| import numpy as np |
| from transformers import BatchFeature, SequenceFeatureExtractor |
|
|
|
|
| class SmadFeatureExtractor(SequenceFeatureExtractor): |
| model_input_names = ["input_features"] |
|
|
| def __init__( |
| self, |
| feature_size=80, |
| sampling_rate=16000, |
| padding_value=0.0, |
| segment_seconds=4.0, |
| n_fft=400, |
| hop_length=160, |
| **kwargs, |
| ): |
| super().__init__( |
| feature_size=feature_size, |
| sampling_rate=sampling_rate, |
| padding_value=padding_value, |
| **kwargs, |
| ) |
| self.segment_seconds = segment_seconds |
| self.n_fft = n_fft |
| self.hop_length = hop_length |
|
|
| def waveform_to_mel(self, waveform, sampling_rate=None): |
| import librosa |
|
|
| sampling_rate = sampling_rate or self.sampling_rate |
| mel = librosa.feature.melspectrogram( |
| y=np.asarray(waveform, dtype=np.float32), |
| sr=sampling_rate, |
| n_fft=self.n_fft, |
| hop_length=self.hop_length, |
| n_mels=self.feature_size, |
| power=2.0, |
| ) |
| return librosa.power_to_db(mel).T.astype(np.float32) |
|
|
| def __call__(self, raw_speech, sampling_rate=None, return_tensors=None, **kwargs): |
| sampling_rate = sampling_rate or self.sampling_rate |
| if sampling_rate != self.sampling_rate: |
| raise ValueError( |
| f"Expected {self.sampling_rate} Hz audio. Resample before calling " |
| f"the feature extractor; received {sampling_rate} Hz." |
| ) |
|
|
| if isinstance(raw_speech, np.ndarray) and raw_speech.ndim == 1: |
| waves = [raw_speech] |
| else: |
| waves = [np.asarray(w, dtype=np.float32) for w in raw_speech] |
|
|
| target_len = int(round(self.segment_seconds * self.sampling_rate)) |
| features = [] |
| for wave in waves: |
| if wave.ndim != 1: |
| raise ValueError("Expected mono audio arrays with shape `(samples,)`.") |
| if wave.shape[0] < target_len: |
| wave = np.pad(wave, (0, target_len - wave.shape[0])) |
| elif wave.shape[0] > target_len: |
| wave = wave[:target_len] |
| features.append(self.waveform_to_mel(wave, sampling_rate=sampling_rate)) |
|
|
| return BatchFeature({"input_features": features}, tensor_type=return_tensors) |
|
|
