Allow dict input for audio classification pipeline (#23445)

* Allow dict input for audio classification pipeline

* make style

* Empty commit to trigger CI

* Empty commit to trigger CI

* check for torchaudio

* add pip instructions
Co-authored-by: Sylvain <sylvain.gugger@gmail.com>

* Update src/transformers/pipelines/audio_classification.py
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>

* asr -> audio class

* asr -> audio class

---------
Co-authored-by: Sylvain <sylvain.gugger@gmail.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>

src/transformers/pipelines/audio_classification.py

@@ -17,7 +17,7 @@ from typing import Union
 import numpy as np
 import requests
 
-from ..utils import add_end_docstrings, is_torch_available, logging
+from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
 from .base import PIPELINE_INIT_ARGS, Pipeline
 
 
@@ -110,12 +110,18 @@ class AudioClassificationPipeline(Pipeline):
         information.
 
         Args:
-            inputs (`np.ndarray` or `bytes` or `str`):
-                The inputs is either a raw waveform (`np.ndarray` of shape (n, ) of type `np.float32` or `np.float64`)
-                at the correct sampling rate (no further check will be done) or a `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 `bytes` it is supposed to be the
-                content of an audio file and is interpreted by *ffmpeg* in the same way.
+            inputs (`np.ndarray` or `bytes` or `str` or `dict`):
+                The inputs is either :
+                    - `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.
+                    - `bytes` it is supposed to be the content of an audio file and is interpreted by *ffmpeg* in the
+                      same way.
+                    - (`np.ndarray` of shape (n, ) of type `np.float32` or `np.float64`)
+                        Raw audio at the correct sampling rate (no further check will be done)
+                    - `dict` form can be used to pass raw audio sampled at arbitrary `sampling_rate` and let this
+                      pipeline do the resampling. The dict must be either be in the format `{"sampling_rate": int,
+                      "raw": np.array}`, or `{"sampling_rate": int, "array": np.array}`, where the key `"raw"` or
+                      `"array"` is used to denote the raw audio waveform.
             top_k (`int`, *optional*, defaults to None):
                 The number of top labels that will be returned by the pipeline. If the provided number is `None` or
                 higher than the number of labels available in the model configuration, it will default to the number of
@@ -151,10 +157,42 @@ class AudioClassificationPipeline(Pipeline):
         if isinstance(inputs, bytes):
             inputs = ffmpeg_read(inputs, self.feature_extractor.sampling_rate)
 
+        if isinstance(inputs, dict):
+            # Accepting `"array"` which is the key defined in `datasets` for
+            # better integration
+            if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
+                raise ValueError(
+                    "When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a "
+                    '"raw" key containing the numpy array representing the audio and a "sampling_rate" key, '
+                    "containing the sampling_rate associated with that array"
+                )
+
+            _inputs = inputs.pop("raw", None)
+            if _inputs is None:
+                # Remove path which will not be used from `datasets`.
+                inputs.pop("path", None)
+                _inputs = inputs.pop("array", None)
+            in_sampling_rate = inputs.pop("sampling_rate")
+            inputs = _inputs
+            if in_sampling_rate != self.feature_extractor.sampling_rate:
+                import torch
+
+                if is_torchaudio_available():
+                    from torchaudio import functional as F
+                else:
+                    raise ImportError(
+                        "torchaudio is required to resample audio samples in AudioClassificationPipeline. "
+                        "The torchaudio package can be installed through: `pip install torchaudio`."
+                    )
+
+                inputs = F.resample(
+                    torch.from_numpy(inputs), in_sampling_rate, self.feature_extractor.sampling_rate
+                ).numpy()
+
         if not isinstance(inputs, np.ndarray):
             raise ValueError("We expect a numpy ndarray as input")
         if len(inputs.shape) != 1:
-            raise ValueError("We expect a single channel audio input for AutomaticSpeechRecognitionPipeline")
+            raise ValueError("We expect a single channel audio input for AudioClassificationPipeline")
 
         processed = self.feature_extractor(
             inputs, sampling_rate=self.feature_extractor.sampling_rate, return_tensors="pt"

tests/pipelines/test_pipelines_audio_classification.py

@@ -103,6 +103,10 @@ class AudioClassificationPipelineTests(unittest.TestCase):
         ]
         self.assertIn(nested_simplify(output, decimals=4), [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2])
 
+        audio_dict = {"array": np.ones((8000,)), "sampling_rate": audio_classifier.feature_extractor.sampling_rate}
+        output = audio_classifier(audio_dict, top_k=4)
+        self.assertIn(nested_simplify(output, decimals=4), [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2])
+
     @require_torch
     @slow
     def test_large_model_pt(self):