Add TFLite accuracy estimation tool

Fixes #1852

evaluate_tflite.py

@@ -0,0 +1,108 @@
+#!/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
+
+from six.moves import zip, range
+from attrdict import AttrDict
+from multiprocessing import JoinableQueue, Pool, Process, Queue, cpu_count
+from deepspeech import Model
+
+from util.preprocess import pmap
+from evaluate import process_decode_result, calculate_report
+from util.text import wer_cer_batch, levenshtein
+
+r'''
+This module should e self-contained:
+  - build a libdeepspeech.so with TFLite:
+    - add a dep in native_client/BUILD against TFlite: '//tensorflow:linux_x86_64': [ "//tensorflow/contrib/lite/kernels:builtin_ops" ]
+    - bazel build [...] --copt=-DUSE_TFLITE [...] //native_client:libdeepspeech.so
+  - make -C native_client/python/ TFDIR=... bindings
+  - setup a virtualenv
+  - pip install numpy attrdict
+  - pip install native_client/python/dist/deepspeech*.whl
+
+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):
+    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')
+    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(cpu_count()):
+        worker_process = Process(target=tflite_worker, args=(args.model, args.alphabet, args.lm, args.trie, work_todo, work_done), 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.extend([0.0])
+        ground_truths.extend([ msg['ground_truth'] ])
+        predictions.extend([ msg['prediction'] ])
+
+    distances = [levenshtein(a, b) for a, b in zip(ground_truths, predictions)]
+
+    wer, samples = calculate_report(ground_truths, predictions, distances, losses)
+    mean_edit_distance = np.mean(distances)
+    mean_loss = np.mean(losses)
+
+    print('Test - WER: %f, CER: %f, loss: %f' %
+          (wer, mean_edit_distance, mean_loss))
+
+if __name__ == '__main__':
+    main()