Delete feature_extraction_granite_speech.py

feature_extraction_granite_speech.py

@@ -1,118 +0,0 @@
-# coding=utf-8
-# Copyright 2025 The HuggingFace Inc. team.
-#
-# 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.
-"""
-Feature extractor class for Speech Granite
-"""
-
-import math
-from typing import List, Optional
-
-from transformers.feature_extraction_utils import BatchFeature, FeatureExtractionMixin
-from transformers.utils import is_torch_available, is_torchaudio_available, logging
-
-
-logger = logging.get_logger(__name__)
-
-if is_torch_available():
-    import torch
-
-if is_torchaudio_available():
-    import torchaudio
-
-
-class GraniteSpeechFeatureExtractor(FeatureExtractionMixin):
-    model_input_names = ["input_features"]
-
-    def __init__(
-        self,
-        sampling_rate=16000,
-        n_fft=512,
-        win_length=400,
-        hop_length=160,
-        n_mels=80,
-        projector_window_size=15,
-        projector_downsample_rate=5,
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-        self.melspec_kwargs = {
-            "sample_rate": sampling_rate,
-            "n_fft": n_fft,
-            "win_length": win_length,
-            "hop_length": hop_length,
-            "n_mels": n_mels,
-        }
-        # HACK - for now, lazily initialize the mel spectrogram transform;
-        # the feature extractor mixin explodes otherwise because
-        # it tries to log the feature extractor, and the melspectrogram
-        # transform isn't json serializable...
-        self.melspec = None
-        self.projector_window_size = projector_window_size
-        self.projector_downsample_rate = projector_downsample_rate
-
-    def _ensure_melspec_transform_is_initialized(self):
-        if self.melspec is None:
-            self.melspec = torchaudio.transforms.MelSpectrogram(**self.melspec_kwargs)
-
-    def __call__(
-        self,
-        x: torch.Tensor,
-        device: Optional[str] = "cpu",
-    ) -> BatchFeature:
-        # TODO there is probably a better way to do both of these things...
-        self._ensure_melspec_transform_is_initialized()
-        if device is not None:
-            melspec = self.melspec.to(device)
-            x = x.to(device)
-        else:
-            melspec = self.melspec
-
-        B, _ = x.shape
-        with torch.no_grad():
-            mel = melspec(x.float())
-            logmel = mel.transpose(-1, -2).clip_(min=1e-10).log10_()
-            mx = logmel.amax(dim=(-2, -1), keepdim=True)
-            logmel = torch.maximum(logmel, mx - 8.0).div_(4).add_(1)
-            if logmel.shape[1] % 2 == 1:
-                logmel = logmel[:, :-1]  # remove last frame if odd
-            x = logmel.reshape(B, -1, 2 * logmel.shape[-1])  # stacking and skipping by 2
-
-        if x.device != "cpu":
-            return x.detach().cpu()
-        return x
-
-    def _get_num_audio_features(self, audio_lengths: List[int]) -> List[int]:
-        """
-        Gets the (variable length) variable length number of features
-        (i.e., projector output) for the sequences being considered.
-        """
-        hop_length = self.melspec_kwargs["hop_length"]
-        effective_window_size = self.projector_window_size // self.projector_downsample_rate
-
-        projector_lengths = []
-        for raw_length in audio_lengths:
-            # mel sequence length computation
-            mel_length = raw_length // hop_length + 1
-            # encoder frame takes two mel features
-            encoder_length = mel_length // 2
-            nblocks = math.ceil(encoder_length / self.projector_window_size)
-            # projector output length
-            projector_length = nblocks * effective_window_size
-            projector_lengths.append(projector_length)
-
-        return projector_lengths
-
-
-__all__ = ["GraniteSpeechFeatureExtractor"]