diff --git a/python/pyarmnn/examples/speech_recognition/README.md b/python/pyarmnn/examples/speech_recognition/README.md
index 2323eac..5ccf003 100644
--- a/python/pyarmnn/examples/speech_recognition/README.md
+++ b/python/pyarmnn/examples/speech_recognition/README.md
@@ -35,6 +35,15 @@
 $ pip install -r requirements.txt
 ```
 
+### Model
+
+The model for this can be found in the Arm Model Zoo repository:
+https://github.com/ARM-software/ML-zoo/tree/master/models
+
+The model we're looking for:
+https://github.com/ARM-software/ML-zoo/tree/master/models/speech_recognition/wav2letter/tflite_int8 
+
+
 ## Performing Automatic Speech Recognition
 
 ### Processing Audio Files
diff --git a/python/pyarmnn/examples/speech_recognition/audio_capture.py b/python/pyarmnn/examples/speech_recognition/audio_capture.py
index 9f28d10..0c89920 100644
--- a/python/pyarmnn/examples/speech_recognition/audio_capture.py
+++ b/python/pyarmnn/examples/speech_recognition/audio_capture.py
@@ -20,7 +20,7 @@
         self.mono = True
         self.dtype = np.float32
         self.samplerate = 16000
-        self.min_samples = 167392
+        self.min_samples = 47712  # (model_input_size-1)*stride + frame_len
 
 
 class AudioCapture:
diff --git a/python/pyarmnn/examples/speech_recognition/audio_utils.py b/python/pyarmnn/examples/speech_recognition/audio_utils.py
index a522a0e..f03d2e1 100644
--- a/python/pyarmnn/examples/speech_recognition/audio_utils.py
+++ b/python/pyarmnn/examples/speech_recognition/audio_utils.py
@@ -17,7 +17,7 @@
     Returns:
         Decoded string.
     """
-    top1_results = [labels[np.argmax(row[0])] for row in model_output]
+    top1_results = [labels[np.argmax(row)] for row in model_output]
     return filter_characters(top1_results)
 
 
@@ -82,7 +82,7 @@
     Slices the text appropriately depending on the window, and decodes for wav2letter output.
         * First run, take the left context, and inner context.
         * Every other run, take the inner context.
-    Stores the current right context, and updates it for each inference. Will get used after last inference
+    Stores the current right context, and updates it for each inference. Will get used after last inference.
 
     Args:
         is_first_window: Boolean to show if it is the first window we are running inference on
@@ -93,16 +93,21 @@
         current_r_context: the current right context
         text: the current text string, with the latest output decoded and appended
     """
+    # For wav2letter with 148 output steps:
+    # Left context is index 0-48, inner context 49-99, right context 100-147
+    inner_context_start = 49
+    inner_context_end = 99
+    right_context_start = 100
 
     if is_first_window:
         # Since it's the first inference, keep the left context, and inner context, and decode
-        text = decode(output_result[0][0:472], labels)
+        text = decode(output_result[0][0][0][0:inner_context_end], labels)
     else:
         # Only decode the inner context
-        text = decode(output_result[0][49:472], labels)
+        text = decode(output_result[0][0][0][inner_context_start:inner_context_end], labels)
 
     # Store the right context, we will need it after the last inference
-    current_r_context = decode(output_result[0][473:521], labels)
+    current_r_context = decode(output_result[0][0][0][right_context_start:], labels)
     return current_r_context, text
 
 
diff --git a/python/pyarmnn/examples/speech_recognition/run_audio_file.py b/python/pyarmnn/examples/speech_recognition/run_audio_file.py
index c7e4c6b..942de20 100644
--- a/python/pyarmnn/examples/speech_recognition/run_audio_file.py
+++ b/python/pyarmnn/examples/speech_recognition/run_audio_file.py
@@ -65,7 +65,7 @@
     mfcc_params = MFCCParams(sampling_freq=16000, num_fbank_bins=128, mel_lo_freq=0, mel_hi_freq=8000,
                                         num_mfcc_feats=13, frame_len=512, use_htk_method=False, n_FFT=512)
     mfcc = MFCC(mfcc_params)
-    preprocessor = Preprocessor(mfcc, model_input_size=1044, stride=160)
+    preprocessor = Preprocessor(mfcc, model_input_size=296, stride=160)
 
     text = ""
     current_r_context = ""