method
result['requestor_id'] = requestor_id
result['requestor_type'] = requestor_type
result['request_id'] = request_id
result['analyzer_id'] = analyzer_id
result['analysis_id'] = analysis_id

ws_req_final = {
"RequestPostTopicUUID": {
"topic_name": "SIFIS:Privacy_Aware_Audio_Anomaly_Detection_Results",
"topic_uuid": "Audio_Anomaly_Detection_Results",
"value": {
"description": "Speech Recognition Results",
"requestor_id": str(requestor_id),
"requestor_type": str(requestor_type),
"request_id": str(request_id),
"analyzer_id": str(analyzer_id),
"analysis_id": str(analysis_id),
"audio_file": str(audio_file),
"method": str(method),
"predictions": label_preds
}
}
}

if args["filter"] is not None and any(x.strip() != "" for x in args["filter"]):
label_preds = [x for x in label_preds if x["label"] in args["filter"]]

result["predictions"] = label_preds
result["status"] = "ok"
result["audio_file"] = audio_file
result["method"] = method
result["requestor_id"] = requestor_id
result["requestor_type"] = requestor_type
result["request_id"] = request_id
result["analyzer_id"] = analyzer_id
result["analysis_id"] = analysis_id

{
"RequestPostTopicUUID": {
"topic_name": "SIFIS:Privacy_Aware_Audio_Anomaly_Detection_Results",
"topic_uuid": "Audio_Anomaly_Detection_Results",
"value": {
"description": "Speech Recognition Results",
"requestor_id": str(requestor_id),
"requestor_type": str(requestor_type),
"request_id": str(request_id),
"analyzer_id": str(analyzer_id),
"analysis_id": str(analysis_id),
"audio_file": str(audio_file),
"method": str(method),
"predictions": label_preds,
},
}
}

return result
11 changes: 7 additions & 4 deletions app.py
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@@ -1,10 +1,13 @@

from maxfw.core import MAXApp

from api import ModelMetadataAPI, ModelPredictAPI
from config import API_TITLE, API_DESC, API_VERSION
from config import API_DESC, API_TITLE, API_VERSION

max_app = MAXApp(API_TITLE, API_DESC, API_VERSION)
max_app.add_api(ModelMetadataAPI, '/metadata')
max_app.add_api(ModelMetadataAPI, "/metadata")
# max_app.add_api(ModelPredictAPI, '/predict')
max_app.add_api(ModelPredictAPI, '/predict/<audio_file>/<method>/<requestor_id>/<requestor_type>/<request_id>')
max_app.add_api(
ModelPredictAPI,
"/predict/<audio_file>/<method>/<requestor_id>/<requestor_type>/<request_id>",
)
max_app.run()
21 changes: 10 additions & 11 deletions config.py
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@@ -1,4 +1,3 @@

# Flask settings
DEBUG = True

Expand All @@ -8,20 +7,20 @@
# Application settings

# API metadata
API_TITLE = 'MAX Audio Classifier'
API_DESC = 'Identify sounds in short audio clips'
API_VERSION = '1.2.0'
API_TITLE = "MAX Audio Classifier"
API_DESC = "Identify sounds in short audio clips"
API_VERSION = "1.2.0"

# default model
MODEL_NAME = 'audio_embeddings'
MODEL_LICENSE = 'Apache 2.0'
MODEL_NAME = "audio_embeddings"
MODEL_LICENSE = "Apache 2.0"

MODEL_META_DATA = {
'id': '{}-tf-imagenet'.format(MODEL_NAME.lower()),
'name': '{} TensorFlow Model'.format(MODEL_NAME),
'description': '{} TensorFlow model trained on Audio Set'.format(MODEL_NAME),
'type': 'image_classification',
'license': '{}'.format(MODEL_LICENSE)
"id": "{}-tf-imagenet".format(MODEL_NAME.lower()),
"name": "{} TensorFlow Model".format(MODEL_NAME),
"description": "{} TensorFlow model trained on Audio Set".format(MODEL_NAME),
"type": "image_classification",
"license": "{}".format(MODEL_LICENSE),
}

DEFAULT_EMBEDDING_CHECKPOINT = "assets/vggish_model.ckpt"
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