Fix benchmark script (SeanNaren#62)

* Added fixes to benchmark script

* Fixed default

benchmark.py

@@ -1,4 +1,5 @@
 import argparse
+import json
 import time
 import torch
 from torch.autograd import Variable
@@ -13,7 +14,8 @@
  help='The size of the fake input in seconds using default stride of 0.01, '
  '15s is usually the maximum duration')
 parser.add_argument('--dry_runs', type=int, default=20, help='Dry runs before measuring performance')
-parser.add_argument('--runs', type=int, default=20, help='Hidden size of RNNs')
+parser.add_argument('--runs', type=int, default=20, help='How many benchmark runs to measure performance')
+parser.add_argument('--labels_path', default='labels.json', help='Path to the labels to infer over in the model')
 parser.add_argument('--hidden_size', default=400, type=int, help='Hidden size of RNNs')
 parser.add_argument('--hidden_layers', default=4, type=int, help='Number of RNN layers')
 parser.add_argument('--rnn_type', default='lstm', help='Type of the RNN. rnn|gru|lstm are supported')
@@ -25,10 +27,18 @@
 
 rnn_type = args.rnn_type.lower()
 assert rnn_type in supported_rnns, "rnn_type should be either lstm, rnn or gru"
+
+with open(args.labels_path) as label_file:
+ labels = str(''.join(json.load(label_file)))
+
+audio_conf = dict(sample_rate=args.sample_rate,
+ window_size=args.window_size)
+
 model = DeepSpeech(rnn_hidden_size=args.hidden_size,
- nb_layers=args.hidden_layers, num_classes=29,
- rnn_type=supported_rnns[rnn_type],
- sample_rate=args.sample_rate, window_size=args.window_size)
+ nb_layers=args.hidden_layers,
+ audio_conf=audio_conf,
+ labels=labels,
+ rnn_type=supported_rnns[rnn_type])
 
 parameters = model.parameters()
 optimizer = torch.optim.SGD(parameters, lr=3e-4,