Add numpy alternative to FE using torchaudio

src/transformers/__init__.py

@@ -146,6 +146,7 @@
     "models.audio_spectrogram_transformer": [
         "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
         "ASTConfig",
+        "ASTFeatureExtractor",
     ],
     "models.auto": [
         "ALL_PRETRAINED_CONFIG_ARCHIVE_MAP",
@@ -514,6 +515,7 @@
     "models.speech_to_text": [
         "SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP",
         "Speech2TextConfig",
+        "Speech2TextFeatureExtractor",
         "Speech2TextProcessor",
     ],
     "models.speech_to_text_2": [
@@ -887,20 +889,6 @@
 else:
     _import_structure["convert_slow_tokenizer"] = ["SLOW_TO_FAST_CONVERTERS", "convert_slow_tokenizer"]
 
-# Speech-specific objects
-try:
-    if not is_speech_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    from .utils import dummy_speech_objects
-
-    _import_structure["utils.dummy_speech_objects"] = [
-        name for name in dir(dummy_speech_objects) if not name.startswith("_")
-    ]
-else:
-    _import_structure["models.audio_spectrogram_transformer"].append("ASTFeatureExtractor")
-    _import_structure["models.speech_to_text"].append("Speech2TextFeatureExtractor")
-
 # Tensorflow-text-specific objects
 try:
     if not is_tensorflow_text_available():
@@ -4275,6 +4263,7 @@
     from .models.audio_spectrogram_transformer import (
         AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         ASTConfig,
+        ASTFeatureExtractor,
     )
     from .models.auto import (
         ALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
@@ -4624,6 +4613,7 @@
     from .models.speech_to_text import (
         SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         Speech2TextConfig,
+        Speech2TextFeatureExtractor,
         Speech2TextProcessor,
     )
     from .models.speech_to_text_2 import (
@@ -4964,15 +4954,6 @@
     else:
         from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS, convert_slow_tokenizer
 
-    try:
-        if not is_speech_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        from .utils.dummy_speech_objects import *
-    else:
-        from .models.audio_spectrogram_transformer import ASTFeatureExtractor
-        from .models.speech_to_text import Speech2TextFeatureExtractor
-
     try:
         if not is_tensorflow_text_available():
             raise OptionalDependencyNotAvailable()

src/transformers/feature_extraction_utils.py

@@ -574,14 +574,15 @@ def from_dict(cls, feature_extractor_dict: Dict[str, Any], **kwargs) -> PreTrain
 
     def to_dict(self) -> Dict[str, Any]:
         """
-        Serializes this instance to a Python dictionary.
-
-        Returns:
-            `Dict[str, Any]`: Dictionary of all the attributes that make up this feature extractor instance.
+        Serializes this instance to a Python dictionary. Returns:
+            `Dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance.
         """
         output = copy.deepcopy(self.__dict__)
         output["feature_extractor_type"] = self.__class__.__name__
-
+        if "mel_filters" in output:
+            del output["mel_filters"]
+        if "window" in output:
+            del output["window"]
         return output
 
     @classmethod

src/transformers/models/audio_spectrogram_transformer/__init__.py

@@ -13,14 +13,15 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
 
 
 _import_structure = {
     "configuration_audio_spectrogram_transformer": [
         "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
         "ASTConfig",
-    ]
+    ],
+    "feature_extraction_audio_spectrogram_transformer": ["ASTFeatureExtractor"],
 }
 
 try:
@@ -36,19 +37,13 @@
         "ASTPreTrainedModel",
     ]
 
-try:
-    if not is_speech_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["feature_extraction_audio_spectrogram_transformer"] = ["ASTFeatureExtractor"]
 
 if TYPE_CHECKING:
     from .configuration_audio_spectrogram_transformer import (
         AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         ASTConfig,
     )
+    from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor
 
     try:
         if not is_torch_available():
@@ -63,14 +58,6 @@
             ASTPreTrainedModel,
         )
 
-    try:
-        if not is_speech_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor
-
 
 else:
     import sys

src/transformers/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.py

@@ -19,12 +19,18 @@
 from typing import List, Optional, Union
 
 import numpy as np
-import torch
-import torchaudio.compliance.kaldi as ta_kaldi
 
+from ...audio_utils import mel_filter_bank, spectrogram, window_function
 from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
 from ...feature_extraction_utils import BatchFeature
-from ...utils import TensorType, logging
+from ...utils import TensorType, is_speech_available, is_torch_available, logging
+
+
+if is_speech_available():
+    import torchaudio.compliance.kaldi as ta_kaldi
+
+if is_torch_available():
+    import torch
 
 
 logger = logging.get_logger(__name__)
@@ -37,8 +43,8 @@ class ASTFeatureExtractor(SequenceFeatureExtractor):
     This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains
     most of the main methods. Users should refer to this superclass for more information regarding those methods.
 
-    This class extracts mel-filter bank features from raw speech using TorchAudio, pads/truncates them to a fixed
-    length and normalizes them using a mean and standard deviation.
+    This class extracts mel-filter bank features from raw speech using TorchAudio if installed or using numpy
+    otherwise, pads/truncates them to a fixed length and normalizes them using a mean and standard deviation.
 
     Args:
         feature_size (`int`, *optional*, defaults to 1):
@@ -83,6 +89,21 @@ def __init__(
         self.std = std
         self.return_attention_mask = return_attention_mask
 
+        if not is_speech_available():
+            mel_filters = mel_filter_bank(
+                num_frequency_bins=256,
+                num_mel_filters=self.num_mel_bins,
+                min_frequency=20,
+                max_frequency=sampling_rate // 2,
+                sampling_rate=sampling_rate,
+                norm=None,
+                mel_scale="kaldi",
+                triangularize_in_mel_space=True,
+            )
+
+            self.mel_filters = np.pad(mel_filters, ((0, 1), (0, 0)))
+            self.window = window_function(400, "hann", periodic=False)
+
     def _extract_fbank_features(
         self,
         waveform: np.ndarray,
@@ -93,17 +114,32 @@ def _extract_fbank_features(
         and hence the waveform should not be normalized before feature extraction.
         """
         # waveform = waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
-        waveform = torch.from_numpy(waveform).unsqueeze(0)
-        fbank = ta_kaldi.fbank(
-            waveform,
-            htk_compat=True,
-            sample_frequency=self.sampling_rate,
-            use_energy=False,
-            window_type="hanning",
-            num_mel_bins=self.num_mel_bins,
-            dither=0.0,
-            frame_shift=10,
-        )
+        if is_speech_available():
+            waveform = torch.from_numpy(waveform).unsqueeze(0)
+            fbank = ta_kaldi.fbank(
+                waveform,
+                sample_frequency=self.sampling_rate,
+                window_type="hanning",
+                num_mel_bins=self.num_mel_bins,
+            )
+        else:
+            waveform = np.squeeze(waveform)
+            fbank = spectrogram(
+                waveform,
+                self.window,
+                frame_length=400,
+                hop_length=160,
+                fft_length=512,
+                power=2.0,
+                center=False,
+                preemphasis=0.97,
+                mel_filters=self.mel_filters,
+                log_mel="log",
+                mel_floor=1.192092955078125e-07,
+                remove_dc_offset=True,
+            ).T
+
+            fbank = torch.from_numpy(fbank)
 
         n_frames = fbank.shape[0]
         difference = max_length - n_frames

src/transformers/models/speech_to_text/__init__.py

@@ -17,14 +17,14 @@
     OptionalDependencyNotAvailable,
     _LazyModule,
     is_sentencepiece_available,
-    is_speech_available,
     is_tf_available,
     is_torch_available,
 )
 
 
 _import_structure = {
     "configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2TextConfig"],
+    "feature_extraction_speech_to_text": ["Speech2TextFeatureExtractor"],
     "processing_speech_to_text": ["Speech2TextProcessor"],
 }
 
@@ -36,14 +36,6 @@
 else:
     _import_structure["tokenization_speech_to_text"] = ["Speech2TextTokenizer"]
 
-try:
-    if not is_speech_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["feature_extraction_speech_to_text"] = ["Speech2TextFeatureExtractor"]
-
 try:
     if not is_tf_available():
         raise OptionalDependencyNotAvailable()
@@ -73,6 +65,7 @@
 
 if TYPE_CHECKING:
     from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, Speech2TextConfig
+    from .feature_extraction_speech_to_text import Speech2TextFeatureExtractor
     from .processing_speech_to_text import Speech2TextProcessor
 
     try:
@@ -83,14 +76,6 @@
     else:
         from .tokenization_speech_to_text import Speech2TextTokenizer
 
-    try:
-        if not is_speech_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .feature_extraction_speech_to_text import Speech2TextFeatureExtractor
-
     try:
         if not is_tf_available():
             raise OptionalDependencyNotAvailable()

src/transformers/models/speech_to_text/feature_extraction_speech_to_text.py

@@ -19,14 +19,17 @@
 from typing import List, Optional, Union
 
 import numpy as np
-import torch
-import torchaudio.compliance.kaldi as ta_kaldi
 
+from ...audio_utils import mel_filter_bank, spectrogram, window_function
 from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
 from ...feature_extraction_utils import BatchFeature
-from ...utils import PaddingStrategy, TensorType, logging
+from ...utils import PaddingStrategy, TensorType, is_speech_available, logging
 
 
+if is_speech_available():
+    import torch
+    import torchaudio.compliance.kaldi as ta_kaldi
+
 logger = logging.get_logger(__name__)
 
 
@@ -37,8 +40,8 @@ class Speech2TextFeatureExtractor(SequenceFeatureExtractor):
     This feature extractor inherits from [`Speech2TextFeatureExtractor`] which contains most of the main methods. Users
     should refer to this superclass for more information regarding those methods.
 
-    This class extracts mel-filter bank features from raw speech using TorchAudio and applies utterance-level cepstral
-    mean and variance normalization to the extracted features.
+    This class extracts mel-filter bank features from raw speech using TorchAudio if installed or using numpy
+    otherwise, and applies utterance-level cepstral mean and variance normalization to the extracted features.
 
     Args:
         feature_size (`int`, defaults to 80):
@@ -77,6 +80,21 @@ def __init__(
         self.normalize_vars = normalize_vars
         self.return_attention_mask = True
 
+        if not is_speech_available():
+            mel_filters = mel_filter_bank(
+                num_frequency_bins=256,
+                num_mel_filters=self.num_mel_bins,
+                min_frequency=20,
+                max_frequency=sampling_rate // 2,
+                sampling_rate=sampling_rate,
+                norm=None,
+                mel_scale="kaldi",
+                triangularize_in_mel_space=True,
+            )
+
+            self.mel_filters = np.pad(mel_filters, ((0, 1), (0, 0)))
+            self.window = window_function(400, "povey", periodic=False)
+
     def _extract_fbank_features(
         self,
         waveform: np.ndarray,
@@ -86,9 +104,27 @@ def _extract_fbank_features(
         and hence the waveform should not be normalized before feature extraction.
         """
         waveform = waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
-        waveform = torch.from_numpy(waveform).unsqueeze(0)
-        features = ta_kaldi.fbank(waveform, num_mel_bins=self.num_mel_bins, sample_frequency=self.sampling_rate)
-        return features.numpy()
+        if is_speech_available():
+            waveform = torch.from_numpy(waveform).unsqueeze(0)
+            features = ta_kaldi.fbank(waveform, num_mel_bins=self.num_mel_bins, sample_frequency=self.sampling_rate)
+            features = features.numpy()
+        else:
+            waveform = np.squeeze(waveform)
+            features = spectrogram(
+                waveform,
+                self.window,
+                frame_length=400,
+                hop_length=160,
+                fft_length=512,
+                power=2.0,
+                center=False,
+                preemphasis=0.97,
+                mel_filters=self.mel_filters,
+                log_mel="log",
+                mel_floor=1.192092955078125e-07,
+                remove_dc_offset=True,
+            ).T
+        return features
 
     @staticmethod
     def utterance_cmvn(