/
evaluate_tflite.py
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evaluate_tflite.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function
import absl.app
import argparse
import numpy as np
import wave
import csv
import os
import sys
from deepspeech import Model
from deepspeech_training.util.evaluate_tools import calculate_and_print_report
from deepspeech_training.util.flags import create_flags
from functools import partial
from multiprocessing import JoinableQueue, Process, cpu_count, Manager
from six.moves import zip, range
r'''
This module should be self-contained:
- build libdeepspeech.so with TFLite:
- bazel build [...] --define=runtime=tflite [...] //native_client:libdeepspeech.so
- make -C native_client/python/ TFDIR=... bindings
- setup a virtualenv
- pip install native_client/python/dist/deepspeech*.whl
- pip install -r requirements_eval_tflite.txt
Then run with a TF Lite model, a scorer and a CSV test file
'''
def tflite_worker(model, scorer, queue_in, queue_out, gpu_mask):
os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_mask)
ds = Model(model)
ds.enableExternalScorer(scorer)
while True:
try:
msg = queue_in.get()
filename = msg['filename']
fin = wave.open(filename, 'rb')
audio = np.frombuffer(fin.readframes(fin.getnframes()), np.int16)
fin.close()
decoded = ds.stt(audio)
queue_out.put({'wav': filename, 'prediction': decoded, 'ground_truth': msg['transcript']})
except FileNotFoundError as ex:
print('FileNotFoundError: ', ex)
print(queue_out.qsize(), end='\r') # Update the current progress
queue_in.task_done()
def main(args, _):
manager = Manager()
work_todo = JoinableQueue() # this is where we are going to store input data
work_done = manager.Queue() # this where we are gonna push them out
processes = []
for i in range(args.proc):
worker_process = Process(target=tflite_worker, args=(args.model, args.scorer, work_todo, work_done, i), daemon=True, name='tflite_process_{}'.format(i))
worker_process.start() # Launch reader() as a separate python process
processes.append(worker_process)
print([x.name for x in processes])
wavlist = []
ground_truths = []
predictions = []
losses = []
wav_filenames = []
with open(args.csv, 'r') as csvfile:
csvreader = csv.DictReader(csvfile)
count = 0
for row in csvreader:
count += 1
# Relative paths are relative to the folder the CSV file is in
if not os.path.isabs(row['wav_filename']):
row['wav_filename'] = os.path.join(os.path.dirname(args.csv), row['wav_filename'])
work_todo.put({'filename': row['wav_filename'], 'transcript': row['transcript']})
wav_filenames.extend(row['wav_filename'])
print('Totally %d wav entries found in csv\n' % count)
work_todo.join()
print('\nTotally %d wav file transcripted' % work_done.qsize())
while not work_done.empty():
msg = work_done.get()
losses.append(0.0)
ground_truths.append(msg['ground_truth'])
predictions.append(msg['prediction'])
wavlist.append(msg['wav'])
# Print test summary
_ = calculate_and_print_report(wav_filenames, ground_truths, predictions, losses, args.csv)
if args.dump:
with open(args.dump + '.txt', 'w') as ftxt, open(args.dump + '.out', 'w') as fout:
for wav, txt, out in zip(wavlist, ground_truths, predictions):
ftxt.write('%s %s\n' % (wav, txt))
fout.write('%s %s\n' % (wav, out))
print('Reference texts dumped to %s.txt' % args.dump)
print('Transcription dumped to %s.out' % args.dump)
def parse_args():
parser = argparse.ArgumentParser(description='Computing TFLite accuracy')
parser.add_argument('--model', required=True,
help='Path to the model (protocol buffer binary file)')
parser.add_argument('--scorer', required=True,
help='Path to the external scorer file')
parser.add_argument('--csv', required=True,
help='Path to the CSV source file')
parser.add_argument('--proc', required=False, default=cpu_count(), type=int,
help='Number of processes to spawn, defaulting to number of CPUs')
parser.add_argument('--dump', required=False,
help='Path to dump the results as text file, with one line for each wav: "wav transcription".')
args, unknown = parser.parse_known_args()
# Reconstruct argv for absl.flags
sys.argv = [sys.argv[0]] + unknown
return args
if __name__ == '__main__':
create_flags()
absl.app.run(partial(main, parse_args()))