97 lines
3.1 KiB
Python
97 lines
3.1 KiB
Python
#!/usr/bin/env python3
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import os
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import os.path
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import logging
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import json
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import tempfile
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import requests
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from pyAudioAnalysis import audioBasicIO
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from pyAudioAnalysis import MidTermFeatures
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import numpy
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class NumpyArrayEncoder(json.JSONEncoder):
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def default(self, obj):
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if isinstance(obj, numpy.ndarray):
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return obj.tolist()
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return json.JSONEncoder.default(self, obj)
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def do_extraction(model_details: dict, file_path: str):
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logging.info("Running extraction...")
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sampling_rate, signal = audioBasicIO.read_audio_file(file_path)
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signal = audioBasicIO.stereo_to_mono(signal)
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if sampling_rate == 0:
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raise Exception("Could not read the file properly: Sampling rate zero")
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if signal.shape[0] / float(sampling_rate) <= model_details['mid_window']:
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raise Exception("Could not read the file properly: Signal shape is not good")
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# feature extraction:
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mid_features, s, _ = \
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MidTermFeatures.mid_feature_extraction(signal, sampling_rate,
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model_details['mid_window'] * sampling_rate,
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model_details['mid_step'] * sampling_rate,
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round(sampling_rate * model_details['short_window']),
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round(sampling_rate * model_details['short_step']))
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# long term averaging of mid-term statistics
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mid_features = mid_features.mean(axis=1)
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if model_details['compute_beat']:
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beat, beat_conf = MidTermFeatures.beat_extraction(s, model_details['short_step'])
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mid_features = numpy.append(mid_features, beat)
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mid_features = numpy.append(mid_features, beat_conf)
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# feature_vector = (mid_features - mean) / std # normalization
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return mid_features
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def run_everything(parameters: dict):
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tag = parameters['tag']
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logging.info(f"Downloading sample: {tag}")
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_, file_path = tempfile.mkstemp(prefix=f"{tag}_", suffix=".wav")
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r = requests.get(f"http://storage-service/object/{tag}")
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with open(file_path, 'wb') as f:
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f.write(r.content)
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logging.debug(f"Downloaded sample to {file_path}")
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logging.info("Getting default model details...")
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r = requests.get("http://model-service/model/$default/details")
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r.raise_for_status()
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model_details = r.json()
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logging.debug(f"Using model {model_details['id']}")
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# download done. Do extraction magic
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try:
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results = do_extraction(model_details, file_path)
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finally:
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os.remove(file_path)
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logging.info(f"Pushing results to Classifier service...")
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response = {
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"tag": tag,
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"features": results,
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"model": model_details['id']
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}
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logging.debug(f"Data being pushed: {str(response)}")
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r = requests.post(
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'http://classification-service/classify',
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data=json.dumps(response, cls=NumpyArrayEncoder),
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headers={'Content-Type': 'application/json'}
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)
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# r.raise_for_status() # An error in a service should not kill other services
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logging.info(f"Classification service response: {r.status_code}")
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