Adding AutomaticSpeechRecognitionPipeline. (#11337)

* Adding `AutomaticSpeechRecognitionPipeline`.

- Because we added everything to enable this pipeline, we probably
should add it to `transformers`.
- This PR tries to limit the scope and focuses only on the pipeline part
(what should go in, and out).
- The tests are very specific for S2T and Wav2vec2 to make sure both
architectures are supported by the pipeline. We don't use the mixin for
tests right now, because that requires more work in the `pipeline`
function (will be done in a follow up PR).
- Unsure about the "helper" function `ffmpeg_read`. It makes a lot of
  sense from a user perspective, it does not add any additional
dependencies (as in hard dependency, because users can always use their
own load mechanism). Meanwhile, it feels slightly clunky to have so much
optional preprocessing.
- The pipeline is not done to support streaming audio right now.

Future work:

- Add `automatic-speech-recognition` as a `task`. And add the
FeatureExtractor.from_pretrained within `pipeline` function.
- Add small models within tests
- Add the Mixin to tests.
- Make the logic between ForCTC vs ForConditionalGeneration better.

* Update tests/test_pipelines_automatic_speech_recognition.py

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Adding docs + main import + type checking + LICENSE.

* Doc style !.

* Fixing TYPE_HINT.

* Specifying waveform shape in the docs.

* Adding asserts + specify in the documentation the shape of the input
np.ndarray.

* Update src/transformers/pipelines/automatic_speech_recognition.py

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Adding require to tests + move the `feature_extractor` doc.

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

docs/source/main_classes/pipelines.rst

@@ -23,6 +23,7 @@ There are two categories of pipeline abstractions to be aware about:
 - The :func:`~transformers.pipeline` which is the most powerful object encapsulating all other pipelines.
 - The other task-specific pipelines:
 
+    - :class:`~transformers.AutomaticSpeechRecognitionPipeline`
     - :class:`~transformers.ConversationalPipeline`
     - :class:`~transformers.FeatureExtractionPipeline`
     - :class:`~transformers.FillMaskPipeline`
@@ -48,6 +49,13 @@ pipeline but requires an additional argument which is the `task`.
 The task specific pipelines
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+AutomaticSpeechRecognitionPipeline
+=======================================================================================================================
+
+.. autoclass:: transformers.AutomaticSpeechRecognitionPipeline
+    :special-members: __call__
+    :members:
+
 ConversationalPipeline
 =======================================================================================================================
 

src/transformers/__init__.py

@@ -233,6 +233,7 @@ _import_structure = {
     "models.xlm_roberta": ["XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMRobertaConfig"],
     "models.xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"],
     "pipelines": [
+        "AutomaticSpeechRecognitionPipeline",
         "Conversation",
         "ConversationalPipeline",
         "CsvPipelineDataFormat",
@@ -1583,6 +1584,7 @@ if TYPE_CHECKING:
 
     # Pipelines
     from .pipelines import (
+        AutomaticSpeechRecognitionPipeline,
         Conversation,
         ConversationalPipeline,
         CsvPipelineDataFormat,

src/transformers/pipelines/__init__.py

@@ -25,6 +25,7 @@ from ..modelcard import ModelCard
 from ..models.auto.tokenization_auto import AutoTokenizer
 from ..tokenization_utils import PreTrainedTokenizer
 from ..utils import logging
+from .automatic_speech_recognition import AutomaticSpeechRecognitionPipeline
 from .base import (
     ArgumentHandler,
     CsvPipelineDataFormat,

src/transformers/pipelines/automatic_speech_recognition.py

@@ -0,0 +1,151 @@
+# Copyright 2021 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import subprocess
+from typing import TYPE_CHECKING, Union
+
+import numpy as np
+
+from ..utils import logging
+from .base import Pipeline
+
+
+if TYPE_CHECKING:
+    from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
+
+logger = logging.get_logger(__name__)
+
+
+def ffmpeg_read(bpayload: bytes, sampling_rate: int) -> np.array:
+    """
+    Helper function to read an audio file through ffmpeg.
+    """
+    ar = f"{sampling_rate}"
+    ac = "1"
+    format_for_conversion = "f32le"
+    ffmpeg_command = [
+        "ffmpeg",
+        "-i",
+        "pipe:0",
+        "-ac",
+        ac,
+        "-ar",
+        ar,
+        "-f",
+        format_for_conversion,
+        "-hide_banner",
+        "-loglevel",
+        "quiet",
+        "pipe:1",
+    ]
+
+    try:
+        ffmpeg_process = subprocess.Popen(ffmpeg_command, stdin=subprocess.PIPE, stdout=subprocess.PIPE)
+    except FileNotFoundError:
+        raise ValueError("ffmpeg was not found but is required to load audio files from filename")
+    output_stream = ffmpeg_process.communicate(bpayload)
+    out_bytes = output_stream[0]
+
+    audio = np.frombuffer(out_bytes, np.float32)
+    if audio.shape[0] == 0:
+        raise ValueError("Malformed soundfile")
+    return audio
+
+
+class AutomaticSpeechRecognitionPipeline(Pipeline):
+    """
+    Pipeline that aims at extracting spoken text contained within some audio.
+
+    The input can be either a raw waveform or a audio file. In case of the audio file, ffmpeg should be installed for
+    to support multiple audio formats
+    """
+
+    def __init__(self, feature_extractor: "SequenceFeatureExtractor", *args, **kwargs):
+        """
+        Arguments:
+            feature_extractor (:obj:`~transformers.SequenceFeatureExtractor`):
+                The feature extractor that will be used by the pipeline to encode waveform for the model.
+            model (:obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`):
+                The model that will be used by the pipeline to make predictions. This needs to be a model inheriting
+                from :class:`~transformers.PreTrainedModel` for PyTorch and :class:`~transformers.TFPreTrainedModel`
+                for TensorFlow.
+            tokenizer (:obj:`~transformers.PreTrainedTokenizer`):
+                The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
+                :class:`~transformers.PreTrainedTokenizer`.
+            modelcard (:obj:`str` or :class:`~transformers.ModelCard`, `optional`):
+                Model card attributed to the model for this pipeline.
+            framework (:obj:`str`, `optional`):
+                The framework to use, either :obj:`"pt"` for PyTorch or :obj:`"tf"` for TensorFlow. The specified
+                framework must be installed.
+
+                If no framework is specified, will default to the one currently installed. If no framework is specified
+                and both frameworks are installed, will default to the framework of the :obj:`model`, or to PyTorch if
+                no model is provided.
+            device (:obj:`int`, `optional`, defaults to -1):
+                Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the
+                model on the associated CUDA device id.
+        """
+        super().__init__(*args, **kwargs)
+        self.feature_extractor = feature_extractor
+
+        if self.framework == "tf":
+            raise ValueError("The AutomaticSpeechRecognitionPipeline is only available in PyTorch.")
+
+    def __call__(
+        self,
+        inputs: Union[np.ndarray, bytes, str],
+        **kwargs,
+    ):
+        """
+        Classify the sequence(s) given as inputs. See the :obj:`~transformers.AutomaticSpeechRecognitionPipeline`
+        documentation for more information.
+
+        Args:
+            inputs (:obj:`np.ndarray` or :obj:`bytes` or :obj:`str`):
+                The inputs is either a raw waveform (:obj:`np.ndarray` of shape (n, ) of type :obj:`np.float32` or
+                :obj:`np.float64`) at the correct sampling rate (no further check will be done) or a :obj:`str` that is
+                the filename of the audio file, the file will be read at the correct sampling rate to get the waveform
+                using `ffmpeg`. This requires `ffmpeg` to be installed on the system. If `inputs` is :obj:`bytes` it is
+                supposed to be the content of an audio file and is interpreted by `ffmpeg` in the same way.
+
+        Return:
+            A :obj:`dict` with the following keys:
+
+            - **text** (:obj:`str`) -- The recognized text.
+        """
+        if isinstance(inputs, str):
+            with open(inputs, "rb") as f:
+                inputs = f.read()
+
+        if isinstance(inputs, bytes):
+            inputs = ffmpeg_read(inputs, self.feature_extractor.sampling_rate)
+
+        assert isinstance(inputs, np.ndarray), "We expect a numpy ndarray as input"
+        assert len(inputs.shape) == 1, "We expect a single channel audio input for AutomaticSpeechRecognitionPipeline"
+
+        processed = self.feature_extractor(
+            inputs, sampling_rate=self.feature_extractor.sampling_rate, return_tensors="pt"
+        )
+
+        name = self.model.__class__.__name__
+        if name.endswith("ForConditionalGeneration"):
+            input_ids = processed["input_features"]
+            tokens = self.model.generate(input_ids=input_ids)
+            tokens = tokens.squeeze(0)
+        elif name.endswith("ForCTC"):
+            outputs = self.model(**processed)
+            tokens = outputs.logits.squeeze(0).argmax(dim=-1)
+
+        skip_special_tokens = False if "CTC" in self.tokenizer.__class__.__name__ else True
+        recognized_string = self.tokenizer.decode(tokens, skip_special_tokens=skip_special_tokens)
+        return {"text": recognized_string}

tests/test_pipelines_automatic_speech_recognition.py

@@ -0,0 +1,89 @@
+# Copyright 2021 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+from transformers import AutoFeatureExtractor, AutoTokenizer, Speech2TextForConditionalGeneration, Wav2Vec2ForCTC
+from transformers.pipelines import AutomaticSpeechRecognitionPipeline
+from transformers.testing_utils import require_datasets, require_torch, require_torchaudio, slow
+
+
+# from .test_pipelines_common import CustomInputPipelineCommonMixin
+
+
+class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase):
+    # pipeline_task = "automatic-speech-recognition"
+    # small_models = ["facebook/s2t-small-mustc-en-fr-st"]  # Models tested without the @slow decorator
+    # large_models = [
+    #     "facebook/wav2vec2-base-960h",
+    #     "facebook/s2t-small-mustc-en-fr-st",
+    # ]  # Models tested with the @slow decorator
+
+    @slow
+    @require_torch
+    @require_datasets
+    def test_simple_wav2vec2(self):
+        import numpy as np
+        from datasets import load_dataset
+
+        model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
+        tokenizer = AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h")
+        feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h")
+
+        asr = AutomaticSpeechRecognitionPipeline(model=model, tokenizer=tokenizer, feature_extractor=feature_extractor)
+
+        waveform = np.zeros((34000,))
+        output = asr(waveform)
+        self.assertEqual(output, {"text": ""})
+
+        ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+        filename = ds[0]["file"]
+        output = asr(filename)
+        self.assertEqual(output, {"text": "A MAN SAID TO THE UNIVERSE SIR I EXIST"})
+
+        filename = ds[0]["file"]
+        with open(filename, "rb") as f:
+            data = f.read()
+        output = asr(data)
+        self.assertEqual(output, {"text": "A MAN SAID TO THE UNIVERSE SIR I EXIST"})
+
+    @slow
+    @require_torch
+    @require_torchaudio
+    @require_datasets
+    def test_simple_s2t(self):
+        import numpy as np
+        from datasets import load_dataset
+
+        model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-mustc-en-it-st")
+        tokenizer = AutoTokenizer.from_pretrained("facebook/s2t-small-mustc-en-it-st")
+        feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/s2t-small-mustc-en-it-st")
+
+        asr = AutomaticSpeechRecognitionPipeline(model=model, tokenizer=tokenizer, feature_extractor=feature_extractor)
+
+        waveform = np.zeros((34000,))
+
+        output = asr(waveform)
+        self.assertEqual(output, {"text": "E questo è il motivo per cui non ci siamo mai incontrati."})
+
+        ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+        filename = ds[0]["file"]
+        output = asr(filename)
+        self.assertEqual(output, {"text": "Un uomo disse all'universo: \"Signore, io esisto."})
+
+        filename = ds[0]["file"]
+        with open(filename, "rb") as f:
+            data = f.read()
+        output = asr(data)
+        self.assertEqual(output, {"text": "Un uomo disse all'universo: \"Signore, io esisto."})