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#!/usr/bin/env python3
"""
Copyright (C) 2021-2022 Intel Corporation
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from argparse import ArgumentParser, SUPPRESS
from itertools import groupby
import json
import logging as log
from pathlib import Path
from time import perf_counter
import sys
import numpy as np
import wave
from openvino.inference_engine import IECore
ie = IECore()
class Wav2Vec:
alphabet = [
"<pad>", "<s>", "</s>", "<unk>", "|",
"e", "t", "a", "o", "n", "i", "h", "s", "r", "d", "l", "u",
"m", "w", "c", "f", "g", "y", "p", "b", "v", "k", "'", "x", "j", "q", "z"]
words_delimiter = '|'
pad_token = '<pad>'
def __init__(self):
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")
@staticmethod
def preprocess(sound):
return (sound - np.mean(sound)) / (np.std(sound) + 1e-15)
def infer(self, audio):
exec_net = ie.load_network(self.nnet, "CPU")
outss = exec_net.infer({"input_values": audio})
# input_data = {next(iter(self.nnet.input_info)): audio}
return outss
def decode(self, logits):
token_ids = np.squeeze(np.argmax(logits, -1))
tokens = [self.decoding_vocab[idx] for idx in token_ids]
tokens = [token_group[0] for token_group in groupby(tokens)]
tokens = [t for t in tokens if t != self.pad_token]
res_string = ''.join([t if t != self.words_delimiter else ' ' for t in tokens]).strip()
res_string = ' '.join(res_string.split(' '))
res_string = res_string.lower()
return res_string
def reshape(self, audio):
self.nnet.reshape({next(iter(self.nnet.input_info)): audio.shape})
def main():
model = Wav2Vec()
start_time = perf_counter()
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:
channel_num, sample_width, sampling_rate, pcm_length, compression_type, _ = wave_read.getparams()
assert sample_width == 2, "Only 16-bit WAV PCM supported"
assert compression_type == 'NONE', "Only linear PCM WAV files supported"
assert channel_num == 1, "Only mono WAV PCM supported"
assert sampling_rate == 16000, "Only 16 KHz audio supported"
audio = np.frombuffer(wave_read.readframes(pcm_length * channel_num), dtype=np.int16).reshape((1, pcm_length))
audio = audio.astype(float) / np.iinfo(np.int16).max
normalized_audio = model.preprocess(audio)
model.reshape(normalized_audio)
character_probs = model.infer(normalized_audio)
print(type(character_probs))
print(character_probs.keys())
transcription = model.decode(character_probs["3761"])
total_latency = (perf_counter() - start_time) * 1e3
# log.info("Metrics report:")
# log.info("\tLatency: {:.1f} ms".format(total_latency))
print(transcription)
print(total_latency)
if __name__ == '__main__':
sys.exit(main() or 0)
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