Source code for gptcache.embedding.data2vec
import numpy as np
from gptcache.utils import import_huggingface, import_torch, import_torchaudio
from gptcache.embedding.base import BaseEmbedding
import_torch()
import_huggingface()
import_torchaudio()
import torch # pylint: disable=C0413
import torchaudio # pylint: disable=C0413
from transformers import Data2VecAudioModel, Wav2Vec2Processor # pylint: disable=C0413
[docs]class Data2VecAudio(BaseEmbedding):
"""Generate audio embedding for given audio using pretrained models from Data2Vec.
:param model: model name, defaults to 'facebook/data2vec-audio-base-960h'.
:type model: str
Example:
.. code-block:: python
from gptcache.embedding import Data2VecAudio
audio_file = 'test.wav'
encoder = Data2VecAudio(model='facebook/data2vec-audio-base-960h')
embed = encoder.to_embeddings(audio_file)
"""
def __init__(self, model_name = "facebook/data2vec-audio-base-960h"):
self.model = Data2VecAudioModel.from_pretrained(model_name)
self.processor = Wav2Vec2Processor.from_pretrained(model_name)
self.__dimension = self.model.config.hidden_size
self.sr = self.processor.feature_extractor.sampling_rate
[docs] def to_embeddings(self, data, **_):
"""Generate embedding given text input
:param data: path to audio file.
:type data: str
:return: a text embedding in shape of (dim,).
"""
audio = self.load_audio(data, self.sr)
inputs = self.processor(audio, sampling_rate=self.sr, return_tensors="pt")
with torch.no_grad():
outputs = self.model(**inputs)
last_hidden_states = outputs.last_hidden_state
feat = last_hidden_states[:,-1,:].flatten().detach().cpu().numpy()
return np.array(feat).astype("float32")
[docs] def load_audio(self, audio_path, target_sr):
waveform, sample_rate = torchaudio.load(audio_path)
waveform = torch.mean(waveform, axis=0)
transform = torchaudio.transforms.Resample(sample_rate, target_sr)
waveform = transform(waveform)
return waveform
@property
def dimension(self):
"""Embedding dimension.
:return: embedding dimension
"""
return self.__dimension