evaluate_tflite.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function

import argparse
import numpy as np
import wave
import csv
import sys
import os

from six.moves import zip, range
from multiprocessing import JoinableQueue, Pool, Process, Queue, cpu_count
from deepspeech import Model

from util.text import levenshtein
from util.evaluate_tools import process_decode_result, calculate_report

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, alphabet, LM/trie and a CSV test file
'''

BEAM_WIDTH = 500
LM_ALPHA = 0.75
LM_BETA = 1.85
N_FEATURES = 26
N_CONTEXT = 9

def tflite_worker(model, alphabet, lm, trie, queue_in, queue_out, gpu_mask):
    os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_mask)
    ds = Model(model, N_FEATURES, N_CONTEXT, alphabet, BEAM_WIDTH)
    ds.enableDecoderWithLM(alphabet, lm, trie, LM_ALPHA, LM_BETA)

    while True:
        msg = queue_in.get()

        fin = wave.open(msg['filename'], 'rb')
        fs = fin.getframerate()
        audio = np.frombuffer(fin.readframes(fin.getnframes()), np.int16)
        audio_length = fin.getnframes() * (1/16000)
        fin.close()
    
        decoded = ds.stt(audio, fs)
        
        queue_out.put({'prediction': decoded, 'ground_truth': msg['transcript']})
        queue_in.task_done()

def main():
    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('--alphabet', required=True,
                        help='Path to the configuration file specifying the alphabet used by the network')
    parser.add_argument('--lm', required=True,
                        help='Path to the language model binary file')
    parser.add_argument('--trie', required=True,
                        help='Path to the language model trie file created with native_client/generate_trie')
    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')
    args = parser.parse_args()

    work_todo = JoinableQueue()   # this is where we are going to store input data
    work_done = 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.alphabet, args.lm, args.trie, 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])

    ground_truths = []
    predictions = []
    losses = []

    with open(args.csv, 'r') as csvfile:
        csvreader = csv.DictReader(csvfile)
        for row in csvreader:
            work_todo.put({'filename': row['wav_filename'], 'transcript': row['transcript']})
    work_todo.join()

    while (not work_done.empty()):
        msg = work_done.get()
        losses.append(0.0)
        ground_truths.append(msg['ground_truth'])
        predictions.append(msg['prediction'])

    distances = [levenshtein(a, b) for a, b in zip(ground_truths, predictions)]

    wer, cer, samples = calculate_report(ground_truths, predictions, distances, losses)
    mean_loss = np.mean(losses)

    print('Test - WER: %f, CER: %f, loss: %f' %
          (wer, cer, mean_loss))

if __name__ == '__main__':
    main()