Hotfix chunk_length_s instead of _ms.

src/transformers/pipelines/automatic_speech_recognition.py

@@ -66,6 +66,39 @@ def ffmpeg_read(bpayload: bytes, sampling_rate: int) -> np.array:
     return audio
 
 
+def apply_stride(tokens, stride):
+    max_token_n = tokens.shape[-1]
+    max_input_n = max(input_n for input_n, _, _ in stride)
+    ratio = max_token_n / max_input_n
+    for i, (input_n, left, right) in enumerate(stride):
+        token_n = int(round(input_n * ratio))
+        left_token = int(round(left / input_n * token_n))
+        right_token = int(round((input_n - right) / input_n * token_n))
+        # This is CTC to preseve decoding, we need to duplicate
+        # next letter, and last letter
+
+        first_letter = tokens[i, left_token]
+        tokens[i, :left_token] = first_letter
+
+        last_letter = tokens[i, right_token - 1]
+        tokens[i, right_token:] = last_letter
+
+
+def chunk_iter(inputs, feature_extractor, chunk_len, stride_left, stride_right):
+    inputs_len = inputs.shape[0]
+    step = chunk_len - stride_left - stride_right
+    for i in range(0, inputs_len, step):
+        # add start and end paddings to the chunk
+        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
+        _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}
+
+
 class AutomaticSpeechRecognitionPipeline(ChunkPipeline):
     """
     Pipeline that aims at extracting spoken text contained within some audio.
@@ -85,11 +118,11 @@ def __init__(self, feature_extractor: Union["SequenceFeatureExtractor", str], *a
             tokenizer ([`PreTrainedTokenizer`]):
                 The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
                 [`PreTrainedTokenizer`].
-            chunk_length_ms (`int`, *optional*, defaults to 0):
+            chunk_length_s (`float`, *optional*, defaults to 0):
                 The input length for in each chunk. If `0` then chunking is disabled (default). Only available for CTC
                 models.
-            stride_length_ms (`int`, *optional*, defaults to `chunk_length_ms / 6`):
-                The length of stride on the left and right of each chunk. Used only with `chunk_length_ms > 0`. This
+            stride_length_s (`float`, *optional*, defaults to `chunk_length_s / 6`):
+                The length of stride on the left and right of each chunk. Used only with `chunk_length_s > 0`. This
                 enables the model to *see* more context and infer letters better than without this context but the
                 pipeline discards the stride bits at the end to make the final reconstitution as perfect as possible.
             framework (`str`, *optional*):
@@ -111,6 +144,7 @@ def __init__(self, feature_extractor: Union["SequenceFeatureExtractor", str], *a
             raise ValueError("The AutomaticSpeechRecognitionPipeline is only available in PyTorch.")
 
         self.check_model_type(dict(MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING.items() + MODEL_FOR_CTC_MAPPING.items()))
+        self.is_ctc = self.model.__class__ in MODEL_FOR_CTC_MAPPING.values()
 
     def __call__(
         self,
@@ -139,13 +173,13 @@ def __call__(
     def _sanitize_parameters(self, **kwargs):
         # No parameters on this pipeline right now
         preprocess_params = {}
-        if "chunk_length_ms" in kwargs:
-            preprocess_params["chunk_length_ms"] = kwargs["chunk_length_ms"]
-        if "stride_length_ms" in kwargs:
-            preprocess_params["stride_length_ms"] = kwargs["stride_length_ms"]
+        if "chunk_length_s" in kwargs:
+            preprocess_params["chunk_length_s"] = kwargs["chunk_length_s"]
+        if "stride_length_s" in kwargs:
+            preprocess_params["stride_length_s"] = kwargs["stride_length_s"]
         return preprocess_params, {}, {}
 
-    def preprocess(self, inputs, chunk_length_ms=0, stride_length_ms=None):
+    def preprocess(self, inputs, chunk_length_s=0, stride_length_s=None):
         if isinstance(inputs, str):
             with open(inputs, "rb") as f:
                 inputs = f.read()
@@ -158,48 +192,37 @@ def preprocess(self, inputs, chunk_length_ms=0, stride_length_ms=None):
         if len(inputs.shape) != 1:
             raise ValueError("We expect a single channel audio input for AutomaticSpeechRecognitionPipeline")
 
-        if chunk_length_ms:
-            if stride_length_ms is None:
-                stride_length_ms = chunk_length_ms // 6
-            inputs_len = len(inputs)
-            chunk_len = chunk_length_ms * self.feature_extractor.sampling_rate // 1000
-            stride_len = stride_length_ms * self.feature_extractor.sampling_rate // 1000
+        if chunk_length_s:
+            if stride_length_s is None:
+                stride_length_s = chunk_length_s / 6
+
+            chunk_len = int(round(chunk_length_s * self.feature_extractor.sampling_rate))
+
+            if isinstance(stride_length_s, (int, float)):
+                stride_length_s = [stride_length_s, stride_length_s]
 
-            # Redefine chunk_len to useful chunk length
-            # Not the size
-            # chunk_len = chunk_len - 2 * stride_len
+            stride_left = int(round(stride_length_s[0] * self.feature_extractor.sampling_rate))
+            stride_right = int(round(stride_length_s[1] * self.feature_extractor.sampling_rate))
 
-            if self.model.__class__ not in MODEL_FOR_CTC_MAPPING.values():
+            if not self.is_ctc:
                 raise ValueError(
-                    "`chunk_length_ms` is only valid for CTC models, use other chunking options for other models"
+                    "`chunk_length_s` is only valid for CTC models, use other chunking options for other models"
                 )
-            if chunk_len < stride_len:
+            if chunk_len < stride_left + stride_right:
                 raise ValueError("Chunk length must be superior to stride length")
 
             # make sure that
-            step = chunk_len
-            for i in range(0, inputs_len, step):
-                # add start and end paddings to the chunk
-                start = 0 if i - stride_len < 0 else i - stride_len
-                stop = inputs_len if i + chunk_len + stride_len > inputs_len else i + chunk_len + stride_len
-                chunk = inputs[start:stop]
-                processed = self.feature_extractor(
-                    chunk, sampling_rate=self.feature_extractor.sampling_rate, return_tensors="pt"
-                )
-                stride_left = i - start
-                stride_right = max(stop - (i + chunk_len), 0)
-                is_last = i + step > inputs_len
-
-                yield {"is_last": is_last, "stride": (stop - start, stride_left, stride_right), **processed}
+            for item in chunk_iter(inputs, self.feature_extractor, chunk_len, stride_left, stride_right):
+                yield item
         else:
             processed = self.feature_extractor(
                 inputs, sampling_rate=self.feature_extractor.sampling_rate, return_tensors="pt"
             )
             yield {"is_last": True, **processed}
 
     def _forward(self, model_inputs):
-        model_class = self.model.__class__
         is_last = model_inputs.pop("is_last")
+        model_class = self.model.__class__
         if model_class in MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING.values():
             encoder = self.model.get_encoder()
             # we need to pass `processed.get("attention_mask")` here since audio encoder
@@ -217,15 +240,7 @@ def _forward(self, model_inputs):
                 if isinstance(stride, tuple):
                     stride = [stride]
 
-                max_token_n = tokens.shape[-1]
-                max_input_n = max(input_n for input_n, _, _ in stride)
-                ratio = max_token_n / max_input_n
-                for i, (input_n, left, right) in enumerate(stride):
-                    token_n = int(input_n * ratio) + 1
-                    left_token = int(left / input_n * token_n)
-                    right_token = int((input_n - right) / input_n * token_n) + 1
-                    tokens[i, :left_token] = self.tokenizer.pad_token_id
-                    tokens[i, right_token:] = self.tokenizer.pad_token_id
+                apply_stride(tokens, stride)
         else:
             logger.warning("This is an unknown class, treating it as CTC.")
             outputs = self.model(**model_inputs)

src/transformers/pipelines/pt_utils.py

@@ -276,7 +276,7 @@ def __next__(self):
             else:
                 item = processed
                 is_last = item.pop("is_last")
-            accumulator.append(item)
+                accumulator.append(item)
         return accumulator
 
 

tests/test_pipelines_automatic_speech_recognition.py

@@ -27,11 +27,24 @@
     Wav2Vec2ForCTC,
 )
 from transformers.pipelines import AutomaticSpeechRecognitionPipeline, pipeline
-from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, require_torchaudio, slow
+from transformers.pipelines.automatic_speech_recognition import apply_stride, chunk_iter
+from transformers.testing_utils import (
+    is_pipeline_test,
+    is_torch_available,
+    nested_simplify,
+    require_tf,
+    require_torch,
+    require_torchaudio,
+    slow,
+)
 
 from .test_pipelines_common import ANY, PipelineTestCaseMeta
 
 
+if is_torch_available():
+    import torch
+
+
 # We can't use this mixin because it assumes TF support.
 # from .test_pipelines_common import CustomInputPipelineCommonMixin
 
@@ -245,17 +258,119 @@ def test_chunking(self):
             tokenizer=tokenizer,
             feature_extractor=feature_extractor,
             framework="pt",
-            chunk_length_ms=10_000,
+            chunk_length_s=10.0,
         )
 
-        from datasets import load_dataset
-
         ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
         audio = ds[40]["audio"]["array"]
 
-        n_repeats = 100
+        n_repeats = 10
         audio = np.tile(audio, n_repeats)
         output = speech_recognizer([audio], batch_size=2)
         expected_text = "A MAN SAID TO THE UNIVERSE SIR I EXIST " * n_repeats
         expected = [{"text": expected_text.strip()}]
         self.assertEqual(output, expected)
+
+    @require_torch
+    def test_chunk_iterator(self):
+        feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h")
+        inputs = torch.arange(100).long()
+
+        outs = list(chunk_iter(inputs, feature_extractor, 100, 0, 0))
+        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])
+
+        # two chunks no stride
+        outs = list(chunk_iter(inputs, feature_extractor, 50, 0, 0))
+        self.assertEqual(len(outs), 2)
+        self.assertEqual([o["stride"] for o in outs], [(50, 0, 0), (50, 0, 0)])
+        self.assertEqual([o["input_values"].shape for o in outs], [(1, 50), (1, 50)])
+        self.assertEqual([o["is_last"] for o in outs], [False, True])
+
+        # two chunks incomplete last
+        outs = list(chunk_iter(inputs, feature_extractor, 80, 0, 0))
+        self.assertEqual(len(outs), 2)
+        self.assertEqual([o["stride"] for o in outs], [(80, 0, 0), (20, 0, 0)])
+        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])
+
+    @require_torch
+    def test_chunk_iterator_stride(self):
+        feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h")
+        inputs = torch.arange(100).long()
+        input_values = feature_extractor(inputs, sampling_rate=feature_extractor.sampling_rate, return_tensors="pt")[
+            "input_values"
+        ]
+
+        outs = list(chunk_iter(inputs, feature_extractor, 100, 20, 10))
+        self.assertEqual(len(outs), 2)
+        self.assertEqual([o["stride"] for o in outs], [(100, 0, 10), (30, 20, 0)])
+        self.assertEqual([o["input_values"].shape for o in outs], [(1, 100), (1, 30)])
+        self.assertEqual([o["is_last"] for o in outs], [False, True])
+
+        outs = list(chunk_iter(inputs, feature_extractor, 80, 20, 10))
+        self.assertEqual(len(outs), 2)
+        self.assertEqual([o["stride"] for o in outs], [(80, 0, 10), (50, 20, 0)])
+        self.assertEqual([o["input_values"].shape for o in outs], [(1, 80), (1, 50)])
+        self.assertEqual([o["is_last"] for o in outs], [False, True])
+
+        outs = list(chunk_iter(inputs, feature_extractor, 90, 20, 0))
+        self.assertEqual(len(outs), 2)
+        self.assertEqual([o["stride"] for o in outs], [(90, 0, 0), (30, 20, 0)])
+        self.assertEqual([o["input_values"].shape for o in outs], [(1, 90), (1, 30)])
+
+        inputs = torch.LongTensor([i % 2 for i in range(100)])
+        input_values = feature_extractor(inputs, sampling_rate=feature_extractor.sampling_rate, return_tensors="pt")[
+            "input_values"
+        ]
+        outs = list(chunk_iter(inputs, feature_extractor, 30, 5, 5))
+        self.assertEqual(len(outs), 5)
+        self.assertEqual([o["stride"] for o in outs], [(30, 0, 5), (30, 5, 5), (30, 5, 5), (30, 5, 5), (20, 5, 0)])
+        self.assertEqual([o["input_values"].shape for o in outs], [(1, 30), (1, 30), (1, 30), (1, 30), (1, 20)])
+        self.assertEqual([o["is_last"] for o in outs], [False, False, False, False, True])
+        # (0, 25)
+        self.assertEqual(nested_simplify(input_values[:, :30]), nested_simplify(outs[0]["input_values"]))
+        # (25, 45)
+        self.assertEqual(nested_simplify(input_values[:, 20:50]), nested_simplify(outs[1]["input_values"]))
+        # (45, 65)
+        self.assertEqual(nested_simplify(input_values[:, 40:70]), nested_simplify(outs[2]["input_values"]))
+        # (65, 85)
+        self.assertEqual(nested_simplify(input_values[:, 60:90]), nested_simplify(outs[3]["input_values"]))
+        # (85, 100)
+        self.assertEqual(nested_simplify(input_values[:, 80:100]), nested_simplify(outs[4]["input_values"]))
+
+
+@require_torch
+class ApplyStrideTest(unittest.TestCase):
+    def test_apply_stride(self):
+        tokens = torch.arange(10).long().reshape((2, 5))
+
+        # No stride
+        apply_stride(tokens, [(100, 0, 0), (100, 0, 0)])
+
+        expected = torch.arange(10).long().reshape((2, 5))
+        self.assertEqual(expected.tolist(), tokens.tolist())
+
+    def test_apply_stride_real_stride(self):
+        # Stride aligned
+        tokens = torch.arange(10).long().reshape((2, 5))
+        apply_stride(tokens, [(100, 20, 0), (100, 0, 20)])
+        self.assertEqual([[1, 1, 2, 3, 4], [5, 6, 7, 8, 8]], tokens.tolist())
+
+        # Stride rounded
+        tokens = torch.arange(10).long().reshape((2, 5))
+        apply_stride(tokens, [(100, 15, 0), (100, 0, 15)])
+        self.assertEqual([[1, 1, 2, 3, 4], [5, 6, 7, 8, 8]], tokens.tolist())
+
+        # No stride rounded
+        tokens = torch.arange(10).long().reshape((2, 5))
+        apply_stride(tokens, [(100, 5, 0), (100, 0, 5)])
+        self.assertEqual([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]], tokens.tolist())
+
+    def test_apply_stride_with_padding(self):
+        # Stride aligned
+        tokens = torch.arange(10).long().reshape((2, 5))
+        apply_stride(tokens, [(100, 20, 0), (60, 0, 20)])
+        self.assertEqual([[1, 1, 2, 3, 4], [5, 6, 6, 6, 6]], tokens.tolist())

tests/test_pipelines_common.py

@@ -584,3 +584,14 @@ def add(number, extra=0):
 
         outputs = [item for item in dataset]
         self.assertEqual(outputs, [[{"id": 2}, {"id": 3}], [{"id": 4}, {"id": 5}]])
+
+        # is_false Across batch
+        dummy_dataset = [{"id": [0, 1, 2], "is_last": [False, False, False]}, {"id": [3], "is_last": [True]}]
+
+        def add(number, extra=0):
+            return {"id": [i + extra for i in number["id"]], "is_last": number["is_last"]}
+
+        dataset = PipelinePackIterator(dummy_dataset, add, {"extra": 2}, loader_batch_size=3)
+
+        outputs = [item for item in dataset]
+        self.assertEqual(outputs, [[{"id": 2}, {"id": 3}, {"id": 4}, {"id": 5}]])