Change the chunk_iter function to handle (#16730)

* Change the chunk_iter function to handle

the subtle cases where the last chunk gets ignored since all the
data is in the `left_strided` data.

We need to remove the right striding on the previous item.

* Remove commented line.

src/transformers/pipelines/automatic_speech_recognition.py

@@ -58,9 +58,8 @@ def chunk_iter(inputs, feature_extractor, chunk_len, stride_left, stride_right):
         chunk = inputs[i : i + chunk_len]
         processed = feature_extractor(chunk, sampling_rate=feature_extractor.sampling_rate, return_tensors="pt")
         _stride_left = 0 if i == 0 else stride_left
-        is_last = i + step >= inputs_len
+        is_last = i + step + stride_left >= inputs_len
         _stride_right = 0 if is_last else stride_right
-
         if chunk.shape[0] > _stride_left:
             yield {"is_last": is_last, "stride": (chunk.shape[0], _stride_left, _stride_right), **processed}
 

tests/pipelines/test_pipelines_automatic_speech_recognition.py

@@ -653,6 +653,17 @@ class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase, metaclass=Pipel
         self.assertEqual([o["input_values"].shape for o in outs], [(1, 80), (1, 20)])
         self.assertEqual([o["is_last"] for o in outs], [False, True])
 
+        # one chunk since first is also last, because it contains only data
+        # in the right strided part we just mark that part as non stride
+        # This test is specifically crafted to trigger a bug if next chunk
+        # would be ignored by the fact that all the data would be
+        # contained in the strided left data.
+        outs = list(chunk_iter(inputs, feature_extractor, 105, 5, 5))
+        self.assertEqual(len(outs), 1)
+        self.assertEqual([o["stride"] for o in outs], [(100, 0, 0)])
+        self.assertEqual([o["input_values"].shape for o in outs], [(1, 100)])
+        self.assertEqual([o["is_last"] for o in outs], [True])
+
     @require_torch
     def test_chunk_iterator_stride(self):
         feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h")