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| #!/usr/bin/env python | |
| # -*- coding: utf-8 -*- | |
| from __future__ import absolute_import, division, print_function | |
| import numpy as np | |
| import sys | |
| import wave | |
| from deepspeech import Model, version | |
| from timeit import default_timer as timer | |
| def client(audio_file, lang="uk"): | |
| model_load_start = timer() | |
| # sphinx-doc: python_ref_model_start | |
| model = "./uk.tflite" | |
| ds = Model(model) | |
| ds.enableExternalScorer("kenlm.scorer") | |
| # sphinx-doc: python_ref_model_stop | |
| model_load_end = timer() - model_load_start | |
| print('Loaded model in {:.3}s.'.format(model_load_end), file=sys.stderr) | |
| desired_sample_rate = ds.sampleRate() | |
| fin = wave.open(audio_file, 'rb') | |
| fs_orig = fin.getframerate() | |
| audio = np.frombuffer(fin.readframes(fin.getnframes()), np.int16) | |
| audio_length = fin.getnframes() * (1/fs_orig) | |
| fin.close() | |
| print('Running inference.', file=sys.stderr) | |
| inference_start = timer() | |
| # sphinx-doc: python_ref_inference_start | |
| result = ds.stt(audio) | |
| print(result) | |
| # sphinx-doc: python_ref_inference_stop | |
| inference_end = timer() - inference_start | |
| print('Inference took %0.3fs for %0.3fs audio file.' % | |
| (inference_end, audio_length), file=sys.stderr) | |
| return result | |