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""" |
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Copyright (C) 2021-2022 Intel Corporation |
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Licensed under the Apache License, Version 2.0 (the "License"); |
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you may not use this file except in compliance with the License. |
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You may obtain a copy of the License at |
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http://www.apache.org/licenses/LICENSE-2.0 |
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Unless required by applicable law or agreed to in writing, software |
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distributed under the License is distributed on an "AS IS" BASIS, |
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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See the License for the specific language governing permissions and |
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limitations under the License. |
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""" |
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from argparse import ArgumentParser, SUPPRESS |
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from itertools import groupby |
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import json |
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import logging as log |
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from pathlib import Path |
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from time import perf_counter |
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import sys |
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import numpy as np |
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import wave |
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from openvino.inference_engine import IECore |
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ie = IECore() |
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class Wav2Vec: |
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alphabet = [ |
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"<pad>", "<s>", "</s>", "<unk>", "|", |
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"e", "t", "a", "o", "n", "i", "h", "s", "r", "d", "l", "u", |
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"m", "w", "c", "f", "g", "y", "p", "b", "v", "k", "'", "x", "j", "q", "z"] |
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words_delimiter = '|' |
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pad_token = '<pad>' |
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def __init__(self): |
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self.nnet = ie.read_network("/home/intel/Documents/ASR/wav2vec2-base-ft-keyword-spotting-int8/ov_model.xml", "/home/intel/Documents/ASR/wav2vec2-base-ft-keyword-spotting-int8/ov_model.bin") |
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@staticmethod |
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def preprocess(sound): |
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return (sound - np.mean(sound)) / (np.std(sound) + 1e-15) |
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def infer(self, audio): |
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exec_net = ie.load_network(self.nnet, "CPU") |
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outss = exec_net.infer({"input_values": audio}) |
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return outss |
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def decode(self, logits): |
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token_ids = np.squeeze(np.argmax(logits, -1)) |
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tokens = [self.decoding_vocab[idx] for idx in token_ids] |
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tokens = [token_group[0] for token_group in groupby(tokens)] |
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tokens = [t for t in tokens if t != self.pad_token] |
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res_string = ''.join([t if t != self.words_delimiter else ' ' for t in tokens]).strip() |
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res_string = ' '.join(res_string.split(' ')) |
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res_string = res_string.lower() |
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return res_string |
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def reshape(self, audio): |
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self.nnet.reshape({next(iter(self.nnet.input_info)): audio.shape}) |
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def main(): |
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model = Wav2Vec() |
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start_time = perf_counter() |
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with wave.open("/home/intel/Documents/ASR/applications.ai.conversational-ai.asr-grpc-security/client_sample_examples/python/audio_data_samples/how_are_you_doing.wav", 'rb') as wave_read: |
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channel_num, sample_width, sampling_rate, pcm_length, compression_type, _ = wave_read.getparams() |
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assert sample_width == 2, "Only 16-bit WAV PCM supported" |
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assert compression_type == 'NONE', "Only linear PCM WAV files supported" |
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assert channel_num == 1, "Only mono WAV PCM supported" |
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assert sampling_rate == 16000, "Only 16 KHz audio supported" |
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audio = np.frombuffer(wave_read.readframes(pcm_length * channel_num), dtype=np.int16).reshape((1, pcm_length)) |
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audio = audio.astype(float) / np.iinfo(np.int16).max |
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normalized_audio = model.preprocess(audio) |
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model.reshape(normalized_audio) |
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character_probs = model.infer(normalized_audio) |
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print(type(character_probs)) |
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print(character_probs.keys()) |
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transcription = model.decode(character_probs["3761"]) |
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total_latency = (perf_counter() - start_time) * 1e3 |
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print(transcription) |
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print(total_latency) |
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if __name__ == '__main__': |
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sys.exit(main() or 0) |
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